Quantum-inspired evolutionary computation offers a practical approach to complex optimization by simulating quantum principles on classical systems. This study proposes a multi-objective weighted portfolio model (MoWPM) based on trend ratio evaluation, along with a multi-objective quantum-inspired tabu search algorithm (MoQTS) for portfolio allocation. MoQTS incorporates superposition and an enhanced entanglement mechanism, which effectively improves convergence and expands the Pareto front. Experimental results indicate that the proposed method performs robustly and shows strong potential in supporting diverse financial decision-making needs.

Image restoration aims to recover content from inputs degraded by various factors, such as adverse weather, blur, and noise. Perceptual Image Restoration (PIR) methods improve visual quality but often do not support downstream tasks effectively. On the other hand, Task-Oriented Image Restoration (TIR) methods focus on enhancing image utility for high-level vision tasks, sometimes compromising visual quality. This paper introduces UniRestore, a unified image restoration model that bridges the gap between PIR and TIR by using a diffusion prior. The diffusion prior is designed to generate images that align with human visual quality preferences, but these images are often unsuitable for TIR scenarios. To solve this limitation, UniRestore utilizes encoder features from an autoencoder to adapt the diffusion prior to specific tasks. We propose a Complementary Feature Restoration Module (CFRM) to reconstruct degraded encoder features and a Task Feature Adapter (TFA) module to facilitate adaptive feature fusion in the decoder. This design allows UniRestore to optimize images for both human perception and downstream task requirements, addressing discrepancies between visual quality and functional needs. Integrating these modules also enhances UniRestore’s adaptability and efficiency across diverse tasks. Extensive experiments demonstrate the superior performance of UniRestore in both PIR and TIR scenarios.

Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction. However, these methods fail scattering medium, such as haze, is prevalent in the scene. To address this challenge, we introduce DehazeNeRF as a framework that robustly operates in hazy conditions. DehazeNeRF extends the volume rendering equation by adding physically realistic terms that model atmospheric scattering. By parameterizing these terms using suitable networks that match the physical properties, we introduce effective inductive biases, which, together with the proposed regularizations, allow DehazeNeRF to demonstrate successful multi-view haze removal, novel view synthesis, and 3D shape reconstruction where existing approaches failed. Our code and pretrained models can be found on this page.

Hybrid quantum computing (QC) combines classical and quantum resources to tackle challenging optimization problems, leveraging the strengths of both within the current constraints of quantum computers, which are limited in size and power. Therefore, we explore the efficacy of a hybrid quantum annealing (QA) search algorithm in improving portfolio optimization. This study pioneers the application of the trend ratio (TR) to a quantum annealing computer, converting it into a constrained quadratic model with a flexible presentation. The TR serves as a promising indicator in portfolio evaluation, considering the great balance between expected returns and risks. Utilizing D- Wave's hybrid solver, we present a thorough analysis and discussion of the proposed model realized in the QA structure. The experimental results demonstrate that our model can discover solutions of comparable quality in a significantly shorter amount of time than an exhaustive search. When extending the search space to sizes challenging for exhaustive search, we conducted experiments comparing our approach with state-of-the-art quantum-inspired artificial intelligence (AI) algorithms. The results show that our method not only constructs higher-quality solutions but also requires the least computation time. The hybrid quantum-classical AI represents a forward-looking technology paradigm poised to revolutionize problem-solving methods.

Generic Face Image Quality Assessment (GFIQA) evalu-ates the perceptual quality of facial images, which is crucial in improving image restoration algorithms and selecting high-quality face images for downstream tasks. We present a novel transformer-based method for GFIQA, which is aided by two unique mechanisms. First, a “Dual-Set Degradation Representation Learning” (DSL) mechanism uses facial images with both synthetic and real degradations to decouple degradation from content, ensuring gen-eralizability to real-world scenarios. This self-supervised method learns degradation features on a global scale, pro-viding a robust alternative to conventional methods that use local patch information in degradation learning. Second, our transformer leverages facial landmarks to emphasize visually salient parts of a face image in evaluating its per-ceptual quality. We also introduce a balanced and diverse Comprehensive Generic Face IQA (CGFIQA-40k) dataset of 40K images carefully designed to overcome the biases, in particular the imbalances in skin tone and gender represen-tation, in existing datasets. Extensive analysis and evaluation demonstrate the robustness of our method, marking a significant improvement over prior methods.

Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation, acclaimed for its robust zero-shot segmentation capabilities and flexible prompting system. Nonetheless, its performance is challenged by images with degraded quality. Addressing this limitation, we propose the Robust Segment Anything Model (RobustSAM), which enhances SAM's performance on low-quality images while preserving its promptability and zero-shot generalization. Our method leverages the pre-trained SAM model with only marginal parameter increments and computational requirements. The additional parameters of RobustSAM can be optimized within 30 hours on eight GPUs, demonstrating its feasibility and practicality for typical research laboratories. We also introduce the Robust-Seg dataset, a collection of 688K image-mask pairs with different degradations designed to train and evaluate our model optimally. Extensive experiments across various segmentation tasks and datasets confirm RobustSAM's superior performance, especially under zero-shot conditions, underscoring its potential for extensive real-world application. Additionally, our method has been shown to effectively improve the performance of SAM-based downstream tasks such as single image dehazing and deblurring.

The Test-of-Time Award recognizes two outstanding papers published 10 years ago at DSN, in the DSN proceedings (research track, practical experience report or tool papers), that have had a sustained and important impact on the theory and/or practice of dependable systems and networks computing research. DSN has several areas under its umbrella and with two awards there are conditions to recognize more than one area. In exceptional situations (not enough nominations), the time frame for awards can be extended to 10-12 years, and only one paper can be awarded, in this order.

Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original training samples. In this study, we delve into the theoretical aspects of backdoor attacks and dataset distillation based on kernel methods. We introduce two new theory-driven trigger pattern generation methods specialized for dataset distillation. Following a comprehensive set of analyses and experiments, we show that our optimization-based trigger design framework informs effective backdoor attacks on dataset distillation. Notably, datasets poisoned by our designed trigger prove resilient against conventional backdoor attack detection and mitigation methods. Our empirical results validate that the triggers developed using our approaches are proficient at executing resilient backdoor attacks.

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with sufficient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low- and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low- and sufficient-luminance domains.

As semiconductor manufacturing technology advances, microarchitecture designers could use the advanced process to create complex microarchitectures, making the design more performant, power-saving, and area efficient. To get better designs for various objectives, designers use hardware description language to deploy a highly parameterizable hardware design generator. However, the more flexible the parameterized design is, the more complex the parameters to choose over the possible parameter space. Traditionally, experienced hardware engineers have to hand-tune each design parameter to achieve better performance, power, and area efficiency. Moreover, the design space is usually too large to practically explore by running place-and-route (PnR) of each design since the electronic design automation (EDA) tool could take days to finish it. Thus, prior works on microarchitecture search only focus on logical results, which leads the search framework to ignore the quality gap between logical and physical design, resulting in inaccurate Pareto frontier prediction. To further address this issue, we use the physical synthesis technique to estimate the physical characteristics and save runtime while exploring the design space simultaneously. In this case, we developed a heuristic idea, Physical-aware Design Space Exploration Framework on RISC-V SonicBOOM Microarchitecture, that optimizes the netlist and physical layout quality. It results in a much more accurate prediction of the Pareto parameter set with a 40% and 28% search time reduction on the three-objective and four-objective problems, respectively, compared to the best baseline model. In addition, our method achieved a seven times smaller hypervolume difference than the best baseline model within the same number of observations.

Breath-first-search (BFS) algorithm is the fundamen-tal building block of broad applications from the electronic design automation (EDA) field to social network analysis. With the targeting data set size growing considerable, researchers have turned to developing parallel BFS (PBFS) algorithms and accelerating them with graph processing units (GPUs). The frontier queue, the core idea among state-of-the-art designs of PBFS, opens the door to neighbor visiting parallelism. However, the traditional centralized frontier queue in PBFS suffers from a dramatic collision and explosive growth of memory space when excessive threads simultaneously operate on it. Therefore, we identify the challenges of current frontier queue implementations. To solve these challenges, we proposed the decentralized frontier queue (DFQ), which separates a centralized queue into multiple tiny sub-queues for scattering the atomic operation collision on these queues. We also developed the novel overflow-free enqueue and asynchronous sub-queue drain methods to avoid dramatic growing size of the frontier queue and the overflow issue on the naive sub-queue design. In our experiments, we showed that our design could have better scalability and grain averagely 1.04x speedup on the execution in the selected benchmark suit with considerable memory space efficiency.

Recently, quantum classifiers have been known to be vulnerable to adversarial attacks, where quantum classifiers are fooled by imperceptible noises to have misclassification. In this paper, we propose one first theoretical study that utilizing the added quantum random rotation noise can improve the robustness of quantum classifiers against adversarial attacks. We connect the definition of differential privacy and demonstrate the quantum classifier trained with the natural presence of additive noise is differentially private. Lastly, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples supported by experimental results.

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code is available in our project page.

The papers in this special section focus on the trustworthiness of artificial intelligence/machine learning models/deep learning models (AI/ML/DL) as it applies to the Industrial Internet of Things (IIot). This includes automated environments, such as smart factories, smart airports, and smart healthcare systems. AI approaches enable automation and data analytic across industrial technologies, including the IIoT, cloud and edge, and fog computing paradigms. Current ML models, such as DL still suffer from designing a generalized trustworthy architecture that reveals semantics and contexts of models and attacks threat surface. The papers in this section were inspired by the convincing challenges and necessities described above and attempt to compile research results that essentially adopts them.

Photographs taken through a glass window are susceptible to disturbances due to reflection. Therefore, single image reflection removal is crucial to image quality enhancement. In this paper, a novel learning architecture that can address this ill-posed problem is proposed. First, a novel reflection removal pipeline was designed to reconstruct the missing information caused by the camera imaging process using the proposed missing recovery network. Second, to address the issues in existing reflection removal strategies, we revisit several auxiliary priors and integrate them by defining an energy function. To solve the energy function, a convolutional neural network-based optimization scheme was proposed. Finally, we investigated the dark channel responses of reflection and clean images and found an interesting way to distinguish between these two types of images. We prove this property mathematically and propose a novel loss function called dark channel loss to improve performance. Experiments show that the proposed method outperforms state-of-the-art reflection removal methods both quantitatively and qualitatively.

Vehicle re-identification (ReID) has attracted considerable attention in computer vision. Although several methods have been proposed to achieve state-of-the-art performance on this topic, re-identifying vehicle in foggy scenes remains a great challenge due to the degradation of visibility. To our knowledge, this problem is still not well-addressed so far. In this paper, to address this problem, we propose a novel training framework called Semi-supervised Joint Defogging Learning (SJDL) framework. First, the fog removal branch and the re-identification branch are integrated to perform simultaneous training. With the collaborative training scheme, defogged features generated by the defogging branch from input images can be shared to learn better representation for the re-identification branch. However, since the fog-free image of real-world data is intractable, this architecture can only be trained on the synthetic data, which may cause the domain gap problem between real-world and synthetic scenarios. To solve this problem, we design a semi-supervised defogging training scheme that can train two kinds of data alternatively in each iteration. Due to the lack of a dataset specialized for vehicle ReID in the foggy weather, we construct a dataset called FVRID which consists of real-world and synthetic foggy images to train and evaluate the performance. Experimental results show that the proposed method is effective and outperforms other existing vehicle ReID methods in the foggy weather. The code and dataset are available in https://github.com/Cihsaing/SJDL-Foggy-Vehicle-Re-Identification--AAAI2022.

In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our goal is to train a model with a ‘unified’ architecture and only one set of pretrained weights that can tackle multiple types of adverse weathers such as haze, snow, and rain simultaneously. To this end, a two-stage knowledge learning mechanism including knowledge collation (KC) and knowledge examination (KE) based on a multi-teacher and student architecture is proposed. At the KC, the student network aims to learn the comprehensive bad weather removal problem from multiple well-trained teacher networks where each of them is specialized in a specific bad weather removal problem. To accomplish this process, a novel collaborative knowledge transfer is proposed. At the KE, the student model is trained without the teacher networks and examined by challenging pixel loss derived by the ground truth. Moreover, to improve the performance of our training framework, a novel loss function called multi-contrastive knowledge regularization (MCR) loss is proposed. Experiments on several datasets show that our student model can achieve promising results on different bad weather removal tasks simultaneously. The code is available in our project page.

Single image reflection removal is a crucial technique which can improve the performance of object detection, semantic segmentation, and various computer vision applications. In this paper, we present a novel reflection removal algorithm using bi-channel priors (i.e., the dark channel prior and the bright channel prior). We observe that the values of dark channel pixels are not near 0, and those of bright channel pixels are not closer to 1 under the reflection scenario. We first demonstrate these phenomena statistically and mathematically. Then, we apply these properties as the constraints in optimizing the proposed reflection removal process. Extensive experiments on several well-known benchmarks demonstrate that our approach achieves desirable reflection suppression results compared with other methods.

Recently, hardware Trojan has become a serious security concern in the integrated circuit (IC) industry. Due to the globalization of semiconductor design and fabrication processes, ICs are highly vulnerable to hardware Trojan insertion by malicious third-party vendors. Therefore, the development of effective hardware Trojan detection techniques is necessary. Testability measures have been proven to be efficient features for Trojan nets classification. However, most of the existing machine-learning-based techniques use supervised learning methods, which involve time-consuming training processes, need to deal with the class imbalance problem, and are not pragmatic in real-world situations. Furthermore, no works have explored the use of anomaly detection for hardware Trojan detection tasks. This paper proposes a semi-supervised hardware Trojan detection method at the gate level using anomaly detection. We ameliorate the existing computation of the Sandia Controllability/Observability Analysis Program (SCOAP) values by considering all types of D flip-flops and adopt semi-supervised anomaly detection techniques to detect Trojan nets. Finally, a novel topology-based location analysis is utilized to improve the detection performance. Testing on 17 Trust-Hub Trojan benchmarks, the proposed method achieves an overall 99.47% true positive rate (TPR), 99.99% true negative rate (TNR), and 99.99% accuracy.

In image processing, smoke may degrade visibility and deteriorate the performance of high-level vision applications. Therefore, single image smoke removal is crucial for computer vision. Currently, existing smoke removal algorithms mainly leverage handcrafted priors. Moreover, these methods usually apply haze removal methods to perform smoke removal due to the similarity between smoke and haze. However, these methods cannot sufficiently address the degradation of thick smoke and may suffer from residual smoke and color distortion problems due to the non-global and non-homogeneous distribution of smoke. In this paper, to solve the aforementioned problems, an end-to-end deep neural network called DesmokeNet is proposed. We construct a two-stage recovered pipeline to remove the smoke in different thicknesses. The light and thick smoke is first removed locally by the smoke removal network (SRN). The missing pixels in the thick smoke are then recovered by the pixel compensation network (PCN). Moreover, we proposed the thickness-aware pixel loss and the dark channel loss to suppress the residual smoke. To further increase the discriminative ability of the DesmokeNet, we proposed self-attentive feature consensus loss and multi-level contrastive regularization loss to improve the performance of smoke removal. Finally, to train the proposed method, we construct the first large-scale dataset containing synthetic and real-world data. Extensive experiments show that the proposed method outperforms favorably against other state-of-the-art methods quantitatively and qualitatively.

Snow is a highly complicated atmospheric phenomenon that usually contains snowflake, snow streak, and veiling effect (similar to the haze or the mist). In this literature, we propose a single image desnowing algorithm to address the diversity of snow particles in shape and size. First, to better represent the complex snow shape, we apply the dual-tree wavelet transform and propose a complex wavelet loss in the network. Second, we propose a hierarchical decomposition paradigm in our network for better under-standing the different sizes of snow particles. Last, we propose a novel feature called the contradict channel (CC) for the snow scenes. We find that the regions containing the snow particles tend to have higher intensity in the CC than that in the snow-free regions. We leverage this discriminative feature to construct the contradict channel loss for improving the performance of snow removal. Moreover, due to the limitation of existing snow datasets, to simulate the snow scenarios comprehensively, we propose a large-scale dataset called Comprehensive Snow Dataset (CSD). Experimental results show that the proposed method can favorably outperform existing methods in three synthetic datasets and real-world datasets. The code and dataset are released in https://github.com/weitingchen83/ICCV2021-Single-Image-Desnowing-HDCWNet.

Single image haze removal is crucial in computer vision. In open literatures, two kinds of dehazing strategies (prior-based and learning-based methods) have been developed. However, they have a trade-off between detail preservation and the image quality. Prior-based methods reconstruct the detail well but have lower image quality while learning-based methods achieve better recovered quality but lose the detail. In this paper, to mitigate this dilemma, a hierarchical architecture using the discrete wavelet transform (DWT) is proposed. It divides the dehazing problem into two parts: detail and background reconstruction. Based on investigating how haze affects the image in the wavelet domain, two networks for detail and background reconstruction are proposed. To avoid color distortion and the detail loss, the anti-vanish wavelet loss and the bound penalty are proposed. The multi-level wavelet component discriminator is proposed for further improvement. Experiments show that the proposed network can achieve superior performance in all metrics.

In this paper, we present a simple, efficient, and highly modularized network architecture for single-image snow-removal. To address the challenging snow-removal problem in terms of network interpretability and computational complexity, we employ a pyramidal hierarchical design with lateral connections across different resolutions. This design enables us to incorporate high-level semantic features with other feature maps at different scales to enrich location information and reduce computational time. In addition, a refinement stage based on recently introduced generative adversarial networks (GANs) is proposed to further improve the visual quality of the resulting snow-removed images and make a refined image and a clean image indistinguishable by a computer vision algorithm to avoid the potential perturbations of machine interpretation. Finally, atrous spatial pyramid pooling (ASPP) is adopted to probe features at multiple scales and further boost the performance. The proposed DesnowGAN (DS-GAN) performs significantly better than state-of-the-art methods quantitatively and qualitatively on the Snow100K dataset.

A modern field-programmable gate array (FPGA) often contains an ASIC-like clocking architecture which is crucial to achieve better skew and performance. Existing conventional FPGA placement algorithms seldom consider clocking resources, and thus may lead to clock routing failures. To address the special FPGA clocking architecture, this article presents an effective clock-aware placement algorithm for large-scale heterogeneous FPGAs. Our algorithm consists of four major technologies: 1) a combinatorial clock fence region method to effectively reduce the overuse of clocking resources; 2) a smoothed heterogeneous density function to lead heterogeneous blocks to desired sites and a coordinate transformation technique to facilitate CLB cell spreading; 3) a heterogeneous force modulation algorithm to stabilize placement movement and a hierarchical contraction technique to remedy an insufficiency of the multilevel placement framework; and 4) a two-level clock-aware packing and legalization scheme to generate an optimized, clocking-violation-free placement. We evaluate our results based on the ISPD 2017 Clock-Aware Placement Contest benchmark suite. Compared with the state-of-the-art placers, the experimental results show that our algorithm achieves the best-routed wirelength.

Trustworthiness is the probability that a system will function according to intended behaviors under a set of circumstances as demonstrated by qualities including, but not limited to safety, security, privacy, reliability, real timeliness. Trustworthiness in the industrial Internet of Things (IIoT) systems and applications is crucial to a vital expectation of industrial investors. Preserving the trustworthiness of such a system and network is crucial to void cost, time, and loss of lives. A trustworthy IIoT system considers both the security characteristics and system functionalities (faults, failures) of IoT trustworthiness. Traditional security systems, tools, techniques, and apps are not enough to protect the IIoT platform due to the practical facts in real industrial environments, diverse protocols, constrained upgrade opportunities, mismatch in protocols, and resource constraints in the industrial system. Regarding these concerns, this special section targets to bring up-to-date research results of IIoT systems and applications with the trustworthiness support. This leads to steady operations and high-quality results and improved safety in the IIoT.

Images captured in a hazy environment usually suffer from bad visibility and missing information. Over many years, learning-based and handcrafted prior-based dehazing algorithms have been rigorously developed. However, both algorithms exhibit some weaknesses in terms of haze removal performance. Therefore, in this work, we have proposed the patch-map-based hybrid learning DehazeNet, which integrates these two strategies by using a hybrid learning technique involving the patch map and a bi-attentive generative adversarial network. In this method, the reasons limiting the performance of the dark channel prior (DCP) have been analyzed. A new feature called the patch map has been defined for selecting the patch size adaptively. Using this map, the limitations of the DCP (e.g., color distortion and failure to recover images involving white scenes) can be addressed efficiently. In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously. A combination of traditional and learning-based methods can efficiently improve the haze removal performance of the network. Experimental results show that the proposed method can achieve better reconstruction results compared to other state-of-the-art haze removal algorithms.

Images are often corrupted by natural obscuration (e.g., snow, rain, and haze) during acquisition in bad weather conditions. The removal of snowflakes from only a single image is a challenging task due to situational variety and has been investigated only rarely. In this article, we propose a novel snow removal framework for a single image, which can be separated into a sparse image approximation module and an adaptive tolerance optimization module. The first proposed module takes the advantage of sparsity-based regularization to reconstruct a potential snow-free image. An auto-tuning mechanism for this framework is then proposed to seek a better reconstruction of a snow-free image via the time-varying inertia weight particle swarm optimizers in the second proposed module. Through collaboration of these two modules iteratively, the number of snowflakes in the reconstructed image is reduced as generations progress. By the experimental results, the proposed method achieves a better efficacy of snow removal than do other state-of-the-art techniques via both objective and subjective evaluations. As a result, the proposed method is able to remove snowflakes successfully from only a single image while preserving most original object structure information.

The importance of effective surgical wound care cannot never be underestimated. Poorly managing surgical wounds may cause many serious complications. Thus, it raises the necessity to develop a patient-friendly self-care system which can help both patients and medical professionals to ensure the state of the surgical wounds without any special medical equipment. In this paper, a surgical wound assessment system for self-care is proposed. The proposed system is designed to enable patients capture surgical wound images of themselves by using a mobile device and upload these images for analysis. Combining image-processing and machine-learning techniques, the proposed method is composed of four phases. First, images are segmented into superpixels where each superpixel contains the pixels in the similar color distribution. Second, these superpixels corresponding to the skin are identified and the area of connected skin superpixels is derived. Third, surgical wounds will be extracted from this area based on the observation of the texture difference between skin and wounds. Lastly, state and symptoms of surgical wound will be assessed. Extensive experimental results are conducted. With the proposed method, more than 90% state assessment results are correct and more than 91% symptom assessment results consistent with the actual diagnosis. Moreover, case studies are provided to show the advantage and limitation of this system. These results show that this system could perform well in the practical self-care scenario.

Given a design variable and its fanin cone, determining whether one fanin variable has controlling power over other fanin variables can benefit many design steps such as verification, synthesis and test generation. In this work we formulate this path controllability problem and propose several algorithms that not only solve this problem but also return values that enable or block other fanin variables. Empirical results show that our algorithms can effectively perform path controllability analysis and help produce high-quality designs.

In this paper, we present a new hybrid representation of slicing trees and multi-packing trees, called multi-domain-packing trees (MDP-trees), for macro placement to handle ultra large-scale multi-domain mixed-size designs. A multi-domain design typically consists of a set of mixed-size domains, each with hundreds/thousands of large macros and (tens of) millions of standard cells, which is often seen in modern high-end applications (e.g., 4G LTE products and upcoming 5G ones). To the best of our knowledge, there is still no published work specifically tackling the domain planning and macro placement simultaneously. Based on binary trees, the MDP-tree is very efficient and effective for handling macro placement with multiple domains. Previous works on macro placement can handle only single-domain designs, which do not consider the global interactions among domains. In contrast, our MDP-trees plan domain regions globally, and optimize the interconnections among domains and macro/cell positions simultaneously. The placement area of each domain is well reserved, and the macro displacement is minimized from initial macro positions of the design prototype. Experimental results show that our approach can significantly reduce both the average half-perimeter wirelength and the average global routing wirelength.

In this paper, we proposed a novel haze removal algorithm based on a new feature called the patch map. Conventional patch-based haze removal algorithms (e.g. the Dark Channel prior) usually performs dehazing with a fixed patch size. However, it may produce several problems in recovered results such as oversaturation and color distortion. Therefore, in this paper, we designed an adaptive and automatic patch size selection model called the Patch Map Selection Network (PMS-Net) to select the patch size corresponding to each pixel. This network is designed based on the convolutional neural network (CNN), which can generate the patch map from the image to image. Experimental results on both synthesized and real-world hazy images show that, with the combination of the proposed PMS-Net, the performance in haze removal is much better than that of other state-of-the-art algorithms and we can address the problems caused by the fixed patch size.

editorial Free Access Share on Dependability in Cyber-Physical Systems and Applications Authors: Md Zakirul Alam Bhuiyan Fordham University, New York, NY, USA Fordham University, New York, NY, USAView Profile , Sy-yen Kuo National Taiwan University, Taipei, Taiwan National Taiwan University, Taipei, TaiwanView Profile , Damian Lyons Fordham University, New York, NY, USA Fordham University, New York, NY, USAView Profile , Zili Shao The Hong Kong Polytechnic University, Hung Hom, Hong Kong The Hong Kong Polytechnic University, Hung Hom, Hong KongView Profile Authors Info & Claims ACM Transactions on Cyber-Physical SystemsVolume 3Issue 1January 2019 Article No.: 1pp 1–4https://doi.org/10.1145/3271432Published:29 September 2018Publication History 4citation680DownloadsMetricsTotal Citations4Total Downloads680Last 12 Months116Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

In-memory techniques keep data into faster and more expensive storage media for improving performance of big data processing. However, existing mechanisms do not consider how to expedite the data processing applications that access the input datasets only once. Another problem is how to reclaim memory without affecting other running applications. In this paper, we provide scheduling-aware data prefetching and eviction mechanisms based on Spark, Alluxio, and Hadoop. The mechanisms prefetch data and release memory resources based on the scheduling information. A mathematical method is proposed for maximizing the reduction of data access time. To make the mechanisms applicable in large-scale environments, we propose a heuristic algorithm to reduce the computational time. Furthermore, an enhanced version of the heuristic algorithm is also proposed to increase the amount of prefetched data. Finally, we perform real-testbed and simulation experiments to show the effectiveness of the proposed mechanisms.

Images acquired from digital cameras are usually interfered by smoke, which may degrade the performance of object detection. There are few algorithms focused on smoke removal for still images so far and we usually use haze removal algorithms to remove smoke instead. However, there exist some differences between haze and smoke (e.g. particle properties and localization). Thus, a dehaze algorithm usually has limited performance for smoke removal. In this paper, we propose a novel smoke removal algorithm based on machine learning and smoke detection techniques. Moreover, we observed that the intensity distributions are not the same for different color channels in smoky images. Therefore, the proposed algorithm trains the models corresponding to each color channel and remove smoke from RGB channels separately. Simulations show that the proposed algorithm can significantly remove smoke. Moreover, as far as we know, the proposed algorithm is the first smoke removal algorithm for static images.

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

In NB-IoT systems, UEs with poor signal quality employ more repetitions to compensate for additional signal attenuation. Excessively high CE levels and repetitions of UEs lead to wastage of valuable wireless resources, whereas inadequate CE levels and repetitions result in data retrieval failure at the receiving end. Therefore, a machine learning-based adaptive repetition scheme for a 3GPP NB-IoT system is proposed in this work to effectively improve overall network transmission efficiency. The results of simulation show the effect of the discount factor? on the convergence behavior of the proposed scheme, with a lower discount factor value denoting the myopic behavior of the proposed scheme, which results from the fact that it places more emphasis on immediate rewards. And the propose scheme is capable of effectively improving the average spectral efficiency.

MapReduce is a software framework for processing data-intensive applications with a parallel manner in cloud computing systems. Some MapReduce jobs have the deadline requirements for their job execution. The existing deadline-constrained MapReduce scheduling schemes do not consider the following two problems: various node performance and dynamical task execution time. In this paper, we utilize the Bipartite Graph modelling to propose a new MapReduce Scheduler called the BGMRS. The BGMRS can obtain the optimal solution of the deadline-constrained scheduling problem by transforming the problem into a well-known graph problem: minimum weighted bipartite matching. The BGMRS has the following features. It considers the heterogeneous cloud computing environment, such that the computing resources of some nodes cannot meet the deadlines of some jobs. In addition to meeting the deadline requirement, the BGMRS also takes the data locality into the computing resource allocation for shortening the data access time of a job. However, if the total available computing resources of the system cannot satisfy the deadline requirements of all jobs, the BGMRS can minimize the number of jobs with the deadline violation. Finally, both simulation and testbed experiments are performed to demonstrate the effectiveness of the BGMRS in the deadline-constrained scheduling.

Outdoor images captured during hazy conditions have degraded visibility. The lack of both a medium transmission and atmospheric lights in a single haze image cause an ill-posed problem in the atmospheric scattering model. This paper proposes a novel haze density estimation model with a universal atmospheric-light extractor for single-image dehazing. The proposed method employs exponential inference to construct an exponential inference model to more accurately estimate haze density compared with the state-of-the-art methods. The coefficients in the proposed haze density estimation model are learned using a turbulent particle swarm optimization technique to obtain the best approximation of medium transmission. Moreover, a novel universal atmospheric-light extractor based on support vector data description is utilized to resolve the problem caused by a lack of atmospheric light. The overall results obtained by conducting qualitative and quantitative evaluations demonstrated that the proposed method has substantially higher dehazing efficacy and produces fewer artifacts than the state-of-the-art haze removal methods.

Restoration of visibility in hazy images is the first relevant step of information analysis in many outdoor computer vision applications. To this aim, the restored image must feature clear visibility with sufficient brightness and visible edges, while avoiding the production of noticeable artifacts. In this paper, we propose a haze removal approach based on the radial basis function (RBF) through artificial neural networks dedicated to effectively removing haze formation while retaining not only the visible edges but also the brightness of restored images. Unlike traditional haze-removal methods that consist of single atmospheric veils, the multiatmospheric veil is generated and then dynamically learned by the neurons of the proposed RBF networks according to the scene complexity. Through this process, more visible edges are retained in the restored images. Subsequently, the activation function during the testing process is employed to represent the brightness of the restored image. We compare the proposed method with the other state-of-the-art haze-removal methods and report experimental results in terms of qualitative and quantitative evaluations for benchmark color images captured in typical hazy weather conditions. The experimental results demonstrate that the proposed method is able to produce brighter and more vivid haze-free images with more visible edges than can the other state-of-the-art methods.

Cloud computing services provide flexible computing and storage resources to process large amount of datasets. In-memory techniques keep the frequently used data into faster and more expensive storage media for improving performance of data processing services. Data prefetching aims to move data to low-latency storage media to meet requirements of performance. However, existing mechanisms do not consider how to benefit the data processing applications which do not frequently access the same datasets. Another problem is how to reclaim memory resources without affecting other running applications. In this paper, we provide a Scheduling-Aware Data Prefetching (SADP) mechanism for data processing services in a cloud data center. The SADP includes data prefetching and data eviction mechanisms. It firstly evicts the data from memory to release resources for hosting other data blocks, and then it caches the data that will be used in near future. Finally, real-testbed experiments are performed to show the effectiveness of the proposed SADP.

For circuit designs in advanced technologies, standard-cell libraries consist of cells with different heights; for example, the number of fins determines the height of cells in the FinFET technology. Cells of larger heights give higher drive strengths, but consume larger areas and power. Such mixed cell heights incur new, complicated challenges for layout designs, due mainly to the heterogeneity in cell dimensions and thus their larger solution spaces. There is not much published work on layout designs with mixed-height standard cells. This paper addresses the legalization problem of mixed-height standard cells, which intends to place cells without any overlap and with minimized displacement. We first study the properties of Abacus, generally considered the best legalization method for traditional single-row-height standard cells but criticized not suitable for handling the new challenge, analyze the capability and insufficiencies of Abacus for tackling the new problem, and remedy Abacuss insufficiencies and extend its advantages to develop an effective and efficient algorithm for the addressed problem. For example, dead spaces become a critical issue in mixed-cell-height legalization, which cannot be handled well with an Abacus variant alone. We thus derive a dead-space-aware objective function and an optimization scheme to handle this issue. Experimental results show that our algorithm can achieve the best wirelength among all published methods in reasonable running time, e.g., about 50% smaller wirelength increase than a state-of-the-art work.

A modern FPGA often contains an ASIC-like clocking architecture which is crucial to achieve better skew and performance. Existing conventional FPGA placement algorithms seldom consider clocking resources, and thus may lead to clock routing failures. To address the special FPGA clocking architecture, this paper presents a novel clock-aware placement algorithm for large-scale heterogeneous FPGAs. Our algorithm consists of three major stages: (1) a nonlinear global placement framework with clock fence region construction, (2) a clock-aware packing scheme, and (3) clock-aware legalization and detailed placement. We evaluate our results based on the 2017 ISPD Clock-Aware Placement Contest benchmark suite. Compared with the top three winners, the results show that our algorithm achieves the best overall routed wirelength. On average, our algorithm outperforms the top-3 winners by 3.6%, 7.5%, and 12.9% in routed wirelength, respectively.

As the number of everyday objects connected to the Internet grows rapidly, securing these connected devices is a big security challenge. Key establishment in Internet of Things (IoT) becomes a challenging problem when considering the resource constrained sensor nodes. In spite of the fact that many clever solutions have been proposed, no practical and suitable scheme has emerged, especially for the extremely large amount of sensor nodes in the wireless sensor network (WSNs) in the future. In this paper, we propose a new key establishment scheme for IoT. The scheme is achieved by Kronecker product and satisfies the following conditions. 1) Substantially decreases the amount of data needs to be stored in a sensor node, 2) efficiently compute the pairwise key, 3) no communication is needed during the computation of the keys. The security evaluation is performed and we also present an in depth analysis of our scheme in terms of computation cost, communication cost and storage cost.

Many elderly persons prefer to stay alone in a single-resident house for seeking an independent life and reducing the cost of health care. However, the independent life cannot be maintained if the resident develops dementia. Thus, an early detection of dementia is essential for the elderly to extend their independent lifetime. One of the early symptoms of dementia is forgetting something when the person leaves the house. In this study, we introduce a novel front-door events (exit, enter, visitor, other, and brief-return-and-exit (BRE)) and their classification scheme that validated by using open datasets (n = 10) collected from ten single-resident testbeds by anonymous binary sensors. BRE events occur when four consecutive events (exit-enter-exit-enter) happen in some certain time intervals (t1, t2, and t3), and some of them may be the forget events. Each testbed had one older adult (aged 73 years and over) during the experimental period (μ = 583.1 ± 297.3 days). The algorithm automatically classifies the resident's front-door events and ignores visitor's entrance and exit events. The experimental results reveal the significance of the tiparameters for the number of BRE events. Since BRE events may include forget events, the proposed algorithm could be a useful tool for the forget event detection.

A novel quality of service (QoS) provisioning algorithm for home networks is presented in this paper. The algorithm carries out the QoS-aware bandwidth allocation using the general regression neural networks (GRNNs). Among all the allocations predicted to receive positive service responses, the algorithm finds the allocation with minimum total bandwidth for the current service. The service response prediction is based on the GRNN with the training set containing the bandwidth allocations and their service responses for past transmissions. The new service responses will then be used to update the training set for the subsequent transmissions. To attain accurate tracking of diversified service requirements, flexible specification of service response levels and QoS levels are provided. Both analytical and numerical studies reveal that the proposed algorithm is able to provide prompt or steady reactions to the service feedback depending on the variations of the source data rates. Because of its simplicity and effectiveness, the proposed algorithm is well suited for dynamic QoS management for heterogeneous home networks.

In this paper we represent our proposed novel morphological filter developed under the scope of Taiwan-Mongolian co-project. We applied the implemented filter in vehicle detection from CCTV video signal. Our goalwas to develop a filter that can reduce the noise in background subtracted binary image, which created by camera shake, and unnecessary moving objects such as wave of the tree etc. We compared our filter performance with morphological open, close, erosion, dilation, and median filters. PSNR (Peak Signal to Noise Ratio) is employed for evaluating the performance of the filters, our filter's PSNR was relatively higher (21.39) than the other method. Furthermore, we used our filter for vehicle detection, and detection rate was 100% as the other methods. Thus, we conclude the new filter is sufficient for denoising binary image, and suitable for vehicle detection.

As interconnects dominate circuit performance in modern chip designs, placement becomes an essential stage in optimizing timing. Recent timing-driven placement (TDP) techniques focus mainly on optimizing late slack rather than early slack. This paper presents a TDP algorithm to improve the early slack while preserving an optimized late slack. The preservation is achieved by accurately predicting optimal Steiner tree topologies after each move in our TDP algorithm. An optimality-preserving pruning scheme for each move is proposed to speed up the optimization process, without sacrificing the solution quality. Experimental results show that our algorithm can substantially improve the early slacks and the overall quality scores of the top-2 winning placers of the 2015 ICCAD Incremental Timing-Driven Placement Contest, while preserving their late slacks.

The use of nuclear energy to generate electric power is crucial to meet the high energy demand of a modern economy. In newly constructed nuclear power plants (NPPs), the trend among control systems is to replace the obsolete analog hard-wired systems with the contemporary digital and cyber-based systems. Therefore, cyberphysical security as well as dependability are critical issues in safety critical NPPs. In this paper, we present different levels/layers of protection to manage cyber/physical security. We also discuss the interrelationship between cyber and physical attacks. We adopt generalized stochastic Petri nets to quantitatively evaluate the intrusion probability. We then propose a new cyberframework and show that the proposed framework not only prevents cyberattacks but also conforms to cybersecurity regulations. We also propose a physical framework to prevent potential physical attacks. Finally, we discuss dependability through three metrics, i.e., reliability, maintainability, and availability. A case study is presented to demonstrate that the proposed cyberframework is highly dependable through analyzing steady-state probabilities.

In this paper, we propose coding-aided K-means clustering (CKMC) blind transceiver for space shift keying (SSK) multiple-input multiple-output (MIMO) systems, where the training of channel state information (CSI) is not required for detection. For the scenario where transmitter is with limited processing capability and limited power such as Internet Of Things (IOT) and wireless sensor network (WSN), SSK is preferable to typical MIMO due to its simplicity and improved energy efficiency. The proposed CKMC blind communication trades off receiver complexity for training overhead, which provides better spectral efficiency compared to training-based transceiver. In CKMC, the blind communication problem is converted to the problems of clustering and permutation; for the clustering problem, we propose K-means clustering (KMC) detector to reduce detection complexity; for the permutation problem, we propose to perform depermutation with the aid of channel decoding. The analysis of CKMC blind transceiver is conducted, and the verification of performance of CKMC is presented in the simulations section. The results show that the performance of CKMC blind communication can closely approach the performance of optimal receiver with perfect CSI under certain scenarios.

Clone detection, aimed at detecting illegal copies with all of the credentials of legitimate sensor nodes, is of great importance for sensor networks because of the severe impact of clones on network operations, like routing, data collection, and key distribution. Various detection methods have been proposed, but most of them are communication-inefficient due to the common use of the witness-finding strategy. In view of the sparse characteristic of replicated nodes, we propose a novel clone detection framework, called CSI, based on a state-of-the-art signal processing technology, compressed sensing. Specifically, CSI bases its detection effectiveness on the compressed aggregation of sensor readings. Due to its consideration of data aggregation, CSI not only achieves the asymptotically lowest communication cost but also makes the network traffic evenly distributed over sensor nodes. In particular, this is achieved by exploiting the sparse property of the clones within the sensor network caused by the clone attack. The performance and security of CSI will be demonstrated by numerical simulations, analyses, and prototype implementation.

Blind quantum computation is an appealing use of quantum information technology because it can conceal both the client's data and the algorithm itself from the server. However, problems need to be solved in the practical use of blind quantum computation and fault-tolerance is a major challenge. Broadbent et al. proposed running error correction over blind quantum computation, and Morimae and Fujii proposed using fault-tolerant entangled qubits as the resource for blind quantum computation. Both approaches impose severe demands on the teleportation channel, the former requiring unrealistic data rates and the latter near-perfect fidelity. To extend the application range of blind quantum computation, we suggest that Alice send input qubits encoded with error correction code instead of single input qubits. Two fault-tolerant protocols are presented and we showed the trade-off of the computational overhead using the ten-bit quantum carry-lookahead adder as an example. Though these two fault-tolerant protocols require the client to have more quantum computing ability than using approaches from prior work, they provide better fault-tolerance when the client and the server are connected by realistic quantum repeater networks.

We propose the application of cosine windowing for the orthogonal frequency-division multiplexing (OFDM) systems to self-cancel intercarrier interference (ICI) in fast time-varying channels prior to receptions. With a time-domain cosine window immediately after the inverse discrete Fourier transform (IDFT) unit in OFDM transmitters, the ICI fractions from adjacent subcarriers significantly cancel one another at the expense of the orthogonality violation among subcarriers in the main lobe. As a result, the frequency-domain channel matrix reshaped by the cosine windowing can be closely approximated to a strictly banded matrix. In the complex exponential basis expansion model (CE-BEM), we present the estimation of the channel matrix with the assistance of the pilot clusters. Simulation results show that the receivers implementing the CE-BEM channel estimation and the low-complexity block turbo minimum mean square error (MMSE) equalization perform with considerably lower bit error rates (BER) even in very fast time-varying channels.

MapReduce is a software framework for processing data-intensive applications with a parallel manner in cloud computing systems. There are also an increasing number of MapReduce jobs that require deadline guarantees. The existing deadline-concerning scheduling schemes do not consider the two problems in the MapReduce computing environment: slot performance heterogeneity and job time variation. In this paper, we utilize the Bipartite Graph modeling to propose a new MapReduce Scheduler called the BGMRS. The BGMRS can obtain the optimal solution of the deadline-constrained scheduling problem by transforming the problem into a well-known graph problem: minimum weighted bipartite matching. The BGMRS has the following features. It considers the heterogeneous cloud computing environment, such that the computing resources of some nodes cannot meet the deadlines of some jobs. As the job progresses, the BGMRS can dynamically find different computing resources for running the job without violating the job deadline. This is beneficial in the computing resource utilization. The BGMRS can also trade the data locality off against the deadline to make more jobs with deadline guarantees. If the available computing resources of the system cannot meet all job deadlines, the BGMRS can minimize the number of jobs with the deadline violation. Finally, simulation experiments are performed to demonstrate the effectiveness of the BGMRS in the deadline-constrained scheduling.

This paper proposes an efficient mechanism, called PCTopk, for functional top-k query with a combination of multiple conditions/dimensions in two-tiered sensor networks to simultaneously preserve data privacy and correctness (i.e., authenticity and integrity). PCTopk constructs a layered authentication tree, cooperated with an order-preserving symmetric encryption technique, for only permitting storage nodes to systematically process inquired data over encryption domain and enabling querists to efficiently verify the authentic and complete query results. To the best of our knowledge, this is the first research work on the issue of secure functional top-k query with a combination of multiple conditions in two-tiered sensor networks. The performance evaluation results show that PCTopk takes significantly less energy consumption and storage space than prior arts while preserving data privacy and correctness.

Storage nodes are expected to be placed as an intermediate tier of large scale sensor networks for caching the collected sensor readings and responding to queries with benefits of power and storage saving for ordinary sensors. Nevertheless, an important issue is that the compromised storage node may not only cause the privacy problem, but also return fake/incomplete query results. We propose a simple yet effective dummy reading-based anonymization framework, under which the query result integrity can be guaranteed by our proposed verifiable top-$k$query (VQ) schemes. Compared with existing works, the VQ schemes have a fundamentally different design philosophy and achieve the lower communication complexity at the cost of slight detection capability degradation. Analytical studies, numerical simulations, and prototype implementations are conducted to demonstrate the practicality of our proposed methods.

OpenFlow (OF) Network is a novel network architecture many famous cloud service providers have applied it to build their data center network. The difference between OF Network and traditional network architecture is the decoupling of controller planes and data planes for network management. Intrusion detection is very important in cloud computing to improve system security. Because OF network can improve the response time of an alert by efficiently configuring network flows, we design an event-based Intrusion Detection System (IDS) architecture on OF network.

This paper examines techniques for a large network infrastructure reconnaissance and dives into a real-world case study of a nation-wide passive network vulnerability assessment. The main goal of this study is to understand methods of a large network risk evaluation and conduct practical experiments using a national network. The main contribution of this paper is a non-intrusive method of a large network infrastructure reconnaissance and an application of acquired data to measure network vulnerability exposures within the analysed network. In this study our assumption is based on an estimation that actual threats come from the actively exploited vulnerabilities. Information on exploit-targeted platforms and vulnerabilities could be easily collected from a large set of malicious websites and automatically turned into signatures. We propose an automated method of building such signatures and use those to analyse the reconnaissance data set to identify ranges of vulnerable systems.

Outdoor images captured during inclement weather conditions generally exhibit visibility degradation. Localized light sources often result from activation of streetlights and vehicle headlights and are common scenarios in these conditions. The presence of localized light sources in hazy images may cause the generation of over saturation artifacts when those images are restored by traditional state-of-the-art haze removal techniques. Therefore, we propose a novel haze removal approach based on the proposed hybrid dark channel prior technique in order to remedy the problems associated with localized light sources during image restoration. The overall results show that the proposed haze removal approach can recover haze-free images more effectively than can the other previous state-of-the-art haze removal approach while avoiding over-saturation.

This paper proposes a clustering-based blind detector for multiple-input multiple-output system using space shift keying modulation. First, we convert the blind detection problem to a clustering problem while considering block fading channel. Second, we use the well-known k-means clustering algorithm to design the blind detector. Third, the proposed k-means clustering detector for a blind receiver can provide comparable performance to that of the optimal receiver with perfect channel state information under the conditions of sufficient channel coherent time and sufficient random initializations of the k-means clustering algorithm. Simulations are conducted to demonstrate the performance of the proposed detector.

In this paper, we consider employing a multi-element antenna (MEA) with close spacing to tackle the challenging channel estimation (CE) in highly mobile OFDM systems. Instead of large spacing for diversity, we propose to place the adjacent elements with one symbol distance in the moving direction to observe the quasi-duplicated channels in temporal difference of one symbol period. In exploiting the quasi-duplicated channels, we developed a novel CE-symbol detection (SD) iteration that cooperates with the standardized comb-type pilots to track fast varying channels. From simulation results, we show the proposed system outperforms the conventional receiver of two antennas with spatial diversity in highly mobile channels as long as the mutual coupling effects with the close-spaced elements are restricted.

Daily life applications of machine‐to‐machine (M2M) communication are constantly increasing. Typically, M2M communication systems comprise numerous small, cheap and autonomous devices that communicate with each other while monitoring environmental conditions. After their deployment, however, remote entity management of a node is still needed for firmware or software updates required for bug fixes, functional changes or other maintenance. Therefore implementing remote entity management for M2M service capability in third generation partnership project machine‐type‐communication is a major challenge. Previous works have attempted to reduce traffic by updating only the software changes in the M2M device. However, the device must still reboot after a software update. Rebooting the device is costly since the previous runtime states are lost. The devices expend time and bandwidth when synchronising with other nodes and when rebuilding the routing table. Hence, the dynamic software update model (DSUM) proposed in this study is designed to enable remote entity management for M2M. A dynamic software update programming model and a prototype implementation for M2M service capability are also presented. By allowing M2M service capabilities to update the software without rebooting the node, the DSUM preserves precious runtime states. The tests in this study showed that software updating required only 88 clock cycles. This framework not only enables dynamic replacement of remote entity management functionalities at runtime, it also reduces power consumption by avoiding the need to rebuild the network topology for devices in an M2M communication network.

We deal with the challenging problem of node replication detection. Although defending against node replication attacks demands immediate attention, compared to the extensive exploration on the defense against node replication attacks in static networks, only a few solutions in mobile networks have been presented. Moreover, while most of the existing schemes in static networks rely on the witness-finding strategy, which cannot be applied to mobile networks, the velocity-exceeding strategy used in existing schemes in mobile networks incurs efficiency and security problems. Therefore, based on our devised challenge-and-response and encounter-number approaches, localized algorithms are proposed to resist node replication attacks in mobile sensor networks. The advantages of our proposed algorithms include 1) localized detection; 2) efficiency and effectiveness; 3) network-wide synchronization avoidance; and 4) network-wide revocation avoidance. Performance comparisons with known methods are provided to demonstrate the efficiency of our proposed algorithms. Prototype implementation on TelosB mote demonstrates the practicality of our proposed methods.

Ensuring the security of communication and access control in Wireless Sensor Networks (WSNs) is of paramount importance. In this paper, we present a security mechanism, MoteSec-Aware, built on the network layer for WSNs with focus on secure network protocol and data access control. In the secure network protocol of MoteSec-Aware, a Virtual Counter Manager (VCM) with a synchronized incremental counter is presented to detect the replay and jamming attacks based on the symmetric key cryptography using AES in OCB mode. For access control, we investigate the Key-Lock Matching (KLM) method to prevent unauthorized access. We implement MoteSec-Aware for the TelosB prototype sensor platform running TinyOS 1.1.15, and conduct field experiments and TOSSIM-based simulations to evaluate the performance of MoteSec-Aware. The results demonstrate that MoteSec-Aware consumes much less energy, yet achieves higher security than several state-of-the-art methods.

ABSTRACT This paper proposes a time–frequency‐based fingerprinting identification approach by extracting the transient characterizations regarding highly integrated wireless local area network (WLAN) cards. The transient energy envelope is derived from the slice of spectrogram in the time–frequency domain. Then this transient response is fitted to a polynomial under least square criteria, and the polynomial coefficients are regarded as the feature vector. A data acquisition system has been set up to capture IEEE 802.11b Wi‐Fi (wireless fidelity) signals. The results exhibit an approving distinctiveness of up to 94% to classify different manufactories of WLAN cards and of 78.4% for the WLAN cards of the same manufactory. Copyright © 2011 John Wiley & Sons, Ltd.

A new efficient algorithm using the compressed domain features of H.264 INTRA frames is proposed for moving object extraction on huge video surveillance archives. To achieve searching efficiency, we propose to locate moving objects by scrutinizing only the INTRA frames in video surveillance archives in H.264 compressed domain with short GOP length. In the proposed structure, a modified codebook algorithm is designed to build the block-based background models from the INTRA coding features. Through the subtraction with the background codebook models, the foreground energy frame is filtered and normalized for detecting the existence of moving objects. To overcome the over-segmentation problem and enable the unsupervised searching, a new structure of hysteresis thresholding, where the thresholds are obtained automatically by an efficient algorithm, is adopted to extract foreground blocks. At the final step, the connected components labeling (CCL) and morphological filters are employed to obtain the list of moving objects. As shown in the experimental results, the proposed algorithm outperforms representative existing works.

A large-scale wireless sensor network constructed in terms of two-tiered architecture, where cloud nodes take charge of storing sensed data and processing queries with respect to the sensing nodes and querists, incurs security breach. This is because the importance of cloud nodes makes them attractive to adversaries and raises concerns about data privacy and query result correctness. To address these problems, we propose an efficient approach, namely EQ (efficient query), which mainly prevents adversaries from gaining the information processed by or stored in cloud nodes, and detects the compromised cloud nodes when they misbehave. EQ can not only achieve the goals of data privacy and integrity preserving but also ensure the secure range query without incurring false positive. For data privacy preserving, EQ presents an order encryption mechanism by adopting stream cipher to encrypt/decrypt all sensed data such that a cloud node can only process issued queries over stored data in the encryption domain. For data integrity/completeness, we manipulate a data structure of XOR linked list (X2L), which allows a querist to verify the integrity of retrieved data via the socalled verification information, i.e., neighborhood difference in a storage-efficient manner. We demonstrate the feasibility and efficiency of EQ via experiments conducted on TelosB prototype sensor platform running TinyOS 1.1.15 and comparisons with state-of-the-arts.

With the growth of different design goals and application requirements, wireless sensor networks (WSNs) are receiving sustained attentions in the recent low-cost industrial automation systems. Moreover, Cognitive Radio (CR) technology gives us a possibility to maximize the utilization efficiency of the limited spectrum resources. However, because the wireless devices coexist in the same radio environment, there are harmful channel conflicts among users, and the increasing radio systems causes great contribution to the increasing energy consumption. In order to realize the industrial circumstance, we complete three major works in this paper. First of all, we describe a practical model of cumulative interferences from the entire cognitive radio-based industrial wireless sensor networks (CR-IWSNs). Then, based on the interference model and the interference avoidance purpose, we propose a self-configurable power control scheme to address the communication requirements on both interference temperature and secondary network Quality-of-Service. Finally, Nonlinear Programming is used to model the scheme and a distributed algorithm is given to solve the problem. Several simulations are given to verify the effectiveness of the proposed power control algorithm on optimizing the total system throughput and energy consumption. Results show that the throughput could be improved and the energy consumption could be reduced with the guarantee that the users are without interference.

In this paper, we investigate resource block assignment in femtocell networks. A resource block assignment algorithm is designed to avoid co-channel intercell interference and ensure service quality for femtocell networks with dense and random femto deployments. We first formulate the optimization problem as the integer linear programming (ILP) on resource block assignment. The goal of the optimization formulation is to maximize the overall utilization of resource blocks with quality of service (QoS) constraints. We propose an efficient and simple algorithm termed interference-aware resource block assignment (IARBA). By considering conditions of resource blocks, the proposed approach achieves better resource block efficiency and assignment within QoS requirements. Our analytical and simulation results show that IARBA not only provides interference-free resource block assignment but also outperforms existing schemes in terms of average throughput with comparable complexities.

In highly mobile OFDM systems, the carrier frequency offsets (CFO) with Doppler spreads for downlink detection can be considerably large, which degrades the frequency alignment for uplink transmission, particularly in employing directional antennas for inter-carrier interference (ICI) reduction. In prior works, the directional antenna was investigated with appropriate frequency alignment in receiver's local oscillator to efficiently reduce ICI in fast time varying OFDM systems. To resolve the optimal frequency offsets problem with Doppler spreads, this paper develops a simple scheme to capture instant Doppler power spectrum density (PSD) through moving directional antennas with arbitrary gain patterns. Thus, the optimal aligning frequency is derived as the center of gravity of the Doppler PSD. Simulations show our approach acquires the highest carrier to interference (C/I) ratio and the lowest bit error rates (BER) compared with other approaches.

The ad hoc network is composed of multiple sensor nodes to serve various applications, such as data collection or environmental monitoring. In many applications, the sensor nodes near the boundary of the deployment region provide biased or low-quality information because they have limited number of neighboring nodes and only partial information is available. Hence, the boundary recognition is an important issue in the ad hoc networks. By the statistical approach in high node density networks, Fekete's pioneer work identified the boundary node by number of neighboring nodes and using a specific threshold. By exploiting the number of nodes in the two-hop region, our proposed algorithm has significant improvement of boundary recognition contrasted with Fekete's algorithm in the low-density network. Given the information topology and the cost function, the analyses provide a framework to obtain the optimal threshold for boundary recognition. Besides, the simulation results reveal the proposed algorithm has greater than 90% detection rate and lower than 10% false alarm rate.

This paper presents a category of detection schemes for Multiple-Input Multiple-Output (MIMO) system called Parallel Sphere Decoder (PSD). Compared to the conventional depth-first sphere decoder with Schnorr-Euchner enumeration (SE-SD), the proposed PSD algorithms use parallel computations and achieve approximately 50% searching time reductions under the same amount of computations. Namely, in hardware implementation, the proposed work provides trade-off between computational time and computing units. Simulations of the proposed algorithms in 4×4 16-QAM and 3×3 64-QAM MIMO systems show the searching time reductions of the proposed algorithms while maintaining ML performances.

This paper proposes cooperative opportunistic routing (COR), a throughput improvement scheme for the cooperative opportunistic routing in multi-hop wireless mesh networks (WMNs). We investigate the two major issues in opportunistic routing, the selection and the prioritization metric for the candidate set. The COR is presented to select and prioritize the candidate node with minimum expected cost. This candidate selection with low expected cost on each transmission constructs a throughput efficient routing path. The COR's robust packet handling strategy is also proposed to avoid duplicated transmission without forwarding list. With more efficient candidate set and packet handling, the average throughput improves by 76% and the end-to-end delay is reduced by 15% in our simulation results.

This article focuses on channel model and capacity of multiple-input multiple-output (MIMO) relay system in a low-rank line-of-sight environment. According to the channel characteristics of MIMO and the correlations among antenna arrays, the expression for system capacity is deduced. Various factors influencing MIMO relay system are analysed based on this expression, and the numerical simulation is performed for verification. The simulation results show that system capacity is greatly increased by adding relay nodes. However, the antenna array spacing and angle at the transmitting and receiving ends, relay node position, Rice K-factor and other factors can all produce certain effects on the capacity of MIMO relay system.

SUMMARY Behavior‐based detection and signature‐based detection are two popular approaches to malware (malicious software) analysis. The security industry, such as the sector selling antivirus tools, has been using signature and heuristic‐based technologies for years. However, this approach has been proven to be inefficient in identifying unknown malware strains. On the other hand, the behavior‐based malware detection approach has a greater potential in identifying previously unknown instances of malicious software. The accuracy of this approach relies on techniques to profile and recognize accurate behavior models. Unfortunately, with the increasing complexity of malicious software and limitations of existing automatic tools, the current behavior‐based approach cannot discover many newer forms of malware either. In this paper, we implement ‘holography platform’, a behavior‐based profiler on top of a virtual machine emulator that intercepts the system processes and analyzes the CPU instructions, CPU registers, and memory. The captured information is stored in a relational database, and data mining techniques are used to extract information. We demonstrate the breadth of the ‘holography platform’ by conducting two experiments: a packed binary behavior analysis and a malvertising (malicious advertising) incident tracing. Both tasks are known to be very difficult to do efficiently using existing methods and tools. We demonstrate how the precise behavior information can be easily obtained using the ‘holography platform’ tool. With these two experiments, we show that the ‘holography platform’ can provide security researchers and automatic malware detection systems with an efficient malicious software behavior analysis solution. Copyright © 2011 John Wiley & Sons, Ltd.

For the obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem, we present a Steiner-point-based algorithm that achieves the best practical performance among existing heuristics. We first propose a new concept of Steiner point locations, creating a linear-space routing graph with satisfactory Steiner point candidates to resolve the bottleneck of most existing heuristics. Then, we propose a Steiner-point-based framework to yield a solution, which is close to the key to the handling of the OARSMT problem. Experimental results show that this algorithm achieves excellent solution quality and speed performance at the same time. We also extend the Steiner-point-based framework to the obstacle-avoiding preferred direction Steiner tree problem with a good performance.

Code coverage is a popular method to find design bugs and verification loopholes. However, once a piece of code is determined to be unreachable, diagnosing the cause of the problem can be challenging: since the code is unreachable, no counterexample can be returned for debugging. Therefore, engineers need to analyze the legality of nonexistent execution paths, which can be difficult. To address such a problem, we analyzed the cause of unreachability in several industrial designs and proposed a diagnosis technique that can explain the cause of unreachability. In addition, our method provides suggestions on how to solve the un-reachability problem, which can further facilitate debugging. Our experimental results show that this technique can greatly reduce an engineer's effort in analyzing unreachable code.

Reset is one of the most important signals in many designs. Since reset is typically not timing critical, it is handled at late physical design stages. However, the large fanout of reset and the lack of routing resources at these stages can create variant delays on different targets of the reset signal, creating reset recovery problems. Traditional approaches address this problem using physical design methods such as buffer insertion or rerouting. However, these methods may invalidate previous optimization efforts, making timing closure difficult. In this work we propose a formal method to calculate reset recovery slacks for registers at the register transfer level. Designers and physical design tools can then utilize this information throughout the design flow to reduce reset problems at later design stages.

Most synthesis tools perform optimizations based on the design itself and do not utilize the information present in the verification environment. Not using such information greatly limits the optimization capabilities of synthesis tools, which is especially serious for circuit customization because most environment constraints are encoded in the testbench. To exploit verification intention, we propose a methodology that utilizes functional assertions for design optimization. To support circuit customization, we also propose a property mining technique that can extract properties from the design under the constraints in the testbench. Our experimental results show that these methods can reduce design size after synthesis, and the optimization is orthogonal to other existing circuit customization methods.

The use of nuclear energy to generate electric power is crucial in meeting the high energy demand of modern economy. The dependability analysis of nuclear power plants has been a critical issue and the reactor containment is the most important safety structure acting as a barrier against the release of radioactive material to the environment. In this paper, we analyze the dependability of the reactor containment. We also propose a tool for design, implementation, and V&V to enhance the dependability of reactor containment through an integrated leakage rate test. Our practical experiences in the on-site tests are also discussed.

This work proposes an adaptive intercell interference avoidance scheme for the self-organization in Orthogonal Frequency Division Multiple Access (OFDMA) femtocell networks. Due to the expected large number of user-deployed cells, the femtocell networks suffers from the intercell interference problem. In this paper, we define the self-organizing resource allocation problem to maximize resource efficiency with OFDMA architecture. The proposed problem formulation supports the desired Quality of Service (QoS) criteria while satisfying reliability constraints. We develop an autonomous resource allocation algorithm to pursue the most efficient frequency allocation. From the simulation results, the proposed approaches can increase the system throughput by over 13%, while the femtocell interference can be avoided completely.

The use of nuclear energy to generate electric power is crucial in meeting the high energy demand of modern economy. The dependability of nuclear power plants has been a critical issue and the reactor containment is the most important safety structure acting as a barrier against the release of radioactive material to the environment. In this paper, we propose a practical framework for design, implementation, and V&V to enhance the dependability of reactor containment through an integrated leakage rate test.

In order to steal valuable data, hackers are uninterrupted research and development new techniques to intrude computer systems. Opposite to hackers, security researchers are uninterrupted analysis and tracking new malicious techniques for protecting sensitive data . There are a lot of existing analyzers can be used to help security researchers to analyze and track new malicious techniques. However, these existing analyzers cannot provide sufficient information to security researchers to perform precise assessment and deep analysis. In this paper, we introduce a behavior-based malicious software profiler, named Holography platform, to assist security researchers to obtain sufficient information. Holography platform analyzes virtualization hardware data, including CPU instructions, CPU registers, memory data and disk data, to obtain high level behavior semantic of all running processes. High level behavior semantic can provide sufficient information to security researchers to perform precise assessment and deep analysis new malicious techniques, such as malicious advertisement attack(malvertising attack).

The ad hoc sensor network has been widely applied to various applications, such as environmental data collection and surveillance. To enable these applications, the accurate localization of sensor nodes is crucial. The DV-Hop provides a basic scheme to retrieve the localization information without GPS. The DV-Hop scheme requires the anchor nodes to be localized in advance, and the locations of the anchor nodes are used to localize other unknown nodes. The hop count between two anchor nodes can be exchanged through multi-hop routing. Using the hop counts and locations of other anchor nodes, the anchor nodes obtain the average distances per hop among one another. The distance estimation errors are caused by the uncertainties in per-hop distance estimation and the communication ranges. We propose a scheme where a node ranks the orders of its neighbor nodes through exchanging neighbor information locally. The neighbor information with orders provides useful information for node localization. Besides, the analysis reveals the probability among the order distance and the node density. Therefore, the distance estimation is improved by using the order information of neighbor nodes. The simulation results show more than 22% error reduction compared with the DV-Hop.

Lane tracing is the problem of estimating the geometric shape of the lane boundaries based on the image grabbed by a camera on board a vehicle. In this paper, a real-time video-based lane detection method is presented. By analysing the result of lane tracing, the behaviour of the vehicle is traced and the focus windows are placeable, which provides useful information to the next round detection recursively. The system is porting on both PC and iPhone 3G to verify the recognition rate and performance through field testing. With the knowledge of road model, it is applicable to the marked and the unmarked roads, as well as the dash and the solid paint line roads. Experimental results show that the proposed method is robust and the performance is capable for practical applications.

X-decomposition Quantum Decision Diagram (XQDD) can represent a quantum operation and perform matrix operations. It can be used to verify quantum and reversible circuits even if the reversible circuits have different number of garbage qubits. It is efficient in terms of space and time. In this paper, we extend the original XQDD to multiple-valued quantum logic. The extended XQDD can represent a multiple-valued quantum operation and perform matrix operations. It can be used to check the equivalence of two multiple-valued quantum or reversible circuits which are synthesized by different approaches. In this paper, we show that the space in multiple-valued XQDD is less than other representations and it is much better than multiple-valued QuIDD and very close to QMDD in terms of time.

The two-tier architecture consisting of a small number of resource-abundant storage nodes in the upper tier and a large number of sensors in the lower tier could be promising for large-scale sensor networks in terms of resource efficiency, network capacity, network management complexity, etc. In this architecture, each sensor having multiple sensing capabilities periodically forwards the multidimensional sensed data to the storage node, which responds to the queries, such as range query, top-kquery, and skyline query. Unfortunately, node compromises pose the great challenge of securing the data collection; the sensed data could be leaked to or could be manipulated by the compromised nodes. Furthermore, chunks of the sensed data could be dropped maliciously, resulting in an incomplete query result, which is the most difficult security breach. Here, we propose a simple yet effective hash tree-based framework, under which data confidentiality, query result authenticity, and query result completeness can be guaranteed simultaneously. In addition, the subtree sampling technique, which could be of independent interest to the other applications, is proposed to efficiently identify the compromised nodes. Last, analytical and extensive simulation studies are conducted to evaluate the performance and security of our methods. Prototype implementation on TelosB mote demonstrates the practicality of our proposed methods.

Sensor networks are vulnerable to false data injection attack and path-based denial of service (PDoS) attack. While conventional authentication schemes are insufficient for solving these security conflicts, an en-route filtering scheme, enabling each forwarding node to check the authenticity of the received message, acts as a defense against these two attacks. To construct an efficient en-route filtering scheme, this paper first presents a Constrained Function-based message Authentication (CFA) scheme, which can be thought of as a hash function directly supporting the en-route filtering functionality. Obviously, the crux of the scheme lies on the design of guaranteeing each sensor to have en-route filtering capability. Together with the redundancy property of sensor networks, which means that an event can be simultaneously observed by multiple sensor nodes, the devised CFA scheme is used to construct a CFA-based en-route filtering (CFAEF) scheme. In addition to the resilience against false data injection and PDoS attacks, CFAEF is inherently resilient against false endorsement-based DoS attack. In contrast to most of the existing methods, which rely on complicated security associations among sensor nodes, our design, which directly exploits an en-route filtering hash function, appears to be novel. We examine the CFA and CFAEF schemes from both the theoretical and numerical aspects to demonstrate their efficiency and effectiveness. Moreover, prototype implementation on TelosB mote demonstrates the practicality of our proposed method.

Optimizing blocks in a System-on-Chip (SoC) circuit is becoming more and more important nowadays due to the use of third-party Intellectual Properties (IPs) and reused design blocks. In this paper, we propose techniques and methodologies that utilize abundant external don't-cares that exist in an SoC environment for block optimization. Our symbolic code-statement reachability analysis can extract don't-care conditions from constrained-random testbenches or other design blocks to identify unreachable conditional blocks in the design code. Those blocks can then be removed before logic synthesis is performed to produce smaller and more power-efficient final circuits. Our results show that we can optimize designs under different constraints and provide additional flexibility for SoC design flows.

This work describes a novel approach to utilizing everyday objects of users as additional, auxiliary, and instant tabletop controllers. Based on this approach, a prototype platform, called iCon, is developed to explore the possible design. Field studies and user studies reveal that utilizing everyday objects such as auxiliary input devices might be appropriate under a multi-task scenario. User studies further demonstrate that daily objects can generally be applied in low precision circumstances, low engagement with selected objects, and medium-to-high frequency of use. The proposed approach allows users to interact with computers while not altering their original work environments.

Due to increases in design complexity, routing a reset signal to all registers is becoming more difficult. One way to solve this problem is to reset only certain registers and rely on a software initialization sequence to reset other registers. This approach, however, may allow unknown values (also called X-values) in uninitialized registers to leak to other registers, leaving the design in a nondeterministic state. Although logic simulation can find some X-problems, it is not accurate and may miss bugs. A recent approach based on symbolic simulation can handle Xs accurately; however, it is not scalable. In this work we analyze the characteristics of X-problems and propose a methodology that leverages the accuracy of formal X-analysis and can scale to large designs. This is achieved by our novel partitioning techniques and the intelligent use of waveforms as stimulus. We applied our methodology to an industrial design and successfully identified several Xs unknown to the designers, including three real bugs, demonstrating the effectiveness of our approach.

Don't-care conditions are utilized by many synthesis tools because such conditions provide additional flexibility for logic optimization. However, most techniques only focus on the gate level because it is difficult to handle such conditions accurately at behavior and register transfer levels. This is problematic since the trend is to move toward high-level synthesis. In this paper, we propose innovative methods to handle such conditions accurately at high-level designs. In addition, we propose three novel algorithms based on our new methods to minimize the number of registers that need to be initialized, which can reduce the routing resources used by the reset signals and alleviate the routing problem. We applied our techniques to a five-stage pipelined processor and successfully reduced the number of control registers that need to be initialized by 53%, demonstrating the effectiveness of our approach.

As a security primitive, key establishment plays the most crucial role in the design of the security mechanisms. Unfortunately, the resource limitation of sensor nodes poses a great challenge for designing an efficient and effective key establishment scheme for wireless sensor networks (WSNs). In spite of the fact that many elegant and clever solutions have been proposed, no practical key establishment scheme has emerged. In this paper, a ConstrAined Random Perturbation-based pairwise keY establishment (CARPY) scheme and its variant, a CARPY+ scheme, for WSNs, are presented. Compared to all existing schemes which satisfy only some requirements in so-called sensor-key criteria, including (1) resilience to the adversary's intervention, (2) directed and guaranteed key establishment, (3) resilience to network configurations, (4) efficiency, and (5) resilience to dynamic node deployment, the proposed CARPY+ scheme meets all requirements. In particular, to the best of our knowledge, CARPY+ is the first noninteractive key establishment scheme with great resilience to a large number of node compromises designed for WSNs. We examine the CARPY and CARPY+ schemes from both the theoretical and experimental aspects. Our schemes have also been practically implemented on the TelosB compatible mote to evaluate the corresponding performance and overhead.

Don't-care conditions provide additional flexibility in logic synthesis and optimization. However, most work only focuses on the gate level because it is difficult to handle such conditions accurately at the behavior and register transfer levels, which is problematic since the trend is to move toward high-level synthesis. In this work we propose innovative methods to handle such conditions accurately at high-level designs. In addition, we propose two novel algorithms based on our new methods to minimize the number of registers that need to be initialized at the architecture level, which can reduce the routing resources used by the reset signals and alleviate the routing problem. Our results show that we can identify 53% of the registers that can be uninitialized in a 5-stage pipelined processor within 5 minutes, demonstrating the effectiveness of our approach.

For the obstacle-avoiding rectilinear Steiner minimal tree problem, this paper presents an O(n log n)-time algorithm with theoretical optimality guarantees on a number of specific cases, which required O(n3) time in previous works. We propose a new framework to directly generate O(n) critical paths as essential solution components, and prove that those paths guarantee the existence of desirable solutions. The path-based framework neither generates invalid initial solutions nor constructs connected routing graphs, and thus provides a new way to deal with the OARSMT problem. Experimental results show that our algorithm achieves the best speed performance, while the average wirelength of the resulting solutions is only 1.1% longer than that of the best existing solutions.

Active operating system fingerprinting is the process of actively determining a target network system's underlying operating system type and characteristics by probing the target system network stack with specifically crafted packets and analyzing received response. Identifying the underlying operating system of a network host is an important characteristic that can be used to complement network inventory processes, intrusion detection system discovery mechanisms, security network scanners, vulnerability analysis systems and other security tools that need to evaluate vulnerabilities on remote network systems.

The miniaturization of transistors in recent technology nodes requires tremendous back-end tuning and optimizations, making bug fixing at later design stages more expensive. Therefore, it is imperative to find design bugs as early as possible. The first defense against bugs is block-level testing performed by designers, and constrained-random simulation is the prevalent method. However, this method may miss corner-case scenarios. In this paper we propose an innovative methodology that reuses existing constrained-random testbenches for formal bug hunting. To support the methodology, we present several techniques to enhance RTL symbolic simulation, and integrate state-of-the-art word-level and Boolean-level verification techniques into a common framework called BugHunter. From case studies DLX, Alpha and FIR, BugHunter found more bugs than constrained-random simulation using fewer cycles, including four new bugs in the verified design previously unknown to the designer. The results demonstrate that the proposed techniques provide a flexible, scalable and robust solution for bug hunting.

For the obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem, this paper presents a Steiner-point based algorithm to achieve the best practical performance in wirelength and run time. Unlike many previous works, the Steiner-based framework is more focused on the usage of Steiner points instead of the handling of obstacles. This paper also proposes a new concept of Steiner point locations to provide an effective as well as efficient way to generate desirable Steiner point candidates. Experimental results show that this algorithm achieves the best solution quality in Θ (n log n) empirical time, which was originally generated by applying the maze routing on an Ω(n2)-space graph. The Steiner-point based framework and the new concept of Steiner point locations can be applied to future research on the OARSMT problem and its generations, such as the multi-layer OARSMT problem.

Quantum communication networks need to securely transmit a quantum frame from source to destination. In the wireless communication network, source and destination have two types of connection: direct or indirect communication. In the direct connected mode, the transmission security can be achieved by using a quantum key distribution. In the indirect connected mode, it is a difficult problem to deal with the unsafe routing path from source to destination due to eavesdropping and man- in-the-middle attack. In this paper, we propose a quantum mechanism that designs a flexible quantum frame to achieve transmission integrity. This frame uses a quantum correlated key to let the receiver has the capability to judge whether the received quantum frame is secure or not. This mechanism provides a new solution to solve the transmission security in the unsafe routing path.

There are various emergency services based on wireless sensor network being proposed recently. However, the ability of these services/networks is inherently limited by geographical restrictions and need to be deployed in advance. This paper proposes an application level approach to enhance the service coverage and availability of emergency services. Specifically, we augment these services with All-IP network infrastructure based on IP Multimedia Subsystem (IMS). Furthermore, we integrate the IMS Emergency Services architecture with Cooperative Network technology to provide ubiquitous emergency services. We also investigate the prime problems of cooperation between heterogeneous networks and IMS. Finally, we present and discuss the experimental results of performance in our Cooperative Emergency IMS Testbed.

The major contribution of this paper is to propose a robust en-route filtering scheme for data authentication in sensor networks without relying on unrealistic assumptions.

Behavior-based detection methods have the ability to detect unknown malicious software (malware). The success of behavior-based detection methods must depend on sufficient number of abnormal behavior models. Insufficient number of abnormal behavior models can lead to high false positive and/or false negative rates. The majority of abnormal behavior models can only be derived by observing application behavior at lower level. However the traditional approaches are not very efficient in this type of analysis. In this paper, we present Holography,a virtual hardware-level tool to capture actions of malware programs. Holography does not rely on any driver that is installed on an operating system to log the execution profile of malware programs. Instead, Holography relies on only hardware level information to capture actions of malware programs. As a result, Holography is invisible to malware programs and therefore cannot be disabled or bypassed by malware programs.

In this paper, a constrained random perturbation based pairwise keY establishment (CARPY) scheme and its variant, a CARPY+ scheme, for Wireless Sensor Networks (WSNs), are presented. Compared to all existing schemes which satisfy only some requirements in so-called sensor-key criteria, including: 1) resilience to the adversary's intervention, 2) directed and guaranteed key establishment, 3) resilience to network configurations, 4) efficiency, and 5) resilience to dynamic node deployment, the proposed CARPY+ scheme meets all requirements. In particular, to the best of our knowledge, CARPY+ is the first non-interactive key establishment scheme with great resilience to a large number of node compromises designed for WSNs. We examine the CARPY and CARPY+ schemes from both the theoretical and experimental aspects. Our schemes have also been practically implemented on the TelosB compatible mote to evaluate the corresponding performance and overhead.

In this paper, we study the challenging problem of node replication detection. Although defending against node replication attacks demands immediate attention, only a few solutions were proposed. In this paper, an Efficient and Distributed Detection (EDD) scheme and its variant, SEDD, are proposed to resist against node replication attacks in mobile sensor networks. The characteristics possessed by EDD and SEDD include (1) Distributed Detection; (2) Efficiency and Effectiveness; (3) Individual Detection; (4) Network-Wide Revocation Avoidance. Performance comparison with known methods are provided to demonstrate the efficiency of the EDD and SEDD schemes.

This paper presents a constrained function based message authentication (CFA) scheme for wireless sensor networks, which meets all the requirements of the so-called sensor authentication criteria, while most of the existing schemes only achieve partial requirements. In particular, to the best of our knowledge, CFA is the first authentication scheme supporting en-route filtering with only a single packet overhead. We examine the CFA scheme from both the theoretical and experimental aspects. Our method has also been practically implemented on the TelosB compatible mote for performance evaluation.

Service-oriented architecture (SOA) paradigm is an inspiring and evolving concept in the context of IT and business services. Recently, various networked embedded systems are penetrating into our life; the unique characteristics of these systems pose many technological challenges. Specifically, the capabilities to interoperate across home appliances and personal digital devices are critical in a smart home environment. As a consequence, the SOA paradigm is introduced as a basis to enhance the service interoperability. However, the service availability is another important concern. In this paper, we focus on augmenting the smart home environment with high service availability. First, we point out the potential problems by adopting the universal plug and play (UPnP) quality of service (QoS) architecture to deliver services with QoS requirements in wireless home networks. After that, we propose a local detour mechanism to tolerate link congestions and node/link failures. Furthermore, the proposed mechanism is based on standard UPnP actions, so that modifications to the existing UPnP QoS architecture are not required. Compared with content-adaptive and resource-adaptive-based approaches operated on fixed service routing paths, our work not only guarantees content quality, but also tolerates topology changes in wireless home networks.

Rectilinear Steiner trees are used to route signal nets by global and detail routers in VLSI design for a long time. However, in current IC industry, there are significantly increasing obstacles to be considered, such as large-scale power networks, pre-routed nets, IP blocks, and antenna jumpers. Accordingly, the obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem has become more important. In this article, we propose a new routing graph, obstacle-avoiding routing graph (OARG), for the OARSMT problem. Due to the important properties of OARG, we construct a 3-step algorithm and a local refinement scheme, which both can take advantage of these properties, to find a suboptimal solution efficiently. Furthermore, each step of our 3-step algorithm as well as the local refinement scheme has theoretical or practical benefits. Therefore, each of them can be applicable to other existing works for general or specific considerations such as efficiency or effectiveness. Extensive experimental results show that our method outperforms all existing works in terms of wirelength and achieves the best speed performance.

In IC design, rectilinear Steiner trees have been used to route signal nets by global and detail routers for a long time. Recently, there are more complicated processing conditions for routing to be considered, such as multiple routing layers, obstacles, and preferred directions. Furthermore, routability is also an important issue for modern routing which handles more than ten thousand signal nets. As a result, how to meet the processing conditions and consider the routability at the same time is becoming important. In this paper, we formulate a routing problem, called the obstacle-avoiding preferred direction Steiner tree (OAPDST) problem, which can deal with more practical processing conditions and achieve acceptable routability. To the best of our knowledge, this is the first attempt to formulate this problem. Then, we propose a routing graph, called preferred direction evading graph (PDEG), for this problem, and prove that at least one optimal solution can be found on PDEG. As a result, by using PDEG as the solution space, more efficient and effective methods can be found for the OAPDST problem. Based on PDEG, we also construct an approximation algorithm for the OAPDST problem to provide stable and effective solutions. Experimental results show that our method can perform well for the OAPDST problem

This paper presents a Constrained Random Perturbation Vector-based (CRPV) pairwise key establishment scheme and its variant, CRPV+ scheme, for wireless sensor networks (WSNs). Compared to all existing schemes which satisfy only some requirements in a so-called versatileness criteria, the CRPV+ scheme meets all requirements. In particular, the performance improvement of our schemes does not rely on tradeoffs among different requirements, but comes from the use of our constrained random vector strategy.

Covert channels are secret communication paths, which existance is not expected in the original system design. Covert channels can be used as legimate tools of censorship resistance, anonimity and privacy preservation to address issues with "national" firewalls, citizen profiling and other "unethical" uses of information technology. Current steganographic methods that implement covert channels within network traffic, are highly dependent on particular media data or network protocol to hide data. In this paper we investigate the methods and an algorithm for implementing adaptive covert communication system that works on real-world Internet, capable of using multiple application-level protocols as its communication media and can be implemented as network application, therefore requires no system modifications of communicating nodes. The key difference from previous solutions is the use of adaptive redundant mechanism, which allows real-time underlying protocol switching and adaptation to the dynamic network configuration changes.

By launching the node replication attack, the adversary can place the replicas of captured sensor nodes back into the sensor networks in order to eavesdrop the transmitted messages or compromise the functionality of the network. Although defending against node replication attacks demands immediate attention, only a few solutions were proposed. Most of the existing distributed protocols adopt the witness finding strategy, which selects a set of sensor nodes somewhere as the witnesses, to detect the replicas. However, the energy consumption of the witness finding strategy is remarkably high and even gets worse in mobile networks. In addition, the location information is necessary for each node if the witness finding strategy is applied. In this paper, a novel protocol, called extremely Efficient Detection (XED), is proposed to resist against node replication attacks in mobile sensor networks. The advantages of XED include (1) only constant communication cost is required for replica detection; (2) the location information of sensor nodes is not required. Performance analyses and comparison with known methods are provided to demonstrate the effectiveness of our protocol.

A systematic procedure was proposed to derive a minimum space quantum circuit for any given classical logic with the generalized quantum Toffoli gate which is universal in Boolean logic. Since quantum computation is reversible, we can apply this property to build quantum iterative logic array (QILA). QILA can be easily tested in constant time (C-testable) if stuck-at fault model is assumed. In this paper, we use Hadamard and general CCN gates to make QILA 1-testable. That is, for any quantum Boolean circuit, the number of test patterns is independent of both the size of the array and the length of the inputs. This property can be applied to perform the quantum built-in self-test (QBIST), which makes any Boolean circuit 1-testable.

The electrostatic discharge (ESD) problem has become a challenging reliability issue in nanometer-circuit design. High voltages that resulted from ESD might cause high current densities in a small device and burn it out, so on-chip protection circuits for IC pads are required. To reduce the design cost, the protection circuit should be added only for the IC pads with an ESD current path, which causes the ESD current path analysis problem. In this paper, we first introduce the analysis problem for ESD protection in circuit design. We then model the circuit as a constraint graph, decompose the ESD connected components (ECCs) linked with the pads, and apply breadth-first search (BFS) to identify the ECCs in each constraint graph and, thus, the current paths. Experimental results show that our algorithm can very efficiently and economically detect all ESD paths. For example, our algorithm can detect all ESD paths in a circuit with more than 1.3 million vertices in 1.39 s and consume only 44-MB memory on a 3.0-GHz Intel Pentium 4 PC. To the best of our knowledge, our algorithm is thefirstpointtoolavailable to the public for the ESD analysis.

In this paper, we focus on enhancing the data accessibility of ad hoc networks, with emphasis on peer-to-peer communications. To achieve this goal, we propose a randomized distributed algorithm for data replication. Furthermore, a probabilistic approach is presented to derive the upper bound of convergence by a novel technique, called path coupling, which gives more insight into factors determining system performance. Our analysis demonstrates that data accessibility can be improved by the proposed approach, with very limited memory consumption.

In order for financial-motivated malware programs such as spyware, virus and worm to survive after system rebooted, they have to modify entries in auto start extensibility points (ASEPs), system calls or system files on a comprised system. We call these system resources which a malware program could attack once it intrudes a host as malware attacking points (MAPs). Based on this observation, we design and implement MAPMon, a monitoring mechanism to detect any suspicious change of malware attacking points. This paper describes the design and implementation tradeoff of the MAPMon tool. The effectiveness of the MAPMon tool for malware detection is evaluated by using real-world malware programs including those that do not have signatures.

Spyware infection that exploits the vulnerabilities of client-side Web application, especially browser, to install malicious programs has gain significant popularity in recent years. Unlike traditional infection vectors such as software bundling in shareware/freeware and placing Trojan in pirated version of commercial software that generally requires user consent to be successfully installed, Web-based spyware attempts exploits on browser vulnerabilities to achieve automatic installation (a.k.a. drive-by download). In this paper, we characterize the behavior of spyware instances collected from software bundling and of those collected from exploit Web pages in terms of auto-start extensibility points (ASEP) and other spyware behaviors. We use a tool called STARS (Stateful Threat-Aware Removal System) that can monitor critical areas of the system and detect advanced feature of a spyware instance such as self- healing. Experimental results show that traditional spyware and Web-based spyware used a different combination set of ASEP to resist deletion. The latter one hooks to low-level system components and loaded as services and/or drivers employing Layered Service Provider (LSP) to interpret network traffic. Our observations identify the unique behaviors performed by the Web-based spyware that are rarely found on traditional spyware.

Peer-to-peer (P2P) communications have attracted a great deal of attention from the network research community in recent years. However, due to the fundamental limitations of wireless environments, providing reliable data availability for P2P applications over wireless ad hoc networks is still a major challenge. To address the problem, a distributed and randomised scheme based on self-avoiding walks is proposed. The scheme concatenates disparate network layers, with the goal of recovering from routing failures that disrupt P2P data accessibility. In addition, a probabilistic approach is presented that explores the tradeoffs between several system parameters. Some new analysis tools, such as path coupling, are utilised which provide a better understanding of the system's operations. That the proposed concepts and techniques make a significant contribution to the design of effective and efficient P2P applications in wireless ad hoc networks is believed.

The twenty-one papers in this special issue are devoted to peer-to-peer communications and their applications. Covers such topics as: overlay networks, searching, video streaming, files and servers, and theories and applications.

Wireless ad hoc networks are drawing increasing attention from the research community because of their potential applications. However, the fundamental capacity limits of these networks pose various technological challenges to designers of network protocols. In this paper, we attempt to capture the inherent constraints on information dissemination in a mobile wireless environment, with the emphasis on peer-to-peer (P2P) communications. More specifically, we introduce the notion of "replication-induced gain" to quantify the impact of data replication under the paradigm of P2P query-response mechanisms. Our major contribution lies in presenting several preliminary results with respect to the complexities and trade-offs involved in enhancing data availability. To the best of our knowledge, the data replication problems that arise because of scarce system resources in wireless ad hoc networks have not been investigated from this perspective. We believe that our results could provide additional insights and practical implications for P2P system designers.

Over the last several decades, many Software Reliability Growth Models (SRGM) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of reliability as software is being improved. However, some research work indicates that the delayed S-shaped model may not fit the software failure data well when the testing-effort spent on fault detection is not a constant. Thus, in this paper, we first review the logistic testing-effort function that can be used to describe the amount of testing-effort spent on software testing. We describe how to incorporate the logistic testing-effort function into both exponential-type, and S-shaped software reliability models. The proposed models are also discussed under both ideal, and imperfect debugging conditions. Results from applying the proposed models to two real data sets are discussed, and compared with other traditional SRGM to show that the proposed models can give better predictions, and that the logistic testing-effort function is suitable for incorporating directly into both exponential-type, and S-shaped software reliability models.

The factoring theorem, and BDD-based algorithms have been shown to be efficient reliability evaluation methods for networks with perfectly reliable vertices. However, the vertices, and the links of a network may fail in the real world. Imperfect vertices can be factored like links, but the complexity increases exponentially with their number. Exact algorithms based on the factoring theorem can therefore induce great overhead if vertex failures are taken into account. To solve the problem, a set of exact algorithms is presented to deal with vertex failures with little additional overhead. The algorithms can be used to solve terminal-pair, k-terminal, and all-terminal reliability problems in directed, and undirected networks. The essential variable is defined to be a vertex or a link of a network whose failure has the dominating effect on network reliability. The algorithms are so efficient that it takes less than 1.2 seconds on a 1.67 GHz personal computer to identify the essential variable of a network having 299paths. When vertex failures in a 3 times 10 mesh network are taken into account, the proposed algorithms can induce as little as about 0.3% of runtime overhead, while the best result from factoring algorithms incurs about 300% overhead

Spyware has become a significant threat to most Internet users as it introduces serious privacy disclosure, and potential security breach to the systems. It has not only utilized critical areas of the computer system to survive reboots, but also grown resilient against current anti-spyware tools; they are capable of self-healing themselves against deletion. Because existing anti-spyware tools are stateless in the sense that they do not remember or monitor the spyware programs that were deleted, they fail to remove self-healing spyware from the system completely. This paper proposes a stateful approach that is based on characterizing spyware invasion as a trust information flow problem, and implements STARS (stateful threat-aware removal system), which is a tool that at run time monitors critical system behaviors, and ensures that removed spyware programs do not reinstall themselves, to enforce information flow policy in the system. If a reinstallation (self-healing) is detected, STARS infers the source of such activities, and discovers additional ldquosuspiciousrdquo programs. Experimental results show that STARS is effective in removing self-healing spyware programs that resist removal by existing anti-spyware tools.

The electrostatic discharge (ESD) problem has become a challenging reliability issue in nanometer circuit design. High voltages resulted from ESD might cause high current densities in a small device and burn it out, so on-chip protection circuits for IC pads are required. To reduce the design cost, the protection circuit should be added only for the IC pads with an ESD current path, which arises the ESD current path analysis problem. In this paper, we first introduce the analysis problem for ESD protection in circuit design. We then model the circuit as a constrained graph, decompose ESD connected components linked with the pads, and apply the breadth-first search (BFS) to identify the ESD connected components in each constrained graph and thus the current paths. Experimental results show that our algorithm can detect all ESD paths very efficiently and economically. To our best knowledge, our algorithm is the first point tool available to the public for the ESD analysis.

Dithering skip mode with a novel load sensor for DC-DC converters is proposed to maintain a high efficiency over a wide load range. Due to the efficiency drop of the transition from the pulse-width modulation (PWM) to pulse-frequency modulation (PFM), a novel dithering skip modulation (DSM) is introduced to smooth the efficiency curve. Importantly, DSM mode can dynamically skip the number of gate driving pulses, which is inverse proportional to load current. Besides, a novel proposed load sensor can automatically select the optimum modulation method from these three modulation methods without an external selection pin. Simulation results shows DSM can maintain the efficiency of converters as high as about 89% over a wide load current range from 3mA to 500mA.

The logical ring topology is a simple protection scheme in WDM networks. The failure of a single physical fiber link may cause the failure of multiple lightpaths. The service becomes unprotected if the failure propagates to the logical ring topology. In this paper, we focus on the elimination of failure propagation from the physical WDM network to the logical topology. We propose a method to make the augmented logical ring topology survivable. The augmented survivable edges (ASE) is based on the connectivity problem of the logical topology. Our method makes the modification of logical ring topology cost-effective, i.e., with the minimal number of the additional edges to the original logical ring topology. Finally, we show the results on various network cases

Quantum entanglement is a phenomenon available only at nanometer scale. In this paper, we show how quantum entanglement can be used to build cryptographic primitives such as oblivious transfer. In addition to studying the protocol itself, we also show how to realize some applications based on our proposal. These include typical e-business applications such as contract signing, certified mail, simultaneous secret exchange, secure transaction and remote coin flip. Unlike classical oblivious transfer, the security of this protocol is based on physical laws, instead of any unproven mathematic conjecture. As a result, our proposal provides unconditional security for e-business

Spyware, a type of potentially unwanted programs (PUPs), has become a significant threat to most Internet users as it introduces serious privacy disclosure and potential security breach to the systems. Current anti-spyware tools use signatures to detect spyware programs. Over time, spyware programs have grown more resilient to this technique; they utilize critical areas of the system to survive reboots and set up mini-installers that re-install a spyware program after it's been detected and removed. Since existing anti-spyware tools are stateless in the sense that they do not remember and monitor the spyware programs that were removed, they fail to permanently remove these self-healing spyware programs. This paper proposes STARS (stateful threat-aware removal system): a tool that at run time intercepts critical system accesses and assures removed spyware does not re-install itself after a successful removal of spyware program in the system. If a re-installation (self-healing) is detected, STARS infers the source of such activities and discovers additional "suspicious" programs. Experimental results show that STARS is effective in removing self-healing spyware programs that existing anti-spyware tools fail to do

The spare allocation problem in redundant RAM is to replace faulty rows/columns of memory cells with spare rows/columns. To solve the problem, comparison-based search tree structures were used in traditional exact algorithms. These algorithms are not efficient for large problems because significant amounts of data have to be retained and copied in order to generate new partial solutions. Many data may need to be compared for the removal of each redundant partial solution. To overcome these drawbacks, an efficient algorithm is proposed in this paper. The algorithm transforms a spare allocation problem into Boolean functions, and the renowned BDD is used to manipulate them. Experimental results indicate that the proposed algorithm is very efficient in terms of speed and memory requirements. It may also be useful for problems which can be modeled as constraint bipartite vertex cover problems.

Rather than conventional segment partition methods in link/path/sub-path based protection switching schemes, we discuss the multi-metrics resource-based network partition approaches to meet the requirement of group switching and adapt to dynamic traffic demands. By the route selection step according to exchanged capacity estimation information, group reconfiguration process and rerouting in each group, channel utilization of network will be maximized and hence the overall quality of protection can be achieved. We consider both the static and dynamic group partition strategies under dynamic traffic demands and examine their relative influence of various network metrics. The simulation results show that resource utilization can be optimized at the expense of moderate restoration penalty under heavy load in the dynamic traffic environment over dense mesh backbone networks.

Virtual router redundancy protocol (VRRP) is designed to eliminate the single point of failure in the static default routing environment in LAN. The original VRRP protocol does not support load balancing for both incoming and outgoing traffic. This paper describes EVRRP, i.e. enhanced VRRP. EVRRP supports an efficient multiple-node cluster and symmetric load balancing among routers. Each router periodically exchanges information to determine the status of the master and backups. The master router distributes and redirects the traffic to one of the backup routers by ICMP redirect message. Backup routers accept the traffic from the master and one of the backup routers takes over the master traffic using a gratuitous ARP message when the master fails. The improved election protocol speeds up the original VRRP election protocol and shortens the failover time by adding a new state in the previous VRRP state diagram and a new protocol type. An extensive evaluation of the EVRRP protocol is described in the paper.

As checking spam became part of our daily life, unsolicited bulk e-mails (UBE) have become unmanageable and intolerable. Bulk volume of spam e-mails delivering to mail transfer agents (MTAs) is similar to the effect of denial of services (DDoS) attacks as it dramatically reduces the dependability and efficiency of networking systems and e-mail servers. Spam mails may also be used to carry viruses and worms which could significantly affect the availability of computer systems and networks. There have been many solutions proposed to filter spam in the past. Unfortunately there is no silver bullet to deter spammers and eliminate spam mails. That is, in isolation, each of existing spam protection mechanisms has its own advantages and disadvantages. In this paper, we analyze the shortcomings of existing anti-spam solutions and propose a multi-faceted approach using the spam-resistible mail agent (SRMA), which provides the most advantages and the least disadvantages of existing anti-spam solutions. Our experiments show that the proposed SRMA is immune to existing spambots and the prototype proves to be effective, feasible and deployable.

The use of IP (Internet protocol) technology in the information and communications industry constitutes a major global trend. A highly efficient service architecture, enabling technologies and advanced applications are essential to rapid multimedia services in an all-IPv6 network environment. This work presents an all-IPv6 service platform based on open service architecture (OSA) to support a set of standard interfaces and applications. The all-IPv6 network environment was integrated using a network-processor-based IPv4/IPv6 translator and a mobile router (MR) supported IPv6 network mobility. The feasibility of the open service platform for all-IPv6 network environments and of the designed application programming interfaces was examined using three applications: e-commerce, video-on-demand, and on-line gaming. The performance analysis indicates that the system throughput increased from 10.5 to 60.5 Mb/s as the number of users increased from 1 to 80; the mean response time increased from approximately 1 to 10.5 ms, and the delay time increased from 0.1 to 1 ms.

Algorithms for evaluating the reliability of a complex system such as a multistate fault-tolerant computer system have become more important. They are designed to obtain the complete results quickly and accurately even when there exist a number of dependencies such as shared loads (reconfiguration), degradation, and common-cause failures. This paper presents an efficient method based on ordered binary decision diagram (OBDD) for evaluating the multistate system reliability and the Griffith's importance measures which can be regarded as the importance of a system-component state of a multistate system subject to imperfect fault-coverage with various performance requirements. This method combined with the conditional probability methods can handle the dependencies among the combinatorial performance requirements of system modules and find solutions for multistate imperfect coverage model. The main advantage of the method is that its time complexity is equivalent to that of the methods for perfect coverage model and it is very helpful for the optimal design of a multistate fault-tolerant system.

Network survivability is a key issue in reliable WDM optical network design to assure the service guarantee to customers. Rather than conventional connection link/ path/subpath based protection switching scheme, we propose protection group (submesh)-based approach to fast recover traffic from single/multiple link/node failures. By the group-based spare resource discovery and exploitation, the local channel utilization of network are maximized and hence enhance the overall quality of protection. The simulation results show that the local resource utilization and restoration time are efficient under heavy load in the dynamic traffic environment over dense mesh backbone networks.

Software reliability is defined as the probability of failure-free software operation for a specified period of time in a specified environment. Over the past 30 years, many software reliability growth models (SRGMs) have been proposed and most SRGMs assume that detected faults are immediately corrected. Actually, this assumption may not be realistic in practice. We first give a review of fault detection and correction processes in software reliability modeling. Furthermore, we show how several existing SRGMs based on NHPP models can be derived by applying the time-dependent delay function. On the other hand, it is generally observed that mutually independent software faults are on different program paths. Sometimes mutually dependent faults can be removed if and only if the leading faults were removed. Therefore, here we incorporate the ideas of fault dependency and time-dependent delay function into software reliability growth modeling. Some new SRGMs are proposed and several numerical examples are included to illustrate the results. Experimental results show that the proposed framework to incorporate both fault dependency and time-dependent delay function for SRGMs has a fairly accurate prediction capability.

The authors describe the use of bounded model checking (BMC) for verifying Web application code. Vulnerable sections of code are patched automatically with runtime guards, allowing both verification and assurance to occur without user intervention. Model checking techniques are relatively complex compared to the typestate-based polynomial-time algorithm (TS) we adopted in an earlier paper, but they offer three benefits - they provide counterexamples, more precise models, and sound and complete verification. Compared to conventional model checking techniques, BMC offers a more practical approach to verifying programs containing large numbers of variables, but requires fixed program diameters to be complete. Formalizing Web application vulnerabilities as a secure information flow problem with fixed diameter allows for BMC application without drawback. Using BMC-produced counterexamples, errors that result from propagations of the same initial error can be reported as a single group rather than individually. This offers two distinct benefits. First, together with the counterexamples themselves, they allow for more descriptive and precise error reports. Second, it allows for automated patching at locations where errors are initially introduced rather than at locations where the propagated errors cause problems. Results from a TS-BMC comparison test using 230 open-source Web applications showed a 41.0% decrease in runtime instrumentations when BMC was used. In the 38 vulnerable projects identified by TS, BMC classified the TS-reported 980 individual errors into 578 groups, with each group requiring a minimal set of patches for repair.

Redundant rows and columns have been used for years to improve the yield of DRAM fabrication. However, finding a memory repair solution has been proved to be an NP-complete problem. This paper presents an efficient algorithm, which is able to find a repair solution for shared spare memory arrays if a solution exists. The remarkable performance of the algorithm can be demonstrated by experimental results.

The World Wide Web has become a sophisticated platform capable of delivering a broad range of applications. However, its rapid growth has resulted in numerous security problems that current technologies cannot address. Researchers from both academic and private sector are devoting a considerable amount of resources to the development of Web application security scanners (i.e., automated software testing platforms for Web application security auditing) with some success. However, little is known about their potential side effects. It is possible for an auditing process to induce permanent changes in an application's state. Due to this potential, we have so far avoided large-scale empirical evaluations of our Web Application Vulnerability and Error Scanner (WAVES). we introduce a testing methodology that allows for harmless auditing, define three testing modes - heavy, relaxed, and safe modes, and report our results from two experiments. In the first, we compared the coverage and side effects of the three scanning modes using 5 real-world Web applications chosen from the 38 found vulnerable in a previous static verification effort. In the second, we used the relaxed mode to conduct a 48-hour test involving 1120 random Web sites, of which 55 were found to be vulnerable.

System reliability evaluation, sensitivity analysis, importance measures, failure frequency analysis and optimal design have become important issues for distributed dependable computing. Finding all the minimal file spanning trees (MFST) and avoiding repeatedly computing the redundant MFSTs is the key technique for evaluating the reliability of a distributed computing system (DCS) in previous works. However, identifying all the disjoint MFSTs is difficult and very time consuming for large-scale networks. Although existing algorithms have been demonstrated that they work fine on medium-scale networks, they have two inherent drawbacks. First, they do not support efficient manipulation of Boolean algebra. The sum-of-disjoint-products method used by them is inefficient in dealing with large Boolean functions. Second, the tree-based partitioning algorithm does not merge isomorphic subproblems and therefore, redundant computations cannot be avoided. We propose a new efficient algorithm for the reliability evaluation of a DCS based on recursive merge and binary decision diagram (BDD). Using the BDD substitution technique, we can easily apply our algorithm to a network with imperfect nodes. The experimental results show a significant improvement on the execution time compared to previous works.

We investigate an optimal resource allocation problem in modular software systems during testing phase. The main purpose is to minimize the cost of software development when the number of remaining faults and a desired reliability objective are given. An elaborated optimization algorithm based on the Lagrange multiplier method is proposed and numerical examples are illustrated. Besides, sensitivity analysis is also conducted. We analyze the sensitivity of parameters of proposed software reliability growth models and show the results in detail. In addition, we present the impact on the resource allocation problem if some parameters are either overestimated or underestimated. We can evaluate the optimal resource allocation problems for various conditions by examining the behavior of the parameters with the most significant influence. The experimental results greatly help us to identify the contributions of each selected parameter and its weight. The proposed algorithm and method can facilitate the allocation of limited testing-resource efficiently and thus the desired reliability objective during software module testing can be better achieved.

Security remains a major roadblock to universal acceptance of the Web for many kinds of transactions, especially since the recent sharp increase in remotely exploitable vulnerabilities have been attributed to Web application bugs. Many verification tools are discovering previously unknown vulnerabilities in legacy C programs, raising hopes that the same success can be achieved with Web applications. In this paper, we describe a sound and holistic approach to ensuring Web application security. Viewing Web application vulnerabilities as a secure information flow problem, we created a lattice-based static analysis algorithm derived from type systems and typestate, and addressed its soundness. During the analysis, sections of code considered vulnerable are instrumented with runtime guards, thus securing Web applications in the absence of user intervention. With sufficient annotations, runtime overhead can be reduced to zero. We also created a tool named.WebSSARI (Web application Security by Static Analysis and Runtime Inspection) to test our algorithm, and used it to verify 230 open-source Web application projects on SourceForge.net, which were selected to represent projects of different maturity, popularity, and scale. 69 contained vulnerabilities. After notifying the developers, 38 acknowledged our findings and stated their plans to provide patches. Our statistics also show that static analysis reduced potential runtime overhead by 98.4%.

In this paper, we present a reservation-based medium access control (MAC) protocol with multicast support for wavelength-division multiplexing networks. Our system is based on the single-hop, passive optical star architecture. Of the available wavelengths (channels), one channel is designated as a control channel, and the remaining channels are used for data transmission. Each node is equipped with a pair of fixed transceiver to access the control channel, and a fixed transmitter and a tunable receiver to access data channels. For easy implementation of the protocol in hardware and for precisely computing the protocol's processing overhead, we give a register-transfer model of the protocol. We simulate the protocol to study its throughput behavior, and present its analytic model. For a node to be able to send data packets in successive data slots with no time gap between them, in spite of the situation that the protocol's execution time may be longer than data transmission time, we propose the idea of multiple MAC units at each node. Unicast throughput of our protocol reaches the theoretically possible maximum throughput for MAC protocols with distributed control, and the multicast throughput is at least as good as, and even better than, those delivered by existing MAC protocols with distributed control.

The recent literature showed that, in many cases, ordered binary decision diagram (OBDD)-based algorithms are more efficient in reliability evaluation compared to other methods such as the inclusion-exclusion (I-E) method and the sum of disjoint products (SDP) method. We present algorithms based on OBDD to compute system failure frequencies and reliability importance measures. Methods are presented to calculate both steady-state and time-specific frequencies of system-failure as well as system-success. The reliability importance measures include the Birnbaum importance, the criticality importance, and other indices for the risk evaluation of a system. In addition, we propose an efficient approach based on OBDD to evaluate the reliability of a nonrepairable system and the availability of a repairable system with imperfect fault-coverage mechanisms. The powerful capability of OBDD for reliability evaluation is fully exploited. Further, we extend all of the proposed algorithms to analyze systems with imperfect fault-coverage.

In this paper, we propose an algorithm to construct the ordered binary decision diagram (OBDD) representing the cut function of a terminal-pair network. The algorithm recognizes isomorphic sub-problems and thus avoids redundant computations. The system reliability could be efficiently computed by the OBDD. Finally, we propose an approach to compute the importance measures for multiple components by traversing the OBDD only once. The correctness and the effectiveness of our approach are demonstrated by experiments on 30 benchmark networks. The experimental results on a 2-by-100 lattice network, which has 2/sup 99/ paths or 10,000 cuts, show an impressive improvement compared to the previous works using the sum of disjoint products method that have exponential complexity. The CPU time of our method, including the calculation of not only the reliability but also the importance measures, for a 100-stage lattice network is only about 0.24 seconds. Thus, this approach is very helpful for the reliability and sensitivity analysis of large networks.

The parameters in these software reliability models are usually directly obtained from the field failure data. Due to the dynamic properties of the system and the insufficiency of the failure data, the accurate values of the parameters are hard to determine. Therefore, the sensitivity analysis is often used in this stage to deal with this problem. Sensitivity analysis provides a way to analyzing the impact of the different parameters. In order to assess the reliability of a component-based software, we propose a new approach to analyzing the reliability of the system, based on the reliabilities of the individual components and the architecture of the system. Furthermore, we present the sensitivity analysis on the reliability of a component-based software in order to determine which of the components affects the reliability of the system most. Finally, three general examples are evaluated to validate and show the effectiveness of the proposed approach.

Heartbeat-style failure detectors are a commonly used building block in practical fault-tolerant distributed systems over unreliable and asynchronous networks. A basic requirement to implement such failure detectors is to diffuse heartbeat information across the underlying network. In wireless ad hoc networks, however, the dynamics of mobility and lack of resource make information dissemination a formidable task. Moreover, as a middleware service, the total bandwidth used for failure detection should be constrained. This paper describes several communication strategies on which heartbeat-style failure detectors can be developed in wireless ad hoc networks. The design goal is to support an effective and robust means of gossiping under a fixed message transmission rate. We show through simulations that the proposed gossiping schemes are resilient to message losses and topology changes. The simulation results also show that the performance of gossiping can be improved by introducing the concept of transient hierarchy under varied network characteristics. 1.

In this paper, we present a localized algorithm for constructing power efficient topology for wireless ad hoc networks. Each mobile node determines its own transmission power based only on local information. The proposed algorithm first constructs the constrained Gabriel graph from the given unit disk graph and then reduces the total transmission power by allowing each node individually excises some replaceable links. The constructed topology is sparse, has a constant bounded power stretch factor, and the total transmission power is lower than those obtained from other proposed algorithms. In addition, compared with others, our algorithm requires lower time complexity to generate a solution, and can thus further save the energy for each mobile node. We demonstrate the performance improvements of our algorithm through simulations.

One of the key tasks in network reliability evaluation is to enumerate all the paths or minimal cutsets of a network. Then the reliability can be calculated from the disjoint form of these terms. Enumerating all the minimal cutsets may be a feasible way to evaluate the reliability of a network if the number of paths is too huge to enumerate practically. One example of this kind of networks is the 2/spl times/100 lattice network. Many algorithms have been proposed to enumerate the minimal cutsets of a graph. Most of them require advanced mathematics or can only be applied to either one of the two broad categories, directed and undirected graphs. This paper presents a simple and systematic recursive algorithm that guarantees the generated cutsets are minimal and the same logic can be applied to both directed and undirected graphs with ease. This algorithm is so simple to implement and efficient that it can also be used to check the correctness of the cutsets generated by the algorithms. This algorithm can also be combined with OBDD (ordered binary decision diagram) to calculate the reliability of a network. Experimental results show that: (1) the running time of enumerating all cutsets versus the graph density is linear for a given number of nodes and (2) it takes 96.71 seconds to evaluate the network reliability of a 2/spl times/100 lattice network which has 2/sup 99/ paths.

Abstract—In this paper, we describe how several existing software reliability growth models based on Nonhomogeneous Poisson processes (NHPPs) can be comprehensively derived by applying the concept of weighted arithmetic, weighted geometric, or weighted harmonic mean. Furthermore, based on these three weighted means, we thus propose a more general NHPP model from the quasi arithmetic viewpoint. In addition to the above three means, we formulate a more general transformation that includes a parametric family of power transformations. Under this general framework, we verify the existing NHPP models and derive several new NHPP models. We show that these approaches cover a number of well-known models under different conditions. Index Terms—Software reliability growth model (SRGM), weighted arithmetic mean, weighted geometric mean, weighted harmonic mean, mean value function (MVF), power transformation, nonhomogeneous Poisson process (NHPP). 1

In this paper we propose an analytical approach for estimating the reliability of a component-based software. This methodology assumes that the software components are heterogeneous and the transfers of control between components follow a discrete time Markov process. Besides, we also formulate and. solve two resource allocation problems. Finally, we demonstrate how these analytical approaches can be employed to measure the reliability of a software system including multiple-input/multiple-output systems and distributed software systems. Experimental results show that the proposed methods can solve the testing-effort allocation problems and improve the quality and reliability of a software system.

We propose a reservation based multicast protocol for the single-hop passive optical star network. Of the available wavelengths (channels), one channel is designated as a control channel, and the remaining channels are used for data transmission. A node accesses the control channel using a fixed transmitter and a fixed receiver. A node sends data packets using a fixed transmitter and receives packets through a tunable receiver (filter). All the channels are viewed as sequences of frames. In addition, frames of the control channel are further divided into mini slots. Corresponding to each node in the network, there is a mini slot in a control frame. A node puts its multicast request in its designated mini slot in a control frame. At the end of a control frame, all nodes receive the multicast requests of all other nodes, and decide which nodes are going to transmit and/or receive during the following data slot. An easily implementable way of resolving destination and source conflicts is presented. We simulate the protocol to study its throughput behavior, and present its analytic model. Simulation results show that our protocol delivers maximum unicast throughput, and the protocol's multicast throughput is much better than existing protocols using a control channel.

In this paper, based on software reliability growth models with generalized logistic testing-effort function, we study three optimal resource allocation problems in modular software systems during the testing phase: 1) minimization of the remaining faults when a fixed amount of testing-effort and a desired reliability objective are given; 2) minimization of the required amount of testing-effort when a specific number of remaining faults and a desired reliability objective are given; and 3) minimization of the cost when the number of remaining faults and a desired reliability objective are given. Several useful optimization algorithms based on the Lagrange multiplier method are proposed and numerical examples are illustrated. Our methodologies provide practical approaches to the optimization of testing-resource allocation with a reliability objective. In addition, we also introduce the testing-resource control problem and compare different resource allocation methods. Finally, we demonstrate how these analytical approaches can be employed in the integration testing. Using the proposed algorithms, project managers can allocate limited testing-resource easily and efficiently and thus achieve the highest reliability objective during software module and integration testing.

This paper presents an efficient approach based on OBDD for the reliability analysis of a multi-state system subject to imperfect fault-coverage with combinatorial performance requirements. Since there exist dependencies between combinatorial performance requirements, we apply the multi-state dependency operation (MDO) of OBDD to deal with these dependencies in a multi-state system. In addition, this OBDD-based approach is combined with the conditional probability methods to find solutions for the multi-state imperfect coverage models. Using conditional probabilities, we can also apply this method for modular structures. The main advantage of this algorithm is that it will take computational time that is equivalent to the same problem without assuming imperfect coverage (i.e. with perfect coverage). This algorithm is very important for complex systems such as fault-tolerant computer systems, since it can obtain the complete results quickly and accurately even when there exist a number of dependencies such as shared loads (reconfiguration), degradation and common-cause failures.

Checkpointing protocols for distributed computing systems can also be applied to mobile computing systems, but the unique characteristics of the mobile environment need to be taken into account. In this paper, an improved time-based checkpointing protocol is proposed, which is suitable for mobile computing systems based on Mobile IP. The main improvement over a traditional time-based protocol is that our protocol reduces the number of checkpoints per checkpointing process to nearly minimum, so that fewer checkpoints need to be transmitted through the bandwidth-limited wireless links. The proposed protocol also performs very well in the aspects of minimizing the number and the size of messages transmitted in the wireless network. Therefore, the protocol brings very little overhead to a mobile host which has limited resource. Additionally, by integrating the improved timer synchronization technique, our protocol can also be applied to wide area networks.

The nodes as well as the links may fail in a real network. Almost all the existing tree-based partitioning algorithms are inefficient in finding the disjoint paths in a large network even if all the nodes are perfect. The number of disjoint paths will increase dramatically if a network has imperfect nodes. In this paper, strategies based on edge expansion diagram using OBDD are proposed to efficiently evaluate the reliability of a network with imperfect nodes. The fixed sink algorithm is proposed to further speed up the process for k-terminal networks. The essential variable is also defined to help us identify the most critical part of the network. Our methods are better than previous numeric algorithms and have two significant results. First, it takes only about 65 seconds to identify the essential variable for a 2/sup 99/-path network on a SPARC 20 with 128 MB of memory. Second, the overhead due to considering imperfect nodes is as low as 0.2% in average for seven st3/spl times/n networks, where n = 13, 14,..., 19.

This paper investigates a SRGM (software reliability growth model) based on the NHPP (nonhomogeneous Poisson process) which incorporates a logistic testing-effort function. SRGM proposed in the literature consider the amount of testing-effort spent on software testing which can be depicted as an exponential curve, a Rayleigh curve, or a Weibull curve. However, it might not be appropriate to represent the consumption curve for testing-effort by one of those curves in some software development environments. Therefore, this paper shows that a logistic testing-effort function can be expressed as a software-development/test-effort curve and that it gives a good predictive capability based on real failure-data. Parameters are estimated, and experiments performed on actual test/debug data sets. Results from applications to a real data set are analyzed and compared with other existing models to show that the proposed model predicts better. In addition, an optimal software release policy for this model, based on cost-reliability criteria, is proposed.

An efficient approach to determining the reliability of an undirected k-terminal network based on 2-terminal reliability functions is presented. First, a feasible set of (k-1) terminal-pairs is chosen, and the 2-terminal reliability functions of the (k-1) terminal-pairs are generated based on the edge expansion diagram using an OBDD (ordered binary decision diagram). Then the k-terminal reliability function can be efficiently constructed by combining these (k-1) reliability expressions with the Boolean and operation. Because building 2-terminal reliability functions and reducing redundant computations by merging reliability functions can be done very efficiently, the proposed approaches are much faster than those which directly expand the entire network or directly factor the k-terminal networks. The effectiveness of this approach is demonstrated by performing experiments on several large benchmark networks. An example of appreciable improvement is that the evaluation of the reliability of a source-terminal 3/spl times/10 all-terminal network took only 2.4 seconds on a SPARC 20 workstation. This is much faster than previous factoring-algorithms.

It is foreseeable that handheld devices will be involved in the arena of distributed computing in the near future. To provide a dependable computing environment, check-pointing and rollback recovery is a useful and important technique for fault-tolerant distributed computing systems. For the most popular platform among handhelds, Palm OS, its built-in HotSync tool can take a partial snapshot of a system state, but it synchronizes only the static data in the handheld with a PC. All dynamic data of applications are lost if a failure occurs and the Palm OS is reset. In order to accommodate mobile computing devices with checkpointing and rollback recovery capability, dynamic data such as global variables should be checkpointed to tolerate system reset/crash failure. Therefore, we developed a checkpointing tool, which provides a set of APIs to checkpoint Palm applications. Using the checkpointing tool, dynamic data in a Palm device can be saved and recovered from a system reset. We describe the tool and demonstrate its usefulness in four popular Palm applications.

Mobile ad hoc networks have gained more and more research attention. They provide wireless communications without location limitations and pre-built fixed infrastructures. Because of the absence of any static support structure, ad hoc networks are prone to link failure. This has become the most serious cause of throughput degradation when using TCP over ad hoc networks. Some researchers chose dynamic source routing (DSR) as the routing protocol and showed that disabling the assignment of a route directly from cache gives better performance. We introduce an efficient cache management mechanism to increase the TCP throughput by replying with a route directly from the cache of DSR and perform cache recovery when a host failure has occurred. We use simulations to compare the performance of our algorithm with the original DSR under the link failure prone environment due to mobility. We also provide the simulation results when host failures are considered in the ad hoc networks.

In the area of high capacity RAMs, the memory columns (rows), including the redundancies, are partitioned into column blocks (row blocks), respectively. If the replacement is performed at the row-block level, then a row block-based FTM (RBFTM) system is used. Alternatively, if the replacement is performed at the column-block level, then a column block-based FTM (CBFTM) system is used. If both approaches are incorporated into a memory chip, then the hybrid FTM (HFTM) system is achieved. Experimental results and analysis show that our fault-tolerant architectures can improve the yield for memory fabrication significantly. The reconfiguration mechanism requires almost negligible hardware overhead for high capacity memories. Moreover, the repair rates among different fault-tolerant strategies are also compared.

With the great progress of distributed object computing, more and more large systems are built using this technology. Thus, fault tolerance for distributed object computing is obviously a significant research domain. The Object Management Group (OMG) recently published the "Fault Tolerant CORBA Specification V1.0" (2000). This specification defines how to achieve fault tolerance for distributed object computing using object group, and failure detection is one of the key elements for fault management. However, the specification does not depict much about failure detection and leaves many specific details to vendors. We propose a simple mechanism for failure detection in distributed object computing. This mechanism is designed to be general rather than application-specific, with no single point of failure, and efficient. While the failure detectors may also crash during operation, we propose a method to handle this condition and to ensure the "no single point of failure" feature. The proposed mechanism has been implemented using CORBA to demonstrate that it works well.

Recent research has shown that voltage scaling is a very effective technique for low-power design. This paper describes a voltage scaling technique to minimize the power consumption of a combinational circuit. First, the converter-free multiple-voltage (CFMV) structures are proposed, including the p-type, the n-type, and the two-way CFMV structures. The CFMV structures make use of multiple supply voltages and do not require level converters. In contrast, previous works employing multiple supply voltages need level converters to prevent static currents, which may result in large power consumption. In addition, the CFMV structures group the gates with the same supply voltage in a cluster to reduce the complexity of placement and routing for the subsequent physical layout stage. Next, we formulated the problem and proposed an efficient heuristic algorithm to solve it. The heuristic algorithm has been implemented in C and experiments were performed on the ISCAS85 circuits to demonstrate the effectiveness of our approach.

This paper proposes a new scheme for constructing software reliability growth models (SRGM) based on a nonhomogeneous Poisson process (NHPP). The main focus is to provide an efficient parametric decomposition method for software reliability modeling, which considers both testing efforts and fault detection rates (FDR). In general, the software fault detection/removal mechanisms depend on previously detected/removed faults and on how testing efforts are used. From practical field studies, it is likely that we can estimate the testing efforts consumption pattern and predict the trends of FDR. A set of time-variable, testing-effort-based FDR models were developed that have the inherent flexibility of capturing a wide range of possible fault detection trends: increasing, decreasing, and constant. This scheme has a flexible structure and can model a wide spectrum of software development environments, considering various testing efforts. The paper describes the FDR, which can be obtained from historical records of previous releases or other similar software projects, and incorporates the related testing activities into this new modeling approach. The applicability of our model and the related parametric decomposition methods are demonstrated through several real data sets from various software projects. The evaluation results show that the proposed framework to incorporate testing efforts and FDR for SRGM has a fairly accurate prediction capability and it depicts the real-life situation more faithfully. This technique can be applied to wide range of software systems.

The authors show that the logistic testing effort function is practically acceptable/helpful for modeling the software reliability growth and providing a reasonable description of resource consumption. Therefore, in addition to the exponential shaped models, we integrate the logistic testing effort function into an S-shaped model for further analysis. The model is designated as the Yamada Delayed S-shaped model. A realistic failure data set is used in the experiments to demonstrate the estimation procedures and results. Furthermore, the analysis of the proposed model under an imperfect debugging environment is investigated. In fact, from these experimental results and discussions, it is apparent that the logistic testing-effort function is very suitable for making estimations of resource consumption during the software development/testing phase.

We first describe how several existing software reliability growth models based on nonhomogeneous Poisson processes (NHPPs) can be derived based on a unified theory for NHPP models. Under this general framework, we can verify existing NHPP models and derive new NHPP models. The approach covers a number of known models under different conditions. Based on these approaches, we show a method of estimating and computing software reliability growth during the operational phase. We can use this method to describe the transitions from the testing phase to operational phase. That is, we propose a method of predicting the fault detection rate to reflect changes in the user's operational environments. The proposed method offers a quantitative analysis on software failure behavior in field operation and provides useful feedback information to the development process.

We study the optimal software release problem considering cost, reliability and testing efficiency. We first propose a generalized logistic testing effort function that can be used to describe the actual consumption of resources during the software development process. We then address the problem of how to decide when to stop testing and when to release software for use. In addressing the optimal release time, we consider cost and reliability factors. Moreover, we introduce the concept of testing efficiency, and describe how reliability growth models can be adjusted to incorporate this new parameter. Theoretical results are shown and numerical illustrations are presented.

For the wavelength division multi-access system (WDMA), the reservation approach and the pre-allocation approach are two major media access protocols to support the packet-switched traffic. In this paper, a new media access control (MAC) protocol, the AP-WDMA (accelerative pre-allocation), is proposed for the WDMA. Although implemented on a simple basic architecture, it does provide a better media access protocol which can be easily extended to all the WDMA. Through evaluations, the AP-WDMA is shown to be able to overcome the wavelength limitation through a channel sharing mechanism and enable efficient transmission with the accelerative mechanism. The AP-WDMA relieves these technology constraints restricting the tunability to only one end and the table size to only n+2 memory spaces, where n is the number of stations.

This paper presents the analysis of multicasting performance in broadcast-and-select wavelength division multiplexing (WDM) networks from two aspects: multicast session length and multicast group size. The protocol analyzed in this paper is based on an existing protocol which can schedule unicast and multicast traffic. The packet distance, determined by the Euclidean distance of session length and the group size of a multicast distance is compared with the multicast distance to select the most appropriate scheduling method for multicast packets. Further comparisons on channel utilization and packet delay show that the session length affects the channel dominance significantly and the group size determines the range of multicast distance such that the performances will remain the same. In addition, multicast traffic with larger mean session length or mean group size will make some specific scheduling strategies fail to achieve optimal performance. If the multicast distance is properly chosen, performance tradeoffs can be made under multicasting environments with large session length or large group size.

Many studies have been performed on the subject of software reliability but few have explicitly considered the impact of software testing on the reliability process. This paper presents two important issues on software reliability modeling and software reliability economics: testing effort and efficiency. First, we discuss on how to extend the logistic testing-effort function into a general form. The generalized logistic testing-effort function has the advantage of relating the work profile more directly to the natural flow of software development. Therefore, it can be used to describe the actual consumption of resources during the software development process and to obtain a conspicuous improvement in modeling testing-effort expenditures. Furthermore, we incorporate the generalized logistic testing-effort function into software reliability modeling and its fault-prediction capability is evaluated through four numerical experiments on real data. Then, we address the effects of automated techniques or tools on increasing the efficiency of software testing. New testing techniques usually increase test coverage. We propose a modified software reliability cost model to reflect these effects. From the simulation results, we obtain a powerful software economic policy which clearly indicates the benefits of applying new automated testing techniques and tools during the software development process.

In order to avoid deadlocks, prevention-based routing algorithms impose certain routing restrictions which lead to high hardware complexity or low adaptability. If deadlock occurrences are extremely rare, recovery-based routing algorithms become more attractive with respect to hardware complexity and routing adaptability. A simple architecture where each router is provided with an additional special flit buffer was developed to achieving deadlock recovery. Disha-SEQ and Disha-CON are two deadlock recovery schemes based on such an architecture to accomplish sequential recovery and concurrent recovery, respectively. In this paper, we propose a simple recovery scheme for a 2D mesh with the same router architecture, and reduce drawbacks in Disha-SEQ or Disha-CON, such as hardwired tokens, finding the Hamiltonian cycle, Hamiltonian path labeling for each node, and non-minimal path routing. Moreover, the simulation results show that the proposed scheme has a similar performance to Disha-CON and is better than Disha-SEQ.

To embed a ring in a hypercube is to find a Hamiltonian cycle through every node of the hypercube. It is obvious that no 2/sup n/-node Hamiltonian cycle exists in an n-dimensional faulty hypercube which has at least one faulty node. However, if a hypercube has faulty links only and the number of faulty links is at most n-2, at least one 2/sup n/-node Hamiltonian cycle can be found. In this paper, we propose a distributed ring-embedding algorithm that can find a Hamiltonian cycle in a fault-free or faulty n-dimensional hypercube (Q,), and the complexity is O(n) parallel steps. The algorithm is based on the recursion property of the hypercube and the free-link dimension concept. In some cases, even when the number of faulty links is larger than n-2, Hamiltonian cycles may still exist. We show that the largest possible number of faulty links that can be tolerated is 2/sup n-1/-1. The performance and the constraints of the fault-tolerant algorithm is also analyzed in detail in this paper. Furthermore, a dynamic reconfiguration algorithm for an embedded ring is proposed and discussed. Due to the distributed nature of the algorithms, they are useful for the simulation of ring-based multiprocessors on MIMD hypercube multiprocessors.

Fault management is an essential function of the network management to secure continuous and efficient operations of a network system. We initiate the fault management study for the adjustable pre-allocation wavelength division multi-access system (AP-WDMA). The subjects investigated include fault detection, fault location, and fault tolerant design. Based on the fault probability consideration for each station, two models are presented. One treats each station as a whole unit and the other further divides each station into 5 parts. For both models, the fault detection and fault location are performed in a two-pass operation. Finally, a fault-tolerant design for the AP-WDMA is proposed. A 2-stage reconfiguration algorithm is also provided in this study.

In this paper, we propose a synchronous flow control mechanism in wormhole routed optical networks. It is expected that the benefit of shorter routing delay and smaller buffer size requirement in wormhole routing will be significant in optical networks. Different from the traditional bi-directional asynchronous back-pressure flow control, the flow control is modified to be unidirectional and synchronous. The size of synchronized control slot does not depend on the routing path length and the number of bits is a constant which is equal to the total number of virtual channels and nodes. The proposed flow control takes advantage of the restricted order of accessing channels in deadlock-free routing to broadcast their control information in a corresponding restricted order. Furthermore, in order to reduce the buffer size to only one unit, the virtual channels which share the same physical channel must be able to simultaneously transmit data. The low channel utilization induced by such mechanism is overcome by our modified source routing. In summary, this paper introduces a flow control mechanism which easily incorporates the benefit of wormhole routing into the limited-resource optical networks.

Numerous stochastic models for the software failure phenomenon based on Nonhomogeneous Poisson Process (NHPP) have been proposed in the last three decades (1968-98). Although these models are quite helpful for software developers and have been widely applied at industrial organizations or research centers, we still need to do more work on examining/estimating the parameters of existing software reliability growth models (SRGMs). We investigate and account for three possible trends of software fault detection phenomena during the testing phase: increasing, decreasing and steady state. We present empirical results from quantitative studies on evaluating the fault detection process and develop a valid time-variable fault detection rate model which has the inherent flexibility of capturing a wide range of possible fault detection trends. The applicability of the proposed model and the related methods of parametric decomposition are illustrated through several real data sets from different software projects. Our evaluation results show that the analytic parametric decomposition approach for SRGM have a fairly accurate prediction capability. In addition, the testing effort control problem based on the proposed model is also demonstrated.

This paper presents an efficient technique to map the minimum vertex cover and two closely related problems (maximum independent set and maximum clique) onto the Hopfield neural networks. The proposed approach can be used to find near-optimum solutions for these problems in parallel, and particularly the network algorithm always yields minimal vertex covers. A systematic way of deriving energy functions is described. Based on these relationships, other NP-complete problems in graph theory can also be solved by neural networks. Extensive simulations were performed, and the experimental results show that the network algorithm outperforms the well-known greedy algorithm for vertex cover problems.

Rollback-Dependency Trackability (RDT) is a property that states that all rollback dependencies between local checkpoints are on-line trackable by using a transitive dependency vector. In this paper, we address three fundamental issues in the design of communication-induced checkpointing protocols that ensure RDT. First, we prove that the following intuition commonly assumed in the literature is in fact false: If a protocol forces a checkpoint only at a stronger condition, then it must take, at most, as many forced checkpoints as a protocol based on a weaker condition. This result implies that the common approach of sharpening the checkpoint-inducing condition by piggybacking more control information on each message may not always yield a more efficient protocol. Next, we prove that there is no optimal on-line RDT protocol that takes fewer forced checkpoints than any other RDT protocol for all possible communication patterns. Finally, since comparing checkpoint-inducing conditions is not sufficient for comparing protocol performance, we present some formal techniques for comparing the performance of several existing RDT protocols.

We investigate a software reliability growth model (SRGM) based on the Non Homogeneous Poisson Process (NHPP) which incorporates a logistic testing effort function. Software reliability growth models proposed in the literature incorporate the amount of testing effort spent on software testing which can be described by an exponential curve, a Rayleigh curve, or a Weibull curve. However it may not be reasonable to represent the consumption curve for testing effort only by an exponential, a Rayleigh or a Weibull curve in various software development environments. Therefore, we show that a logistic testing effort function can be expressed as a software development/test effort curve and give a reasonable predictive capability for the real failure data. Parameters are estimated and experiments on three actual test/debug data sets are illustrated. The results show that the software reliability growth model with logistic testing effort function can estimate the number of initial faults better than the model with Weibull type consumption curve. In addition, the optimal release policy of this model based on cost reliability criterion is discussed.

The Hyper-Geometric Distribution software reliability growth Model (HGDM) was developed to estimate the number of remaining software faults after completing the test/debug phase. An important problem in the software development process is to determine when to stop testing and release the software to the users. In this paper, the cost optimal release policy, which minimizes the total expected software cost, is discussed. The total expected software cost here includes the penalty cost, which should be paid by the manufacturer if the software is delivered after the scheduled delivery time. The underlying software reliability growth model in our approach is the HGDM. Numerical examples are presented for illustration.

Highly reliable interleaved memory systems for uniprocessor and multiprocessor computer architectures are presented. The memory systems are divided into groups. Each group consists of several banks and each bank has several modules. The error model is defined at the memory-module level. A module is faulty if any single or multiple faults result in loss of the entire module. Spare modules, as well as spare banks, are included in the systems to enhance reliability and availability. A faulty module is replaced by a spare module within a bank first, and, if the bank has no redundancy remaining for the faulty module, the whole bank will be replaced by a spare bank at the next higher level. The structure of the reconfigurable memory system is designed in such a way that the replacement of faulty modules (banks) by spare modules (banks) will not disturb memory references if each bank (group) has at most two spare modules (banks). If there are more than two spare modules (banks) in a bank (group), a second-level address translator is designed which can prohibit references to faulty modules by address remapping. The address translator can be implemented with a CAM or switches. Analysis results show that the system reliability can be significantly improved with little hardware overhead. Also, a typical system with one redundant row of modules has the highest cost-effectiveness during its useful lifetime period. User transparency in memory access is retained.

This paper presents a new and efficient strategy of pseudorandom pattern generation (PRPG) for IC testing. It uses a general programmable LFSR (P-LFSR) to offer multiple-seed and multiple-polynomial PRPG. The deterministic pattern set generated by an ATPG tool or supplied by the designers is used to guide the generation of pseudorandom patterns. A novel application of the Gauss-elimination procedure is proposed to find the seeds as well as the polynomials. With an intelligent heuristic to further utilize the essential faults, this approach becomes very efficient, even for the random pattern resistant (RPR) circuits. Experiments are conducted on the ISCAS-85 benchmarks and the full scan version of the ISCAS-89 benchmarks. For all benchmark circuits, complete fault coverage is achieved with good balance on the hardware overhead and the test lengths as compared to other schemes.

One major drawback of the LFSR-based BIST is its low fault coverage. To obtain the complete fault coverage, multiple seeds and multiple polynomials are usually required. One way to find the seeds and polynomials for the LFSR was utilizing the Gauss-elimination procedure. In this approach, the test patterns which are generated by LFSR are modeled as a set of multivariable linear equations. It is created from a given deterministic test set. The corresponding seed and polynomial are then obtained from the solution of this equations set. However, given the original deterministic test set without don't cares, it were not acceptable on the random pattern resistant circuits. In this paper, we allow the test patterns to have don't care values. With an intelligent heuristic of further utilizing the essential faults, this approach becomes much more efficient even for the random pattern resistant circuits. The experimental results on the ISCAS-85 and the ISCAS-89 benchmarks show that a significant improvement can be obtained both on the hardware overhead and the test length.

Most existing behavioral synthesis systems concentrate on area and performance optimization, while ignoring other design qualities such as testability. In this paper/sup /spl Dagger//, we present three algorithms for register, module, and interconnection allocation of behavioral synthesis respectively to improve testability in data path allocation without assuming any specific test strategy. By using primary input/output registers effectively, the proposed algorithms produce RTL designs with better testability, while incur low or even no hardware overhead. Four benchmarks are synthesized using the proposed approaches and the results are compared with the best results of similar works in the literature. It shows that our approaches give both higher fault coverage and lower hardware overhead.

A considerable amount of testing resources is required during software module testing. In this paper, based on the HGDM (Hyper-Geometric Distribution Model) software reliability growth model, we investigate the following optimal resource allocation problems in software module testing: (1) minimization of the number of software faults still undetected in the system after testing given a total amount of testing resources, and (2) minimization of the total amount of testing resources repaired, given the number of software faults still undetected in the system after testing. Furthermore, based on the concepts of "average allocation" and "proportional allocation", two simple allocation methods are also introduced. Experimental results show that the optimal allocation method can improve the quality and reliability of the software system much more significantly than these simple allocation methods can. Therefore, the optimal allocation method is very efficient for solving the testing resource allocation problem.

The tree communication scheme was shown to be very efficient for global operations on data residing in the processors of a hypercube with time complexity of O(log/sub 2/N), where N is the number of processors. This communication scheme is very useful for many parallel algorithms on hypercube multiprocessors. If a problem can be divided into independent subproblems, each subproblem can first be solved by one of the processors. Then, the tree communication scheme is invoked to merge the subresults into the final results. All the algorithms for problems with this property can benefit from the tree communication scheme. We propose a more general and efficient tree communication scheme in this paper. In addition, we also propose fault-tolerant algorithms for the tree communication scheme, by exploiting the unique properties of the tree communication scheme. The computation and communication slowdown is small (<2) under the effect of multiple link and/or node failures.

Considerable testing resources are required during software module testing. This paper, based on the 'hyper-geometric distribution software reliability growth model' (HGDM) investigates two optimal resource allocation (OPT/RA) problems in software module testing: (1) minimization of the number of software faults (NSF) still undetected in the system after testing, given a fixed amount of testing resources; and (2) minimization of the total amount of testing resources required, given the NSF still undetected in the system after testing. Based on the concepts of average allocation and proportional allocation, two simple allocation methods are introduced. Experimental results show that the OPT/RA method can improve the quality and reliability of the software system much more than the simple allocation methods. Therefore, the OPT/RA method is very efficient for solving the 'testing resource allocation' problem.

The hyper-geometric distribution software-reliability growth model (HGDM) can estimate the number of initial faults in a software program. An important problem in software development is to determine when to stop testing and then release the software. This paper mainly investigates the optimal software release policies which minimize the mean total software cost and satisfy the software-reliability requirement based on the HGDM. The optimal software release times are determined and shown to be finite. A numerical example illustrates these optimal software release policies.

Debugging actions during the test/debug phase of software development are not always performed perfectly. That is, not all the software faults detected are perfectly removed without introducing new faults. This phenomenon is called imperfect debugging. The hyper-geometric distribution software reliability growth model (HGDM) was developed for estimating the number of software faults initially in a program. We propose an extended model based on the HGDM incorporating the notion of imperfect debugging.

The hyper-geometric distribution model (HGDM) of software reliability growth has been used for estimating the number of initial faults in a software program. Another important problem in the software development process is to determine when to stop testing and release the software. In this paper, we investigate the optimal release policies minimizing the total expected software cost with a scheduled software delivery time for the HGDM. The total expected software cost includes the penalty cost which should be paid by the manufacturer if the software is delivered after the scheduled delivery time. The main result is that the optimal release time can be determined and shown to be finite. Numerical examples illustrating the optimal software release problem are also presented.>

In this paper, the partitioning scheme is used to achieve fault tolerance in hyperbus and hypercube multiprocessors. Unlike other schemes, processor faults are assumed to be randomly distributed. We propose a novel and practical load redistribution method to tolerate processor faults in a hyperbus structure with insignificant overhead (a slowdown of 2 for computation and a slowdown of 3 for communication in the worst case). Standard routing and broadcasting algorithms were implemented on hypercube computers. To achieve fault tolerance, we present routing and broadcasting algorithms for a faulty hypercube with at most n-1 faults. Compared with other existing algorithms, our methods have better performance in most measures.

This paper examines the problem of recovering from processor transient faults in pipelined multiprocessor systems. A pipelined machine allows out of order instruction execution and branch prediction to increase performance, thus a precise computation state may not be available. We propose a modified scheme to implement the precise computation state in a pipelined machine. The goal of this research is to implement checkpointing and rollback for error recovery in a pipelined system based on the technique to achieving precise computation state. Detailed analysis has been performed to demonstrate the effectiveness of this method.

This paper presents a novel technique to map the minimum vertex cover and related problems onto the Hopfield neural networks. The proposed approach can be used to find near-optimum solutions for these problems in parallel, and particularly the network algorithm always yields minimal vertex covers. Further, the relationships between Boolean equations and arithmetic functions are presented. Based on these relationships, other NP-complete problems in graph theory can also be solved by neural networks. Extensive simulation was performed and the experimental results demonstrate that the network algorithm outperforms the well-known greedy algorithm for the vertex cover problem.>

The hyper-geometric distribution software reliability growth model (HGDM) has been shown to be able to estimate the number of faults initially resident in a program at the beginning of the test-and-debug phase. A key factor of the HGDM is the "sensitivity factor", which represents the number of faults discovered and rediscovered at the application of a test instance. The learning curve incorporated in the sensitivity factor is generally assumed to be linear in the literature. However, this assumption is apparently not realistic in many applications. We propose two new sensitivity factors based on the exponential learning curve and the S-shaped learning curve, respectively. Furthermore, the growth curves of the cumulative number of discovered faults for the HGDM with the proposed learning curves are investigated. Extensive experiments have been performed based on two real test/debug data sets, and the results show that the HGDM with the proposed learning curves estimates the number of initial faults better than previous approaches.>

Several new algorithms for matrix-matrix multiplications on hypercube multiprocessors are presented and evaluated based on the number of multiplications, additions, and transfers. The matrices to be multiplied are uniformly distributed to all processors of a hypercube system. Each processor owns some submatrices which are derived by dividing the source matrices. Each submatrix multiplication can now be performed independently within a processor. All the partial results are then summed up and transferred to a single processor. An orthogonal tree is used for efficient communication. The time complexity is O(log/sub 2/p) if p /spl times/ p processors are used. In addition, the UDD (Uniform Data Distribution) approach is employed when some processors do not work properly and the faulty effects have been detected. Two classes of fault patterns are considered and evaluated.>

Article Free Access Share on The sea-of-wires array synthesis system Authors: Ing-Yi Chen View Profile , Geng-Lin Chen View Profile , Fredrick J. Hill View Profile , Sy-Yen Kuo View Profile Authors Info & Claims DAC '93: Proceedings of the 30th international Design Automation ConferenceJuly 1993 Pages 188–193https://doi.org/10.1145/157485.164664Published:01 July 1993Publication History 2citation442DownloadsMetricsTotal Citations2Total Downloads442Last 12 Months13Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The author points out that the goal of a channel routing algorithm is to route all the nets with as few tracks as possible to minimize chip areas and achieve 100% connection. However, the manufacturing yield may not reach a satisfactory level if care is not taken to reduce critical areas which are susceptible to defects. These critical areas are caused by the highly compacted adjacent wires and vias in the routing region. A channel routing algorithm, the yield optimizing routing (YOR) algorithm, is presented to deal with this problem. It systematically eliminates critical areas by floating, burying, and bumping net segments as well as shifting vias. The YOR algorithm also minimizes the number of vias since vias in a chip will increase manufacturing complexity and hence degrade the yield. YOR has been implemented and applied to benchmark routing layouts in the literature. Experimental results show that large reduction in the number of critical areas and significant improvement in yield are achieved, particularly for practical size channels such as Deutsch's difficult problem.>

A hierarchical modular sorting network which achieves a balance in area-time cost between the odd-even transposition sort and the bitonic sort is presented. It consumes less hardware than a single-level odd-even sorter and reduces the wire complexity of the bitonic sorter in VLSI or WSI (wafer-scale integration) implementation. The optimal number of levels in the hierarchy is evaluated, and the sorting capability of each level is derived so as to minimize the hardware overhead. The hierarchical sorting network is very regular in structure and hence defect tolerance capability can be included more easily than in any existing sorting network with the same time complexity. Redundancy is provided at every level of the hierarchy. Hierarchical reconfiguration is performed by replacing the defective cells at the bottom level with the spare cells first and repeating the process at the next higher level if there is not enough redundancy at the current level. Yield analysis is performed to demonstrate the effectiveness of the approach.>

Reconfigurable logic and memory structures are an important means of increasing manufacturing yield as both circuit density and chip size continue to increase. Yield enhancement through reconfiguration, however, necessarily relies on accurate diagnosis of fault locations. Although a substantial body of literature exists concerning testing of logic arrays, little is known regarding diagnosis of the specific locations of multiple faults in such arrays. In the paper a fault diagnosis algorithm is presented for large programmable logic arrays (PLAs).>

A novel approach to online error detection and correction for high-throughput VLSI sorting arrays is presented. The error model is defined at the sorting element level and both functional errors and data errors are considered. Functional errors are detected and corrected by exploiting inherent properties as well as newly discovered special properties of the sorting array. Coding techniques are used to locate data errors. All the checkers are designed to be totally self-checking and hence the sorting array is highly reliable. Two-level pipelining is employed, making the design very efficient and suitable for real-time application. The structure is very regular and therefore is very attractive for VLSI or WSI implementation.>

The development of efficient algorithms for constructing a flawless subarray from a defective VLSI/WSI (wafer scale integration) array is discussed. The array consists of identical elements such as processors or memory cells embedded in a switch lattice in the form of a rectangular grid, in contrast to the redundancy approach in which some elements are dedicated as spares, all the elements in the degradation approach are treated in a uniform way. Each element can be either fault-free or defective, and a subarray which contains no faulty element is derived under constraints of switching and routing mechanisms. Although extensive literature exists concerning spare allocation and reconfiguration in arrays with redundancy, little research has been published on optimal reconfiguration in a degradable array. A graph formulation is used to describe the problem, and reconfiguration is found to relate to finding an independent set of a graph. Efficient heuristic algorithms are presented to determine a target subarray from the defective host array.>

The authors present an integrated computer-aided design environment, the VAR (VHDL-based array reconfiguration) system, for the tasks of design, reconfiguration, simulation, and evaluation in an architecture modeled by VHDL. An easily diagnosable and reconfigurable two-dimensional defect-tolerant processing element (PE) switch lattice array is used as an example to illustrate the methodology of VAR. VAR allows the designers study and evaluate fault diagnosis and reconfiguration algorithms by inserting faults, which are generated based on manufacturing yield data, into the array and then locating the fault PEs as well as simulating the reconfiguration process. Thus, VAR can assist the designers in evaluating the different combinations of fault patterns, fault diagnosis algorithms, and reconfigurable architectures through a complete set of figures of merit which aim at architectural improvements. Extensive simulation and evaluation have been performed to demonstrate and support the effectiveness of VAR.>

A systematic and efficient fault diagnosis methodology in reconfigurable VLSI array architectures is presented. This methodology utilizes the output data path independence of subsets of processing elements (PEs) based on the topology of the arrays. The 'divide the conquer' technique is applied to reduce testing complexity and enhances the controllability and observability of arrays. An array under test is divided into several nonoverlapping parallel partitions. Those PEs in the same partition can be diagnosed simultaneously. The problem to find parallel partitions is shown equivalent to a generalized Eight Queens problem. Three types of easily testable PEs are designed to illustrate this approach. The main contribution of this paper is a novel PE fault diagnosis approach which speeds up the testing by at least O( mod V mod /sup 1/2/) for the arrays considered, where mod V mod is the number of PEs. This approach requires little or no hardware overhead depending on the types of architectures and can diagnose multiple PE faults.>

The issue of yield degradation due to physical failures in large memory and processor arrays is of significant importance to semiconductor manufacturers. One method of increasing the yield for iterated arrays of memory cells or processing elements is by incorporating spare rows and columns in the die or wafer which can be programmed into the array. This paper addresses the issue of computer-aided design approaches to optimal reconfiguration of such arrays. The paper presents the first formal analysis of the problem. The complexity of optimal reconfiguration is shown to be NP-complete for rectangular arrays utilizing spare rows and columns. In contrast to previously proposed exhaustive search and greedy algorithms, this paper develops a heuristic branch and bound approach based on the complexity analysis, which allows for flexible and highly efficient reconfiguration. Initial screening is performed by a bipartite graph matching algorithm.