In industrial scenarios, semantic segmentation of surface defects is vital for identifying, localizing, and delineating defects. However, new defect types constantly emerge with product iterations or process updates. Existing defect segmentation models lack incremental learning capabilities, and direct fine-tuning (FT) often leads to catastrophic forgetting. Furthermore, low contrast between defects and background, as well as among defect classes, exacerbates this issue. To address these challenges, we introduce a plug-and-play Transformer-based semantic complement module (TSCM). With only a few added parameters, it injects global contextual features from multi-head self-attention into shallow convolutional neural network (CNN) feature maps, compensating for convolutional receptive-field limits and fusing global and local information for better segmentation. For incremental updates, we propose multi-scale spatial pooling distillation (MSPD), which uses pseudo-labeling and multi-scale pooling to preserve both short- and long-range spatial relations and provides smooth feature alignment between teacher and student. Additionally, we adopt an adaptive weight fusion (AWF) strategy with a dynamic threshold that assigns higher weights to parameters with larger updates, achieving an optimal balance between stability and plasticity. The experimental results on two industrial surface defect datasets demonstrate that our method outperforms existing approaches in various incremental segmentation scenarios.

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Wearable Internet of Things (IoT) devices are widely used in many fields. Such as smart bracelets for health, smart watches for sports, smart safety helmets for industry, and so on. These devices make life easier and more efficient. Multiple-input-multiple-output (MIMO) antenna technology improves data transmission rates and communication reliability by using multiple antennas in a device, so as to meet the demand for real-time data transmission and multidevice connectivity in the IoT. A compact tri-port MIMO antenna with high isolation suitable for wearable applications is proposed. The antenna is designed to work in the frequency band of 2.45 GHz industrial, scientific, and medical (ISM). The antenna has two layers and three ports with low profile suitable for wearable applications. The upper layer is a circular patch with four shorting pins, which excites the${\mathrm { TM}}_{01}$mode with a conical radiation pattern. The lower layer is a circular patch inlaid in an annular patch, which respectively excite the${\mathrm { TM}}_{11}$mode and the${\mathrm { TM}}_{31}$mode with a broadside radiation pattern. To achieve high isolation, characteristic mode analysis (CMA) was conducted on the circular patch, the circular patch with four shorting pins, and the annular patch. The positions of the three ports are reasonably arranged based on mode diversity. The measured bandwidth of the port for conical radiation is 35 MHz (2.43–2.465 GHz), while the measured bandwidths of the two ports for broadside radiation are basically the same which is 50 MHz (2.417–2.467 GHz). The measured overall isolation exceeds 50 dB.

In this article, we propose a new adaptive fuzzy fault-tolerant control (FTC) for a three-dimensional riser-vessel system with unknown backlash nonlinearity. A model for the smooth inverse dynamics of the backlash is introduced; then, the control input is divided into an expected input and a compensation error. Considering the imprecision of system modeling and unknown external disturbances, we employ a fuzzy adaptive technology to achieve compensation. By incorporating the actuator fault term and backlash error, the adaptive FTC is developed to resolve loss faults in the actuator and compensate for the unknown backlash to some extent. The direct Lyapunov method is used to demonstrate the system’s bounded stability. Finally, simulation results demonstrate the effectiveness of the derived scheme.

To meet the content delivery requirement of the sky-rocketing increase in video requests, crowdsourced content delivery network (crowdsourced CDN) provides a new promising paradigm for low-cost and low-latency video distribution. However, due to the low contribution of storage and upload bandwidth resources from edge network owners, the resources in crowdsourced CDN are always scare. So how to incentivize crowdsourced resource supply from edge network owners are the key in the crowdsourced CDN paradigm. In this paper, we propose an incentive mechanism to address the challenge. More specifically, a Stackelberg game is formulated to model the âĂIJbargainâĂİ interaction between owners and content provides (CPs). With the game, we propose a genetic algorithm to reach the equibibrium. Finally, trace-driven experiments show that effectiveness of our design.

Compressed bitmap indexes are increasingly used in databases and search engines. By exploiting bit-level parallelism and bitwise operations, e.g. AND/OR operations, they can significantly accelerate the development of many areas. The Word Aligned Hybrid (WAH) bitmap compression scheme using run-length encoding (RLE), is commonly recognized as the most efficient scheme in terms of CPU-performance. This paper presents a new form of compressed bitmap indexes named Adaptive Partitioned Position List Encoding (APPLE), which uses packed position lists for compression. For experiments, we compare it with Huffman encoding, and two enhanced variants of WAH : Concise and COMPAX. Our empirical results show this scheme achieves significant improvement.

This work explores super resolution (SR) with a deep network based on a pixel probabilistic model, where particular small inputs and large magnification factors make the problem highly underspecified since fairly large amounts of high-frequency details are missing in low resolution (LR) source. In this paper, we develop a deep architecture comprising of a PixelCNN and a residual network (ResNet), in which PixelCNN predicts the serial dependencies of the pixel sequence and ResNet for capturing the global structure of LR input. A human visual saliency mechanism (HVSM) by employing accurate SR in salient regions and fast interpolation in nonsalient regions is integrated within the pixel probabilistic model to efficiently reduce the computational complexity while maintaining the desired visual quality. Additionally, we present a Bayesian optimization technique to automatically determine the optimal weight of loss function. Furthermore, a modified image quality assessment taking into account HVSM is introduced, trying to align with the human visual perception. Experiments demonstrate that the proposed algorithm could generate more plausible facial features than previous deep learning methods, offering finer details and significant improvement in visual quality.

Today's mobile users achieve unsatisfactory quality of experience mainly due to the large network distance to the centralized infrastructure. To improve users' experiences, caching at the wireless access points (APs) has been proposed for bringing the contents closer to users. However, the wireless content placement is challenging as the placement is affected by many realistic constraints, such as a large number of APs, interaction among neighboring APs, various local content popularities. In this paper, we study the wireless caching problem, i.e., which contents should be stored by which APs. First, we fulfil these constraints to formulate our problem and introduce an objective function that maximizes the total cache hit rate of all APs. Next, we prove the NP-hardness of the problem and propose a local distributed caching algorithm to address it. Furthermore, we provide a game theoretic perspective on the problem and prove that the proposed algorithm can converge to the Nash Equilibrium in polynomial time. Finally, we perform simulations on a real-world dataset to demonstrate the effectiveness of our algorithm.

Today's internet has witnessed a fast growth of mobile video streaming. Different from traditional PC/laptop-based video streaming, mobile video streaming relies on the usage of mobile devices and wireless networks, allowing people to receive video content on the move. The change has challenged traditional video content delivery, which uses centralized infrastructure (e.g., CDN) inside the network for content distribution, in a sense that mobile users (connected to Wi-Fi or cellular networks) encounter large delay and small download speed. One promising solution is to prefetch content in the edge of the network, e.g., on access points (APs). However, it faces the great challenges: 1) It is difficult to prefetch content in such edge APs with limited storage capacity; 2) Users' mobility cross APs affects the content delivery; 3) Popularity of content may change significantly across APs. Previous approaches make mobile video content delivery inefficient without jointly considering these problems. In this paper, we propose an AP-assisted mobile video delivery framework to solve these problems. First, using large-scale measurement studies of users' trajectories and preferences of videos, we reveal that both users' mobility patterns and their intrinsic preferences are important for AP-assisted content delivery. Second, we formulate the AP content prefetching as an optimization problem, and develop an online solution, APRank, to solve it. Third, we evaluate the effectiveness of our design, compared with four baselines, random-based, popularity-based, preference-based and offline prefetching.

Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks-from regional content delivery network (CDN) servers to peer CDN servers (e.g., smartrouters in users' homes)-to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world data sets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over two weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand the performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching, including least recently used (LRU) and least frequently used (LFU), in terms of supporting mobile video requests. We reveal that content, location, and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30% compared with LRU/LFU.

More and more applications in industrial or medical CT have to adopt limited-angle projection data, such as restricted scanning, decreasing radiation dose and so on. Traditional reconstruction methods for incomplete projection data, including analytic reconstruction and iterative reconstruction, can't meet the demand of limited-angle reconstruction. To deal with TV reconstruction model's imbalance in reconstructing image information of different direction, this paper proposes a reconstruction model and its corresponding hybrid algorithm, which utilize horizontal direction gradient to assist TV reconstruction in vertical direction. Experiments results demonstrate the proposed method's effectiveness and performance in image reconstruction from limited-angle projection data.

Bitmap indexing is flexible to conduct boolean operations in data retrieval. Besides, the query processing based on bitmap indexing is also very fast. Therefore it has been widely used in various big data analytics platforms, such as Druid and Spark etc. However, bitmap index can consume a large amount of memory, which leads to the invention of different kinds of bitmap index compression algorithms without sacrificing temporal performance. In practice, we are often discommoded by choosing a proper algorithm when handling specific problems. Besides, after devising a new algorithm that may outperform existing ones, it is essential to evaluate its performance in theory. Without appropriate theoretical analysis, the deficit of a new algorithm can only be spotted until final experimental results are drawn, thus wasting much time and effort. In this paper, we propose a general analytical model to analyze both the spatial and temporal performance for bitmap index compression algorithms, which can be applied to analyze all kinds of algorithms derived from WAH (word-aligned hybrid). In this model, two types of distributed bitmaps, uniformly distributed bitmaps and clustered bitmaps, are used separately. In order to illustrate this model, several bitmap index compression algorithms are analyzed and compared with each other. Algorithms herein are COMBAT (COMbining Binary And Ternary encoding), SECOMPAX (Scope Extended COMPAX) and CONCISE (Compressed 'n' Composable Integer Set), which are all derived from WAH. Evaluation results by MATLAB simulation about these algorithms are also presented. This paper paves the way for further researches on the performance evaluation of various bitmap index compression algorithms in the future.

Archiving of Internet traffic is essential for analyzing network events in the field of network security. Currently, bitmap indexing is used to accelerate the indexing and search queries for archival traffic data. However, the generation of bitmap index needs large storage space, which makes bitmap index compression is a must-have function. In this paper, we propose a new bitmap index encoding algorithm named SECOMPAX (Scope-Extended COMPressed Adaptive indeX), which performs better compression ratio and fast encoding speed compared with the state-of-art bitmap index compression algorithm WAH (Word-Aligned-Hybrid), PLWAH(Position list word aligned hybrid) and COMPAX (COMPressed Adaptive indeX). The comparison among WAH, PLWAH, COMPAX and SECOMPAX shows that SECOMAX accomplishes the smallest bitmap index in size and the comparable encoding time with other three methods. We also use real Internet trace from CAIDA to prove the validity of SECOMPAX. SECOMPAX has the best compression ratio in compared with other bitmap index encoding algorithms in our experiments. The encoding time is measured, and statistics of the distribution of codeword used in SECOMPAX is also investigated in experiments. It shows that SECOMPAX's extra time consumption is acceptable as the new designed codebook work effectively in encoding bit sequence which cannot be compressed in other bitmap encoding schemes.

With the pretty prompt growth in Internet content, future Internet is emerging as the main usage shifting from traditional host-to-host model to content dissemination model, e.g. video makes up more than half of Internet traffic. ISPs, content providers and other third parties have widely deployed content delivery networks (CDNs) to support digital content distribution. Though CDN is an ad-hoc solution to the content dissemination problem, there are still big challenges, such as complicated control plane. By contrast, as a wholly new designed network architecture, named data networking (NDN) incorporates content delivery function in its network layer, its stateful routing and forwarding plane can effectively detect and adapt to the dynamic and ever-changing Internet. In this paper, we try to explore the similarities and differences between CDN and NDN. Hence, we evaluate the distribution efficiency, network security and protocol overhead between CDN and NDN. Especially in the implementation phase, we conduct their testbeds separately with the same topology to derive their performance of content delivery. Finally, summarizing our main results, we gather that: 1) NDN has its own advantage on lots of aspects, including security, scalability and quality of service (QoS); 2) NDN make full use of surrounding resources and is more adaptive to the dynamic and ever-changing Internet; 3) though CDN is a commercial and mature architecture, in some scenarios, NDN can perform better than CDN under the same topology and caching storage. In a word, NDN is practical to play an even greater role in the evolution of the Internet based on the massive distribution and retrieval in the future.

Nowadays, rapid evolution of computers and mobile devices has caused the explosive increase in network traffic. So it becomes more and more necessary to archive network traffic for analyzing network events and a lot of emerging applications. Compression is fundamental for traffic archival solution to save the storage space, and indexing is effective to accelerate search queries for archive of traffic data. In this paper, we propose BreadZip (blocks row-reordering and adaptive index zip), a combination of initial traffic data and index compression. BreadZip has three main advantages. 1) to improve compressing efficiency and reduce memory footprint, traffic data is reordered in sequence and divided into fixed-size blocks; 2) to accelerate queries, an improved bitmap indexes with smaller volume than traditional will be introduced; 3) to save space, both traffic blocks and bitmap indexes are compressed in different simple run-length encoding methods respectively. Finally, our empirical results on network traffic from CAIDA (Cooperative Association for Internet Data Analysis) show that our solution can significantly reduce the volume of traffic data, while simultaneously preserving the ability to perform selectively queries with response times in seconds.

This paper explores techniques for automatically recognizing the sentiment of facial expressions in social photos, especially those of politicians in the context of elections. We first use the Active Shape Model (ASM) to extract facial feature points. Next, the shape model points from the ASM are normalized to a standard shape and then submitted to a trained AdaBoost classifier to recognize the sentiment of facial expressions. Three types of sentiment are of primary interest: flattering, neutral and unflattering. Finally, the approach is evaluated by experiments, which indicate that the proposed method is sufficiently effective for facial expression analysis of images of election candidates and thus can be used to gauge the public opinion during the election.