As global population aging accelerates, fall-related injuries among elderly individuals have become a critical public health challenge, with falls being the leading cause of injury-related deaths in adults aged 65 and older. Current fall detection methods face a fundamental trade-off between accuracy and computational efficiency, limiting their deployment in real-time eldercare monitoring systems. This paper presents RATTCN-SVM, a novel hybrid architecture that integrates Support Vector Machines with Temporal Convolutional Networks enhanced by a Route Attention mechanism specifically designed for fall detection. The Route Attention mechanism dynamically prioritizes critical temporal features by computing route-wise attention weights, enabling the model to focus on subtle motion patterns that precede fall events while maintaining computational efficiency. Our architecture leverages TCN’s ability to capture long-range temporal dependencies through dilated convolutions, combined with SVM’s robust classification capabilities in high-dimensional feature spaces. Comprehensive experiments on the UP-Fall and HARTH datasets demonstrate that RATTCN-SVM achieves superior performance with MAE of 0.1452, RMSE of 0.2305, and MAPE of 0.8938, outperforming the state-of-the-art Reformer model by 4.1% in MAE and 7.3% in MAPE. Critically, our model maintains detection latency under 300ms with only 7.8MB memory footprint, making it suitable for deployment on resource-constrained edge devices in eldercare environments. The proposed method advances fall detection technology by providing both improved accuracy and practical deployment capabilities for real-world healthcare monitoring applications.

The development of autonomous laparoscope control in robot-assisted surgery has emerged as a significant research area, particularly due to its potential to reduce assistant fatigue and minimize miscommunication between the surgeon and assistant. A notable challenge, however, is the tendency of autonomous control strategies to override the surgeon’s direct command occasionally. To address this issue, we propose a novel haptic feedback-based cooperative control strategy that enhances the surgeon’s command of laparoscopic field of view (FOV) movement in robot-assisted laparoscopic surgery. Specifically, we first established a dynamic model of the laparoscope-holding robot, which serves as a link between the movement of the laparoscopic FOV and the surgical instruments to deliver haptic feedback to the surgeon. Next, a motion observer was developed to transform 30 Hz visual feedback into 1 kHz haptic feedback by integrating visual tracking data with kinematic information, ensuring smoother and more continuous haptic feedback. Finally, we propose two distinct collaboration modes: the plane tracking mode (PTM) ensures instruments remain within the laparoscopic image, and the space tracking mode (STM) synchronizes the laparoscope with instrument movement. The laboratory experiments validated the effectiveness of the proposed method in enhancing the cooperative performance of robot-assisted laparoscope systems while animal experiments demonstrated the feasibility of the PTM design.

The accurate identification of low-risk and high-risk lung tumors is essential for clinicians to develop lung cancer treatment strategies during surgery. Despite the achievements in deep learning, most medical image analysis applications are still hampered by the difficulty of obtaining large amounts of labeled data. In this paper, a semi-supervised deep learning framework, DS-FixMatch, is proposed for identifying lung tumors intra-operatively while alleviating the issue of sparse annotations. DS-FixMatch combines selective labeling and semi-supervised training: (1) For the acquired unlabeled images, a subset that best represents the distribution of the entire dataset is selected by an unsupervised algorithm. Compared to the traditional random labeling strategy, this method can avoid introducing samples that are highly influenced by the intraoperative environment for labeling. This subset is then sent to a human expert for labeling. (2) Supervised training is performed using labeled images. For the remaining unlabeled samples, DS-FixMatch utilizes model predictions to generate pseudo-labels for consistency regularization, further enhancing the model’s generalization ability. A dataset consisting of 2221 natural images, each capturing the Region of Interest (ROI) in lung tumors, is constructed to evaluate the effectiveness of the designed framework. Experiments show that DS-FixMatch leads in performance for the task of lung tumor recognition compared to other baselines.

Although some pyroptosis-related (PR) prognostic models for cancers have been reported, pyroptosis-based features have not been fully discovered at the single-cell level in hepatocellular carcinoma (HCC). In this study, by deeply integrating single-cell and bulk transcriptome data, we systematically investigated significance of the shared pyroptotic signature at both single-cell and bulk levels in HCC prognosis. Based on the pyroptotic signature, a robust PR risk system was constructed to quantify the prognostic risk of individual patient. To further verify capacity of the pyroptotic signature on predicting patients' prognosis, an attention mechanism-based deep neural network classification model was constructed. The mechanisms of prognostic difference in the patients with distinct PR risk were dissected on tumor stemness, cancer pathways, transcriptional regulation, immune infiltration and cell communications. A nomogram model combining PR risk with clinicopathologic data was constructed to evaluate the prognosis of individual patients in clinic. The PR risk could also evaluate therapeutic response to neoadjuvant therapies in HCC patients. In conclusion, the constructed PR risk system enables a comprehensive assessment of tumor microenvironment characteristics, accurate prognosis prediction and rational therapeutic options in HCC.

EOSIO smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. As EOSIO smart contracts manage valuable assets, they become high-value targets and are subjected to more and more attacks. Tools for protecting EOSIO smart contracts are imperative. This paper proposes EOSIOAnalyzer, an effective static secu-rity analysis framework for EOSIO smart contracts to counter the three most common attacks. The framework consists of three components, the control flow graph builder, the static analyzer and the vulnerability detector. This paper implements an approach to transforming low-level Wasm bytecode into a high-level intermediate representation (Register Transfer Language). Besides, this paper also implements vulnerability detection speci-fications for three popular EOSIO smart contracts vulnerabilities, including Fake EOS Transfer, Forged Transfer Notification and Block Information Dependency. As a proof of concept, this paper conducts experiments to evaluate the effectiveness and efficiency of the EOSIOAnalyzer. The experiment results show that the detection accuracy of the three vulnerabilities is 100 %, 98.8 % and 100%, respectively.

The financial property of Ethereum makes smart contract attacks frequently bring about tremendous economic loss. Method for effective detection of vulnerabilities in contracts imperative. Existing efforts for contract security analysis heavily rely on rigid rules defined by experts, which are labor-intensive and non-scalable. There is still a lack of effort that considers combining expert-defined security patterns with deep learning. This paper proposes EtherGIS, a vulnerability detection framework that utilizes graph neural networks (GNN) and expert knowledge to extract the graph feature from smart contract control flow graphs (CFG). To gain multi-dimensional contract information and reinforce the attention of vulnerability-related graph features, sensitive EVM instruction corpora are constructed by analyzing EVM underlying logic and diverse vulnerability triggering mechanisms. The characteristic of nodes and edges in a CFG is initially confirmed according to the corpora, generating the corresponding attribute graph. GNN is adopted to aggregate the whole graph's attribute and structure information, bridging the semantic gap between low-level graph features and high-level contract features. The feature representation of the graph is finally input into the graph classification model for vulnerability detection. Furthermore, automated machine learning (AutoML) is adopted to automate the entire deep learning process. Data for this research was collected from Ethereum to build up a dataset of six vulnerabilities for evaluation. Experimental results demonstrate that EtherGIS can productively detect vulnerabilities in Ethereum smart contracts in terms of accuracy, precision, recall, and F1-score. All aspects outperform the existing work.

MOTIVATION: Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject. RESULTS: In this work, we developed OmicsLonDA, a statistical method that provides robust identification of time intervals of temporal omics biomarkers. OmicsLonDA is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked OmicsLonDA on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.87. Applying OmicsLonDA to the iPOP cohort revealed temporal patterns of genes, proteins, metabolites and microbes that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied OmicsLonDA to a longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and OmicsLonDA identified potential lipid markers that are temporally significantly different between the two groups. AVAILABILITY AND IMPLEMENTATION: We provide an open-source R package (https://bioconductor.org/packages/OmicsLonDA), to enable widespread use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Consider a distributed multi-task learning (MTL) framework where the distributed users first train their own models based on the local data and then send the local updates to the server, and the server sends back helpful information by which each user can learn its task independently. Compared to the single-task case, the distributed MTL suffers more severely from the communication bottleneck, because the users wish to learn multiple models, causing the downlink communication load to linearly increase with the total number of tasks. In this paper, we propose a novel scheme named coded distributed multi-task learning, to reduce the communication loads both in the uplink and downlink. The key idea is to exploit the local information stored at the users during the local training, and utilize a particular repetitive placement and computation on the publicly shared dataset such that coded multicasting opportunities can be created at the server and users. Our method for the first time applies coding strategy to reduce the communication cost for distributed MTL framework. Experiments on real-world dataset show that the proposed scheme can substantially reduce the communication load compared to the traditional uncoded approach.

MOTIVATION: Data normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity. RESULTS: To robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm.In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. AVAILABILITYAND IMPLEMENTATION: https://github.com/mwgrassgreen/RobNorm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In medicine, chromosome karyotype analysis is vital for genetic disease diagnoses, such as Edward syndrome, Patau syndrome, and Down syndrome. However, the chromosome karyotype analysis is usually manually done by extensive experienced clinical analysts, which is tedious and time-consuming. Consequently, automatic or partial automatic chromosome karyotyping is essential to alleviate clinical analysts' jobs. This paper proposes a chromosome cluster identification framework based on geometric chromosome features and machine learning algorithms to identify chromosome clusters needed to further process in the automated instance segmentation task. In the proposed framework, we first collect multiple dimensions of chromosome geometric features into a feature tuple, including object area, bounding box area, convex area, extent, solidity, perimeter, equivalent diameter, eccentricity, major axis length, minor axis length, and minor-major axis ratio. Second, we classify these chromosome feature tuples utilizing different machine learning classification algorithms. The experiment results show that our proposed method yields 96.21 ± 0.91% classification accuracy and 0.9832 ± 0.0111AUC (Area Under The Curve) value in the clinical dataset. The highlight of this paper is that the performance of the proposed approach has exceeded the existing geometric features threshold-based methods and multiple end-to-end deep learning-based baselines. Moreover, our proposed method can be deployed on any devices and platforms with a Python running environment, which significantly improves application flexibility. To facilitate peers in reproducing and employing our work, we release the code and the corresponding clinical dataset on Github.

MOTIVATION: DNA N4-methylcytosine (4mC) is a crucial epigenetic modification. However, the knowledge about its biological functions is limited. Effective and accurate identification of 4mC sites will be helpful to reveal its biological functions and mechanisms. Since experimental methods are cost and ineffective, a number of machine learning-based approaches have been proposed to detect 4mC sites. Although these methods yielded acceptable accuracy, there is still room for the improvement of the prediction performance and the stability of existing methods in practical applications. RESULTS: In this work, we first systematically assessed the existing methods based on an independent dataset. And then, we proposed DNA4mC-LIP, a linear integration method by combining existing predictors to identify 4mC sites in multiple species. The results obtained from independent dataset demonstrated that DNA4mC-LIP outperformed existing methods for identifying 4mC sites. To facilitate the scientific community, a web server for DNA4mC-LIP was developed. We anticipated that DNA4mC-LIP could serve as a powerful computational technique for identifying 4mC sites and facilitate the interpretation of 4mC mechanism. AVAILABILITY AND IMPLEMENTATION: http://i.uestc.edu.cn/DNA4mC-LIP/. CONTACT: [email protected] or [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Transcription termination is an important regulatory step of gene expression. If there is no terminator in gene, transcription could not stop, which will result in abnormal gene expression. Detecting such terminators can determine the operon structure in bacterial organisms and improve genome annotation. Thus, accurate identification of transcriptional terminators is essential and extremely important in the research of transcription regulations. RESULTS: In this study, we developed a new predictor called 'iTerm-PseKNC' based on support vector machine to identify transcription terminators. The binomial distribution approach was used to pick out the optimal feature subset derived from pseudo k-tuple nucleotide composition (PseKNC). The 5-fold cross-validation test results showed that our proposed method achieved an accuracy of 95%. To further evaluate the generalization ability of 'iTerm-PseKNC', the model was examined on independent datasets which are experimentally confirmed Rho-independent terminators in Escherichia coli and Bacillus subtilis genomes. As a result, all the terminators in E. coli and 87.5% of the terminators in B. subtilis were correctly identified, suggesting that the proposed model could become a powerful tool for bacterial terminator recognition. AVAILABILITY AND IMPLEMENTATION: For the convenience of most of wet-experimental researchers, the web-server for 'iTerm-PseKNC' was established at http://lin-group.cn/server/iTerm-PseKNC/, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

Promoters are DNA regulatory elements located directly upstream or at the 5' end of the transcription initiation site (TSS), which are in charge of gene transcription initiation. With the completion of a large number of microorganism genomics, it is urgent to predict promoters accurately in bacteria by using the computational method. In this work, a sequence-based predictor named "iPro70-PseZNC" was designed for identifying sigma70 promoters in prokaryote. In the predictor, the samples of DNA sequences are formulated by a novel pseudo nucleotide composition, called PseZNC, into which the multi-window Z-curve composition and six local DNA structural properties are incorporated. In the 5-fold cross-validation, the area under the curve of receiver operating characteristic of 0.909 was obtained on our benchmark dataset, indicating that the proposed predictor is promising and will provide an important guide in this area. Further studies showed that the performance of PseZNC is better than it of multi-window Z-curve composition. For the sake of convenience for researchers, a user-friendly online service was established and can be freely accessible at http://lin.uestc.edu.cn/server/iPro70-PseZNC. The PseZNC approach can be also extended to other DNA-related problems.

Massive multiple-input multiple-output (MIMO) has shown its great capabilities in improving spectral efficiency and energy efficiency, yet the high complexity and cost of hardware are huge challenges. In massive MIMO, antenna selection (AS) is still an effective way to decrease the number of radio frequency chains. In this letter, the AS problem in massive MIMO systems is studied with the consideration of maximizing the channel capacity. To eliminate the limitation of the traditional square maximum-volume (SMV) AS method, the theory of rectangular maximum-volume (RMV) submatrices is introduced. An AS algorithm based on RMV method is also developed for massive MIMO, which can be regarded as a postprocessing of the results of SMV method. Based on the result obtained from SMV method, the proposed algorithm can select a rectangular submatrix with maximum-volume from the channel matrix. Finally, the numerical simulations are presented to show the validity of the proposed RMV method.

Summary: In prokaryotes, the σ54 promoters are unique regulatory elements and have attracted much attention because they are in charge of the transcription of carbon and nitrogen-related genes and participate in numerous ancillary processes and environmental responses. All findings on σ54 promoters are favorable for a better understanding of their regulatory mechanisms in gene transcription and an accurate discovery of genes missed by the wet experimental evidences. In order to provide an up-to-date, interactive and extensible database for σ54 promoter, a free and easy accessed database called Pro54DB (σ54 promoter database) was built to collect information of σ54 promoter. In the current version, it has stored 210 experimental-confirmed σ54 promoters with 297 regulated genes in 43 species manually extracted from 133 publications, which is helpful for researchers in fields of bioinformatics and molecular biology. Availability and Implementation: Pro54DB is freely available on the web at http://lin.uestc.edu.cn/database/pro54db with all major browsers supported. Contacts: [email protected] or [email protected]

Considerable progress has been made recently in the development of techniques to determine exactly two-point resistances in networks of various topologies. In particular, a general resistance formula of a non-regular m × n resistor network with an arbitrary boundary is determined by the recursion-transform (RT) method. However, research on the complex impedance network is more difficult than that on the resistor network, and it is a problem worthy of study since the equivalent impedance has many different properties from equivalent resistance. In this study, the equivalent impedance of a non-regular m × n RLC network with an arbitrary boundary is studied based on the resistance formula, and the oscillation characteristics and resonance properties of the equivalent impedance are discovered. In the RLC network, it is found that our formula leads to the occurrence of resonances at the boundary condition holding a series of specific values with an external alternating current source. This curious result suggests the possibility of practical applications of our formula to resonant circuits.

Variable block size motion estimation (VBSME) is becoming the new coding technique in H.264/AVC. This paper presents a low-power VLSI implementation for full-search VBSME. Compared to existing hardware architectures and implementations for VBSME, the proposed design employs a fast full-search block matching algorithm to reduce power consumption, while preserving the optimal solution and the throughput. The proposed architecture has been implemented and tested in Xilinx XtremeDSP Video Starter Kit Spartan-3ADSP 3400A Edition, and also verified using standard cell approach in UMC 0.18μm CMOS technology. Compared to other VBSME designs that give optimal solutions of Motion Vectors (MV), the proposed design can save power consumption by more than 56%.

In speech recognition systems, CHMM (Continuous Hidden Markov Model) based speech recognition algorithms have the best accuracy but with the most computational cost. Neither General Purpose Processor (GPP) nor dedicated hardware implementation is a good solution for the algorithm, due to high power consumption for the former and lack of flexibility for the later. To reduce power consumption and enhance flexibility, this paper presents a speech recognition system composed of a coprocessor and a MCU. The coprocessor is a dedicated hardware design for Output Probability Calculation (OPC), which is the most computation-intensive part in CHMM, and MCU is a 32 bit RISC (ARM). Tested with a 358-state 3-mixture 27-feature 800-word HMM, MCU operates at 40 MHz and coprocessor operates at 10 MHz to meet real-time requirement. The power consumption of MCU is 10 mW, and coprocessor 1.8 mW.

In the CHMM (Continuous Hidden Markov Model) based speech recognition algorithm, Output Probability Calculation (OPC) is the most computation-intensive part. To reduce power consumption and design cost, this paper presents a custom-designed co-processor to implement OPC. The standard SRAM interface of the co-processor allows it to be controlled by various micro-controllers. The co-processor has been implemented in standard-cell based approach and manufactured in 0.18 mum UMC technology. Tested with a 358-state 3-mixture 27-feature 800-word HMM, the co-processor operates at 10 MHz to meet real-time requirement. The power consumption of this co-processor is 1.6 mW, and the die size is 1.18 mm2.

Continuous-time Delta-Sigma modulators are very sensitive to clock jitter effects. In this paper, we present a simple technique to reduce clock jitter effects. The technique employs two delayed elements to generate a feedback current waveform with a fixed-width return-to-zero time period, followed a fixed-width time period for active feedback, which is followed by another variable return-to-zero time period subject to clock jitter. It has been shown in the paper through behavioral simulation models that this technique is very effective to reduce independent clock jitter effects.

Traditional system-level design of Delta-Sigma modulators follows a method based on linear modeling of the modulators without considering circuit-level non-idealities. In this paper, we propose to post-optimize system-level Delta-Sigma modulators considering circuit-level non-idealities after the traditional design. In addition, due to stochastic modeling of circuit-level non-idealities, performance evaluation of the system-level modulators is noisy. Therefore, stochastic post-optimization of modulators is needed so that noisy performance evaluation is not misleading the search for an optimal design solution.

In this paper, we present an empirical method for efficient analog design retargeting by combining design knowledge reuse and circuit synthesis. The method first decomposes the source system into circuit blocks and extracts the performance parameter specifications of each circuit block. Then, it scales each circuit block and defines a design space in the target technology. Subsequently, each circuit block is synthesized. Our assumption is that if the synthesized circuit blocks retain the same set of performance specifications, then the overall system after retargeting would have the same performance specification as the source system. We experiment the method on a fourth order continuous-time Delta-Sigma modulator.

Statistical analysis has become increasingly important with increasing process parameter variations in manufacturing. Monte Carlo method has been most popular for statistical analysis, but it is not efficient for complex circuits/systems due to overwhelming computational time. In this paper, we present a general hierarchical method for efficient statistical analysis of performance parameter variations for complex circuits/systems and conduct a case study on a 4th order continuous-time Delta Sigma modulator. At circuit-level, we use response surface modeling method to extract quadratic models of circuit- level performance parameters in terms of process parameter variations. Then, at system-level, we use behavioral models to extract statistical distribution of the overall system performance parameter. The method can achieve a good tradeoff between computational efficiency and accuracy.

This paper proposes a systematic methodology for designing reconfigurable continuous-time DeltaSigma modulator topologies. Topologies are optimized by minimizing the complexity of the topologies, maximizing the sharing of circuits between the different modes, maximizing the topology robustness with respect to circuit nonidealities, and minimizing the total power consumption. This paper presents a case study for designing topologies for a three-mode reconfigurable DeltaSigma modulator and compares the obtained topologies with a state-of-the-art design and topologies obtained using the DeltaSigma Toolbox

This paper proposes a methodology for designing reconfigurable continuous-time DeltaSigma modulator topologies. The methodology is based on the concept of generic topology that expresses all possible signal paths in a reconfigurable topology. Topologies are optimized for minimizing the complexity of the topologies, maximizing the sharing of circuitry for different modes, maximizing the topology robustness with respect to circuit nonidealities, and minimizing total power consumption. The paper presents a case study for designing topologies for a three mode reconfigurable DeltaSigma modulator, and compares topologies with state-of-the-art design

This paper proposes a novel topology-synthesis methodology for single-loop single-bit /spl Delta//spl Sigma/ modulators. The goal is to explore all possible topologies and to obtain the optimal topology under various design considerations, such as hardware complexity, modulator sensitivity, and power consumption. A generic modulator architecture that incorporates all possible feedback and feedforward signal paths was defined and the symbolic noise transfer function (NTF) and signal transfer function (STF) for the generic topology were derived. The symbolic functions were then used to formulate the topology-exploration problem as a mixed-integer nonlinearly constrained programming (MINLP) problem that simultaneously generates and selects the optimal modulator topology with respect to the cost function. Experiments show the superiority of synthesized topologies as compared to traditional modulator topologies.

This paper presents a novel algorithm for synthesis of continuous-time analog filters. The goal is to find as many "very good" design points as possible without requiring feasible designs as starting points or any other additional designer knowledge as input. This problem is challenging for present exploration-based analog-synthesis methods, including existing commercial tools, which have difficulties in locating diverse constraint-satisfying designs. The proposed algorithm conducts a three-step refinement process, in which poor-quality solution-space regions are eliminated through different strategies. It starts with the step of parameter-domain pruning, which identifies parameter subdomains that are more likely to produce many feasible solution points. Domains are found using interval arithmetic and the proposed simplified affine transformation operators. In the second step, selected variable subdomains are searched using plateau search, a novel exploration technique described in this paper. The algorithm addresses the three main types of solution-space regions: 1) convex, quasi-convex, and /spl delta/-convex regions; 2) rifts; and 3) plateau. The technique expands descendant-gradient-based search with a systematic way of sampling plateau. Finally, promising regions that remained after step 2 are further refined during the step of search with adaptive-sampling step length. Using four filter examples, experiments observed the quality of results and convergence of synthesis. Plateau search was also experimented for synthesis of two /spl Delta//spl Sigma/ modulators.

The paper proposes a novel architecture synthesis algorithm for single-loop single-bit /spl Delta//spl Sigma/ modulators. We defined a generic modulator architecture and derived its noise and signal transfer function (NTF/STF) in symbolic forms. We then used the TF in MINLP (mixed integer nonlinearly constrained programming) to generate optimal topologies for a variety of design requirements, such as modulator complexity, sensitivity and power consumption, which appeared as cost functions. Experiments show the superiority of the synthesized topologies as compared to traditional solutions.

This paper outlines the design and implementation of WSMQ, which is a message-oriented middleware specifically designed to enhance the reliability of Web services. Highlights of this application feature fault tolerance of Web services communication, Quality of Services including authentication and prioritization, security enhancement and performance improvements in Web services over the existing architecture. The implementation of these features aims to address the existing issues surrounding Web services, and further its advancement towards a new standard for distributed application development.

This paper presents our experience on high-level synthesis of Σ - Δ analog to digital converters (ADC) from VHDL-AMS descriptions. The proposed VHDL-AMS subset for synthesis is discussed. The subset has the composition semantics, so that specifications offer enough insight into the system structure for automated architecture generation and optimization. A case study for the synthesis of a fourth order Σ - Δ ADC is detailed. Compared to similar work, the method is more flexible in tackling new designs, and more tolerant to layout parasitic.

This paper describes a layout-aware analog synthesis methodology. The methodology includes parameter exploration and classification, parameter domain pruning and sampling, and identification of parameter dependencies. The optimization process executes a combined constraint transformation, floorplanning and global routing. The paper presents results for a high frequency continuous-time filter, and two ̿Δ ADCs. Compared to similar work, the methodology is more flexible in handling new designs, and more tolerant in accommodating layout parasitics.