The compressive sensing has been applied to develop an effective framework for simultaneously localizing multiple targets in wireless sensor networks. Nevertheless, existing methods implicitly use analog measurements, which have infinite bit precision. In this letter, we focus on off-grid target localization using quantized measurements with only several bits. To address this, we propose a novel localization framework for jointly estimating target locations and dealing with quantization errors, based on the novel application of the variational Bayesian Expectation-Maximization methodology. Simulation results highlight its superior performance.

Currently, visual perceptions generated by visual prosthesis are low resolution with unruly color and restricted grayscale. This severely restricts the ability of prosthetic implant to complete visual tasks in daily scenes. Some studies explore existing image processing techniques to improve the percepts of objects in prosthetic vision. However, most of them extract the moving objects and optimize the visual percepts in general dynamic scenes. The application of visual prosthesis in daily life scenes with high dynamic is greatly limited. Hence, in this study, a novel unsupervised moving object segmentation model is proposed to automatically extract the moving objects in high dynamic scene. In this model, foreground cues with spatiotemporal edge features and background cues with boundary-prior are exploited, the moving object proximity map are generated in dynamic scene according to the manifold ranking function. Moreover, the foreground and background cues are ranked simultaneously, and the moving objects are extracted by the two ranking maps integration. The evaluation experiment indicates that the proposed method can uniformly highlight the moving object and keep good boundaries in high dynamic scene with other methods. Based on this model, two optimization strategies are proposed to improve the perception of moving objects under simulated prosthetic vision. Experimental results demonstrate that the introduction of optimization strategies based on the moving object segmentation model can efficiently segment and enhance moving objects in high dynamic scene, and significantly improve the recognition performance of moving objects for the blind.

Hardware prefetching involves a sophisticated balance between accuracy, coverage, and timeliness while minimizing hardware cost. Recent prefetchers have achieved these goals, but they still require complex hardware and a significant amount of storage. In this paper, we propose an efficient Per-page Most-Offset Prefetcher (PMOP) that minimizes hardware cost and simultaneously improves accuracy while maintaining coverage and timeliness. We achieve these objectives using an enhanced offset prefetcher that performs well with a reasonable hardware cost. Our approach first addresses coverage and timeliness by allowing multiple Most-Offset predictions. To minimize offset interference between pages, the PMOP leverages a fine-grain per-page offset filter. This filter records the access history with page-IDs, which enables efficient mapping and tracking of multiple offset streams from diverse pages. Analysis results show that PMOP outperforms the state-of-the-art Signature Path Prefetcher while reducing storage overhead by a factor of 3.4.

This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.

This paper presents a type-I digital ring-based PLL with wide loop bandwidth to lower the ring oscillator's noise contribution. The loop delay due to the D flip-flops at filter's output is compensated in order to lower the noise peak and stably achieve wide loop bandwidth. The input-referred jitter is lowered by using a successive-approximated-register analog-to-digital converter (SAR-ADC)-based sampling phase detector (SPD). A stacked reference buffer is introduced to reduce the transient short-circuit current for low power and low reference spur. The locking issue due to the steady-state phase error in a type-I PLL and the limited range of the phase detector is addressed using a TDC-assisted loop. The loop stability and phase noise are analyzed, suggesting a trade-off for the minimum jitter. The solutions in detail are described. The prototype PLL fabricated in 65 nm CMOS demonstrates 2.0 ps RMS jitter, 3.1 mW power consumption, and 0.067 mm2 area, with 50 MHz reference frequency and 2.0 GHz output frequency.

Some of the most serious threats to network security involve malware. One common way to detect malware-infected machines in a network is by monitoring communications based on blacklists. However, such detection is problematic because (1) no blacklist is completely reliable, and (2) blacklists do not provide the sufficient evidence to allow administrators to determine the validity and accuracy of the detection results. In this paper, we propose a malicious DNS query clustering approach for blacklist-based detection. Unlike conventional classification, our cause-based classification can efficiently analyze malware communications, allowing infected machines in the network to be addressed swiftly.

This letter presents an objective quality index for benchmarking image inpainting algorithms. Under the guidance of the masks of damaged areas, the boundary region and the inpainting region are first located. Then, the statistical features are extracted from the boundary and inpainting regions respectively. For the boundary region, we utilize Weibull distribution to fit the gradient magnitude histograms of the exterior and interior regions around the boundary, and the Kullback-Leibler Divergence (KLD) is calculated to measure the boundary distortions caused by imperfect inpainting. Meanwhile, the quality of the inpainting region is measured by comparing the naturalness factors between the inpainted image and the reference image. Experimental results demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics.

This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).

Redundant relations refer to explicit relations which can also be deducted implicitly. Although there exist several ontology redundancy elimination methods, they all do not take equivalent relations into consideration. Actually, real ontologies usually contain equivalent relations; their redundancies cannot be completely detected by existing algorithms. Aiming at solving this problem, this paper proposes a super-node based ontology redundancy elimination algorithm. The algorithm consists of super-node transformation and transitive redundancy elimination. During the super-node transformation process, nodes equivalent to each other are transferred into a super-node. Then by deleting the overlapped edges, redundancies relating to equivalent relations are eliminated. During the transitive redundancy elimination process, redundant relations are eliminated by comparing concept nodes' direct and indirect neighbors. Most notably, we proposed a theorem to validate real ontology's irredundancy. Our algorithm outperforms others on both real ontologies and synthetic dynamic ontologies.

In this paper, we propose a novel framework for structural magnetic resonance image (sMRI) classification of Alzheimer's disease (AD) with data combination, outlier removal, and entropy-based data selection using AlexNet. In order to overcome problems of conventional classical machine learning methods, the AlexNet classifier, with a deep learning architecture, was employed for training and classification. A data permutation scheme including slice integration, outlier removal, and entropy-based sMRI slice selection is proposed to utilize the benefits of AlexNet. Experimental results show that the proposed framework can effectively utilize the AlexNet with the proposed data permutation scheme by significantly improving overall classification accuracies for AD classification. The proposed method achieves 95.35% and 98.74% classification accuracies on the OASIS and ADNI datasets, respectively, for the binary classification of AD and Normal Control (NC), and also achieves 98.06% accuracy for the ternary classification of AD, NC, and Mild Cognitive Impairment (MCI) on the ADNI dataset. The proposed method can attain significantly improved accuracy of up to 18.15%, compared to previously developed methods.

Machine learning algorithms are becoming more and more popular in current era. Data preprocessing especially feature selection is helpful for improving the performance of those algorithms. A new powerful feature selection algorithm is proposed. It combines the advantages of ant colony optimization and brain storm optimization which simulates the behavior of human beings. Six classical datasets and five state-of-art algorithms are used to make a comparison with our algorithm on binary classification problems. The results on accuracy, percent rate, recall rate, and F1 measures show that the developed algorithm is more excellent. Besides, it is no more complex than the compared approaches.

In recent years, intelligent transportation system (ITS) techniques have been widely exploited to enhance the quality of public services. As one of the worldwide leaders in recycling, Taiwan adopts the waste collection and disposal policy named “trash doesn't touch the ground”, which requires the public to deliver garbage directly to the collection points for awaiting garbage collection. This study develops a travel time prediction system based on data clustering for providing real-time information on the arrival time of waste collection vehicle (WCV). The developed system consists of mobile devices (MDs), on-board units (OBUs), a fleet management server (FMS), and a data analysis server (DAS). A travel time prediction model utilizing the adaptive-based clustering technique coupled with a data feature selection procedure is devised and embedded in the DAS. While receiving inquiries from users' MDs and relevant data from WCVs' OBUs through the FMS, the DAS performs the devised model to yield the predicted arrival time of WCV. Our experiment result demonstrates that the proposed prediction model achieves an accuracy rate of 75.0% and outperforms the reference linear regression method and neural network technique, the accuracy rates of which are 14.7% and 27.6%, respectively. The developed system is effective as well as efficient and has gone online.

360-degree video delivery in Virtual Reality is very challenging due to the fact that 360-degree videos require much higher bandwidth than conventional videos. To overcome this problem, viewport-adaptive streaming has been introduced. In this study, we propose a new adaptation method for tiling-based viewport-adaptive streaming of 360-degree videos. For content preparation, the Cubemap projection format is used, where faces or parts of a face are encoded as tiles. Also, the problem is formulated as an optimization problem, in which each visible tile is weighted based on how that tile overlaps with the viewport. To solve the problem, an approximation algorithm is proposed in this study. An evaluation of the proposed method and reference methods is carried out under different tiling schemes and bandwidths. Experiments show that the Cubemap format with tiling provides a lot of benefits in terms of storage, viewport quality across different viewing directions and bandwidths, and tolerance to prediction errors.

Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

In this paper, to make asynchronous circuit design easy, we propose a conversion method from synchronous Register Transfer Level (RTL) models to asynchronous RTL models with bundled-data implementation. The proposed method consists of the generation of an intermediate representation from a given synchronous RTL model and the generation of an asynchronous RTL model from the intermediate representation. This allows us to deal with different representation styles of synchronous RTL models. We use the eXtensible Markup Language (XML) as the intermediate representation. In addition to the asynchronous RTL model, the proposed method generates a simulation model when the target implementation is a Field Programmable Gate Array and a set of non-optimization constraints for the control circuit used in logic synthesis and layout synthesis. In the experiment, we demonstrate that the proposed method can convert synchronous RTL models specified manually and obtained by a high-level synthesis tool to asynchronous ones.

VLSIs that perform signal processing near infrared sensors cooled to ultra-low temperature are demanded. Delay test of those chips must be executed at ultra-low temperature while functional test could be performed at room temperature as long as hold timing errors do not occur. In this letter, we focus on the hold timing violation and evaluate the feasibility of functional test of ultra-low temperature circuits at room temperature. Experimental evaluation with a case study shows that the functional test at room temperature is possible.

We have developed a novel array configuration based on the combination of sum and difference co-arrays. There have been many studies on array antenna configurations that enhance the degree of freedom (DOF) of an array, but the maximum DOF of the difference co-array configuration is often limited. With our proposed array configuration, called “sum and difference composite co-array”, we aim to further enhance the DOF by combining the concept of sum co-array and difference co-array. The performance of the proposed array configuration is evaluated through computer simulated beamforming*.

The symbol flipping decoding algorithms based on prediction (SFDP) for non-binary LDPC codes perform well in terms of error performances but converge slowly when compared to other symbol flipping decoding algorithms. In order to improve the convergence rate, we design new flipping rules with two phases for the SFDP algorithms. In the first phase, two or more symbols are flipped at each iteration to allow a quick increase of the objective function. While in the second phase, only one symbol is flipped to avoid the oscillation of the decoder when the objective function is close to its maximum. Simulation results show that the SFDP algorithms with the proposed flipping rules can reduce the average number of iterations significantly, whereas having similar performances when compared to the original SFDP algorithms.

A physical-layer network coding (PNC) scheme is developed using serially concatenated continuous phase modulation (SCCPM) with symbol interleavers in a two-way relay channel (TWRC), i.e., SCCPM-PNC. The decoding structure of the relay is designed and the corresponding soft input soft output (SISO) iterative decoding algorithm is discussed. Simulation results show that the proposed SCCPM-PNC scheme performs good performance in bit error rate (BER) and considerable improvements can be achieved by increasing the interleaver size and number of iterations.

Controller area network (CAN) has been widely adopted as an in-vehicle communications standard. CAN with flexible data-rate (CAN FD) is defined in the ISO standards to achieve higher data rates than the legacy CAN. A number of CAN nodes can be connected by a single transmission medium, i.e. CAN enables us to constitute cost-effective bus-topology networks. CAN puts carrier sense multiple access with collision resolution (CSMA/CR) into practice by using bit-wise arbitration based on wired logical AND in the physical layer. The most prioritized message is delivered without interruption if two or more CAN nodes transmit messages at the same time due to the bit-wise arbitration. However, the scalability of CAN networks suffers from ringing caused by the signaling mechanism establishing the wired logical AND. We need to reduce networking material in a car in order to reduce the car weight, save the fuel and the cost, and develop a sustainable society by establishing more scalable CAN networks. In this paper, we show a reduced wiring technology for CAN to enhance the network scalability and the cost efficiency.