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In digital circuit designs, sequential components such as flip-flops are used to synchronize signal propagations. Logic computations are aligned at and thus isolated by flip-flop stages. Although this fully synchronous style can reduce design efforts significantly, it may affect circuit performance negatively, because sequential components can only introduce delays into signal propagations but never accelerate them. In this article, we propose a new timing model, VirtualSync+, in which signals, specially those along critical paths, are allowed to propagate through several sequential stages without flip-flops. Timing constraints are still satisfied at the boundary of the optimized circuit to maintain a consistent interface with existing designs. By removing clock-to-q delays and setup time requirements of flip-flops on critical paths, the performance of a circuit can be pushed even beyond the limit of traditional sequential designs. In addition, we further enhance the optimization with VirtualSync+ by fine-tuning with commercial design tools, e.g., design compiler from Synopsys, to achieve more accurate result. To achieve this fine-tuning, we first optimize the circuits by reallocating sequential components with sequential and combinational components as delay units. Afterward, the removal locations of flip-flops with respect to the circuits under optimization are extracted and the corresponding wave-pipelining timing constraints compatible with commercial design tools are established. These timing constraints are then incorporated into the optimization flow of commercial tools to generate the optimized circuits. The experimental results demonstrate that circuit performance can be improved by up to 4% (average 1.5%) compared with that after extreme retiming and sizing, while the increase of area is still negligible. This timing performance is enhanced beyond the limit of traditional sequential designs. It also demonstrates that compared with those after retiming and sizing, the circuits with VirtualSync+ can achieve better timing performance under the same area cost or smaller area cost under the same clock period, respectively. [ABSTRACT FROM AUTHOR]

This article studies the robust adaptive asymptotic tracking control problem for uncertain nonlinear systems with event-triggered inputs. Contrary to the existing results, a novel nonaffine nonlinear control input separated design scheme is developed based on the Taylor decoupling technique instead of the previous approximation ways. Correspondingly, the augmented dimension parameter updated laws and the unknown bound estimation of the compounded disturbance with a positive continuous integrable function are skillfully introduced. Also, by introducing a suitable decreasing function of tracking error variables, an improved adaptive event-triggered mechanism is constructed to achieve the asymptotic tracking and the communication load can be effectively alleviated. Moreover, it is shown that all the signals in the closed-loop system are uniformly bounded and the tracking errors converge to zero in a preset compact set. Finally, the validity of the proposed method is illustrated by simulation on an inverted pendulum model. [ABSTRACT FROM AUTHOR]

With the advancement of deep neural networks (DNNs), the applications driven by DNNs have been spread from the cloud to the edge. However, the intensive computations and data movements in CNNs impede the adoption of DNNs in resource-constraint edge devices. Quantization, a common model compression method, has attracted a lot of attention as it enables efficient inference by lowering the data bit-width of CNN parameters. Due to the features of massive storage and computing-in-memory array, resistive memory (RRAM) has established the energy efficiency and small area processing-in-memory (PIM) for the acceleration of DNNs at the edge end. However, when deploying the network onto resistive-memory-based PIM (RRAM-based PIM), there will be tremendous unused cells due to the mismatch between the structure of the neural network layer and memory array, resulting in the resource under-utilization and low computation efficiency. In this work, we observed prior quantization approaches fail to improve hardware resource utilization as they ignored the hardware structure information in RRAM. Thus, combining the information of the neural network model and hardware information is essential for a high-utilization RRAM-based PIM design. Considering the vast model parameters and heterogeneous RRAM crossbar structure, we develop a novel quantization framework by leveraging the AutoML technique, i.e., RaQu, which automatically generates a fine-grained quantization strategy for any model that fully utilizes the resource of RRAM-based PIM. The experimental results show that RaQu achieves at most 29.2%–37.4% and 1.8%–3.3% improvement in resource utilization and model accuracy, respectively, compared to prior coarse-grained quantization methods. [ABSTRACT FROM AUTHOR]

Traditional active learning algorithms have several limitations: 1) they cannot obtain satisfactory results on high dimensional datasets, especially for multivariate time series (MTS) data; 2) traditional crowd-based labeling approaches do not consider the swarm intelligence of crowds, which cannot guarantee the confidence of labeling results; and 3) up to now, few works have addressed the issue of tradeoff between the labeling accuracy and the labeling cost with crowdsourcing labelers. There is also a lack of research on crowd-based active learning on MTS data. To efficiently address the above issues, we propose a new framework of active semi-supervised learning. First, two criteria are advanced to measure the importance of an unlabeled sample from different perspectives. A dynamic time wrapping (DTW)-based similarity matrix is used to represent the MTS data. Next, to confidently label the most valuable samples with minimum cost, we advance a cost-effective crowd selection model and an adaptive labeler selection approach (ALS) to select the most suitable labelers, which could minimize the total cost of labeling and achieve better classification performance. Experiments on ten datasets show the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

We put forward a focusing formula to describe the guided waves periodical focusing phenomenon, which, apart from being a fundamental problem in the guided waves’ propagation in the pipes, has essential applications in the field of nondestructive evaluation. Due to the partial circumferential loads or nonaxisymmetric defects, the guided waves are not only in the zeroth-order axisymmetric forms but also in the higher order nonaxisymmetric forms. When multiple orders of the same mode exist simultaneously, the angular profile is adopted to describe the circumferential energy distribution of the superposed wave field. However, the angular profile varies in the propagation process. In our finding, this variation is periodic, meaning that the circumferential energy will repeat the process of dispersing and focusing. Thus, we put forward a focusing formula to describe the phenomenon. The proposed formula indicates that the angular profile varies periodically with the ratio of propagation distance to wavenumber, and this period is only related to the pipe radius. Thus, three factors, including propagation distance, excitation frequency, and pipe radius, will affect the angular profile. Moreover, we established an experimental system to verify this phenomenon, based on which we have designed three groups of experiments to investigate these three factors. The experimental results are in good agreement with the theoretical predictions. [ABSTRACT FROM AUTHOR]

Secure downlink transmission in massive multiple-input multiple-output (MIMO) systems has sparked great research enthusiasm. However, the secrecy of uplink transmission is equally important although relatively less attention has been dedicated to. In this paper, we show that a multi-antenna eavesdropper could potentially reduce the secrecy rate of legitimate communications severely through channel estimation in the training phase and coherent detection in the payload data transmission phase. To safeguard uplink transmission, an artificial noise (AN)-assisted scheme is proposed. Specifically, the optimization problem that aims to optimize the power allocation between AN and data symbols in the sense of maximizing the secrecy rate is formulated. We investigate optimal power allocation strategies for two distinct cases. That is, the base station and the eavesdropper know the channel state information to the users, and the opposite case. In each case, we further consider whether the accurate position of the eavesdropper is known to the users. Due to the complexity of the cost function, a closed-form solution is intractable. As a result, the bisection method is employed to obtain the numerical results. The impacts of the non-idealities, including the channel estimation error and the uncertainty of eavesdropper's position, on the power allocation strategy are discussed. Finally, extensive simulations are carried out to validate our proposed algorithms. [ABSTRACT FROM AUTHOR]

Viewing various stereo images under different viewing conditions has escalated the need for effective object-level remapping techniques. In this paper, we propose a new object spatial mapping scheme, which adjusts the depth and size of the selected object to match user preference and viewing conditions. Existing warping-based methods often distort the shape of important objects or cannot faithfully adjust the depth/size of the selected object due to improper warping such as local rotations. In this paper, by explicitly reducing the transformation freedom degree of warping, we propose an optimization model based on axis-aligned warping for object spatial remapping. The proposed axis-aligned warping based optimization model can simultaneously adjust the depths and sizes of selected objects to their target values without introducing severe shape distortions. Moreover, we propose object consistency constraints to ensure the size/shape of parts inside a selected object to be consistently adjusted. Such constraints improve the size/shape adjustment performance while remaining robust to some extent to incomplete object extraction. Experimental results demonstrate that the proposed method achieves high flexibility and effectiveness in adjusting the size and depth of objects compared with existing methods. [ABSTRACT FROM AUTHOR]

To protect underage people from accessing objectionable videos in the Internet, an effective objectionable video recognition algorithm is necessary for web filtering. Recently, the multi-instance learning has been introduced for objectionable video recognition and achieves impressive results. However, hand-crafted features as well as redundant and noisy frames in objectionable videos become an intractable problem that inevitably degrades the recognition performance. In this paper, we propose a novel representative prototype selection algorithm embedding deep multi-instance representation learning. In the proposed method, an improved convolutional neural network is designed for multimodal multi-instance feature learning and a self-expressive dictionary learning model based on sparse and low rank constraint is designed to select the representative prototypes from each subspace of instances. Then the bag-level feature is constructed via mapping the bag to the selected prototypes. Experiments on three objectionable video sets show the effectiveness of our method for objectionable video recognition. [ABSTRACT FROM AUTHOR]

Deconvolution is a key component in contemporary neural networks, especially, generative adversarial networks (GANs) and fully convolutional networks (FCNs). Due to extra operations of deconvolution compared to convolution, considerable degradation of performance, as well as energy efficiency is incurred when implementing deconvolution on the existing resistive random access memory (ReRAM)-based processing-in-memory (PIM) accelerators. In this article, we propose an ReRAM-based accelerator design, RED, for providing high-performance and low-energy deconvolution. We analyze the deconvolution execution on the existing ReRAM-based PIMs and utilize its interior computation pattern for design optimization. RED includes two major contributions: 1) pixel-wise mapping scheme and 2) zero-skipping data flow. Pixel-wise mapping scheme removes the zero insertion and performs convolutions over several ReRAM arrays and thus enables parallel computations with nonzero inputs. Zero-skipping data flow, assisted with customized input buffers design, enhances the computation parallelism and input data reuse. In evaluation, we compare RED against the existing ReRAM-based PIMs and CMOS-based counterpart with a variety of GAN and FCN models, each of which contains multiple deconvolution layers. The experimental results show that RED achieves a $4.0\times $ – $56.16\times $ speedup and a $1.05\times $ – $18.17\times $ energy efficiency improvement over previous related accelerator designs. [ABSTRACT FROM AUTHOR]

With recent advances in reverse engineering, attackers can reconstruct a netlist to counterfeit chips by opening the die and scanning all layers of authentic chips. This relatively easy counterfeiting is made possible by the use of the standard simple clocking scheme, where all combinational blocks function within one clock period, so that a netlist of combinational logic gates and flip-flops is sufficient to duplicate a design. In this article, we propose to invalidate the assumption that a netlist completely represents the function of a circuit with unconventional timing. With the introduced wave-pipelining (WP) paths, attackers have to capture gate and interconnect delays during reverse engineering, or to test a huge number of combinational paths to identify the WP paths. To hinder the test-based attack, we construct false paths with WP to increase the counterfeiting challenge. The experimental results confirm that WP true paths and false paths can be constructed in benchmark circuits successfully with only a negligible cost, thus thwarting the potential attack techniques. [ABSTRACT FROM AUTHOR]

Flow-based microfluidic biochips have become a promising platform for complex biochemical assays. As the integration of such chips is increasing, a flexible general reconfigurable platform, fully programmable valve array (FPVA), has emerged. Such a 2-D array comprises regularly arranged valves using which flow-networks with different geometry, size, and connectivity can be constructed dynamically. However, the test generation for such arrays becomes challenging due to the large number of potential flow-networks and transportation paths that can be configured on-chip. In this article, we propose a strategy to generate efficient test patterns for FPVAs based on the concepts of test paths and cuts. These patterns together can cover multiple faults in both flow and control layers. We also introduce the concept of test trees and multiple cuts for a test pattern to deal with faults in FPVAs with multiple ports. Moreover, the proposed method can be applied to generate test patterns for traditional flow-based biochips with predefined architectures. The simulation results demonstrate that defects in FPVAs can be detected reliably by a limited number of test patterns generated by the proposed method. For traditional biochips with predefined architectures, these patterns also exhibit an improved test efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses the adaptive fault-tolerant control (FTC) problem of uncertain switched nonaffine nonlinear systems with actuator failures and time delays. Unlike the literature employing the FTC methods, the novel nonlinear fault compensate function with adjustable parameter factor, whose norm is used for approximation with fuzzy logic systems, is constructed based on the mean value theorem. Accordingly, a standard transformation scheme from the multi-input and single-output nonaffine nonlinear system to the corresponding affine nonlinear system is first to be given; then, the designed adaptation mechanism with state-dependent switching strategy can effectively eliminate the effects of multiple time delays and actuator faults, including outage and stuck modes. Furthermore, by using the Lyapunov–Krasovskii stability analysis method, it is proved that the proposed adaptive switched controller can guarantee the states of the overall closed-loop systems to asymptotically converge to zero. Finally, the simulation results are displayed to verify the feasibility of the presented approach. [ABSTRACT FROM AUTHOR]

The images used in various practices are often disturbed by noise, such as Gaussian noise, speckled noise, and salt and pepper noise. Images with noise are one of the challenges for segmentation, since the noise may cause inaccurate segmented results. To cope with the effect of noise on images during segmentation, a novel active contour model is proposed in this paper. The newly proposed model consists of fitting term, regularization term and penalty term. The fitting term is designed using a Gaussian kernel function and fractional order differentiation with an adaptively defined fractional order, which applies different orders to different pixels. The regularization term is applied to maintain the smoothness of curves. In order to ensure stable evolution of curves, a penalty term is added into the proposed model. Comparison experiments are conducted to show the effectiveness and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Color constancy is one of the fundamental tasks in computer vision. Many supervised methods, including recently proposed Convolutional Neural Networks (CNN)-based methods, have been proved to work well on this problem, but they often require a sufficient number of labeled data. However, it is expensive and time-consuming to collect a large number of labeled training images with accurately measured illumination. In order to reduce the dependence on labeled images and leverage unlabeled ones without measured illumination, we propose a novel semi-supervised framework with limited training samples for illumination estimation. Our key insight is that the images with similar features from different cues will share similar lighting conditions. Consequently, three graphs based on three visual cues, low-level RGB color distribution, mid-level initial illuminant estimates and high-level scene content, are constructed to represent the relationship among different images. Then a multi-cue semi-supervised color constancy method (MSCC) is proposed after integrating these three graphs into a unified model. Extensive experiments on benchmark datasets demonstrate that our proposed MSCC method outperforms nearly all the existing supervised methods with limited labeled samples. Even with no unlabeled samples, MSCC still obtains better performance and stableness than most supervised methods. [ABSTRACT FROM AUTHOR]

Convolutional neural networks are built upon simple but useful convolution modules. The traditional convolution has a limitation on feature extraction and object localization due to its fixed scale and geometric structure. Besides, the loss of spatial information also restricts the networks’ performance and depth. To overcome these limitations, this paper proposes a novel anisotropic convolution by adding a scale factor and a shape factor into the traditional convolution. The anisotropic convolution augments the receptive fields flexibly and dynamically depending on the valid sizes of objects. In addition, the anisotropic convolution is a generalized convolution. The traditional convolution, dilated convolution and deformable convolution can be viewed as its special cases. Furthermore, in order to improve the training efficiency and avoid falling into a local optimum, this paper introduces a simplified implementation of the anisotropic convolution. The anisotropic convolution can be applied to arbitrary convolutional networks and the enhanced networks are called ACNs (anisotropic convolutional networks). Experimental results show that ACNs achieve better performance than many state-of-the-art methods and the baseline networks in tasks of image classification and object localization, especially in classification task of tiny images. [ABSTRACT FROM AUTHOR]

Stereo video retargeting aims at minimizing shape and depth distortions with temporal coherence in resizing a stereo video content to a desired size. Existing methods extend stereo image retargeting schemes to stereo video retargeting by adding additional temporal constraints that demand temporal coherence in all corresponding regions. However, such a straightforward extension incurs conflicts among multiple requirements (i.e., shape and depth preservation and their temporal coherence), thus failing to meet one or more of these requirements satisfactorily. To mitigate conflicts among depth, shape, and temporal constraints and avoid degrading temporal coherence perceptually, we relax temporal constraints for non-paired regions at frame boundaries, derive new temporal constraints to improve human viewing experience of a 3D scene, and propose an efficient grid-based implementation for stereo video retargeting. Experimental results demonstrate that our method achieves superior visual quality over existing methods. [ABSTRACT FROM AUTHOR]

The conventional calculation of rotor losses in cage induction motors by the time-stepping finite element method requires the full slip waves of the rotor flux densities and currents. The calculations of the full slip waves are extremely expensive in CPU time, memory, and hard disk space for the induction motors with skewed slots and pulsewidth modulated supply. This article proposes a technique that reproduces those full slip waves by utilizing both the time varying and the spatial information of the rotor electromagnetic quantities and, hence, significantly saves the calculation cost. The more rotor bars per pair of poles the induction motor has, the more calculation cost this technique saves. The calculation costs of the proposed and conventional methods are compared for the rotor losses of a 5.5-kW inverter-fed skewed induction motor under load condition, and the calculated losses are validated by tests. [ABSTRACT FROM AUTHOR]

Magnetic resonance elastography (MRE) offers a noninvasive solution to visualize the mechanical properties of soft tissue, but the study suffers from expensive magnetic resonance scanning. Moreover, translating MRE wave images into soft tissue elasticity is a nontrivial issue for clinical professionals and healthcare practitioners. An interactive system—OpenMRE—is thus developed with the aid of ImageJ for numerical MRE study. It is comprised of two comparatively independent toolkits, namely MREA for simulation and MREP for interpretation. MREA mainly deals with the forward problem of MRE, and provides a numerical platform to determine the propagation and distribution of specially designed elastic wave. It is possible to numerically study some state-of-the-art paradigms including multisource and multifrequency MRE. The resultant wave images are interpretable in MREP that is designed for the inverse problem of MRE. It consists of the algorithms for phase unwrapping, directional filtering, and elasticity reconstruction. In a word, OpenMRE offers the MRE community a convenient and well-functioning system for interactive MRE study. [ABSTRACT FROM AUTHOR]

Many-core systems connected by 3D Networks-on-Chip (NoC) are emerging as a promising computation engine for systems like cloud computing servers, big data systems, etc. Mapping applications at runtime to 3D NoCs is the key to maintain high throughput of the overall chip under a thermal/power constraint. However, the goals of optimizing both the communication latency and chip peak temperature are contradicting due to several reasons. First, exploiting the vertical TSV links can accelerate communications, while low peak temperature prefers that the tasks to be mapped closer to the heat sink, instead of using the vertical links. Second, mapping tasks in close proximity can reduce communication latency, but at the cost of poor heat dissipation. To address these issues, in this paper, we propose an efficient runtime mapping algorithm to reduce both communication latency and overall application running time under thermal constraint. In essence, this algorithm first selects a 3D cuboid core region of a specific shape for each incoming application by setting the region's number of occupied vertical layers and its distance to the heat sink, in order to optimize its communication performance and peak temperature. Next, the exact locations of the core regions in the chip are determined, followed by a task-to-core mapping. A defragmentation algorithm is also proposed to keep free core regions contiguous. The experimental results have confirmed that, compared to two recently proposed runtime mapping algorithms, our proposed approach can reduce the total running time by up to 48% and communication cost by up to 44%, with a low runtime overhead. [ABSTRACT FROM AUTHOR]

This paper presents a cache-based popular services pushing solution on high-speed train (HST) by using converged wireless broadcasting and cellular networks. Pushing and caching popular services on the HST to improve the capacity of the network is a very efficient way; and it can also bring a better user experience. The most popular services are transmitted and cached on the vehicle relay station of the train ahead the departure time in the proposed model. Then, the most popular services are broadcasted and cached on the User Equipment after all the passengers are on the train; the less popular services are delivered to the passengers by P2P mode through the relayed cellular network on the train. Specifically, we firstly use the dynamic programming algorithm to maximize the network capacity in limited pushing time, which can be converted to the 0–1 Knapsack problem. Furthermore, we propose three greedy algorithms to approximate the optimal solution on account of the high time complexity of dynamic programming when the input scale gets bigger. And simulation results show that the proposed popularity-based greedy algorithm performs well. Moreover, as the passengers may get on and off the HST when arriving at an intermediate station, a services rebroadcast algorithm is employed when more intermediate stations are considered. U-shaped distribution is adopted to indicate the number of passengers getting on and off the train. Simulations also show that the proposed rebroadcast algorithm can efficiently improve the capacity of the converged networks. [ABSTRACT FROM AUTHOR]

This paper is concerned with the input-to-state stabilization problem for a class of delayed differential systems. Both time-delay in state and bounded exogenous disturbances are taken into account in the model. An event-triggered strategy, which depends simultaneously on the latest sampled state and a non-negative threshold, is proposed to reduce the transmission frequency of the feedback control signals with guaranteed performance requirements. The notion of input-to-state practical stability is introduced to evaluate the dynamical performance of the controlled systems with considering the effects from both exogenous disturbances and event-triggered scheme. The estimations of the upper bounds for the system state and the measurement error are employed to analyze and further exclude the Zeno behavior for the proposed event-triggered scheme. The controller gain and the event-trigger parameters are co-designed in terms of the feasibility of certain matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

Reported researches on smart charging methods have the disadvantages of low calculation efficiency or have not simultaneously taken the three-phase imbalance, voltage, and power flow constraints into account. It is an important topic to improve the computational speed to meet the online rolling optimization requirement for EVCC problems. In this paper, the branch power flow equations of balanced and unbalanced distribution system are derived. The linearization methods for the nonlinear terms of the branch power flow equations are proposed. Two stages linear programming (LP) is introduced for EVCC to minimize the total charging costs of the holders where three-phase imbalance, charging demand, voltage, and power flow constraints have been taken into account. Via ignoring the nonlinear terms of the branch power flow equations, the first stage LP is formulated to calculate the estimated branch power and node voltages as the initial points for linearizing the nonlinear terms of branch power flow equations. The second stage LP is formulated to calculate the optimal charging power using the linearized branch power flow equations. Four case studies show that the proposed method without the compromise of precision is significantly faster than state-of-the-art works with respect to the computational speed. [ABSTRACT FROM AUTHOR]

At nanometer manufacturing technology nodes, process variations affect circuit performance significantly. This trend leads to a large timing margin and thus overdesign in the traditional worst-case circuit design flow. To combat this pessimism, post-silicon clock tuning buffers can be deployed to balance timing slacks of consecutive combinational paths in individual chips by tuning clock skews after manufacturing. A challenge of this method is that path delays of each chip with timing failures should be measured to gather the information for clock skew configuration. However, current methods for delay measurement rely on path-wise frequency stepping, which requires much time from expensive testers. In this paper, we propose an efficient delay test framework (EffiTest2) to solve the post-silicon testing problem by testing only representative paths with delay alignment using the already-existing tunable buffers in the circuit. Experimental results demonstrate that EffiTest2 can reduce the number of frequency stepping iterations by more than 94% with only a slight yield loss. [ABSTRACT FROM AUTHOR]

A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches often suffer from losing domain terminologies since they neglect the impact of domain-level topical distribution. In this paper, we propose an efficient method for high quality and cohesive topical phrase mining. A high quality phrase should satisfy frequency, phraseness, completeness, and appropriateness criteria. In our framework, we integrate quality guaranteed phrase mining method, a novel topic model incorporating the constraint of phrases, and a novel document clustering method into an iterative framework to improve both phrase quality and topical cohesion. We also describe efficient algorithmic designs to execute these methods efficiently. The empirical verification demonstrates that our method outperforms the state-of-the-art methods from the aspects of both interpretability and efficiency. [ABSTRACT FROM AUTHOR]

Wheels are one of the three components of rail vehicles that are most affected by wear, having significant implications on safety and comfort. For the safety of vehicles, maintenance activities are needed for worn wheels to ensure their normal geometry, where reprofiling plays the most important role in maintenance. How to reprofile wheels scientifically and economically has become a vital issue, which needs to be solved in the field of wheel maintenance. This study focuses on high-speed train wheels and proposes a dynamic reprofiling policy based on condition monitoring at equidistant discrete time inspections. The problem is modeled in the framework of a semi-Markov decision process considering both wear and external shocks. The objective of the maintenance model is to minimize the long-term expected cost per unit time. We apply a policy-iteration algorithm to determine the optimal reprofiling policy. To demonstrate the model's effectiveness, actual degradation data on high-speed train wheels are used as an illustration. The results show that the dynamic reprofiling policy reduces wheel maintenance costs and extends their service life, also ensuring wheels’ safety. [ABSTRACT FROM AUTHOR]

This paper investigates the open-circuit faults of switching devices in the three-level T-Type inverter and proposes a new tolerant control method. The three-phase current and variation of the neutral-point voltage are detected to determine the fault switch. A carrier-based pulse width modulation is developed to tolerate switch fault by transiting from three-level to two-level switching. This makes the modulation scheme easier to be implemented compared to space vector modulation. For the neutral-point fault, the fault phase is operated by two-level modulation to produce the same rating voltage. On the other hand, the inverter is able to produce balanced voltage with reduced rating by keeping the phase of half-bridge fault to the neutral-point voltage. The neutral-point voltage is also able to be balanced by using two-level modulation since it does not contribute the neutral-point current. The performance and effectiveness of the proposed tolerant control are verified by a laboratory build. [ABSTRACT FROM AUTHOR]

The linear canonical transform (LCT) has been shown to be one of the most powerful tools in signal processing, and in this paper, we propose an adaptive approach for the computation of the discrete LCT (DLCT), termed the sliding discrete linear canonical transform (SDLCT). First, we introduce a scheme for the single-point DLCT, which can effectively calculate a single or a few linear canonical spectra. Second, the SDLCT is proposed based on an iterative algorithm to meet the requirements of online spectral analysis when only a subset of $N$ frequencies are required from an $\tilde{N}\hbox{--}$ point discrete LCT ($N\leq \tilde{N}$). The additivity and reversibility properties of the proposed algorithms are also discussed in detail. Third, the DLCT convolution operation is obtained to reduce the spectral leakage of the proposed algorithm, and time-domain windowing is implemented via frequency-domain convolution. Finally, we present two methods to assess performance with regard to computational complexity and precision and to show the correctness of the derived results. [ABSTRACT FROM AUTHOR]

At submicrometer manufacturing technology nodes, process variations affect circuit performance significantly. To counter these variations, engineers are reserving more timing margin to maintain yield, leading to an unaffordable overdesign. Most of these margins, however, are wasted after manufacturing, because process variations cause only some chips to be really slow, while other chips can easily meet given timing specifications. To reduce this pessimism, we can reserve less timing margin and tune failed chips after manufacturing with clock buffers to make them meet timing specifications. With this post-silicon clock tuning, critical paths can be balanced with neighboring paths in each chip specifically to counter the effect of process variations. Consequently, chips with timing failures can be rescued and the yield can thus be improved. This is specially useful in high-performance designs, e.g., high-end CPUs, where clock binning makes chips with higher performance much more profitable. In this paper, we propose a method to determine where to insert post-silicon tuning buffers during the design phase to improve the overall profit with clock binning. This method learns the buffer locations with a Sobol sequence iteratively and reduces the buffer ranges afterward with tuning concentration and buffer grouping. Experimental results demonstrate that the proposed method can achieve a profit improvement of about 14% on average and up to 26%, with only a small number of tuning buffers inserted into the circuit. [ABSTRACT FROM AUTHOR]

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (M$^2$ IL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse $\varepsilon$ -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M $^2$IL. Experiments and analyses in many practical applications prove the effectiveness of the M $^2$ IL. [ABSTRACT FROM PUBLISHER]

In this paper, we consider different types of networks, and investigate the characteristics of the frequency responses of loads, which are distributed in the networks. Without loss of generality, both frequency-independent and frequency-dependent loads are discussed, respectively. Beginning with a transmission-line (TL) network with frequency-independent loads, via the TL theory and Baum–Liu–Tesche equation, we demonstrate that the frequency responses are periodic in the frequency domain, where the periodicity is derived and verified. Subsequently, our study is extended to the complex networks that consist of multiple junctions and branches. By using the statistical method, we generate random loads with different attributes, i.e., resistive, inductive, or capacitive, and mainly study the effect of the number of branches and junctions on the frequency response of targeted load in various networks. From the perspective of protections for the targeted load in networks, results indicate that, for lossless and good dielectric (i.e., low-loss) media, it is crucial to consider the frequency responses at the critical frequencies in a periodical manner, rather than at a single frequency. Furthermore, it is worth noting that, the frequency response of targeted load behaves differently when varying the attributes of other loads in the network. The variation of network topology, i.e., increasing the number of junctions or branches, also influences the frequency response. [ABSTRACT FROM AUTHOR]

With advancing process technologies and booming Internet of Things markets, millimeter-wave CMOS RFICs have evolved rapidly and been widely applied in recent years. The performance of CMOS RFICs is very sensitive to the chip layout, and a tiny variation of the microstrip length can cause a large impact to the circuit performance. This results in a time-consuming tuning process including much simulation effort for chip design, which becomes the major bottleneck for time to market. This paper introduces a progressive integer-linear-programming-based method consisting of two phases: 1) global layout generation and 2) iterative validation. In the global layout generation phase, we focus on the most critical constraints such as layout planarity and device connection relations to determine the topology of the final design. This provides a basis for constructing the accurate model in the iterative validation phase. The layouts generated by applying our method can satisfy very stringent routing requirements of microstrip lines, including spacing/noncrossing rules, precise length, and bend number minimization, within a given layout area. The resulting RFIC layouts excel in both performance and area with much fewer bends compared with the simulation-tuning based manual layout, while the layout generation time is significantly reduced from weeks to a few minutes. [ABSTRACT FROM AUTHOR]

The asymmetry/coating-induced mode conversion (ACIMC) effect in a transmission line system is discussed in this letter. This effect is proved only to be induced when asymmetry and coating exist simultaneously. The reason for ACIMC is that asymmetry and coating lead to the inconsistency of current division factors for electric and magnetic effects. Based on this idea, ACIMC is modeled by adding common-mode current-controlled voltage sources in differential circuits, and the results agree well with those obtained by the method of moments. Moreover, it is proved that balanced-state-matched terminal loads do not exist for a asymmetric and coated transmission line system, but approximate ones at low frequencies could still be found. [ABSTRACT FROM PUBLISHER]

Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics. [ABSTRACT FROM PUBLISHER]

The breakdown voltages in a needle-plate geometry have been measured by 50 Hz power frequency tests for the CF3I-N2 gas mixtures (5?30% CF3I) over the gas pressure from 0.1 to 0.3 MPa. The influence of different conditions on the breakdown performance is investigated. The synergistic effect of CF3I-N2 gas mixtures is verified from the respect of breakdown voltage. Moreover, the behavior of the gas mixtures in highly divergent fields is characterized by a pressure range of corona stabilization, which can explain the relation of the breakdown strength to gas pressure and mixing ratio. A comparison to the theoretical breakdown voltages in uniform electric field shows that the breakdown strength reduces with the rising non-uniform coefficient of electric field at relatively short gap distance. Furthermore, the sensitivity of the insulating ability to divergent field is weakened by the increasing CF3I ratio. [ABSTRACT FROM PUBLISHER]

In flow-based biochips, peristaltic pumps consisting of valves are essential to generate circulation flow in a mixer. When a peristaltic pump is activated, the related valves for peristalsis are required to be actuated for many times. However, the roles of valves in traditional chips are fixed, and therefore the valves for peristalsis can wear out much faster than the valves for guiding fluid transportation. This could lead to a reduced lifetime of the chip, because the whole chip function can be affected when just a few or even only a single valve wears out. In this paper, we propose a valve-centered architecture with virtual valves, based on which we introduce a valve-role-changing concept to balance the valve actuations. By switching a valve into different roles, microfluidic components such as mixers, storages, and flow channels can be formed dynamically during the assay process, which enables us to balance the utilization of valves, and synthesize designs that support various kinds of operations. Compared with our preliminary work, we further decrease the largest number of valve actuation as well as the number of valves by the revised dynamic device mapping and fluid path routing. For dynamic device mapping, we introduce a virtual-boundary concept to generate devices at better places while connections between devices are still guaranteed. For fluid path routing, we accurately model valve actuation resulting from our valve-actuation-aware routing, and revise the results by rip-up and reroute. In addition to performance, we improve the reliability of our method by assuring fluid paths from devices to chip boundaries. Experiments show that the new method can be eight times better than the traditional method, and outperforms our preliminary work for large cases even with fewer valves. [ABSTRACT FROM PUBLISHER]

This paper studied the insulation characteristics of C3F8 gas mixtures with N2 and CO2 from the point view of electron swarm parameters. The density-normalized effective ionization coefficient (α-η)/N and electron drift velocity Ve in binary C3F8-N2 and C3F8- CO2 gas mixtures are calculated by solving the Boltzmann equation in the condition of steady-state Townsend discharge experiment. Values of the limiting electric field strength for which the electron ionization exactly balances the attachment are derived from the effective ionization coefficient data. It is found that the limiting field strength of C3F8-N2 gas mixture is greater than that of C3F8-CO2 for C3F8 gas content k varied from 0% to 100%, and the C3F8-N2 gas mixture containing 20% proportion C3F8 can achieve 60% dielectric strength of pure C3F8. Additionally, the synergistic effects of the insulation strength for C3F8 gas mixtures are also evaluated and compared with that of SF6-N2 mixture. The calculation results, together with the discussions on liquefaction temperature and global warming potential indicate that gas mixtures containing C3F8 are feasible for applying in electric power apparatus as insulation medium. [ABSTRACT FROM AUTHOR]

Post-silicon clock tuning elements are widely used in high-performance designs to mitigate the effects of process variations and aging. Located on clock paths to flip-flops, these tuning elements can be configured through the scan chain so that clock skews to these flip-flops can be adjusted after manufacturing. Owing to the delay compensation across consecutive register stages enabled by the clock tuning elements, higher yield and enhanced robustness can be achieved. These benefits are, nonetheless, attained by increasing die area due to the inserted clock tuning elements. For balancing performance improvement and area cost, an efficient timing analysis algorithm is needed to evaluate the performance of such a circuit. So far this evaluation is only possible by Monte Carlo simulation which is very time-consuming. In this paper, we propose an alternative method using graph transformation, which computes a parametric minimum clock period and is more than 10^ 4 times faster than Monte Carlo simulation while maintaining a good accuracy. This method also identifies the gates that are critical to circuit performance, so that a fast analysis-optimization flow becomes possible. [ABSTRACT FROM AUTHOR]

Length-matching is an important technique to balance delays of bus signals in high-performance printed circuit board (PCB) routing. Existing routers, however, may generate very dense meander segments. Signals propagating along these meander segments exhibit a speedup effect due to crosstalk between the segments of the same wire, thus leading to mismatch of arrival times even under the same physical wire length. In this paper, we present a post-processing method to enlarge the width and the distance of meander segments and hence distribute them more evenly on the board so that crosstalk can be reduced. In the proposed framework, we model the sharing of available routing areas after removing dense meander segments from the initial routing, as well as the generation of relaxed meander segments and their groups for wire length compensation. This model is transformed into an ILP problem and solved for a balanced distribution of wire patterns. In addition, we adjust the locations of long wire segments according to wire priorities to swap free spaces toward critical wires that need much length compensation. To reduce the problem space of the ILP model, we also introduce a progressive fixing technique so that wire patterns are grown gradually from the edge of the routing toward the center area. Experimental results show that the proposed method can expand meander segments significantly even under very tight area constraints, so that the speedup effect can be alleviated effectively in high-performance PCB designs. [ABSTRACT FROM PUBLISHER]

Passive ultrahigh frequency (UHF) radio frequency identification (RFID) is a powerful automatic contactless identification technique that has numerous applications. The performance of passive UHF RFID system is affected seriously by the application environments. In this paper, we propose a metrical method of application environment impact for passive UHF RFID system based on fuzzy comprehensive evaluation. The metric of application environment impact is graded by the degradation of reading distance, reading rate, and reading speed of passive UHF RFID system. Analytic hierarchy process is used to calculate the weight of factors, which reduces the subjectivity of evaluation results. For avoiding the loss of valid information, weighted average operator is employed to replace traditional max min operator. The end user can know the real performance of product selected by the grade of application environment impact visually and easily use the method proposed. Experimental results indicate that the proposed method is simple, valid, fast, accurate, and practical. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the challenges of applying statistical static timing analysis in hierarchical design flow, where modules supplied by IP vendors are used to hide design details for IP protection and to reduce the complexity of design and verification. For the three basic circuit types, combinational, flip-flop-based, and latch-controlled, we propose methods for extracting timing models that contain interfacing and compressed internal constraints. Using these compact timing models, the runtime of full-chip timing analysis can be reduced, while circuit details from IP vendors are not exposed. We also propose a method for reconstructing correlation between modules during full-chip timing analysis. This correlation cannot be incorporated into timing models because it depends on the layout of the corresponding modules in the chip. In addition, we investigate how to apply the extracted timing models with the reconstructed correlation to evaluate the performance of the complete design. Experiments demonstrate that using the extracted timing models and reconstructed correlation full-chip timing analysis can be several times faster than applying the flattened circuit directly, while the accuracy of statistical timing analysis is still well maintained. [ABSTRACT FROM AUTHOR]

Node localization is a main application of wireless sensor networks. However, the measurement error and noise in a real environment make the positioning of information nonlinear and have a great effect on the performance of localization methods. In this paper, we propose a novel isometric mapping (Isomap) node localization algorithm based on partial least squares (PLS-Isomap). For topological stability, the critical outlier points are eliminated by comparing the contribution rate of all data points. Then, we employ the PLS method to solve the Isomap. The adoption of PLS reduces the noise sensitivity of Isomap, which achieves solution by least squares. Moreover, the proposed approach applies a projection method to construct a new kernel matrix between new and original data points. Compared with Isomap and the multidimensional scale method, experimental and simulation results indicate that the PLS-Isomap algorithm has good topological stability, robustness, positioning accuracy, and lower computational complexity. [ABSTRACT FROM PUBLISHER]

Level-sensitive latches are widely used in high-performance designs. For such circuits, efficient statistical timing analysis algorithms are needed to take increasing process variations into account. The existing methods for solving this problem are still computationally expensive and can only provide the yield at a given clock period. In this paper, we propose a method combining reduced iterations and graph transformations. The reduced iterations extract setup time constraints and identify a subgraph for the following graph transformations handling the constraints from nonpositive loops. The combined algorithms are very efficient, more than ten times faster than other existing methods, and result in a parametric minimum clock period, which, together with the hold-time constraints, can be used to compute the yield at any given clock period very easily. [ABSTRACT FROM AUTHOR]

Presents a stochastic approach called SAGACIA based on proper integration of simulated annealing algorithm (SAA), genetic algorithm (GA) and chemotaxis algorithm (CA) for solving complex optimization problems. Comparison of SAA, GA and CA; Outline of SAGACIA; Applications of the approach; Conclusion of the study.

Extracting channel state information (CSI) from WiFi signals is of proved high-effectiveness in locating human locations in a device-free manner. However, existing localization/positioning systems are mainly trained and deployed in a fixed environment, and thus they are likely to suffer from substantial performance declines when immigrating to new environments. In this paper, we address the fundamental problem of WiFi-based cross-environment indoor localization using a semi-supervised approach, in which we only have access to the annotations of the source environment while the data in the target environments are un-annotated. This problem is of high practical values in enabling a well-trained system to be scalable to new environments without tedious human annotations. To this end, a deep neural forest is introduced which unifies the ensemble learning with the representation learning functionalities from deep neural networks in an end-to-end trainable fashion. On top of that, an adversarial training strategy is further employed to learn environment-invariant feature representations for facilitating more robust localization. Extensive experiments on real-world datasets demonstrate the superiority of the proposed methods over state-of-the-art baselines. Compared with the best-performing baseline, our model excels with an average 12.7% relative improvement on all six evaluation settings. [ABSTRACT FROM AUTHOR]

Although transformer has achieved great progress on computer vision tasks, the scale variation in dense image prediction is still the key challenge. Few effective multi-scale techniques are applied in transformer and there are two main limitations in the current methods. On the one hand, self-attention module in vanilla transformer fails to sufficiently exploit the diversity of semantic information because of its rigid mechanism. On the other hand, it is difficult to build attention and interaction among different levels due to the heavy computational burden. To alleviate this problem, we first revisit multi-scale problem in dense prediction, verifying the significance of diverse semantic representation and multi-scale interaction, and exploring the adaptation of transformer to pyramidal structure. Inspired by these findings, we propose a novel Semantic-aware Decoupled Transformer Pyramid (SDTP) for dense image prediction, consisting of Intra-level Semantic Promotion (ISP), Cross-level Decoupled Interaction (CDI) and Attention Refinement Function (ARF). ISP explores the semantic diversity in different receptive space through more flexible self-attention strategy. CDI builds the global attention and interaction among different levels in decoupled space which also solves the problem of heavy computation. Besides, ARF is further added to refine the attention in transformer. Experimental results demonstrate the validity and generality of the proposed method, which outperforms the state-of-the-art by a significant margin in dense image prediction tasks. Furthermore, the proposed components are all plug-and-play, which can be embedded in other methods. [ABSTRACT FROM AUTHOR]