When using a gesture-based interface for pointing to targets on a wide screen, displaying a large pointer instead of a typical spot pattern reduces disturbance caused by measurement errors of user's pointing posture. However, it remains unclear why a large pointer helps facilitate easy pointing. To examine this issue, in this study we propose a mathematical model that formulates human pointing motions affected by a large pointer. Our idea is to describe the effect of the large pointer as human visual perception, because the user will perceive the pointer-target distance as being shorter than it actually is. We embedded this scheme, referred to as non-linear distance filter (NDF), into a typical feedback loop model designed to formulate human pointing motions. We also proposed a method to estimate NDF mapping from pointing trajectories, and used it to investigate the applicability of the model under three typical disturbance patterns: small vibration, smooth shift, and step signal. Experimental results demonstrated that the proposed NDF-based model could accurately reproduced actual pointing trajectories, achieving high similarity values of 0.89, 0.97, and 0.91 for the three respective disturbance patterns. The results indicate the applicability of the proposed method. In addition, we confirmed that the obtained NDF mappings suggested rationales for why a large pointer helps facilitate easy pointing.

Multi-cycle Test looks promising a way to reduce the test application time of POST (Power-on Self-Test) for achieving a targeted high fault coverage specified by ISO26262 for testing automotive devices. In this paper, we first analyze the mechanism of Stuck-at Fault Detection Degradation problem in multi-cycle test. Based on the result of our analysis we propose a novel solution named FF-Control Point Insertion technique (FF-CPI) to achieve the reduction of scan-in patterns by multi-cycle test. The FF-CPI technique modifies the captured values of scan Flip-Flops (FFs) during capture operation by directly reversing the value of partial FFs or loading random vectors. The FF-CPI technique enhances the number of detectable stuck-at faults under the capture patterns. The experimental results of ISCAS89 and ITC99 benchmarks validated the effectiveness of FF-CPI technique in scan-in pattern reduction for POST.

A grating consisting of a periodic array of InSb-coated dielectric cylinders on a substrate is analyzed at THz frequencies using the frequency-dependent finite-difference time-domain method based on the trapezoidal recursive convolution technique. The transmission characteristics of an infinite periodic array are investigated not only at normal incidence but also at oblique incidence. The incident field is shown to be coupled to the substrate due to the guided-mode resonance (GMR), indicating the practical application of a grating coupler. For the sensor application, the frequency shift of the transmission dip is investigated with attention to the variation of the background refractive index. It is found that the shift of the dip involving the surface plasmon resonance is almost ten times as large as that of the dip only from the GMR. We finally analyze a finite periodic array of the cylinders. The field radiation from the array is discussed, when the field propagates through the substrate. It is shown that the radiation direction can be controlled with the frequency of the propagating field.

We demonstrated a compact 16×16 multicast switch (MCS) made from a silica-based planar lightwave circuit (PLC). The switch utilizes a new electrical connection method based on surface mount technology (SMT). Five electrical connectors are soldered directly to the PLC by using the standard reflow process used for electrical devices. We reduced the chip size to half of one made with conventional wire bonding technology. We obtained satisfactory solder contacts and excellent switching properties. These results indicate that the proposed method is suitable for large-scale optical switches including MCSs, variable optical attenuators, dispersion compensators, and so on.

In recent years, deep neural network (DNN) has achieved considerable results on many artificial intelligence tasks, e.g. natural language processing. However, the computation complexity of DNN is extremely high. Furthermore, the performance of traditional von Neumann computing architecture has been slowing down due to the memory wall problem. Processing in memory (PIM), which places computation within memory and reduces the data movement, breaks the memory wall. ReRAM PIM is thought to be a available architecture for DNN accelerators. In this work, a novel design of ReRAM neuromorphic system is proposed to process DNN fully in array efficiently. The binary ReRAM array is composed of 2T2R storage cells and current mirror sense amplifiers. A dummy BL reference scheme is proposed for reference voltage generation. A binary DNN (BDNN) model is then constructed and optimized on MNIST dataset. The model reaches a validation accuracy of 96.33% and is deployed to the ReRAM PIM system. Co-design model optimization method between hardware device and software algorithm is proposed with the idea of utilizing hardware variance information as uncertainness in optimization procedure. This method is analyzed to achieve feasible hardware design and generalizable model. Deployed with such co-design model, ReRAM array processes DNN with high robustness against fabrication fluctuation.

Sketch face recognition is to match sketch face images to photo face images. The main challenge of sketch face recognition is learning discriminative feature representations to ensure intra-class compactness and inter-class separability. However, traditional sketch face recognition methods encouraged samples with the same identity to get closer, and samples with different identities to be further, and these methods did not consider the intra-class compactness of samples. In this paper, we propose triplet-margin-center loss to cope with the above problem by combining the triplet loss and center loss. The triplet-margin-center loss can enlarge the distance of inter-class samples and reduce intra-class sample variations simultaneously, and improve intra-class compactness. Moreover, the triplet-margin-center loss applies a hard triplet sample selection strategy. It aims to effectively select hard samples to avoid unstable training phase and slow converges. With our approach, the samples from photos and from sketches taken from the same identity are closer, and samples from photos and sketches come from different identities are further in the projected space. In extensive experiments and comparisons with the state-of-the-art methods, our approach achieves marked improvements in most cases.

In this letter, we propose a novel robust transferable subspace learning (RTSL) method for cross-corpus facial expression recognition. In this method, on one hand, we present a novel distance metric algorithm, which jointly considers the local and global distance distribution measure, to reduce the cross-corpus mismatch. On the other hand, we design a label guidance strategy to improve the discriminate ability of subspace. Thus, the RTSL is much more robust to the cross-corpus recognition problem than traditional transfer learning methods. We conduct extensive experiments on several facial expression corpora to evaluate the recognition performance of RTSL. The results demonstrate the superiority of the proposed method over some state-of-the-art methods.

The dispersion problem is a variant of the facility location problem. Given a set P of n points and an integer k, we intend to find a subset S of P with |S|=k such that the cost minp∈S{cost(p)} is maximized, where cost(p) is the sum of the distances from p to the nearest c points in S. We call the problem the dispersion problem with partial c sum cost, or the PcS-dispersion problem. In this paper we present two algorithms to solve the P2S-dispersion problem(c=2) if all points of P are on a line. The running times of the algorithms are O(kn2 log n) and O(n log n), respectively. We also present an algorithm to solve the PcS-dispersion problem if all points of P are on a line. The running time of the algorithm is O(knc+1).

In this paper, we present a joint multi-patch and multi-task convolutional neural networks (JMM-CNNs) framework to learn more descriptive and robust face representation for face recognition. In the proposed JMM-CNNs, a set of multi-patch CNNs and a feature fusion network are constructed to learn and fuse global and local facial features, then a multi-task learning algorithm, including face recognition task and pose estimation task, is operated on the fused feature to obtain a pose-invariant face representation for the face recognition task. To further enhance the pose insensitiveness of the learned face representation, we also introduce a similarity regularization term on features of the two tasks to propose a regularization loss. Moreover, a simple but effective patch sampling strategy is applied to make the JMM-CNNs have an end-to-end network architecture. Experiments on Multi-PIE dataset demonstrate the effectiveness of the proposed method, and we achieve a competitive performance compared with state-of-the-art methods on Labeled Face in the Wild (LFW), YouTube Faces (YTF) and MegaFace Challenge.

Automatic disassembly of railway fasteners is of great significance for improving the efficiency of replacing rails. The accurate positioning of fastener is the key factor to realize automatic disassembling. However, most of the existing literature mainly focuses on fastener region positioning and the literature on accurate positioning of fasteners is scarce. Therefore, this paper constructed a visual inspection system for accurate positioning of fastener (VISP). At first, VISP acquires railway image by image acquisition subsystem, and then the subimage of fastener can be obtained by coarse-to-fine method. Subsequently, the accurate positioning of fasteners can be completed by three steps, including contrast enhancement, binarization and spike region extraction. The validity and robustness of the VISP were verified by vast experiments. The results show that VISP has competitive performance for accurate positioning of fasteners. The single positioning time is about 260ms, and the average positioning accuracy is above 90%. Thus, it is with theoretical interest and potential industrial application.

Intersymbol interference (ISI) is a significant source of degradation in many digital communication systems including our proposed non-far region communication system using large array antennas in the millimeter-wave band in which the main cause of ISI can be attributed to the path delay differences among the elements of an array antenna. This paper proposes a quantitative method to evaluate the ISI estimated from the measured near-field distribution of the array antenna. The influence of the uniformity in the aperture field distribution in ISI is discussed and compared with an ideally uniform excitation. The reliability of the proposed method is verified through a comparison with another method based on direct measurements of the transmission between the actual antennas. Finally, the signal to noise plus interference is evaluated based on the estimated ISI results and ISI is shown to be the dominant cause of the degradation in the reception zone of the system.

Chopping technique up-modulates amplifier's offset and low-frequency noise up to its switching frequency, and therefore can achieve low offset and low temperature drift. On the other hand, it generates unwanted AC and DC errors due to its switching artifacts such as up-modulated ripple and glitches. This paper summarizes various circuit techniques of reducing such switching artifacts, and then discusses the advantages and disadvantages of each technique. The comparison shows that newer designs with advanced circuit techniques can achieve lower DC and AC errors with higher chopping frequency.

This paper introduces a user study regarding the effects of hand- and ocular-dominances to pointing gestures. The result of this study is applicable for designing new gesture interfaces which are close to a user's cognition, intuitive, and easy to use. The user study investigates the relationship between the participant's dominances and pointing gestures. Four participant groups—right-handed right-eye dominant, right-handed left-eye dominant, left-handed right-eye dominant and left-handed left-eye dominant—were prepared, and participants were asked to point at the targets on a screen by their left and right hands. The pointing errors among the different participant groups are calculated and compared. The result of this user study shows that using dominant eyes produces better results than using non-dominant eyes and the accuracy increases when the targets are located at the same side of dominant eye. Based on these interesting properties, a method to find the dominant eye for pointing gestures is proposed. This method can find the dominant eye of an individual with more than 90% accuracy.

This paper proposes an ultra-low quiescent current low-dropout regulator (LDO) with a flipped voltage follower (FVF)-based load transient enhanced circuit for wireless sensor network (WSN). Some characteristics of an FVF are low output impedance, low voltage operation, and simple circuit configuration [1]. In this paper, we focus on the characteristics of low output impedance and low quiescent current. A load transient enhanced circuit based on an FVF circuit configuration for an LDO was designed in this study. The proposed LDO, including the new circuit, was fabricated in a 0.6 µm CMOS process. The designed LDO achieved an undershoot of 75 mV under experimental conditions of a large load transient of 100 µA to 10 mA and a current slew rate (SR) of 1 µs. The quiescent current consumed by the LDO at no load operation was 204 nA.

Plane wave scattering from a circular conducting cylinder and a circular conducting strip has been formulated by equivalent surface currents which are postulated from the scattering geometrical optics (GO) field. Thus derived radiation far fields are found to be the same as those formulated by a conventional physical optics (PO) approximation for both E and H polarizations.

Simultaneous multithreading technology (SMT) can effectively improve the overall throughput and fairness through improving the resources usage efficiency of processors. Traditional works have proposed some metrics for evaluation in real systems, each of which strikes a trade-off between fairness and throughput. How to choose an appropriate metric to meet the demand is still controversial. Therefore, we put forward suggestions on how to select the appropriate metrics through analyzing and comparing the characteristics of each metric. In addition, for the new application scenario of cloud computing, the data centers have high demand for the quality of service for killer applications, which bring new challenges to SMT in terms of performance guarantees. Therefore, we propose a new metric P-slowdown to evaluate the quality of performance guarantees. Based on experimental data, we show the feasibility of P-slowdown on performance evaluation. We also demonstrate the benefit of P-slowdown through two use cases, in which we not only improve the performance guarantee level of SMT processors through the cooperation of P-slowdown and resources allocation strategy, but also use P-slowdown to predict the occurrence of abnormal behavior against security attacks.

To solve the area and power problems in Finite Impulse Response (FIR) implementations, a faithfully truncated adder-based FIR design is presented in this paper for significant area and power savings while the predefined output accuracy can still be obtained. As a solution to the accuracy loss caused by truncated adders, a static error analysis on the utilization of truncated adders in FIRs was performed. According to the mathematical analysis, we show that, with a given accuracy constraint, the optimal truncated adder configuration for an area-power efficient FIR design can be effortlessly determined. Evaluation results on various FIR implementations by using the proposed faithfully truncated adder designs showed that up to 35.4% and 27.9% savings in area and power consumption can be achieved with less than 1 ulp accuracy loss for uniformly distributed random inputs. Moreover, as a case study for normally distributed signals, a fixed 6-tap FIR is implemented for electrocardiogram (ECG) signal filtering was implemented, in which even with the increased truncated bits up to 10, the mean absolute error (Ē) can be guaranteed to be less than 1 ulp while up to 29.7% and 25.3% savings in area and power can be obtained.

Green cognitive radio (CR) plays an important role in offering secondary users (SUs) with more spectrum with smaller energy expenditure. However, the energy efficiency (EE) issues associated with green CR for fading channels have not been fully studied. In this paper, we investigate the average EE maximization problem for spectrum-sharing CR in fading channels. Unlike previous studies that considered either the peak or the average transmission power constraints, herein, we considered both of these constraints. Our aim is to maximize the average EE of SU by optimizing the transmission power under the joint peak and average transmit power constraints, the rate constraint of SU and the quality of service (QoS) constraint of primary user (PU). Specifically, the QoS for PU is guaranteed based on either the average interference power constraint or the PU outage constraint. To address the non-convex optimization problem, an iterative optimal power allocation algorithm that can tackle the problem efficiently is proposed. The optimal transmission powers are identified under both of perfect and imperfect channel side information (CSI). Simulations show that our proposed scheme can achieve higher EE over the existing scheme and the EE achieved under perfect CSI is better than that under imperfect CSI.

In the statistic en-route filtering, each report generation node must collect a certain number of endorsements from its neighboring nodes. However, at some point, a node may fail to collect an insufficient number of endorsements since some of its neighboring nodes may have dead batteries. This letter presents a report generation method that can enhance the generation process of sensing reports under such a situation. Simulation results show the effectiveness of the proposed method.

Some non-acoustic modalities have the ability to reveal certain speech attributes that can be used for synthesizing speech signals without acoustic signals. This study validated the use of ultrasonic Doppler frequency shifts caused by facial movements to implement a silent speech interface system. A 40kHz ultrasonic beam is incident to a speaker's mouth region. The features derived from the demodulated received signals were used to estimate the speech parameters. A nonlinear regression approach was employed in this estimation where the relationship between ultrasonic features and corresponding speech is represented by deep neural networks (DNN). In this study, we investigated the discrepancies between the ultrasonic signals of audible and silent speech to validate the possibility for totally silent communication. Since reference speech signals are not available in silently mouthed ultrasonic signals, a nearest-neighbor search and alignment method was proposed, wherein alignment was achieved by determining the optimal pair of ultrasonic and audible features in the sense of a minimum mean square error criterion. The experimental results showed that the performance of the ultrasonic Doppler-based method was superior to that of EMG-based speech estimation, and was comparable to an image-based method.