Narrow swipe sensor based systems have drawn more and more attention in recent years. However, the size of captured image is significantly smaller than that obtained from the traditional area fingerprint sensor. Under this condition the available minutiae number is also limited. Therefore, only employing minutiae with the standard associated feature can hardly achieve high verification accuracy. To solve this problem, we present a novel Hybrid Minutiae Descriptor (HMD) which consists of two modules. The first one: Minutiae Ridge-Valley Orientation Descriptor captures the orientation information around minutia and also the trace points located at associated ridge and valley. The second one: Gabor Binary Code extracts and codes the image patch around minutiae. The proposed HMD enhances the representation capability of minutiae feature, and can be matched very efficiently. Experiments conducted over public databases and the database captured by the narrow swipe sensor show that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate).

This work reports a novel power-rail electrostatic discharge (ESD) clamp circuit with parasitic bipolar-junction-transistor (BJT) and channel parallel shunt paths. The parallel shunt paths are formed by delivering a tiny ratio of drain voltage to the gate terminal of the clamp device in ESD events. Under such a mechanism, the proposed circuit achieves enhanced robustness over those of both gate-grounded NMOS (ggNMOS) and the referenced gate-coupled NMOS (gcNMOS). Besides, the proposed circuit also achieves improved fast power-up immunity over that of the referenced gcNMOS. All investigated designs are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and human-body-model (HBM) test results have both confirmed the performance enhancements of the proposed circuit. Finally, the validity of the achieved performance enhancements on other trigger circuits is essentially revealed in this work.

The Android pattern unlock is a widely adopted graphical password system that requires a user to draw a secret pattern connecting points arranged in a grid. The theoretical security of pattern unlock can be defined by the number of possible patterns. However, only upper bounds of the number of patterns have been known except for 3×3 and 4×4 grids for which the exact number of patterns was found by brute-force enumeration. In this letter, we present the first lower bound by computing the minimum number of visible points from each point in various subgrids.

The quality assessment of screen content images (SCIs) has been attractive recently. Different from natural images, SCI is usually a mixture of picture and text. Traditional quality metrics are mainly designed for natural images, which do not fit well into the SCIs. Motivated by this, this letter presents a simple and effective method to naturalize SCIs, so that the traditional quality models can be applied for SCI quality prediction. Specifically, bicubic interpolation-based up-sampling is proposed to achieve this goal. Extensive experiments and comparisons demonstrate the effectiveness of the proposed method.

A text-dependent i-vector extraction scheme and a lexicon-based binary vector (L-vector) representation are proposed to improve the performance of text-dependent speaker verification. I-vector and L-vector are used to represent the utterances for enrollment and test. An improved cosine distance kernel is constructed by combining i-vector and L-vector together and is used to distinguish both speaker identity and lexical (or text) diversity with back-end support vector machine (SVM). Experiments are conducted on RSR 2015 Corpus part 1 and part 2, the results indicate that at most 30% improvement can be obtained compared with traditional i-vector baseline.

Wireless sensor networks are being used in many disaster-related applications. Certain types of disasters are studied and modeled with different and dynamic risk estimations in different areas, hence requiring different levels of monitoring. Such nonuniform and dynamic coverage requirements pose a challenge to a sensor coverage problem. This work proposes the Mobile sensor Relocation using Delaunay triangulation And Shifting on Hill climbing (MR-DASH) approach, which calculates an appropriate location for each mobile sensor as an attempt to maximize coverage ratio. Based on a probabilistic sensing model, it constructs a Delaunay triangulation from static sensors' locations and vertices of interesting regions. The resulting triangles are then prioritized based on their sizes and corresponding levels of requirement so that mobile sensors can be relocated accordingly. The proposed method was both compared with an existing previous work and demonstrated with real-world disaster scenarios by simulation. The result showed that MR-DASH gives appropriate target locations that significantly improve the coverage ratio with relatively low total sensors' moving distance, while properly adapting to variations in coverage requirements.

Zero correlation zone (ZCZ) aperiodic complementary sequence (ZACS) sets have potential applications in multi-carriers (MC) CDMA communication systems, which can support more users than traditional complementary sequence sets. In this letter, methods for constructing ZACS sets based on orthogonal matrices are proposed. The new constructions may propose ZACS sets with optimal parameters. The new ZACS sets can be applied in approximately synchronized MC-CDMA to remove interferences.

In this paper, a hamburger architecture with a 3D stacked reconfigurable memory is proposed for a 4K motion estimation (ME) processor. By positioning the memory dies on both the top and bottom sides of the processor die, the proposed hamburger architecture can reduce the usage of the signal through-silicon via (TSV), and balance the power delivery network and the clock tree of the entire system. It results in 1/3 reduction of the usage of signal TSVs. Moreover, a stacked reconfigurable memory architecture is proposed to reduce the fabrication complexity and further reduce the number of signal TSVs by more than 1/2. The reduction of signal TSVs in the entire design is 71.24%. Finally, we address unique issues that occur in electronic design automation (EDA) tools during 3D large-scale integration (LSI) designs. As a result, a 4K ME processor with 7-die stacking 3D system-on-chip design is implemented. The proposed design can support real time 3840 × 2160 @ 120 fps encoding at 130 MHz with less than 540 mW.

Recent research has shown that the class of rotation symmetric Boolean functions is beneficial to cryptographics. In this paper, for an odd prime p, two sufficient conditions for p-variable rotation symmetric Boolean functions to be 1-resilient are obtained, and then several concrete constructions satisfying the conditions are presented. This is the first time that resilient rotation symmetric Boolean functions have been systematically constructed. In particular, we construct a class of 2-resilient rotation symmetric Boolean functions when p=2m+1 for m ≥ 4. Moreover, several classes of 1-order correlation immune rotation symmetric Boolean functions are also got.

In this paper, we propose to use a strategy for the two-user Gaussian X channel with limited receiver cooperation in the general case consisting of two parts: 1) the transmission scheme where the superposition coding is used and 2) the cooperative protocol where the two-round strategy based on quantize-map-and-forward (QMF) is employed. We image that a Gaussian X channel can be considered as a superposition of two Gaussian interference channels based on grouping of the sent messages from each transmitter to the corresponding receivers. Finally, we give an achievable rate region for the general case of this channel.

We proposed to apply compressed sensing to realize information sharing of link quality for wireless mesh networks (WMNs) with grid topology. In this paper, we extend the link quality sharing method to be applied for WMNs with arbitrary topology. For arbitrary topology WMNs, we introduce a link quality matrix and a matrix formula for compressed sensing. By employing a diffusion wavelets basis, the link quality matrix is converted to its sparse equivalent. Based on the sparse matrix, information sharing is achieved by compressed sensing. In addition, we propose compressed transmission for arbitrary topology WMNs, in which only the compressed link quality information is transmitted. Experiments and simulations clarify that the proposed methods can reduce the amount of data transmitted for information sharing and maintain the quality of the shared information.

The solution of the standard 2-norm-based multiple kernel regression problem and the theoretical limit of the considered model space are discussed in this paper. We prove that 1) The solution of the 2-norm-based multiple kernel regressor constructed by a given training data set does not generally attain the theoretical limit of the considered model space in terms of the generalization errors, even if the training data set is noise-free, 2) The solution of the 2-norm-based multiple kernel regressor is identical to the solution of the single kernel regressor under a noise free setting, in which the adopted single kernel is the sum of the same kernels used in the multiple kernel regressor; and it is also true for a noisy setting with the 2-norm-based regularizer. The first result motivates us to develop a novel framework for the multiple kernel regression problems which yields a better solution close to the theoretical limit, and the second result implies that it is enough to use the single kernel regressors with the sum of given multiple kernels instead of the multiple kernel regressors as long as the 2-norm based criterion is used.

We propose a cache-aware method to accelerate texture-based volume rendering on a graphics processing unit (GPU) that is compatible with the compute unified device architecture. The proposed method extends a previous method such that it can maximize the average rendering performance while rotating the viewing direction around a volume. To realize this, the proposed method performs in-place rotation of volume data, which rearranges the order of voxels to allow consecutive threads (warps) to refer to voxels with the minimum access strides. Experiments indicate that the proposed method replaces the worst texture cache (TC) hit rate of 42% with the best TC hit rate of 93% for a 10243-voxel volume. Thus, the average frame rate increases by a factor of 1.6 in the proposed method compared with that in the previous method. Although the overhead of in-place rotation slightly decreases the frame rate from 2.0 frames per second (fps) to 1.9 fps, this slowdown occurs only with a few viewing directions.

Image deconvolution is the task to recover the image information that was lost by taking photos with blur. Especially, to perform image deconvolution without prior information about blur kernel, is called blind image deconvolution. This framework is seriously ill-posed and an additional operation is required such as extracting image features. Many blind deconvolution frameworks separate the problem into kernel estimation problem and deconvolution problem. In order to solve the kernel estimation problem, previous frameworks extract the image's salient features by preprocessing, such as edge extraction. The disadvantage of these frameworks is that the quality of the estimated kernel is influenced by the region with no salient edges. Moreover, the optimization in the previous frameworks requires iterative calculation of convolution, which takes a heavy computational cost. In this paper, we present a blind image deconvolution framework using a specified high-pass filter (HPF) for feature extraction to estimate a blur kernel. The HPF-based feature extraction properly weights the image's regions for the optimization problem. Therefore, our kernel estimation problem can estimate the kernel in the region with no salient edges. In addition, our approach accelerates both kernel estimation and deconvolution processes by utilizing a conjugate gradient method in a frequency domain. This method eliminates costly convolution operations from these processes and reduces the execution time. Evaluation for 20 test images shows our framework not only improves the quality of recovered images but also performs faster than conventional frameworks.

A wideband noise-cancelling receiver front-end is proposed in this brief. As a basic architecture, a low-noise transconductance amplifier, a passive mixer, and a transimpedance amplifier are employed to compose the wideband receiver. To achieve wideband input matching for the transconductor, a global feedback method is adopted. Since the wideband receiver has to minimize linearity degradation if a large blocker signal exists out-of-band, a linearization technique is applied for the transconductor circuit. The linearization cancels third-order intermodulation distortion components and increases linearity; however, the additional circuits used in linearization generate excessive noise. A noise-cancelling architecture that employs an auxiliary path cancels noise signals generated in the main path. The designed receiver front-end is fabricated using a 65-nm CMOS process. The receiver operates in the frequency range of 25 MHz-2 GHz with a gain of 49.7 dB. The in-band input-referred third-order intercept point is improved by 12.3 dB when the linearization is activated, demonstrating the effectiveness of the linearization technique.

For reliable communication, this letter proposes cooperative transmission scheme with spatial phase coding (SPC) in the edge area among base stations. The diversity method has the a difficulty in terms of the price and complexity in a base station with multiple antennas. Thus, this problem may be resolved by using the cooperative scheme among the base stations and the proposed scheme increases that uses economically resource by using less feedback bits. Especially, if the coverage of many base stations is overlapped, the performance of the proposed scheme is improved. From the simulation results, the proposed scheme has the better performance compared to the conventional scheme in heterogeneous network.

Traditional speech enhancement (SE) algorithms usually have fluctuant performance when they deal with different types of noisy speech signals. In this paper, we propose multi-task Bayesian compressive sensing based speech enhancement (MT-BCS-SE) algorithm to achieve not only comparable performance to but also more stable performance than traditional SE algorithms. MT-BCS-SE algorithm utilizes the dependence information among compressive sensing (CS) measurements and the sparsity of speech signals to perform SE. To obtain sufficient sparsity of speech signals, we adopt overcomplete dictionary to transform speech signals into sparse representations. K-SVD algorithm is employed to learn various overcomplete dictionaries. The influence of the overcomplete dictionary on MT-BCS-SE algorithm is evaluated through large numbers of experiments, so that the most suitable dictionary could be adopted by MT-BCS-SE algorithm for obtaining the best performance. Experiments were conducted on well-known NOIZEUS corpus to evaluate the performance of the proposed algorithm. In these cases of NOIZEUS corpus, MT-BCS-SE is shown that to be competitive or even superior to traditional SE algorithms, such as optimally-modified log-spectral amplitude (OMLSA), multi-band spectral subtraction (SSMul), and minimum mean square error (MMSE), in terms of signal-noise ratio (SNR), speech enhancement gain (SEG) and perceptual evaluation of speech quality (PESQ) and to have better stability than traditional SE algorithms.

In this paper, a non-isolated bidirectional DC-DC converter with zero voltage switching and constant switching frequency is proposed. Unlike the active clamp bidirectional converters, to create soft switching condition in both direction, only one auxiliary switch is used, which reduces conduction losses and the complexity of the circuit. The proposed converter is controlled by pulse width modulation and the switches are gated complementary, thus the implementation of the control circuit is simple. Low switching losses, high efficiency, high power density, are the advantages of this converter. The simulation and experimental results of the converter verify theoretical analysis. Based on an implemented prototype of the proposed converter at 80 watts, the measured efficiency is 96.5%.

Periodic interference frequently affects the measurement of small signals and causes problems in clinical diagnostics. Adaptive filters can be used as potential tools for cancelling such interference. However, when the interference has a frequency fluctuation, the ideal adaptive-filter coefficients for cancelling the interference also fluctuate. When the adaptation property of the algorithm is slow compared with the frequency fluctuation, the interference-cancelling performance is degraded. However, if the adaptation is too quick, the performance is degraded owing to the target signal. To overcome this problem, we propose an adaptive filter that suppresses the fluctuation of the ideal coefficients by utilizing a $rac{pi}{2}$ phase-delay device. This method assumes a frequency response that characterizes the transmission path from the interference source to the main input signal to be sufficiently smooth. In the numerical examples, the proposed method exhibits good performance in the presence of a frequency fluctuation when the forgetting factor is large. Moreover, we show that the proposed method reduces the calculation cost.

To deal with the recent explosion of mobile data traffic, heterogeneous cellular networks, in which a large number of small cells are deployed in a macro-cell coverage area, are considered to be a promising approach. However, when a mobile terminal (MT) traveling at a high velocity moves through several small cells in a short period of time, the frequent handovers (HOs) that occur between small cells lead to a deterioration of user quality of experience. To avoid such HO problems, while improving the network capacity in the heterogeneous cellular network, it is effective to introduce an inter-layer HO control policy where MTs traveling at high velocities are connected to the macro-cell layer to reduce the number of HOs and MTs traveling at low velocities or which are stationary are connected to the small-cell layer for offloading traffic from the macro-cells to the small-cells. However, to realize such inter-layer HO control policy in the heterogeneous cellular network, it is crucial to estimate the velocity of each MT. Due to the technological constraints of MT velocity estimation based on the Global Positioning Systems (GPS), we focus on MT velocity estimation algorithms which do not require information provided by GPS. First, we discuss the issues of the existing MT velocity estimation algorithms and then focus on a MT velocity estimation algorithm based on a conventional Doppler spread detection using Fast Fourier Transform (FFT). Since few studies have evaluated Doppler spread based MT velocity estimation techniques for practical communication systems in actual radio propagation environments, we implement the MT velocity estimation algorithm to a Long Term Evolution (LTE) based experimental system, and perform its field experiments. Based on these experimental results we also evaluate the high or low velocity decision accuracy for the inter-layer HO control policy and show that good decision accuracy is achieved in both line-of-sight (LOS) and non-line-of-sight (NLOS) outdoor propagation environment. These results show its feasibility for practical mobile communication systems in actual radio propagation environments.