This paper presents measurement results of bit error rate (BER) and soft error rate (SER) improvement on 150-nm FD-SOI 7T/14T (7-transistor/ 14-transistor) SRAM test chips. The reliability of the 7T/14T SRAM can be dynamically changed by a control signal depending on an operating condition and application. The 14T dependable mode allocates one bit in a 14T cell and improves the BER in a read operation and SER in a retention state, simultaneously. We investigate its error rate mitigating mechanisms using Synopsys TCAD simulator. In our measurements, the minimum operating voltage was improved by 100 mV, the alpha-induced SER was suppressed by 80.0%, and the neutron-induced SER was decreased by 34.4% in the 14T dependable mode over the 7T normal mode.

This letter proposes a new image colorization algorithm based on the sparse optimization. Introducing some assumptions, a problem of recovering a color image from a grayscale image with the small number of known color pixels is formulated as a mixed l0/l1 norm minimization, and an iterative reweighted least squares (IRLS) algorithm is proposed. Numerical examples show that the proposed algorithm colorizes the grayscale image efficiently.

Secure computation of the set intersection functionality allows n parties to find the intersection between their datasets without revealing anything else about them. An efficient protocol for such a task could have multiple potential applications in commerce, health care, and security. However, all currently known secure set intersection protocols for n > 2 parties have computational costs that are quadratic in the (maximum) number of entries in the dataset contributed by each party, making secure computation of the set intersection only practical for small datasets. In this paper, we describe the first multi-party protocol for securely computing the set intersection functionality with both the communication and the computation costs that are quasi-linear in the size of the datasets. For a fixed security parameter, our protocols require O(n2k) bits of communication and Õ(n2k) group multiplications per player in the malicious adversary setting, where k is the size of each dataset. Our protocol follows the basic idea of the protocol proposed by Kissner and Song, but we gain efficiency by using different representations of the polynomials associated with users' datasets and careful employment of algorithms that interpolate or evaluate polynomials on multiple points more efficiently. Moreover, the proposed protocol is robust. This means that the protocol outputs the desired result even if some corrupted players leave during the execution of the protocol.

In this paper, we propose a novel method of measuring the perceived picture quality of H.264 coded video based on parametric analysis of the coded bitstream. The parametric analysis means that the proposed method utilizes only bitstream parameters to evaluate video quality, while it does not have any access to the baseband signal (pixel level information) of the decoded video. The proposed method extracts quantiser-scale, macro block type and transform coefficients from each macroblock. These parameters are used to calculate spatiotemporal image features to reflect the perception of coding artifacts which have a strong relation to the subjective quality. A computer simulation shows that the proposed method can estimate the subjective quality at a correlation coefficient of 0.923 whereas the PSNR metric, which is referred to as a benchmark, correlates the subjective quality at a correlation coefficient of 0.793.

A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on bi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality.

High-definition (HD) videos become more and more popular on portable devices these years. Due to the resolution mismatch between the HD video sources and the relative low-resolution screens of portable devices, the HD videos are usually fully decoded and then down-sampled (FDDS) for the displays, which not only increase the cost of both computational power and memory bandwidth, but also lose the details of video contents. In this paper, an encoder-unconstrained partial decoding scheme for H.264/AVC is presented to solve the problem by only decoding the object of interest (OOI) related region, which is defined by users. A simplified compression domain tracking method is utilized to ensure that the OOI locates in the center of the display area. The decoded partial area (DPA) adaptation, the reference block relocation (RBR) and co-located temporal Intra prediction (CTIP) methods are proposed to improve the visual quality for the DPA with low complexity. The simulation results show that the proposed partial decoding scheme provides an average of 50.16% decoding time reduction comparing to the fully decoding process. The displayed region also presents the original HD granularity of OOI. The proposed partial decoding scheme is especially useful for displaying HD video on the devices of which the battery life is a crucial factor.

This letter presents the architecture of multi-gigabit parallel demodulator suitable for demodulating high order QAM modulated signal and easy to implement on FPGA platform. The parallel architecture is based on frequency domain implementation of matched filter and timing phase correction. Parallel FIFO based delete-keep algorithm is proposed for timing synchronization, while a kind of reduced constellation phase-frequency detector based parallel decision feedback PLL is designed for carrier synchronization. A fully pipelined parallel adaptive blind equalization algorithm is also proposed. Their parallel implementation structures suitable for FPGA platform are investigated. Besides, in the demonstration of 2 Gbps demodulator for 16QAM modulation, the architecture is implemented and validated on a Xilinx V6 FPGA platform with performance loss less than 2 dB.

We investigate a sparsely encoded Hopfield model with unit replacement by using a statistical mechanical method called self-consistent signal-to-noise analysis. We theoretically obtain a relation between the storage capacity and the number of replacement units for each sparseness a. Moreover, we compare the unit replacement model with the forgetting model in terms of the network storage capacity. The results show that the unit replacement model has a finite value of the optimal sparseness on an open interval 0 (1/2 coding) < a < 1 (the limit of sparseness) to maximize the storage capacity for a large number of replacement units, although the forgetting model does not.

In this paper, we propose a novel architecture for large-scale regular expression matching, called dynamically reconfigurable bit-parallel NFA architecture (Dynamic BP-NFA), which allows dynamic loading of regular expressions on-the-fly as well as efficient pattern matching for fast data streams. This is the first dynamically reconfigurable hardware with guaranteed performance for the class of extended patterns, which is a subclass of regular expressions consisting of union of characters and its repeat. This class allows operators such as character classes, gaps, optional characters, and bounded and unbounded repeats of character classes. The key to our architecture is the use of bit-parallel pattern matching approach, in which the information of an input non-deterministic finite automaton (NFA) is first compactly encoded in bit-masks stored in a collection of registers and block RAMs. Then, the NFA is efficiently simulated by a fixed circuitry using bitwise Boolean and arithmetic operations consuming one input character per clock regardless of the actual contents of an input text. Experimental results showed that our hardwares for both string and extended patterns were comparable to previous dynamically reconfigurable hardwares in their performances.

An asymmetric classifier based on kernel partial least squares is proposed for software defect prediction. This method improves the prediction performance on imbalanced data sets. The experimental results validate its effectiveness.

In this letter, a new method is proposed to solve the direction-of-arrivals (DOAs) estimation problem of coherently distributed sources based on the block-sparse signal model of compressed sensing (CS) and the convex optimization theory. We make use of a certain number of point sources and the CS array architecture to establish the compressive version of the discrete model of coherently distributed sources. The central DOA and the angular spread can be estimated simultaneously by solving a convex optimization problem which employs a joint norm constraint. As a result we can avoid the two-dimensional search used in conventional algorithms. Furthermore, the multiple-measurement-vectors (MMV) scenario is also considered to achieve robust estimation. The effectiveness of our method is confirmed by simulation results.

A flexible and computationally efficient method for spectral analysis of sinusoidal signals using the Basis Pursuit De-Noising (BPDN) is proposed. This method estimates a slotted Auto-Correlation Function (ACF) and computes the spectrum as the sparse representation of the ACF in a dictionary of cosine functions. Simulation results illustrate flexibility and effectiveness of the proposed method.

This paper presents a novel algorithm to generate homogeneous superpixels from Markov random walks. We exploit Markov clustering (MCL) as the methodology, a generic graph clustering method based on stochastic flow circulation. In particular, we introduce a graph pruning strategy called compact pruning in order to capture intrinsic local image structure. The resulting superpixels are homogeneous, i.e. uniform in size and compact in shape. The original MCL algorithm does not scale well to a graph of an image due to the square computation of the Markov matrix which is necessary for circulating the flow. The proposed pruning scheme has the advantages of faster computation, smaller memory footprint, and straightforward parallel implementation. Through comparisons with other recent techniques, we show that the proposed algorithm achieves state-of-the-art performance.

Existing pose estimation algorithms suffer from either low performance or heavy computation cost. In this letter, we present an approach to improve the attractive algorithm called Orthogonal Iteration. A new form of fundamental equations is derived which reduces the computation cost significantly. And paraperspective camera model is used instead of weak perspective camera model during initialization which improves the stability. Experiment results validate the accuracy and stability of the proposed algorithm and show that its computational complexity is favorably compare to the O(n) non-iterative algorithm.

A differential pair of convergent and divergent lenses with adjustable lens spacing (“differential lens”) was devised as a varifocal lens and was successfully integrated into an object-space telecentric lens to build a focus mechanism with constant magnification. This integration was done by placing the front principal point of the varifocal lens at the rear focal point of the telecentric lens within a practical tolerance of positioning. Although the constant-magnification focus mechanism is a parallel projection system, a system for perfect perspective projection imaging without shifting the projection center during focusing could be built simply by properly setting this focus mechanism between an image-taking lens with image-space telecentricity and an image sensor. The focus resolution experimentally obtained was 0.92 µm (σ) for the parallel projection system with a depth range of 1.0 mm and this was 0.25 mm (σ) for the perspective projection system with a range from 120 to 350 mm within a desktop space. A marginal image resolution of 100 lp/mm was obtained with optical distortion of less than 0.2% in the parallel projection system. The differential lens could work up to 55 Hz for a sinusoidal change in lens spacing with a peak-to-valley amplitude of 425 µm when a tiny divergent lens that was plano-concave was translated by a piezoelectric positioner. Therefore, images that were entirely in focus were generated at a frame rate of 30 Hz for an object moving at a speed of around 150 mm/s in depth within the desk top space. Thus, three-dimensional (3-D) imaging that provided 3-D resolution based on fast focusing was accomplished in both microscopic and macroscopic spaces.

The lateral velocity is of importance in cases like target identification and traffic management. Conventional Doppler methods are not capable of measuring lateral velocities since they quantify only the radial component. Based on the spectrogram characteristic of laterally moving targets, an algorithm based on fractional Fourier transform has been studied in the signal processing literature. The algorithm searches the peak position of the transformation, and calculates the lateral velocity from the peak position. The performance analysis of this algorithm is carried out in this paper, which shows that this algorithm approaches Cramer-Rao bound with reasonable computational complexity. Simulations are conducted at last to compare the analytical performance and the experimental result.

In this paper, we propose a new method for the bias-dependent parameter extraction of a MOSFET, which covers DC to over 100 GHz. The DC MOSFET model provided by the chip foundry is assumed to be correct, and the core DC characteristics are designed to be asymptotically recovered at low frequencies. This is carried out by representing the corrections required at high frequencies using a bias-dependent Y matrix, assuming that a parasitic nonlinear two-port matrix (Y-wrapper) is connected in parallel with the core MOSFET. The Y-wrapper can also handle the nonreciprocity of the parasitic components, that is, the asymmetry of the Y matrix. The reliability of the Y-wrapper model is confirmed through the simulation and measurement of a one-stage common-source amplifier operating at several bias points. This paper will not discuss about non-linearity.

While phrase-based statistical machine translation systems prefer to translate with longer phrases, this may cause errors in a free word order language, such as Japanese, in which the order of the arguments of the predicates is not solely determined by the predicates and the arguments can be placed quite freely in the text. In this paper, we propose to reorder the arguments but not the predicates in Japanese using a dependency structure as a kind of reordering. Instead of a single deterministically given permutation, we generate multiple reordered phrases for each sentence and translate them independently. Then we apply a re-ranking method using a discriminative approach by Ranking Support Vector Machines (SVM) to re-score the multiple reordered phrase translations. In our experiment with the travel domain corpus BTEC, we gain a 1.22% BLEU score improvement when only 1-best is used for re-ranking and 4.12% BLEU score improvement when n-best is used for Japanese-English translation.

In this paper, we propose an optimized virtual re-convergence system especially to reduce the visual fatigue caused by binocular stereoscopy. Our unique idea to reduce visual fatigue is to utilize the virtual re-convergence based on the optimized disparity-map that contains more depth information in the negative disparity area than in the positive area. Therefore, our system facilitates a unique search-range scheme, especially for negative disparity exploration. In addition, we used a dedicated method, using a so-called Global-Shift Value (GSV), which are the total shift values of each image in stereoscopy to converge a main object that can mostly affect visual fatigue. The experimental result, which is a subjective assessment by participants, shows that the proposed method makes stereoscopy significantly comfortable and attractive to view than existing methods.

Sparse representation based classification (SRC) has emerged as a new paradigm for solving face recognition problems. Further research found that the main limitation of SRC is the assumption of pixel-accurate alignment between the test image and the training set. A. Wagner used a series of linear programs that iteratively minimize the sparsity of the registration error. In this paper, we propose another face registration method called three-point positioning method. Experiments show that our proposed method achieves better performance.