Chronic liver disease is a major worldwide health problem. Diagnosis and staging of chronic liver diseases is an important issue. In this paper, we propose a quantitative method of analyzing local morphological changes for accurate and practical computer-aided diagnosis of cirrhosis. Our method is based on sparse and low-rank matrix decomposition, since the matrix of the liver shapes can be decomposed into two parts: a low-rank matrix, which can be considered similar to that of a normal liver, and a sparse error term that represents the local deformation. Compared with the previous global morphological analysis strategy based on the statistical shape model (SSM), our proposed method improves the accuracy of both normal and abnormal classifications. We also propose using the norm of the sparse error term as a simple measure for classification as normal or abnormal. The experimental results of the proposed method are better than those of the state-of-the-art SSM-based methods.

In consideration of the image noise captured by photoelectric cameras at nighttime, a robust motion detection algorithm based on sparse representation is proposed in this study. A universal dictionary for arbitrary scenes is presented. Realistic and synthetic experiments demonstrate the robustness of the proposed approach.

The reliability of electromagnetic relay (EMR) which contains a permanent magnet (PM) can be improved by a robust design method. In this parameter design process, the calculation of electromagnetic system is very important. In analytical calculation, PM is often equivalent to a lumped parameter model of one magnetic resistance and one magnetic potential, but significant error is often caused; in order to increase the accuracy, a distributed parameter calculation model (DPM) of PM bar is established; solution procedure as well as verification condition of this model is given; by a case study of the single PM bar, magnetic field lines division method is adopted to build the DPM, the starting point and section magnetic flux of each segment are solved, a comparison is made with finite element method (FEM) and measured data; the accuracy of this magnetic field line based distributed parameter model (MFDPM) in PM bar is verified; this model is applied to the electromagnetic system of a certain type EMR, electromagnetic system calculation model is established based on MFDPM, and the static force is calculated under different rotation angles; compared with traditional lumped parameter model and FEM, it proves to be of acceptable calculation accuracy and high calculation speed which fit the requirement of robust design.

Recently, design of sparse finite impulse response (FIR) digital filters has attracted much attention due to its ability to reduce the implementation cost. However, finding a filter with the fewest number of nonzero coefficients subject to prescribed frequency domain constraints is a rather difficult problem because of its non-convexity. In this paper, an algorithm based on binary particle swarm optimization (BPSO) is proposed, which successively thins the filter coefficients until no sparser solution can be obtained. The proposed algorithm is evaluated on a set of examples, and better results can be achieved than other existing algorithms.

Single-thread performance has not improved much over the past few years, despite an ever increasing transistor budget. One of the reasons for this is that there is a speed gap between the processor and main memory, known as the memory wall. A promising method to overcome this memory wall is aggressive out-of-order execution by extensively enlarging the instruction window resources to exploit memory-level parallelism (MLP). However, simply enlarging the window resources lengthens the clock cycle time. Although pipelining the resources solves this problem, it in turn prevents instruction-level parallelism (ILP) from being exploited because issuing instructions requires multiple clock cycles. This paper proposed a dynamic scheme that adaptively resizes the instruction window based on the predicted available parallelism, either ILP or MLP. Specifically, if the scheme predicts that MLP is available during execution, the instruction window is enlarged and the window resources are pipelined, thereby exploiting MLP. Conversely, if the scheme predicts that less MLP is available, that is, ILP is exploitable for improved performance, the instruction window is shrunk and the window resources are de-pipelined, thereby exploiting ILP. Our evaluation results using the SPEC2006 benchmark programs show that the proposed scheme achieves nearly the best performance possible with fixed-size resources. On average, our scheme realizes a performance improvement of 21% over that of a conventional processor, with additional cost of only 6% of the area of the conventional processor core or 3% of that of the entire processor chip. The evaluation results also show 8% better energy efficiency in terms of 1/EDP (energy-delay product).

We propose a synthesis method of nested loops into parallelized circuits by integrating the polyhedral optimization, which is a state-of-the-art technique in the field of software, into high-level synthesis. Our method constructs circuits equipped with multiple processing elements (PEs), using information generated by the polyhedral optimizing compiler. Since multiple PEs cannot concurrently access the off-chip RAM, a method for constructing on-chip buffers is also proposed. Our buffering method reduces the off-chip RAM access conflicts and further enables burst accesses and data reuses. In our experimental result, the buffered circuits generated by our method are 8.2 times on average and 26.5 times at maximum faster than the sequential non-buffered ones, when each of the parallelized circuits is configured with eight PEs.

High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the intra prediction accuracy, 35 intra prediction modes were used in the prediction units (PUs), with partition sizes ranging from 4 × 4 to 64 × 64 in HEVC. However, the manifold prediction modes dramatically increase the encoding complexity. This paper proposes a fast mode- and depth-decision algorithm based on edge detection and reconfiguration to alleviate the large computational complexity in intra prediction with trivial degradation in accuracy. For mode decision, we propose pixel gradient statistics (PGS) and mode refinement (MR). PGS uses pixel gradient information to assist in selecting the prediction mode after rough mode decision (RMD). MR uses the neighboring mode information to select the best PU mode (BPM). For depth decision, we propose a partition reconfiguration algorithm to replace the original partitioning order with a more reasonable structure, by using the smoothness of the coding unit as a criterion in deciding the prediction depth. Smoothness detection is based on the PGS result. Experiment results show that the proposed method saves about 41.50% of the original processing time with little degradation (BD bitrate increased by 0.66% and BDPSNR decreased by 0.060dB) in the coding gain.

This paper presents a spatial division (SD) transmission method based on two-ray fading that dispenses with the high signal processing cost of multiple-input and multiple-output (MIMO) detection and antennas with narrow beamwidth. We show the optimum array geometries as functions of the transmission distance for providing a concrete array design method. Moreover, we clarify achievable channel capacity considering reflection coefficients that depend on the polarization, incident angle, and dielectric constant. When the ground surface is conductive, for two- and three-element arrays, channel capacity is doubled and tripled, respectively, over that of free space propagation. We also clarify the application limit of this method for a dielectric ground by analyzing the channel capacity's dependency on the dielectric constant. With this method, increased channel capacity by SD transmission can be obtained merely by placing antennas of wireless transceiver sets that have only SISO (single-input and single-output) capability in a two-ray propagation environment. By using formulations presented in this paper for the first time and adding discussions on the adoption of polarization multiplexing, we clarify antenna geometries of SD transmission systems using polarization multiplexing for up to six streams.

Post-Silicon Tunable (PST) buffers are widely adopted in high-performance integrated circuits to fix timing violations introduced by process variations. In typical optimization procedures, the statistical timing analysis of the circuits with PST clock buffers will be executed more than 2000 times for large scale circuits. Therefore, the efficiency of the statistical timing analysis is crucial to the PST clock buffer optimization algorithms. In this paper, we propose a stochastic collocation based efficient statistical timing analysis method for circuits with PST buffers. In the proposed method, we employ the Howard algorithm to calculate the clock periods of the circuits on less than 100 deterministic sparse-grid collocation points. Afterwards, we use these obtained clock periods to derive the yield of the circuits according to the stochastic collocation theory. Compared with the state-of-the-art statistical timing analysis method for the circuits with PST clock buffers, the proposed method achieves up to 22X speedup with comparable accuracy.

Reducing accident severity is an effective way to improve road safety. In the literature of accident severity analysis, two main disadvantages exist: most studies use classification accuracy to measure the quality of a classifier which is not appropriate in the condition of unbalanced dataset; the other is the results are not easy to be interpreted by users. Aiming at these drawbacks, a novel multi-objective particle swarm optimization (MOPSO) method is proposed to identify the contributing factors that impact accident severity. By employing Pareto dominance concept, a set of Pareto optimal rules can be obtained by MOPSO automatically, without any pre-defined threshold or variables. Then the rules are used to form a non-ordered classifier. A MOPSO is applied to discover a set of Pareto optimal rules. The accident data of Beijing between 2008 and 2010 are used to build the model. The proposed approach is compared with several rule learning algorithms. The results show the proposed approach can generate a set of accurate and comprehensible rules which can indicate the relationship between risk factors and accident severity.

The dynamic time warping (DTW) algorithm is widely used to determine time series similarity search. As DTW has quadratic time complexity, the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. In this paper, we present a parallel approach for DTW on a heterogeneous platform with a graphics processing unit (GPU). In order to exploit fine-grained data-level parallelism, we propose a specific parallel decomposition in DTW. Furthermore, we introduce an optimization technique called diamond tiling to improve the utilization of threads. Results show that our approach substantially reduces computational time.

This paper presents a prediction model based on historical data to achieve optimal values of pipelining, concurrency and parallelism (PCP) in GridFTP data transfers in Cloud systems. Setting the correct values for these three parameters is crucial in achieving high throughput in end-to-end data movement. However, predicting and setting the optimal values for these parameters is a challenging task, especially in shared and non-predictive network conditions. Several factors can affect the optimal values for these parameters such as the background network traffic, available bandwidth, Round-Trip Time (RTT), TCP buffer size, and file size. Existing models either fail to provide accurate predictions or come with very high prediction overheads. The author shows that new model based on historical data can achieve high accuracy with low overhead.

A fully-integrated double-channel 5-Gb/s/ch 2:1 serializer array is designed and fabricated in a standard 0.18-$mu $m CMOS technology, which can be easily expanded to any even-number-channel array, e.g. 12 channels, by means of arrangement in a parallel manner. Besides two conventional half-rate 2:1 serializers, both phase-locked loop and delay-locked loop techniques are employed locally to deal with the involved clocking-related issues, which make the serializer array self-contained, compact and automatic. The system architecture, circuit and layout designs are discussed and analyzed in detail. The chip occupies a die area of 673,$mu $m$, imes ,$667,$mu $m with a core width of only 450,$mu $m. Measurement results show that it works properly without a need for additional clock channels, reference clocks, off-chip tuning, external components, and so on. From a single supply of 1.8,V, a power of 200,mW is consumed and a single-ended swing of above 300,mV for each channel is achieved.

There exist two intrinsic issues in multiple-shot person re-identification: (1) large differences in camera view, illumination, and non-rigid deformation of posture that make the intra-class variance even larger than the inter-class variance; (2) only a few training data that are available for learning tasks in a realistic re-identification scenario. In our previous work, we proposed a local distance comparison framework to deal with the first issue. In this paper, to deal with the second issue (i.e., to derive a reliable distance metric from limited training data), we propose an adaptive learning method to learn an adaptive distance metric, which integrates prior knowledge learned from a large existing auxiliary dataset and task-specific information extracted from a much smaller training dataset. Experimental results on several public benchmark datasets show that combined with the local distance comparison framework, our adaptive learning method is superior to conventional approaches.

In this paper, we investigate the minimum-weight codewords of array LDPC codes C(m,q), where q is an odd prime and m ≤ q. Using some analytical approaches, the lower bound on the number of minimum-weight codewords of C(m,q) given by Kaji (IEEE Int. Symp. Inf. Theory, June/July 2009) is proven to be tight for m = 4 and q > 19. In other words, C(4,q) has 4q2(q-1) minimum-weight codewords for all q > 19. In addition, we show some interesting universal properties of the supports of generators of minimum-weight codewords of the code C(4,q)(q > 19).

In this paper, we propose a parameter estimation method using Volterra kernels for the nonlinear IIR filters, which are used for the linearization of closed-box loudspeaker systems. The nonlinear IIR filter, which originates from a mirror filter, employs nonlinear parameters of the loudspeaker system. Hence, it is very important to realize an appropriate estimation method for the nonlinear parameters to increase the compensation ability of nonlinear distortions. However, it is difficult to obtain exact nonlinear parameters using the conventional parameter estimation method for nonlinear IIR filter, which uses the displacement characteristic of the diaphragm. The conventional method has two problems. First, it requires the displacement characteristic of the diaphragm but it is difficult to measure such tiny displacements. Moreover, a laser displacement gauge is required as an extra measurement instrument. Second, it has a limitation in the excitation signal used to measure the displacement of the diaphragm. On the other hand, in the proposed estimation method for nonlinear IIR filter, the parameters are updated using simulated annealing (SA) according to the cost function that represents the amount of compensation and these procedures are repeated until a given iteration count. The amount of compensation is calculated through computer simulation in which Volterra kernels of a target loudspeaker system is utilized as the loudspeaker model and then the loudspeaker model is compensated by the nonlinear IIR filter with the present parameters. Hence, the proposed method requires only an ordinary microphone and can utilize any excitation signal to estimate the nonlinear parameters. Some experimental results demonstrate that the proposed method can estimate the parameters more accurately than the conventional estimation method.

In this paper, we introduce a parallax barrier system that shows high definition autostereoscopy and holds wide viewing zone. The proposed method creates a 4-view parallax barrier system with full display resolution per view by setting aperture ratio to one quarter and using time-division quadplexing, then applies obtained 4-view to 2-view, so that the viewing zone for each eye becomes wider than that from the conventional methods. We build a prototype with two 120,Hz LCD panels and manage to achieve continuous viewing zone with common head-tracking device involved. However, moire patterns and flickers stand out, which are respectively caused by the identical alignments of the color filters on the overlaid LCD panels and a lack of refresh rate of 240,Hz. We successfully remove the moire patterns by changing the structure of the system and inserting a diffuser. We also reduce the flickers by proposing 1-pixel aperture, while stripe shaped noise due to the lack of refresh rate occurs during a blink or a saccade. The stripe noise can be effectively weakened by applying green and magenta anaglyph to the proposed system, where extra crosstalk takes place since the default RGB color filters on LCD panels share certain ranges of wavelength with each other. Although a trade-off turns out to exist between stripe noise and crosstalk from our comparison experiment, results from different settings all hold acceptable quality and show high practicability of our method. Furthermore, we propose a solution that shows possibility to satisfy both claims, where extra color filters with narrow bandwidths are required.

In this paper, we present update rules for convolutive nonnegative matrix factorization (NMF) in which cost functions are based on the squared Euclidean distance, the Kullback-Leibler (KL) divergence and the Itakura-Saito (IS) divergence. We define an auxiliary function for each cost function and derive the update rule. We also apply this method to the single-channel signal separation in speech signals. Experimental results showed that the convergence of our KL divergence-based method was better than that in the conventional method, and our method achieved single-channel signal separation successfully.

This letter proposes an efficient lossless compression method for high dynamic range (HDR) images in OpenEXR format. The proposed method transforms an HDR image to an indexed image and packs the histogram of the indexed image. Finally the packed image is losslessly compressed by using any existing lossless compression algorithm such as JPEG 2000. Experimental results show that the proposed method reduces the bit rate of compressed OpenEXR images compared with equipped lossless compression methods of OpenEXR format.

Much attention has recently been paid to direction of arrival (DOA) estimation using compressed sensing (CS) techniques, which are sparse signal reconstruction methods. In our previous study, we developed a method for estimating the DOAs of multi-band signals that uses CS processing and that is based on the assumption that incident signals have the same complex amplitudes in all the bands. That method has a higher probability of correct estimation than a single-band DOA estimation method using CS. In this paper, we propose novel DOA estimation methods for multi-band signals with frequency characteristics using the Khatri-Rao product. First, we formulate a method that can estimate DOAs of multi-band signals whose phases alone have frequency dependence. Second, we extend the scheme in such a way that we can estimate DOAs of multi-band signals whose amplitudes and phases both depend on frequency. Finally, we evaluate the performance of the proposed methods through computer simulations and reveal the improvement in estimation performance.