NAND multilevel cell flash memory devices are gaining popularity because they can increase the memory capacity by storing two or more bits to a single cell. However, when the number of levels of a cell increases, the inter-cell interference which shifts threshold voltage becomes more critical. There are two approaches to alleviate the errors caused by the voltage shift. One is the error correcting codes, and the other is the signal processing methods. In this paper, we focus on signal processing methods to reduce the inter-cell interference which causes the voltage shift, and propose two algorithms which reduce the voltage shift effects by adjusting read voltages. The simulation results show that the proposed algorithms are effective for interference mitigation.

The on-chip interconnection network (OCIN) is an integrated solution for system-on-chip (SoC) designs. The buffer architecture and size, however, dominate the performance of OCINs and affect the design of routers. This work analyzes different buffer architectures and uses a data-link two-level FIFO (first-in first-out) buffer architecture to implement high-performance routers. The concepts of shared buffers and multiple accesses for buffers are developed using the two-level FIFO buffer architecture. The proposed two-level FIFO buffer architecture increases the utilities of the storage elements via the centralized buffer organization and reduces the area and power consumption of routers to achieve the same performance achieved by other buffer architectures. Depending on a cycle-accurate simulator, the proposed data-link two-level FIFO buffer can realize performance similar to that of the conventional virtual channels, while using 25% of the buffers. Consequently, the two-level FIFO buffer can achieve about 22% power reduction compared with the similar performance of the conventional virtual channels using UMC 65 nm CMOS technology.

Overcoming the highly organized and coordinated malware threats by botnets on the Internet is becoming increasingly difficult. A honeypot is a powerful tool for observing and catching malware and virulent activity in Internet traffic. Because botnets use systematic attack methods, the sequences of malware downloaded by honeypots have particular forms of coordinated pattern. This paper aims to discover new frequent sequential attack patterns in malware automatically. One problem is the difficulty in identifying particular patterns from full yearlong logs because the dataset is too large for individual investigations. This paper proposes the use of a data-mining algorithm to overcome this problem. We implement the PrefixSpan algorithm to analyze malware-attack logs and then show some experimental results. Analysis of these results indicates that botnet attacks can be characterized either by the download times or by the source addresses of the bots. Finally, we use entropy analysis to reveal how frequent sequential patterns are involved in coordinated attacks.

Kudekar et al. recently proved that for transmission over the binary erasure channel (BEC), spatial coupling of LDPC codes increases the BP threshold of the coupled ensemble to the MAP threshold of the underlying LDPC codes. One major drawback of the capacity-achieving spatially-coupled LDPC codes is that one needs to increase the column and row weight of parity-check matrices of the underlying LDPC codes. It is proved, that Hsu-Anastasopoulos (HA) codes and MacKay-Neal (MN) codes achieve the capacity of memoryless binary-input symmetric-output channels under MAP decoding with bounded column and row weight of the parity-check matrices. The HA codes and the MN codes are dual codes each other. The aim of this paper is to present an empirical evidence that spatially-coupled MN (resp. HA) codes with bounded column and row weight achieve the capacity of the BEC. To this end, we introduce a spatial coupling scheme of MN (resp. HA) codes. By density evolution analysis, we will show that the resulting spatially-coupled MN (resp. HA) codes have the BP threshold close to the Shannon limit.

In this paper, we present a construction method of non-binary low-density parity-check (LDPC) convolutional codes. Our construction method is an extension of Felstrom and Zigangirov construction [1] for non-binary LDPC convolutional codes. The rate-compatibility of the non-binary convolutional code is also discussed. The proposed rate-compatible code is designed from one single mother (2,4)-regular non-binary LDPC convolutional code of rate 1/2. Higher-rate codes are produced by puncturing the mother code and lower-rate codes are produced by multiplicatively repeating the mother code. Simulation results show that non-binary LDPC convolutional codes of rate 1/2 outperform state-of-the-art binary LDPC convolutional codes with comparable constraint bit length. Also the derived low-rate and high-rate non-binary LDPC convolutional codes exhibit good decoding performance without loss of large gap to the Shannon limits.

We propose a novel background and foreground estimation algorithm in MAP-MRF approach for binarization of degraded document image. In the proposed algorithm, an assumption that background whiteness and foreground blackness is not employed differently from the conventional algorithm, and we employ character's irregularities based on local statistics. This makes the method possible to apply to the image with various colored characters, ex. outlined characters by colored background. The effectiveness and the validity are shown by applying the proposed method to various degraded document images.

Adaptive interference suppression strategies based on the transform domain approach are proposed for a satellite on-board filter bank under tone-type interferences. In the proposed methods, the three kinds of algorithms to compute the threshold level are jointly employed with the notch filter or the clipper. Simulation results show that the proposed schemes significantly improve performance under interfering environments, compared to the no suppression case.

High Dynamic Range (HDR) images have been widely applied in daily applications. However, HDR image is a special format, which needs to be pre-processed known as tone mapping operators for display. Since the visual quality of HDR images is very sensitive to luminance value variations, conventional watermarking methods for low dynamic range (LDR) images are not suitable and may even cause catastrophic visible distortion. Currently, few methods for HDR image watermarking are proposed. In this paper, two watermarking schemes targeting HDR images are proposed, which are based on µ-Law and bilateral filtering, respectively. Both of the subjective and objective qualities of watermarked images are greatly improved by the two methods. What's more, these proposed methods also show higher robustness against tone mapping operations.

This letter proposes a novel technique for detecting a target signal buried in clutter using principal component analysis (PCA) for pulse-Doppler radar systems. The conventional detection algorithm is based on the fast Fourier transform-constant false alarm rate (FFT-CFAR) approaches. However, the detection task becomes extremely difficult when the Doppler spectrum of the target is completely buried in the spectrum of clutter. To enhance the detection probability in the above situations, the proposed method employs the PCA algorithm, which decomposes the target and clutter signals into uncorrelated components. The performances of the proposed method and the conventional FFT-CFAR based detection method are evaluated in terms of the receiver operating characteristics (ROC) for various signal-to-clutter ratio (SCR) cases. The results of numerical simulations show that the proposed method significantly enhances the detection probability compared with that obtained using the conventional FFT-CFAR method, especially for lower SCR situations.

The fixed points of the belief propagation decoder for non-binary low-density parity-check (LDPC) codes are referred to as stopping constellations. In this paper, we give the stopping constellation distributions for the irregular non-binary LDPC code ensembles defined over the general linear group. Moreover, we derive the exponential growth rate of the average stopping constellation distributions in the limit of large codelength.

In this paper, we propose an energy efficient MAC protocol for wireless sensor networks. In sensor networks, reducing energy consumption is one of the critical issues for extending network lifetime. One good solution to resolve this issue is introducing listen-sleep cycles, allowing sensor nodes to turn their transceiver off during sleep periods, which was adopted by S-MAC [1]. However, in S-MAC, due to the synchronized scheduling, transmission collisions will increase in heavy traffic situations, resulting in energy waste and low throughput. Hence, in this paper, we propose probabilistic scheduled MAC (PS-MAC), in which each node determines ‘listen’ or ‘sleep’ pseudo-randomly based on its own pre-wakeup probability and pre-wakeup probabilities of its neighbor nodes in each time slot. This allows the listen-sleep schedule of nodes in each transmitter and receiver pair to be synchronized, while maintaining those of the rest of nodes to be asynchronous. Therefore, collisions can be reduced even under heavy traffic conditions, resulting in reduced energy waste and high throughput. In addition, by dynamically adjusting the pre-wakeup probabilities of sensor nodes based on the change of the network environment, system throughput and latency can be further improved. Simulation results show that PS-MAC provides significant energy savings, low delay, and high network throughput.

The doubly constrained robust Capon beamformer (DCRCB), which employs a spherical uncertainty set of the steering vector together with the constant norm constraint, can provide robustness against arbitrary array imperfections. However, its performance can be greatly degraded when the uncertainty bound of the spherical set is not properly selected. In this paper, combining the DCRCB and the weight-vector-norm-constrained beamformer (WVNCB), we suggest a new robust adaptive beamforming method which allows us to overcome the performance degradation due to improper selection of the uncertainty bound. In WVNCB, its weight vector norm is limited not to be larger than a threshold. Both WVNCB and DCRCB belong to a class of diagonal loading methods. The diagonal loading range of WVNCB, which dose not consider negative loading, is extended to match that of DCRCB which can have a negative loading level as well as a positive one. In contrast to the conventional DCRCB with a fixed uncertainty bound, the bound in the proposed method varies such that the weight vector norm constraint is satisfied. Simulation results show that the proposed beamformer outperforms both DCRCB and WVNCB, being far less sensitive to the uncertainty bound than DCRCB.

The involvement of IEEE 802.15.4 Wireless Sensor Networks (WSNs) in diverse applications has made the realistic analysis of sensor power dissipation in distributed network environments an essential research issue. In this paper, we propose and thoroughly analyze a power dissipation model for Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) based IEEE 802.15.4 distributed multi-hop WSNs. Our model takes the loss rate of frames, neighbor sensors density in communication range of a sensor, number of hops, distance of source to the sink, and density of the network into account. We evaluate the impact of these factors on overall power dissipation. We also perform comprehensive analysis of overheads caused by message routing through multi-hop distributed networks. We validate our proposed model through Monte Carlo simulations. Results show that our power dissipation model is more realistic compared to other proposed models in terms of accuracy and multiplicity of the environments.

We propose a high-speed and low-complexity architecture for the very large-scale integration (VLSI) implementation of the maximum a posteriori probability (MAP) algorithm suited to the double binary turbo decoder. For this purpose, equation manipulations on the conventional Linear-Log-MAP algorithm and architectural optimization are proposed. It is shown by synthesized simulations that the proposed architecture improves speed, area and power compared with the state-of-the-art Linear-Log-MAP architecture. It is also observed that the proposed architecture shows good overall performance in terms of error correction capability as well as decoder hardware's speed, complexity and throughput.

This paper proposes a novel waveguide intersection separating two H-plane waveguide systems from each other. If a four-port network in a four-fold rotational symmetry is completely matched, it has necessarily intersection properties. The proposed waveguide intersection consists of a square H-plane waveguide planar circuit connected four input/output waveguide ports in a four-fold rotational symmetry, and several metallic posts inserted at the junction without destroying the symmetry to realize a perfect matching. By optimizing the circuit parameters, high isolation properties are obtained in a relatively wide frequency band of about 8.6% for return loss and isolation better than 20 and 30 dB, respectively, for a circuit designed at 10 GHz. The proposed waveguide intersection can be analyzed by H-plane planar circuit approach, and possess advantages of compactness, simplicity, and high-power handling capability. Furthermore, an SIW intersection is designed by applying H-plane planar circuit approach to a waveguide circuit filled with dielectric material, and high isolation properties similar to H-plane waveguide intersection can be realized. The validity of these design concepts is confirmed by em-simulations and experiments.

In this study, the design and fabrication of a 110–140-GHz varistor mode frequency tripler made with four Schottky diodes pair are presented. Nonlinear simulations were performed to calculate the optimum diode embedding impedance and the required input power. A compact microstrip resonant cell (CMRC) filter was introduced for the first time in submillimeter multiplier, instead of the traditional low-and-high impedance microstrip filter. The shorter size and the wider stop band of the CMRC filter improved the performance of the tripler. The tripler exhibited the best conversion efficiency of 5.2% at 129 GHz and peak output power of 5.3 mW at 125 GHz. Furthermore, within the output bandwidth from 110 to 140 GHz, the conversion efficiency was greater than 1.5%.

Kernel biased discriminant analysis (KBDA), as a subspace learning algorithm, has been an attractive approach for the relevance feedback in content-based image retrieval. Its performance, however, still suffers from the “small sample learning” problem and “kernel learning” problem. Aiming to solve these problems, in this paper, we present a new semi-supervised scheme of KBDA (S-KBDA), in which the projection learning and the “kernel learning” are interweaved into a constrained optimization framework. Specifically, S-KBDA learns a subspace that preserves both the biased discriminant structure among the labeled samples, and the geometric structure among all training samples. In kernel optimization, we directly optimize the kernel matrix, rather than a kernel function, which makes the kernel learning more flexible and appropriate for the retrieval task. To solve the constrained optimization problem, a fast algorithm based on gradient ascent is developed. The image retrieval experiments are given to show the effectiveness of the S-KBDA scheme in comparison with the original KBDA, and the other two state-of-the-art algorithms.

With the increasing demand of high video quality and large image size, adaptive interpolation filter (AIF) addresses these issues and conquers the time varying effects resulting in increased coding efficiency, comparing with recent H.264 standard. However, currently most AIF algorithms are based on either frame level or macroblock (MB) level, which are not flexible enough for different video contents in a real codec system, and most of them are facing a severe time consuming problem. This paper proposes a content based coarse to fine AIF algorithm, which can adapt to video contents by adding different filters and conditions from coarse to fine. The overall algorithm has been mainly made up by 3 schemes: frequency analysis based frame level skip interpolation, motion vector modeling based region level interpolation, and edge detection based macroblock level interpolation. According to the experiments, AIF are discovered to be more effective in the high frequency frames, therefore, the condition to skip low frequency frames for generating AIF coefficients has been set. Moreover, by utilizing the motion vector information of previous frames the region level based interpolation has been designed, and Laplacian of Gaussian based macroblock level interpolation has been proposed to drive the interpolation process from coarse to fine. Six 720p and six 1080p video sequences which cover most typical video types have been tested for evaluating the proposed algorithm. The experimental results show that the proposed algorithm reduce total encoding time about 41% for 720p and 25% for 1080p sequences averagely, comparing with Key Technology Areas (KTA) Enhanced AIF algorithm, while obtains a BDPSNR gain up to 0.004 and 3.122 BDBR reduction.

This paper investigates characteristics of periodic structure of ferrite and dielectric slabs in cutoff waveguide which include left-handed operation. Transmission line model and finite element simulation are used to get dispersion characteristics and scattering parameters. Band pass response of left-handed ferrite mode at negative permeability region are discussed with backward wave phenomenon. Theoretical results show that by choosing appropriate ratio of (1) ferrite width and dielectric width, and (2) ferrite length and dielectric length, band pass response with steep edge characteristics can be obtained by the LH ferrite mode, which are confirmed with experiments using single crystal of yttrium iron garnet ferrite. Good band pass and phase shift responses are observed in S band.

The contribution of this paper is two-fold. Firstly, we conduct a large-scale real-world evaluation of the effectiveness of integrating an automatic transliteration system with a machine translation system. A human evaluation is usually preferable to an automatic evaluation, and in the case of this evaluation especially so, since the common machine translation evaluation methods are affected by the length of the translations they are evaluating, often being biassed towards translations in terms of their length rather than the information they convey. We evaluate our transliteration system on data collected in field experiments conducted all over Japan. Our results conclusively show that using a transliteration system can improve machine translation quality when translating unknown words. Our second contribution is to propose a novel Bayesian model for unsupervised bilingual character sequence segmentation of corpora for transliteration. The system is based on a Dirichlet process model trained using Bayesian inference through blocked Gibbs sampling implemented using an efficient forward filtering/backward sampling dynamic programming algorithm. The Bayesian approach is able to overcome the overfitting problem inherent in maximum likelihood training. We demonstrate the effectiveness of our Bayesian segmentation by using it to build a translation model for a phrase-based statistical machine translation (SMT) system trained to perform transliteration by monotonic transduction from character sequence to character sequence. The Bayesian segmentation was used to construct a phrase-table and we compared the quality of this phrase-table to one generated in the usual manner by the state-of-the-art GIZA++ word alignment process used in combination with phrase extraction heuristics from the MOSES statistical machine translation system, by using both to perform transliteration generation within an identical framework. In our experiments on English-Japanese data from the NEWS2010 transliteration generation shared task, we used our technique to bilingually co-segment the training corpus. We then derived a phrase-table from the segmentation from the sample at the final iteration of the training procedure, and the resulting phrase-table was used to directly substitute for the phrase-table extracted by using GIZA++/MOSES. The phrase-table resulting from our Bayesian segmentation model was approximately 30% smaller than that produced by the SMT system's training procedure, and gave an increase in transliteration quality measured in terms of both word accuracy and F-score.