Short-range Multiple-Input-Multiple-Output (SR-MIMO) transmission is an effective technique for achieving high-speed and short-range wireless communication. With this technique, however, the optimum aperture size of array antennas grows when the transmission distance is increased. Thus, antenna miniaturization is an important issue in SR-MIMO. In this paper, we clarify the effectiveness of using dual-polarized planar antennas as a means of miniaturizing SR-MIMO array antennas by measurements and analysis of MIMO transmission characteristics. We found that even in SR-MIMO transmission, the use of dual-polarized transmission enables higher channel capacity. Dual-polarized antennas can reduce by two thirds the array area that is needed to obtain the same channel capacity. For a transmission distance of two wavelengths, the use of a dual-polarized antenna improved the channel capacity by 26 bit/s/Hz while maintaining the same number of transmitters and receivers and the same antenna aperture size. Moreover, dual-polarized SR-MIMO has a further benefit when zero-forcing (ZF) reception without transmit beamforming is adopted, i.e., it effectively simplifies hardware configuration because it can reduce spatial correlation even in narrow element spacing. In this work, we confirmed that the application of dual-polarization to SR-MIMO is an effective way to both increase channel capacity and enhance transceiver simplification.

This article presents an algorithm that solves an on-line version of the longest common subsequence (LCS) problem for two strings over a constant alphabet in O(d+n) time and O(m+d) space, where m is the length of the shorter string, the whole of which is given to the algorithm in advance, n is the length of the longer string, which is given as a data stream, and d is the number of dominant matches between the two strings. A new upper bound, O(p(m-q)), of d is also presented, where p is the length of the LCS of the two strings, and q is the length of the LCS of the shorter string and the m-length prefix of the longer string.

A multiband monopole antenna with modified fractal loop parasitic is presented. Especially, bow-tie stubs and a modified fractal loop are attached to the sides and bottom of a strip line monopole antenna, respectively, in order to generate the multi-resonant frequencies for the applications of wireless communication systems. The characteristics of the presented antenna have been examined by using the simulation software. The comparison between the simulated and measured results confirms the good agreement. The results show good multiband operation with 10 dB impedance bandwidths of 15.55%, 8.75%, and 31.94% at the resonant frequencies of 1.8 GHz, 2.4 GHz, and 3.6 GHz, respectively, which cover the operating band applications of DCS 1800, WLAN (IEEE802.11 b/g), WiMAX, and IMT advanced system (4G mobile communication system).

Irregular low-density parity-check (LDPC) codes generally have good decoding performance in the waterfall region, but they exhibit higher error floors than regular ones. In this letter, we present a hybrid method, which combines code construction and the iterative decoding algorithm, to tackle this problem. Simulation results show that the proposed scheme decreases the error floor significantly for irregular LDPC codes over binary-input additive white Gaussian noise (BIAWGN) channel.

Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.

This paper deals with reflection and transmission of a TE plane wave from a two-dimensional random slab with slanted fluctuation by means of the stochastic functional approach. Such slanted fluctuation of the random slab is written by a homogeneous random field having a power spectrum with a rotation angle. By starting with the previous paper [IEICE Trans. Electron., Vol. E92-C, no.1, pp.77–84, January 2009], any statistical quantities are immediately obtained even for slanted fluctuation cases. The first-order incoherent scattering cross section is numerically calculated and illustrated in figures. It is then newly found that shift and separation phenomena of the leading or enhanced peaks at four characteristic scattering angles take place in the transmission and reflection sides, respectively.

In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.

Beamforming with sparse constraint has shown significant performance improvement. In this letter, a least squares constant modulus blind adaptive beamforming with sparse constraint is proposed. Simulation results indicate that the proposed approach exhibits better performance than the well-known least squares constant modulus algorithm (LSCMA).

We propose a capacitive averaging technique applied to a double-tail latched comparator without a preamplifier for an offset reduction technique. Capacitive averaging can be introduced by considering the first stage of the double-tail latched comparator as a capacitive loaded amplifier. This makes it possible to reduce the offset voltage while preventing an increase in power dissipation. A positive feedback technique is also used for the first stage, which maximizes the effectiveness of the capacitive averaging. The capacitive averaging mechanism and the relationship between the offset reduction and the linearity of the amplifier is discussed in detail. Simulation results for a 90-nm CMOS process show that the proposed technique can reduce the offset voltage by 1/3.5 (3 mV) at a power dissipation of only 45 µW.

For measuring the similarity of biological sequences and structures such as DNA sequences, protein sequences, and tertiary structures, several compression-based methods have been developed. However, they are based on compression algorithms only for sequential data. For instance, protein structures can be represented by two-dimensional distance matrices. Therefore, it is expected that image compression is useful for measuring the similarity of protein structures because image compression algorithms compress data horizontally and vertically. This paper proposes series of methods for measuring the similarity of protein structures. In the methods, an original protein structure is transformed into a distance matrix, which is regarded as a two-dimensional image. Then, the similarity of two protein structures is measured by a kind of compression ratio of the concatenated image. We employed several image compression algorithms, JPEG, GIF, PNG, IFS, and SPC. Since SPC often gave better results among the other image compression methods, and it is simple and easy to be modified, we modified SPC and obtained MSPC. We applied the proposed methods to clustering of protein structures, and performed Receiver Operating Characteristic (ROC) analysis. The results of computational experiments suggest that MSPC has the best performance among existing compression-based methods. We also present some theoretical results on the time complexity and Kolmogorov complexity of image compression-based protein structure comparison.

A finite buffer shared by multiple packet queues is considered. Partitioning the buffer to maximize total throughput is formulated as a resource allocation problem, the solution is shown to be achieved by a greedy incremental algorithm in polynomial time. The optimal buffer allocation strategy is applied to different models for a wireless downlink. First, a set of parallel M/M/1/mi queues, corresponding to a downlink with orthogonal channels is considered. It is verified that at high load, optimal buffer partitioning can boost the throughput significantly with respect to complete sharing of the buffer. Next, the problem of optimal combined buffer allocation and channel assignment problems are shown to be separable in an outage scenario. Motivated by this observation, buffer allocation is considered in a system where users need to be multiplexed and scheduled based on channel state. It is observed that under finite buffers in the high load regime, scheduling simply with respect to channel state with a simply partitioned buffer achieves comparable throughput to combined channel and queue-aware scheduling.

Device-parameter estimation sensors inside a chip are gaining its importance as the post-fabrication tuning is becoming of a practical use. In estimation of variational parameters using on-chip sensors, it is often assumed that the outputs of variation sensors are not affected by random variations. However, random variations can deteriorate the accuracy of the estimation result. In this paper, we propose a device-parameter estimation method with on-chip variation sensors explicitly considering random variability. The proposed method derives the global variation parameters and the standard deviation of the random variability using the maximum likelihood estimation. We experimentally verified that the proposed method improves the accuracy of device-parameter estimation by 11.1 to 73.4% compared to the conventional method that neglects random variations.

A signal model and weighted-average based estimation techniques are proposed to estimate the angle-of-arrival (AOA) parameters of multiple clusters for a low data rate ultrawide band (LR-UWB) based wireless positioning system. The optimal AOA estimation techniques for the LR-UWB wireless positioning system according to the cluster condition are introduced and it is shown that the proposed techniques are superior to the conventional technique from the standpoint of performance.

In the near future, interconnection networks of massively parallel computer systems will connect more than a hundred thousands of computing nodes. The performance evaluation of the interconnection networks can provide real insights to help the development of efficient communication library. Hence, to evaluate the performance of such interconnection networks, simulation tools capable of modeling the networks with sufficient details, supporting a user-friendly interface to describe communication patterns, providing the users with enough performance information, completing simulations within a reasonable time, are a real necessity. This paper introduces a novel interconnection network simulator NSIM, for the evaluation of the performance of extreme-scale interconnection networks. The simulator implements a simplified simulation model so as to run faster without any loss of accuracy. Unlike the existing simulators, NSIM is built on the execution-driven simulation approach. The simulator also provides a MPI-compatible programming interface. Thus, the simulator can emulate parallel program execution and correctly simulate point-to-point and collective communications that are dynamically changed by network congestion. The experimental results in this paper showed sufficient accuracy of this simulator by comparing the simulator and the real machine. We also confirmed that the simulator is capable of evaluating ultra large-scale interconnection networks, consumes smaller memory area, and runs faster than the existing simulator. This paper also introduces a simulation service built on a cloud environment. Without installing NSIM, users can simulate interconnection networks with various configurations by using a web browser.

In this paper, we propose an approach to obtaining enhanced performance of the Linpack benchmark on a GPU-accelerated PC cluster connected via relatively slow inter-node connections. For one node with a quad-core Intel Xeon W3520 processor and a NVIDIA Tesla C1060 GPU card, we implement a CPU–GPU parallel double-precision general matrix–matrix multiplication (dgemm) operation, and achieve a performance improvement of 34% compared with the GPU-only case and 64% compared with the CPU-only case. For an entire 16-node cluster, each node of which is the same as the above and is connected with two gigabit Ethernet links, we use a computation-communication overlap scheme with GPU acceleration for the Linpack benchmark, and achieve a performance improvement of 28% compared with the GPU-accelerated high-performance Linpack benchmark (HPL) without overlapping. Our overlap GPU acceleration solution uses overlaps in which the main inter-node communication and data transfer to the GPU device memory are overlapped with the main computation task on the CPU cores. These overlaps use multi-core processors, which almost all of today's high-performance computers use. In particular, as well as using a CPU core for communication tasks, we also simultaneously use other CPU cores and the GPU for computation tasks. In order to enable overlap between inter-node communication and computation tasks, we eliminate their close dependence by breaking the main computation task into smaller tasks and rescheduling. Based on a scheme in which part of the CPU computation power is simultaneously used for tasks other than computation tasks, we experimentally find the optimal computation ratio for CPUs; this ratio differs from the case of parallel dgemm operation of one node.

Multi-operand adders, which calculates the summation of more than two operands, usually consist of compressor trees which reduce the number of operands to two without any carry propagation, and a carry-propagate adder for the two operands in ASIC implementation. The former part is usually realized using full adders or (3;2) counters like Wallace-trees in ASIC, while adder trees or dedicated hardware are used in FPGA. In this paper, an approach to realize compression trees on FPGAs is proposed. In case of FPGA with m-input LUT, any counters with up to m inputs can be realized with one LUT per an output. Our approach utilizes generalized parallel counters (GPCs) with up to m inputs and synthesizes high-performance compressor trees by setting some intermediate height limits in the compression process like Dadda's multipliers. Experimental results show that the number of GPCs are reduced by up to 22% compared to the existing heuristic. Its effectivity on reduction of delay is also shown against existing approaches on Altera's Stratix III.

In this paper, we consider the signal processing algorithm on each subcarrier for the downlink of Multi-User Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MU-MIMO OFDM) system. A novel transmit scheme is proposed for the cancellation of Inter-User Interference (IUI) at the Base Station (BS). The improved performance of each user is obtained by optimizing the transmit scheme on each subcarrier, where the Particle Swarm Optimization (PSO) algorithm is employed to solve the constrained nonlinear optimization problem. Compared with the conventional Zero Forcing Dirty Paper Coding (ZF-DPC) having only single receive antenna at each Mobile Station (MS), the proposed scheme also applies the principle of DPC to cancel the IUI, but the MS users can be equipped with multiple receive antennas producing their increased receive SNR's. With the Channel State Information (CSI) being known at the BS and the MS, the eigenvalues for all the user channels are calculated first and then the user with the maximum eigenvalue is selected as the 1-st user. The remaining users are ordered and sequentially processed, where the transmit weights are generated from the previously selected users by the Particle Swarm Optimization (PSO) algorithm which ensures the transmit gain for each user as large as possible. The computational complexity analysis, BER performance and achievable sum-rate analysis of system verify the effectiveness of the proposed scheme.

In this paper, we propose a hybrid test application in partial skewed-load (PSL) scan design. The PSL scan design in which some flip-flops (FFs) are controlled as skewed-load FFs and the others are controlled as broad-side FFs was proposed in [1]. We notice that the PSL scan design potentially has a capability of two test application modes: one is the broad-side test mode, and the other is the hybrid test mode which corresponds to the test application considered in [1]. According to this observation, we present a hybrid test application of the two test modes in the PSL scan design. In addition, we also address a way of skewed-load FF selection based on propagation dominance of FFs in order to take advantage of the hybrid test application. Experimental results for ITC'99 benchmark circuits show that the hybrid test application in the proposed PSL scan design can achieve higher fault coverage than the design based on the skewed-load FF selection [1] does.

Finite difference time domain (FDTD) method has been accelerated on the Cell Broadband Engine (Cell B.E.). However the problem has arisen that speedup is limited by the bandwidth of the main memory on large-scale analysis. As described in this paper, we propose a novel algorithm and implement FDTD using it. We compared the novel algorithm with results obtained using region segmentation, thereby demonstrating that the proposed algorithm has shorter calculation time than that provided by region segmentation.

Recently, a number of location-based services such as navigation and mobile advertising have been proposed. Such services require real-time user positions. Since a global positioning system (GPS), which is one of the most well-known techniques for real-time positioning, is unsuitable for indoor uses due to unavailability of GPS signals, many indoor positioning systems (IPSs) using WLAN, radio frequency identification tags, and so forth have been proposed. However, most of them suffer from high installation costs. In this paper, we propose a novel IPS for real-time positioning that utilizes a digital audio watermarking technique. The proposed IPS first embeds watermarks into an audio signal to generate watermarked signals, each of which is then emitted from a corresponding speaker installed in a target environment. A user of the proposed IPS receives the watermarked signals with a mobile device equipped with a microphone, and the watermarks are detected in the received signal. For positioning, we model various effects upon watermarks due to propagation in the air, i.e., delays, attenuation, and diffraction. The model enables the proposed IPS to accurately locate the user based on the watermarks detected in the received signal. The proposed IPS can be easily deployed with a low installation cost because the IPS can work with off-the-shelf speakers that have been already installed in most of the indoor environments such as department stores, amusement arcades, and airports. We experimentally evaluate the accuracy of positioning and show that the proposed IPS locates the user in a 6 m by 7.5 m room with root mean squared error of 2.25 m on average. The results also demonstrate the potential capability of real-time positioning with the proposed IPS.