The social network model has been regarded as a promising mechanism to defend against Sybil attack. This model assumes that honest peers and Sybil peers are connected by only a small number of attack edges. Detection of the attack edges plays a key role in restraining the power of Sybil peers. In this paper, an attack-resisting, distributed algorithm, named Random walk and Social network model-based clustering (RSC), is proposed to detect the attack edges. In RSC, peers disseminate random walk packets to each other. For each edge, the number of times that the packets pass this edge reflects the betweenness of this edge. RSC observes that the betweennesses of attack edges are higher than those of the non-attack edges. In this way, the attack edges can be identified. To show the effectiveness of RSC, RSC is integrated into an existing social network model-based algorithm called SOHL. The results of simulations with real world social network datasets show that RSC remarkably improves the performance of SOHL.

A square-root (SR) algorithm, an SR architecture and a leakage current reduction circuit were developed to reduce dynamic power (PAT) and leakage power (PST), while maintaining the speed of a CMOS SR circuit. Using these techniques, a 90-nm CMOS LSI was fabricated. The PAT of the new SR circuit at a clock frequency (fc) of 490 MHz and a supply voltage (VDD) of 0.75 V was 104.1 µW, i.e., 21.6% that (482.3 µW) of a conventional SR circuit. The PST of the new SR circuit was markedly reduced to 19.51 nW, which was only 1.69% that (1,153 nW) of the conventional SR circuit.

A 3.3-V 512-k 18-b 2-bank synchronous DRAM (SDRAM) has been developed using a novel 3-stage-pipelined architecture. The address-access path which is usually designed by analog means is digitized, separated into three stages by latch circuits at the column switch and data-out buffer. Since this architecture requires no additional read/write bus and data amp, it minimizes an increase in die size. Using the standardized GTL interface, a 250-Mbyte/s synchronous DRAM with die size of 113.7-mm2, which is the same die size as our conventional DRAM, has been achieved with 0.50-µm CMOS process technology.

The recording density 34 KFRPI was achieved by the use of transversal recording. A tape orientated transversal direction was employed and a ferrite head with 3.8 µm gap length and 15.5 µm track width was used. The frequency characteristics of transversal recording and its bitter patterns are described.

A multilayer perceptron is usually considered a passive learner that only receives given training data. However, if a multilayer perceptron actively gathers training data that resolve its uncertainty about a problem being learnt, sufficiently accurate classification is attained with fewer training data. Recently, such active learning has been receiving an increasing interest. In this paper, we propose a novel active learning strategy. The strategy attempts to produce only useful training data for multilayer perceptrons to achieve accurate classification, and avoids generating redundant training data. Furthermore, the strategy attempts to avoid generating temporarily useful training data that will become redundant in the future. As a result, the strategy can allow multilayer perceptrons to achieve accurate classification with fewer training data. To demonstrate the performance of the strategy in comparison with other active learning strategies, we also propose an empirical active learning algorithm as an implementation of the strategy, which does not require expensive computations. Experimental results show that the proposed algorithm improves the classification accuracy of a multilayer perceptron with fewer training data than that for a conventional random selection algorithm that constructs a training data set without explicit strategies. Moreover, the algorithm outperforms typical active learning algorithms in the experiments. Those results show that the algorithm can construct an appropriate training data set at lower computational cost, because training data generation is usually costly. Accordingly, the algorithm proves the effectiveness of the strategy through the experiments. We also discuss some drawbacks of the algorithm.

Live streaming media are delay sensitive and have limited allowable delays. Current conventional multicast protocols do not have a loss retransmission mechanism. Even though several reliable multicast protocols with retransmission mechanisms have been proposed, the long delay and high packet loss rate make them inefficient for live streaming. This paper proposes a multicast protocol focusing on the allowable delay called the QoS Multicast for Live Streaming (QMLS) protocol. QMLS routers are placed along the multicast tree to detect and retransmit lost packets. We propose a method that enables data recovery to be done immediately after lost packets are detected by the QMLS router and a method that reduces the unnecessary packets sent to end receivers. This paper discusses the mathematical analysis of the proposed protocol and compares it with other multicast protocols. The results reveal that our protocol is more effective in live streaming. Finally, we do a simulation to evaluate its performance and study the effect of consecutive losses. The simulation reveals that consecutive losses can slightly increase losses with our protocol.

In this note, we consider the problem of finding a step-by-step transformation between two longest increasing subsequences in a sequence, namely LONGEST INCREASING SUBSEQUENCE RECONFIGURATION. We give a polynomial-time algorithm for deciding whether there is a reconfiguration sequence between two longest increasing subsequences in a sequence. This implies that INDEPENDENT SET RECONFIGURATION and TOKEN SLIDING are polynomial-time solvable on permutation graphs, provided that the input two independent sets are largest among all independent sets in the input graph. We also consider a special case, where the underlying permutation graph of an input sequence is bipartite. In this case, we give a polynomial-time algorithm for finding a shortest reconfiguration sequence (if it exists).

An analysis is presented of the performance of the optimum detection for NRZ magnetic recording. The error rate performance of the optimum detection is obtained by theoretical analysis. And the recording density with the optimum detection is compared with that with some other well-known detecting methods.

Quaternionic neural networks are extensions of neural networks using quaternion algebra. 3-D and 4-D quaternionic MLPs have been studied. 3-D quaternionic neural networks are useful for handling 3-D objects, such as Euclidean transformation. As for Hopfield neural networks, only 4-D quaternionic Hopfield neural networks (QHNNs) have been studied. In this work, we propose the 3-D QHNNs. Moreover, we define the energy, and prove that it converges.

The equidistortion principle[1] has recently been proposed as a basic principle for design of an optimal vector quantization (VQ) codebook. The equidistortion principle adjusts all codebook vectors such that they have the same contribution to quantization error. This paper introduces a novel VQ codebook design algorithm based on the equidistortion principle. The proposed algorithm is a variant of the law-of-the-jungle algorithm (LOJ), which duplicates useful codebook vectors and removes useless vectors. Due to the LOJ mechanism, the proposed algorithm can establish the equidistortion condition without wasting learning steps. This is significantly effective in preventing performance degradation caused when initial states of codebook vectors are improper to find an optimal codebook. Therefore, even in the case of improper initialization, the proposed algorithm can achieve minimization of quantization error based on the equidistortion principle. Performance of the proposed algorithm is discussed through experimental results.

We developed and applied a new circuit, called the “Self-controllable Voltage Level (SVL)” circuit, not only to expand both “write” and “read” stabilities, but also to achieve a low stand-by power and data holding capability in a single low power supply, 90-nm, 2-kbit, six-transistor CMOS SRAM. The SVL circuit can adaptively lower and higher the word-line voltages for a “read” and “write” operation, respectively. It can also adaptively lower and higher the memory cell supply voltages for the “write” and “hold” operations, and “read” operation, respectively. This paper focuses on the “hold” characteristics and the standby power dissipations (PST) of the developed SRAM. The average PST of the developed SRAM is only 0.984µW, namely, 9.57% of that (10.28µW) of the conventional SRAM at a supply voltage (VDD) of 1.0V. The data hold margin of the developed SRAM is 0.1839V and that of the conventional SRAM is 0.343V at the supply voltage of 1.0V. An area overhead of the SVL circuit is only 1.383% of the conventional SRAM.

Sparse Matrix-Vector multiplication (SpMV) is a computational kernel widely used in many applications. Because of the importance, many different implementations have been proposed to accelerate this computational kernel. The performance characteristics of those SpMV implementations are quite different, and it is basically difficult to select the implementation that has the best performance for a given sparse matrix without performance profiling. One existing approach to the SpMV best-code selection problem is by using manually-predefined features and a machine learning model for the selection. However, it is generally hard to manually define features that can perfectly express the characteristics of the original sparse matrix necessary for the code selection. Besides, some information loss would happen by using this approach. This paper hence presents an effective deep learning mechanism for SpMV code selection best suited for a given sparse matrix. Instead of using manually-predefined features of a sparse matrix, a feature image and a deep learning network are used to map each sparse matrix to the implementation, which is expected to have the best performance, in advance of the execution. The benefits of using the proposed mechanism are discussed by calculating the prediction accuracy and the performance. According to the evaluation, the proposed mechanism can select an optimal or suboptimal implementation for an unseen sparse matrix in the test data set in most cases. These results demonstrate that, by using deep learning, a whole sparse matrix can be used to do the best implementation prediction, and the prediction accuracy achieved by the proposed mechanism is higher than that of using predefined features.

In this paper we present an analysis based on the indirect Lyapunov criteria, that is used to study the convergence of an infinite impulse response (IIR) adaptive digital filter (ADF) based on estimation of the allpass system. The analysis is then extended to investigate the necessity of directly estimating the transfer level of the unknown system. We consider two cases of modeling the ADF. In the first system, the allpass section of the ADF estimates only the real poles of the unknown system while in the second system, both real and complex poles the allpass section are estimated. From the analysis and computer simulation, we realize that the poles of the ADF converge selectively to the poles of the unknown system, depending on the sign of the step size of adaptation. Using these results we proposed a new method to control the convergence of the poles the IIR ADF based on estimation of the allpass system.

In camera-based object recognition and classification, surface color is one of the most important characteristics. However, apparent object color may differ significantly according to the illumination and surface conditions. Such a variation can be an obstacle in utilizing color features. Geusebroek et al.'s color invariants can be a powerful tool for characterizing the object color regardless of illumination and surface conditions. In this work, we analyze the estimation process of the color invariants from RGB images, and propose a novel invariant feature of color based on the elementary invariants to meet the circular continuity residing in the mapping between colors and their invariants. Experiments show that the use of the proposed invariant in combination with luminance, contributes to improve the retrieval performances of partial object image matching under varying illumination conditions.

Complex-valued Associative Memory (CAM) is an advanced model of Hopfield Associative Memory. The CAM is based on multi-state neurons and has the high ability of representation. Lee proposed gradient descent learning for the CAM to improve the storage capacity. It is based on only the phases of input signals. In this paper, we propose another type of gradient descent learning based on both the phases and the amplitude. The proposed learning method improves the noise robustness and accelerates the learning speed.

We present a mechanism, named the law of the jungle (LOJ), to improve the Kohonen learning. The LOJ is used to be an adaptive vector quantizer for approximating nonstationary probability distribution functions. In the LOJ mechanism, the probability that each node wins in a competition is dynamically estimated during the learning. By using the estimated win probability, "strong" nodes are increased through creating new nodes near the nodes, and "weak" nodes are decreased through deleting themselves. A pair of creation and deletion is treated as an atomic operation. Therefore, the nodes which cannot win the competition are transferred directly from the region where inputs almost never occur to the region where inputs often occur. This direct "jump" of weak nodes provides rapid convergence. Moreover, the LOJ requires neither time-decaying parameters nor a special periodic adaptation. From the above reasons, the LOJ is suitable for quick approximation of nonstationary probability distribution functions. In comparison with some other Kohonen learning networks through experiments, only the LOJ can follow nonstationary probability distributions except for under high-noise environments.

Recently, in the modem, the spread spectrum communication system and the software radio, Digital Signal Processor type Squaring Loop (DSP-squaring-loop) is employed in the demodulation of Binary Phase Shift Keying (BPSK) signal. The DSP-squaring-loop extracts the carrier signal that is used for the coherent detection. However, in case the Signal to Noise Ratio (SNR) is low, the DSP-Phase Locked Loop (DSP-PLL) can not pull in the frequency offset and the phase offset. In this paper, we propose a DSP-squaring-loop that is robust against noise and which uses the adaptive notch filter type frequency estimator and the adaptive Band Pass Filter (BPF). The proposed method can extract the carrier signal in the low SNR environment. The effectiveness of the proposed method is confirmed by the computer simulation results.

Learning for boltzmann machines deals with each state individually. If given data is categorized, the probabilities have to be distributed to each state, not to each catetory. We propose boltzmann machines identifying the states in the same categories. Boltzmann machines with hidden units are the special cases. Boltzmann learning and em algorithm are effective learning methods for boltzmann machines. We solve boltzmann learning and em algorithm for the proposed models.

To improve picture quality of a color-under VCR, chrominance bandwidth expansion is studied using subsampling technique. It is concluded that line offset subsampling and field offset subsampling are effective methods of improving reproduced picture quality.

Given a set P of n points and an integer k, we wish to place k facilities on points in P so that the minimum distance between facilities is maximized. The problem is called the k-dispersion problem, and the set of such k points is called a k-dispersion of P. Note that the 2-dispersion problem corresponds to the computation of the diameter of P. Thus, the k-dispersion problem is a natural generalization of the diameter problem. In this paper, we consider the case of k=3, which is the 3-dispersion problem, when P is in convex position. We present an O(n2)-time algorithm to compute a 3-dispersion of P.