Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L*u*v* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L*u*v* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L*u*v* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.

Superluminal group velocity in dispersive media has long been controversial. A partial source of confusion seems to be the absence of high precision numerical results concerning the waveform of the transmitted signal. This paper gives the precise waveforms of a causal half-sine-modulated pulse and a triangle-modulated pulse propagating in the Lorentz medium. Thus, the effects of analyticity of signal are clarified, which the analysis using Gaussian pulse cannot. Further, to deepen understanding of the mechanism of superluminal group velocity, we give a network theoretic consideration.

In this paper, the magnetic field properties around household appliances are investigated with the single coil model and equivalent source model, which are used as main source models in the European standard EN50366 (CENELEC). The accuracy of the field properties is conducted for the coil model (defined in the EN50366), by comparing with the results of the equivalent source model, which allow the reproduction of the complicated inhomogeneous magnetic field around the appliance with full generality (i.e. supports three dimensional vector fields).

A simple millimeter-wave quasi-maximal-ratio-combin-ing antenna diversity system based on the millimeter-wave self-heterodyne transmission technique is described. The millimeter-wave self-heterodyne transmission technique is useful for developing millimeter-wave systems with enhanced characteristics in regard to system miniaturization, development and fabrication cost, and the frequency stability of the signal transmission. We also show that applying this technique with an antenna diversity receiver configuration can easily solve a problem peculiar to millimeter-wave systems--the fact that the transmission link always requires a line-of-sight path--without requiring hardware designed with millimeter-scale precision. In this paper, we theoretically analyze the operating principle of a combining antenna diversity system based on the millimeter-wave self-heterodyne transmission technique. We further prove that we can obtain a diversity gain in accordance with that of a maximal-ratio combining diversity system without resorting to any complicated control of the received signal envelope and phase. Our experiments using the simplest two-branch diversity structure have validated the operating principle derived in our theoretical analysis. Our results show that a received CNR improvement of 3 dB is obtained as a diversity gain. We also demonstrate that circuit precision corresponding to the wavelength of the intermediate frequency, rather than to the millimeter wavelength, is sufficient to obtain the diversity effect when we control the signal phase or delay in combining the received signals.

This paper addresses the issue of reconfiguring multi-layer neural networks implemented in single or multiple VLSI chips. The ability to adaptively reconfigure network configuration for a given application, considering the presence of faulty neurons, is a very valuable feature in a large scale neural network. In addition, it has become necessary to achieve systems that can automatically reconfigure a network and acquire optimal weights without any help from host computers. However, self-reconfigurable architectures for neural networks have not been studied sufficiently. In this paper, we propose an architecture for a self-reconfigurable multi-layer neural network employing both reconfiguration with spare neurons and weight training by GAs. This proposal offers the combined advantages of low hardware overhead for adding spare neurons and fast weight training time. To show the possibility of self-reconfigurable neural networks, the prototype system has been implemented on a field programmable gate array.

In a high-rate indoor wireless personal communication system, the delay spread due to multi-path propagation results in intersymbol interference which can significantly increase the transmission bit error rate (BER). The technique most commonly used for combating the intersymbol interference and frequency-selective fading found in communications channels is the adaptive equalization. In this paper, we propose a novel neural detector based on self-organizing map (SOM) to improve the system performance of the receiver. In the proposed scheme, the SOM is used as an adaptive detector of equalizer, which updates the decision levels to follow the received faded signal. To adapt the proposed scheme to the time-varying channel, we use the Euclidean distance, which will be updated automatically according to the received faded signal, as an adaptive radius to define the neighborhood of the winning neuron of the SOM algorithm. Simulations on a 16 QAM system show that the receiver using the proposed neural detector has a significantly better BER performance than the traditional receiver.

DualFS is a next-generation journaling file system which has the same consistency guaranties as traditional journaling file systems but better performance. This paper introduces three new enhancements which significantly improve DualFS performance during normal operation, and presents different experimental results which compare DualFS and other traditional file systems, namely, Ext2, Ext3, XFS, JFS, and ReiserFS. The experiments carried out prove, for the first time, that a new file system design based on separation of data and metadata can significantly improve file systems' performance without requiring several storage devices.

This paper describes an analysis of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, this paper used a self-organizing map, which provides a way to map high-dimensional data onto a low-dimensional domain. Also, in the LRD-based analysis, this paper employed detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. In applying this method to traffic analysis, this paper performed two kinds of traffic measurement: one based on IP-network traffic flowing into NTT Musashino R&D center (Tokyo, Japan) from the Internet and the other based on IP-network traffic flowing through at an interface point between an access provider (Tokyo, Japan) and the Internet. Based on sequential measurements of IP-network traffic, this paper derived corresponding values for the LRD-related parameter α of measured traffic. As a result, we found that the characteristic of self-similarity seen in the measured traffic fluctuated over time, with different time variation patterns for two measurement locations. In training the self-organizing map, this paper used three parameters: two α values for different plot ranges, and Shannon-based entropy, which reflects the degree of concentration of measured time-series data. We visually confirmed that the traffic data could be projected onto the map in accordance with the traffic properties, resulting in a combined depiction of the effects of the degree of LRD and network utilization rates. The proposed method can deal with multi-dimensional parameters, projecting its results onto a two-dimensional space in which the projected data positions give us an effective depiction of network conditions at different times.

This paper presents a compensating method based on Self-Organizing Map (SOM) for nonlinear distortion, which is caused by high-power amplifier (HPA) in 16-QAM-OFDM system. OFDM signals are sensitive to nonlinear distortions and different methods are studied to solve them. In the proposed scheme, the correction is done at the receiver by a SOM algorithm. Simulations are carried out considering an additive white Gaussian (AWG) transmission channel. Simulation results show that the SOM algorithm brings perceptible gains in a complete 16-QAM-OFDM system.

In this paper, we propose a noise-robust automatic speech recognition system that uses orthogonalized distinctive phonetic features (DPFs) as input of HMM with diagonal covariance. In an orthogonalized DPF extraction stage, first, a speech signal is converted to acoustic features composed of local features (LFs) and ΔP, then a multilayer neural network (MLN) with 153 output units composed of context-dependent DPFs of a preceding context DPF vector, a current DPF vector, and a following context DPF vector maps the LFs to DPFs. Karhunen-Loeve transform (KLT) is then applied to orthogonalize each DPF vector in the context-dependent DPFs, using orthogonal bases calculated from a DPF vector that represents 38 Japanese phonemes. Each orthogonalized DPF vector is finally decorrelated one another by using Gram-Schmidt orthogonalization procedure. In experiments, after evaluating the parameters of the MLN input and output units in the DPF extractor, the orthogonalized DPFs are compared with original DPFs. The orthogonalized DPFs are then evaluated in comparison with a standard parameter set of MFCCs and dynamic features. Next, noise robustness is tested using four types of additive noise. The experimental results show that the use of the proposed orthogonalized DPFs can significantly reduce the error rate in an isolated spoken-word recognition task both with clean speech and with speech contaminated by additive noise. Furthermore, we achieved significant improvements when combining the orthogonalized DPFs with conventional static MFCCs and ΔP.

High-frequency characteristics of SiGe heterojunction bipolar transistors with different emitter sizes are studied based on pulsed measurements. Because the self-heating effect in transistors will enhance the Kirk effect, as the devices operate in high current region, the measured cutoff frequency and maximum oscillation frequency decrease with measurement time in the pulsed duration. By analyzing the equivalent small-signal device parameters, we know the reduction of cutoff frequency and maximum oscillation frequency is attributed to the reduction of transconductance and the increase of junction capacitances for fixed base-emitter voltage, while it is only attributed to the degradation of transconductance for fixed collector current. Besides, the degradation of high-frequency performance due to self-heating effect would be improved with the layout design combining narrow emitter finger and parallel-interconnected subcells structure.

Measuring and modeling network traffic is of key importance for the traffic engineering of IP networks, due to the growing diversity of multimedia applications and the need to efficiently support QoS differentiation in the network. Several recent measurements have shown that Internet traffic may incorporate long-range dependence and self-similar characteristics, which can have significant impact on network performance. Self-similar traffic shows variability over many time scales, and this behavior must be taken into account for accurate prediction of network performance. In this paper, we propose a new parameter fitting procedure for a superposition of Markov Modulated Poisson Processes (MMPPs), which is able to capture self-similarity over a range of time scales. The fitting procedure matches the complete distribution of the arrival process at each time scale of interest. We evaluate the procedure by comparing the Hurst parameter, the probability mass function at each time scale, and the queuing behavior (as assessed by the loss probability and average waiting time), corresponding to measured traffic traces and to traces synthesized according to the proposed model. We consider three measured traffic traces, all exhibiting self-similar behavior: the well-known pOct Bellcore trace, a trace of aggregated IP WAN traffic, and a trace corresponding to the popular file sharing application Kazaa. Our results show that the proposed fitting procedure is able to match closely the distribution over the time scales present in data, leading to an accurate prediction of the queuing behavior.

This letter presents a significant property of the mapping parameters that play a central role to represent a given signal in Fractal Interpolation Functions (FIF). Thanks to our theoretical analysis, it is derived that the mapping parameters required to represent a given signal are also applicable to represent the upsampled signal of a given one. Furthermore, the upsampled signal obtained by using the property represents the self-affine property more distinctly than the given signal. Experiments show the validity and usefulness of the significant property.

High-quality Fe3O4 thin films have been fabricated onto metallic underlayers of Cr/Cu and Al by rf-magnetron sputtering at low substrate temperatures (<573 K). The measured saturation magnetizations Ms are 462 emu/cm3 for Al (50 nm)/Fe3O4 (200 nm) and 422 emu/cm3 for Cr (45 nm)/Cu (300 nm)/Fe3O4 (200 nm), which are markedly enhanced compared with that for the reference sample deposited directly on a glass substrate, and practically comparable to the bulk value of 477 emu/cm3. Highly conductive transport with an order-disorder change of the Verwey transition was observed in the current-perpendicular-to-plane geometry. The order of decrease in coercive field was achieved by exchange coupling with an overlaid NiFe layer.

In this paper, we propose a self-nonself recognition algorithm based on positive and negative selection used in the developing process of T cells. The anomaly detection algorithm based on negative selection is a representative model among self-recognition method and it has been applied to computer immune systems in recent years. In biological immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigens, or nonself cells. In this paper, we propose a self-recognition algorithm based on the positive selection and also propose a fusion algorithm based on both positive and negative selection. To verify the effectiveness of the proposed system, we show simulation results for detecting some infected data obtained from cell changes and string changes in the self-file.

Self-stabilization is a theoretical framework of non-masking fault-tolerant distributed algorithms. In this paper, we investigate the Steiner tree problem in distributed systems, and propose a self-stabilizing heuristic solution to the problem. Our algorithm is constructed by four layered modules (sub-algorithms): construction of a shortest path forest, transformation of the network, construction of a minimum spanning tree, and pruning unnecessary links and processes. Competitiveness is 2(1-1/l), where l is the number of leaves of optimal solution.

In 1999, Boneh et al. proposed the Lattice Factoring Method (LFM) for the integer factoring problem for a composite of the form N = prq by employing the LLL-algorithm. Time complexity of LFM is measured by the number of calls of the LLL-algorithm. In the worst case, the number is 2log p for a certain constant c. In 2001, Uchiyama and Kanayama introduced a novel criterion and provided an improved algorithm which runs (2k-p)/|p-Nr+1| times faster (for certain constants k, Nr+1). In this letter, we note another practical improvement applicable to the original and the improved LFM, which enables to provide about 2 times speed-up.

Reseeding technique is proposed to improve the fault coverage in pseudo-random testing. However most of previous works on reseeding is based on storing the seeds in an external tester or in a ROM. In this paper we present a built-in reseeding technique for LFSR-based test pattern generation. The proposed structure can run both in pseudorandom mode and in reseeding mode. Besides, our method requires no storage for the seeds since in reseeding mode the seeds can be generated automatically in hardware. In this paper we also propose an efficient grouping algorithm based on simulated annealing to optimize test vector grouping. Experimental results for benchmark circuits indicate the superiority of our technique against other reseeding methods with respect to test length and area overhead. Moreover, since the theoretical properties of LFSRs are preserved, our method could be beneficially used in conjunction with any other techniques proposed so far.

We propose a technique of selecting seeds for the LFSR-based test pattern generators that are used in VLSI BISTs. By setting the computed seed as an initial value, target fault coverage, for example 100%, can be accomplished with minimum test length. We can also maximize fault coverage for a given test length. Our method can be used for both test-per-clock and test-per-scan BISTs. The procedure is based on vector representations over GF(2m), where m is the number of LFSR stages. The results indicate that test lengths derived through selected seeds are about sixty percent shorter than those derived by simple seeds, i.e. 0001, for a given fault coverage. We also show that seeds obtained through this technique accomplish higher fault coverage than the conventional selection procedure. In terms of the c7552 benchmark, taking a test-per-scan architecture with a 20-bit LFSR as an example, the number of undetected faults can be decreased from 304 to 227 for 10,000 LFSR patterns using our proposed technique.

In this paper, a new grouping method for Group Technology using Self-Organizing Map (SOM) is proposed. The purpose of our study is to divide machines in a factory into any number of cells so that the machines in each cell can process a similar set of parts to increase productivity. A main feature of our method is to specify not only the number of the cells but also the maximum and minimum numbers of machines in a cell. Some experimental results show effectiveness of our proposed algorithm.