To develop human interfaces such as home information equipment, highly capable word learning ability is required. In particular, in order to realize user-customized and situation-dependent interaction using language, a function is needed that can build new categories online in response to presented objects for an advanced human interface. However, at present, there are few basic studies focusing on the purpose of language acquisition with category formation. In this study, taking hints from an analogy between machine learning and infant developmental word acquisition, we propose a taxonomy-based word-learning model using a neural network. Through computer simulations, we show that our model can build categories and find the name of an object based on categorization.

A rate splitting algorithm is presented for a multiple video transmission system to transfer the aggregation (or statical multiplexing) of multiple video streams to multiple clients so that each client can receive the requested video stream with the reliable fidelity. Computer simulations for transmission of a set of 128 MPEG compressed video streams show that the proposed algorithm alleviates the variability of the aggregate video transmission comparing with a scheme to smooth individually each of videos using the traditional online smoothing algorithm. Besides, the proposed is 2 time faster than the traditional one.

Principal Component Analysis (PCA) has been applied in various areas such as pattern recognition and data compression. In some cases, however, PCA does not extract the characteristics of the data-distribution efficiently. In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of the principal components. In this paper, we reduce the dimensionality of image data using the proposed method, and examine its effectiveness in the compression and recognition of images.

This paper presents a novel method of structure selection and identification for Hammerstein type nonlinear systems. An unknown nonlinear static part to be estimated is approximately represented by an automatic choosing function (ACF) model. The connection coefficients of the ACF and the system parameters of the linear dynamic part are estimated by the linear least-squares method. The adjusting parameters for the ACF model structure, i.e. the number and widths of the subdomains and the shape of the ACF are properly selected by using a genetic algorithm, in which the Akaike information criterion is utilized as the fitness value function. The effectiveness of the proposed method is confirmed through numerical experiments.

In deep submicron designs, the interconnect wires play a major role in the timing behavior of logic gates. The effective capacitance Ceff concept is usually used to calculate the delay of gate with interconnect loads. In this paper, we present a new method of Integration Approximation to calculate Ceff. In this new method, the complicated nonlinear gate output is assumed as a piecewise linear (PWL) waveform. A new model is then derived to compute the value of Ceff. The introduction of Integration Approximation results in Ceff being insensitive to output waveform shape. Therefore, the new method can be applied to various output waveforms of CMOS gates with RC-π loads. Experimental results show a significant improvement in accuracy.

In this letter, we compare a Multiple-Input Single-Output (MISO)-Ultra WideBand (UWB)- Impulse Radio (IR) system and a Single-Input Single-Output (SISO)-UWB-IR system at high transmission rates. We evaluate the Bit Error Rate (BER) of the two systems with some RAKE receivers under heavy multipath environments. From the results of our computer simulation, we show that the SISO-UWB-IR system with Minimum Mean Square Error (MMSE)-RAKE receiver is a good candidate to achieve high transmission rates.

This paper presents Dynamic Attention Map by Ising model for face detection. In general, a face detector can not know where faces there are and how many faces there are in advance. Therefore, the face detector must search the whole regions on the image and requires much computational time. To speed up the search, the information obtained at previous search points should be used effectively. In order to use the likelihood of face obtained at previous search points effectively, Ising model is adopted to face detection. Ising model has the two-state spins; "up" and "down". The state of a spin is updated by depending on the neighboring spins and an external magnetic field. Ising spins are assigned to "face" and "non-face" states of face detection. In addition, the measured likelihood of face is integrated into the energy function of Ising model as the external magnetic field. It is confirmed that face candidates would be reduced effectively by spin flip dynamics. To improve the search performance further, the single level Ising search method is extended to the multilevel Ising search. The interactions between two layers which are characterized by the renormalization group method is used to reduce the face candidates. The effectiveness of the multilevel Ising search method is also confirmed by the comparison with the single level Ising search method.

This paper present three characteristic functions which can express the luminance distribute characteristic much better. Based on these functions a region classification algorithm is presented. The algorithm can offer more information on regions' similarity and greatly improve the efficiency and performance of match seeking in fractal coding. It can be widely applied to many kinds of fractal coding algorithms. Analysis and experimental results proved that it can offer more information on luminance distribute characteristics among regions and greatly improve the decoding quality and compression ratio with holding the running speed.

The resource allocation problem in multi-agent systems is one of the crucial problems hindering the development of multi-agent technologies. This study demonstrates that "time delay" functions as an effective factor in a resource allocation, contrasting to the conventional real-time oriented multi-agent paradigm by 1) introducing a "fickle" agent, whose own strategy fluctuates randomly, and 2) an agent repository mechanism. This study also demonstrates that in the resource allocation process, time delay induces dramatic changes in performance, the specific phenomenon is the so-called "phase transition phenomenon". This finding means emergence of the phase transition is cited as a major factor governing multi-agent system performance. This knowledge is of essential importance in the regulation in multi-agent performance.

This paper presents an RTOS-based methodology for design and validation of embedded systems. The heart of our methodology is the use of an RTOS simulation model from the very early stage of the system design. A case study with a JPEG decoder application is also presented in order to demonstrate the effectiveness of our methodology.

A novel phase error correction scheme is proposed for the high rate OFDM-based wireless local area networks (WLANs). The proposed scheme makes the system capable of efficiently compensating the whole phase error due to the residual sampling clock offset and frequency offset estimation error after timing and frequency offset compensation, as well as the phase noise.

We propose a new two-stage blind separation and deconvolution strategy for multiple-input multiple-output (MIMO)-FIR systems driven by colored sound sources, in which single-input multiple-output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. After the separation by the SIMO-ICA, a blind deconvolution technique for the SIMO model can be applied even when each source signal is temporally correlated and the mixing system has a nonminimum phase property. The simulation results reveal that the proposed algorithm can successfully achieve separation and deconvolution of a convolutive mixture of speech, and outperforms a number of conventional ICA-based BSD methods.

In virtual cellular network (VCN), proposed for high-speed packet mobile communications, the signal transmitted from a mobile terminal is received by wireless ports distributed in each virtual cell and relayed to the central port that acts as a gateway to the core network. In this letter, we apply the multi-hop maximal ratio combining (MHMRC) diversity and propose the route modification algorithm in order to improve transmit power efficiency degradation caused by the carrier frequency difference between the control and the data communication channels for VCN. The transmit power efficiency and the distribution of the number of hops are evaluated by computer simulation for a VCN.

We propose a method for the recovery of projective structure and motion by the factorization of the rank 1 matrix containing the images of all points in all views. In our method, the unknowns are the 3D motion and relative depths of the set of points, not their 3D positions. The coordinates of the points along the camera plane are given by their image positions in the first frame. The knowledge of the coordinates along the camera plane enables us to solve the SFM problem by iteratively factorizing the rank 1 matrix. This simplifies the decomposition compared with the SVD (Singular Value Decomposition). Experiments with both simulated and real data show that the method is efficient for the recovery of projective structure and motion.

This paper proposes the design to reduce the number of fed elements by replacing with parasitic elements in an array antenna. The study depends on the analysis of electromagnetic wave fields in consideration of the coupling between the half-wavelength dipoles. The case of 2 fed elements and 2 parasitic elements is considered as a unit cell to form the total array. After optimizing the element arrangement, the antenna gain can match that of the equivalent 4-fed element case. Feeding networks in a high power radiating system are analyzed in terms of the length and matching of feed lines, and the arrangement of amplifiers.

This letter proposes two very-low-complexity maximum-likelihood (ML) detection algorithms based on QR decomposition for the quasi-orthogonal space-time code (QSTBC) with four transmit antennas [3]-[5], called VLCMLDec1 and VLCMLDec2 decoders. The first decoder, VLCMLDec1, can be used to detect transmitted symbols being extracted from finite-size constellations such as phase-shift keying (PSK) or quadrature amplitude modulation (QAM). The second decoder, VLCMLDec2, is an enhanced version of the VLCMLDec1, developed mainly for QAM constellations. Simulation results show that both of the proposed decoders enable the QSTBC to achieve ML performance with significant reduction in computational load.

In this letter a planar tapered-slot-fed annular slot antenna with band-notched characteristics is presented for ultra-wideband radios. By etching a narrowband resonance slit on the non-radiating part of the antenna, the proposed antenna is capable of not only reducing the interference at the WLAN bands but also of avoiding the spatial-dependent bandstop characteristics that have commonly occurred in previous works.

To increase the number of the embedded secrets and to improve the quality of the stego-image in the vector quantization (VQ)-based information hiding scheme, in this paper, we present a novel information-hiding scheme to embed secrets into the side match vector quantization (SMVQ) compressed code. First, a host image is partitioned into non-overlapping blocks. For these seed blocks of the image, VQ is adopted without hiding secrets. Then, for each of the residual blocks, SMVQ or VQ is employed according to the smoothness of the block such that the proper codeword is chosen from the state codebook or the original codebook to compress it. Finally, these compressed codes represent not only the host image but also the secret data. Experimental results show that the performance of the proposed scheme is better than other VQ-based information hiding scheme in terms of the embedding capacity and the image quality. Moreover, in the proposed scheme, the compression rate is better than the compared scheme.

Over the past few years, a considerable number of studies have been conducted on modeling the bargaining game using artificial agents on within-model interaction. However, very few attempts have been made at study on the interaction and co-evolutionary process among heterogeneous artificial agents. Therefore, we present two kinds of artificial agents, based on genetic algorithm (GA) and reinforcement learning (RL), which play a game on between-model interaction. We investigate their co-evolutionary processes and analyze their parameters using the analysis of variance.

This paper presents a low-complexity equalization for M-ary biorthogonal keying based direct sequence ultra wideband (MBOK DS-UWB) systems. We focus on a Viterbi equalizer, which is based on maximum likelihood sequence estimation (MLSE). To reduce the computational complexity of MLSE-based equalizer, we use two strategies. One is the use of delayed-decision feedback sequence estimation (DDFSE), which is a hybrid estimation between MLSE and decision feedback estimation (DFE). And the other is the truncation of state transition in MLSE by considering MBOK pulse mapping. The reduced complexity sequence estimation is named as reduced state (RS)-DDFSE. By the use of RS-DDFSE, the complexity of Viterbi equalizer for MBOK DS-UWB is significantly reduced, by comparison with that of MLSE. The performance of RS-DDFSE based equalizer is evaluated on multipath fading channel models provided by IEEE802.15.3a. An analysis on trellis diagram of RS-DDFSE and simulation results show that the impact on error rate performance generated by the complexity lower is slight.