In this letter, we propose a new method to detect faces in color images based on the characterized convex regional relationship. We detect skin and hair likeness regions using the derived skin and hair color models and the convex skin likeness and hair likeness regions are adopted as the characteristic convex regions. Finally, human faces can be detected via their intersection relationship. The proposed algorithm can accomplish face detection in an image including not only single face but also multi-faces and also detect deformed faces efficiently. To validity the effectiveness of the proposed method, we make experiments with various cases.

In the last three decades of the 20th Century, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems for business automation, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. Although we have witnessed many new technological promises, we have also encountered a number of practical limitations that hinder a widespread deployment of applications and services. On one hand, fast progress was observed in statistical speech and language modeling. On the other hand only spotty successes have been reported in applying knowledge sources in acoustics, speech and language science to improving speech recognition performance and robustness to adverse conditions. In this paper we review some key advances in several areas of speech recognition. A bottom-up detection framework is also proposed to facilitate worldwide research collaboration for incorporating technology advances in both statistical modeling and knowledge integration into going beyond the current speech recognition limitations and benefiting the society in the 21st century.

A soft-decision decoding scheme of low-density parity-check codes (LDPC) is proposed for hard-detected signals of optical fiber communication (OFC) systems. Based on the error detection, the proposed scheme converts the received hard-decision into soft reliability for the input of the LDPC decoder, and soft-decision decoding is performed. Simulation results under OFC channels are shown and superior performance is obtained by using the proposed decoding scheme of the LDPC codes.

We describe progress we have achieved in the development of our quantum transport modeling for nano-scale devices. Our simulation is based upon either the non-equilibrium Green's function method (NEGF) or the quantum correction (QC) associated with density gradient method (DG) and/or effective potential method (EP). We show the results of our modeling methods applied to several devices and discuss issues faced with regards to computational time, open boundary conditions, and their relationship to self-consistent solution of the Poisson-NEGF equations. We also discuss those for efficiently tailored QC Monte Carlo techniques.

Interactions between ESD protection devices and other components of a chip can lead to complex and not easily anticipated discharge bevahior. Triggering of a protection MOSFET is equivalent to the closing of a fast switch and can cause substantial transient discharge currents. The peak value of this current depends on the chip capacitance, resistance, properties of the protection clamp, etc. Careful optimization of the protection circuit is therefore necessary to avoid current overstress and circuit failure.

This paper presents novel techniques for blind separation and blind extraction of instantaneously mixed binary sources, which are suitable for the case with less sensors than sources. First, a solvability analysis is presented for a general case. Necessary and sufficient conditions for recoverability of all or some part of sources are derived. A new deterministic blind separation algorithm is then proposed to estimate the mixing matrix and separate all sources efficiently in the noise-free or low noise level case. Next, using the Maximum Likelihood (ML) approach for robust estimation of centers of clusters, we have extended the algorithm for high additive noise case. Moreover, a new sequential blind extraction algorithm has been developed, which enables us not only to extract the potentially separable sources but also estimate their number. The sources can be extracted in a specific order according to their dominance (strength) in the mixtures. At last, simulation results are presented to illustrate the validity and high performance of the algorithms.

Though, high dielectric constant material is a possible near future solution in order to suppress gate current densities of MOSFETs, the barrier height generally decreases with an increasing dielectric constant. In this paper, the injection current through gate stacks has been calculated while taking into account the electron temperature using the W.K.B. method to understand the impact of the injection current from the drain edge.

This paper concerns recognizing 3-dimensional object using proposed multi-layer block model. In particular, we aim to achieve desirable recognition performance while restricting the computational load to a low level using 3-step feature extraction procedure. An input image is first precisely partitioned into hierarchical layers of blocks in the form of base blocks and overlapping blocks. The hierarchical blocks are merged into a matrix, with which abundant local feature information can be obtained. The local features extracted are then employed by the kernel based support vector machines in tournament for enhanced system recognition performance while keeping it to low dimensional feature space. The simulation results show that the proposed feature extraction method reduces the computational load by over 80% and preserves the stable recognition rate from varying illumination and noise conditions.

It is well known that KT method proposed by R. Kumaresan and D. W. Tufts is used as a popular parameter estimation method of exponentially damped signal. It is based on linear backward-prediction method and singular value decomposition (SVD). However, it is difficult to estimate parameters correctly by KT method in the case when high noise exists in the signal. In this paper, we propose a parameter (frequency components and damping factors) estimation method to improve the performance of KT method under high noise. In our proposed method, we find the signal zero groups by calculating zeros with different data record lengths according to the combination of forward-prediction and backward-prediction, the mean value of the zeros in the signal zero groups are calculated to estimate the parameters of the signal. The proposed method can estimate parameters correctly and accurately even when high noise exists in the signal. Simulation results are shown to confirm the effectiveness of the proposed method.

In certain computer and communication systems, the significant number of byte errors are not hard errors, but a few transient bit errors confined to byte regions. This kind of byte errors are called spotty byte errors, meaning, not all, but only 2 or 3 random bits, are corrupted in a byte. Especially, the codewords of memory systems which use recent high density wide I/O data semiconductor DRAM chips are prone to this kind of spotty byte errors. This is because, the presence of strong electromagnetic waves in the environment or the bombardment of an energetic particle on a DRAM chip is highly likely to upset more than just one bit stored in that chip. Under this situation, codes capable of correcting single spotty byte errors are suitable for application in semiconductor memory systems. This paper defines a spotty byte error as a random t-bit error confined to a b-bit byte and proposes a class of codes called Single t/b-error Correcting (St/bEC) codes which are capable of correcting single spotty byte errors occurring in computer and communication systems. For the case where the chip data output is 16 bits, i.e., b=16, the S3/16EC code proposed in this paper requires only 16 check bits, that is, only one chip is required for check bits at practical information lengths such as 64, 128 and 256 bits. Furthermore, this S3/16EC code is capable of detecting more than 95% of all single 16-bit byte errors at information length 64 bits.

The effect of a conductive sheet placed over a PCB with a microstrip line on electromagnetic noise shielding is investigated. As a typical conductive sheet, a copper sheet is used, and is not grounded. First, the input impedance of the microstrip line and the magnetic field when varying the distance between the PCB and the conductive sheet are measured, and the distance that does not affect the signal transmission is set at 8 mm. Second, the effect of the conductive sheet size on the magnetic field radiation is discussed by measurements and FDTD modeling, and the magnetic near-field distribution around the PCB is visualized by using the FDTD calculation. A conductive sheet whose width is larger than the PCB width should be effective for suppression of the magnetic near-field noise radiation just above a PCB.

A high power dissipation density in today's miniature electronic/mechanical systems makes on-chip thermal management very important. In order to achieve quick to evaluate, yet accurate electro-thermal models, needed for the thermal management of microsystems, a model order reduction is necessary. In this paper, we present an automatic, Krylov-subspace-based order reduction of a electro-thermal model, which we illustrate by a novel type of micropropulsion device. Numerical simulation results of the full finite element model and the reduced order model, that describes the transient electro-thermal behavior, are presented. A comparison between Krylov-subspace-based order reduction, order reduction using control theoretical approaches and commercially available reduced order modeling has been performed. A Single-Input-Single-Output setup for the Arnoldi reduction algorithm was proved to be sufficient to accurately represent the complete time-dependent temperature distribution of the device.

The implant-anneal cycle for B doping during Si device fabrication causes transient enhanced diffusion (TED) of B and the formation of small immobile B-interstitial clusters (BICs) which deactivate the B. Additionally, since modern ultrashallow devices put most of the B in immediate proximity of the Si/SiO2 interface, interface-dopant interactions like segregation become increasingly important. In this work, we use density-functional theory calculations to study TED, clustering, and segregation of B during annealing and discuss a continuum model which combines the TED and clustering results.

Changing vehicle structures and backgrounds makes it very difficult to correctly extract a license plate region from a vehicle image. In this paper, we propose a simple method to extract the license plate region using edge properties of wavelet subband. The High Frequency Subband (HFS) of an image has edge information for each direction. Edge information is concentrated in each direction of the Headlight-Radiator-Headlight (H-R-H) and the license plate region compared to other regions in the vehicle image. This paper shows a license plate region extraction method using these edge properties and our experimental results with various vehicle images.

In this paper we consider blind source separation (BSS) problem of signals which are spatially uncorrelated of order four, but temporally correlated of order four (for instance speech or biomedical signals). For such type of signals we propose a new sufficient condition for separation using fourth order statistics, stating that the separation is possible, if the source signals have distinct normalized cumulant functions (depending on time delay). Using this condition we show that the BSS problem can be converted to a symmetric eigenvalue problem of a generalized cumulant matrix Z(4)(b) depending on L-dimensional parameter b, if this matrix has distinct eigenvalues. We prove that the set of parameters b which produce Z(4)(b) with distinct eigenvalues form an open subset of RL, whose complement has a measure zero. We propose a new separating algorithm which uses Jacobi's method for joint diagonalization of cumulant matrices depending on time delay. We empasize the following two features of this algorithm: 1) The optimal number of matrices for joint diago- nalization is 100-150 (established experimentally), which for large dimensional problems is much smaller than those of JADE; 2) It works well even if the signals from the above class are, additionally, white (of order two) with zero kurtosis (as shown by an example).

This paper presents a new hierarchical scheduling method for a large-scale manufacturing system based on the hybrid Petri-net model, which consists of CPN (Continuous Petri Net) and TPN (Timed Petri Net). The study focuses on an automobile production system, a typical large-scale manufacturing system. At a high level, CPN is used to represent continuous flow in the production process of an entire system, and LP (Linear Programming) is applied to find the optimal flow. At a low level, TPN is used to represent the manufacturing environment of each sub-production line in a decentralized manner, and the MCT algorithm is applied to find feasible semi-optimal process sequences for each sub-production line. Our proposed scheduling method can schedule macroscopically the flow of an entire system while considering microscopically any physical constraints that arise on an actual shop floor.

Transmit adaptive array requires the forward link channel state for evaluating the optimum transmit weight in which a feedback channel transports the forward link channel state to the base station. Since the feedback information limits the transmission rate of the reverse link traffic, it is necessary to keep the number of feedback bits to a minimum. This paper presents a system in which the N transmit antennas are extended to the 2N transmit antennas while the feedback channel is limited as that of N-transmit antenna system. The increased antennas can give additional diversity gain but requires higher rate of feedback bits. The limited feedback channel increases the quantization error of feedback information since the number of feedback bits assigned to each antenna is reduced. In order to overcome the limited rate of feedback channel problem, this paper proposes the transmit antenna selection schemes which can effectively use the limited feedback bits, reduce the computational complexity at the mobile station, and eventually achieve diversity gain. System performances are investigated for the case of N=4 for the various antenna selection schemes on both flat fading and multi-path fading channels.

In this paper, we discuss crosstalk equalization technique for high-speed digital transmission systems. This equalization technique makes use of the cyclostationarity of the crosstalk interferer. We first analyze the eigenstructure of the equalizer in the presence of cyclostationary crosstalk interference. It is shown that the eigenvalues of the equalizer depend upon the folded signal and interferer power spectra, and the cross power spectrum between the signal and the interferer. The expressions of the minimum mean square error (MMSE) and the excess MSE are then obtained by using the equalizer's eigenstructure. Analysis and simulation results indicate that such peculiar equalizer's eigenstructure in the presence of cyclostationary interference results in significantly different initial convergence and steady-state behaviors as compared with the stationary noise case. We also show that the performance of the equalizer varies depending on the relative clock phase of the symbol clocks used by the signal and the crosstalk interferer.

In this paper, a grey filtering approach based on GM(1,1) model is proposed. Then the grey filtering is applied to speech enhancement. The fundamental idea in the proposed grey filtering is to relate estimation error of GM(1,1) model to additive noise. The simulation results indicate that the additive noise can be estimated accurately by the proposed grey filtering approach with an appropriate scaling factor. Note that the spectral subtraction approach to speech enhancement is heavily dependent on the accuracy of statistics of additive noise and that the grey filtering is able to estimate additive noise appropriately. A magnitude spectral subtraction (MSS) approach for speech enhancement is proposed where the mechanism to determine the non-speech and speech portions is not required. Two examples are provided to justify the proposed MSS approach based on grey filtering. The simulation results show that the objective of speech enhancement has been achieved by the proposed MSS approach. Besides, the proposed MSS approach is compared with HFR-based approach in [4] and ZP approach in [5]. Simulation results indicate that in most of cases HFR-based and ZP approaches outperform the proposed MSS approach in SNRimp. However, the proposed MSS approach has better subjective listening quality than HFR-based and ZP approaches.

It is believed that distant-talking speech recognition in a noisy environment requires a large-scale microphone array. However, this cannot fit into small consumer devices. Our objective is to improve the performance with a limited number of microphones (preferably only left and right). In this paper, we focused on a profile that is the shape of the power distribution according to the beamforming direction. An observed profile can be decomposed into known profiles for directional sound sources and a non-directional background sound source. Evaluations confirmed this method reduced the CER (Character Error Ratio) for the dictation task by more than 20% compared to a conventional 2-channel Adaptive Spectral Subtraction beamformer in a non-reverberant environment.