This letter describes two low complexity receiver structures over a multi-broadcast channel of an orthogonal frequency division multiple access (OFDMA) multi-user system. The first is a one-group occupied receiver structure, whose complexity is much lower than that of a conventional OFDMA receiver structure. The other one, a multi-group occupied receiver structure, exploits multiple groups for one user, by which users' down-link data rate can be adaptively controlled by a base station (BS). Unlike unchangeable complexity of an OFDMA receiver structure that performs full-size of a fast Fourier transform (FFT) operation although only a few subcarriers are taken, its complexity linearly increases with the number of occupied subcarrier groups. The proposed receiver structures can meet the possible high-rate demand in the down-link and will become one of the strong candidates in next generation mobile communication systems.

Ultrahigh-speed continuous-tine delta-sigma modulators (DSMs) have been designed by using a fully-differential comparator consisting of resonant-tunneling diodes (RTDs) and HEMTs. Continuous-time lowpass and bandpass filters using HEMTs have also been incorporated to obtain lowpass- and bandpass-type DSMs, respectively. Circuit simulation assuming 0.1-µm InP-based HEMT and RTD technology has revealed a successful operation of the 2nd-order lowpass DSM at a sampling frequency of 20 GHz. The clock frequency was 10 GHz because of the double sampling function of the present comparator. The 2nd-order bandpass DSM has also been designed with a center frequency of 3 GHz. These results clearly show high potential of the present delta-sigma modulators.

Coexisting with many concurrent narrowband services, the performance of UWB systems will be affected considerably by them. Specifically, IEEE 802.11a systems which operate around 5 GHz and overlap the band of UWB signals will interfere with UWB systems significantly. In this paper, a novel narrow-band interferences (NBI) suppression technique based on singular value decomposition (SVD) algorithm for a direct sequence ultra-wideband (DS-UWB) communication system is presented. SVD is used to approximate the interferences which then are subtracted from the received signals. The proposed technique is simple and robust. Simulation results show that the proposed new technique is very effective.

Natural, continuous tone images have a very important property of high correlation of adjacent pixels. Images which we wish to compress are usually non-stationary and can be reasonably modeled as smooth and textured areas separated by edges. This property has been successfully exploited in LOCO-I and CALIC by applying gradient based predictive coding as a major de-correlation tool. However, they only examine the horizontal and vertical gradients, and assume the local edge can only occur in these two directions. Their over-simplified assumptions hurt the robustness of the prediction in higher complex areas. In this paper, we propose an accurate gradient selective prediction (AGSP) algorithm which is designed to perform robustly around any type of image texture. Our method measures local texture information by comparison and selection of normalized scalar representation of the gradients in four directions. An adaptive predictor is formed based on the local gradient information and immediate causal pixels. Local texture properties are also exploited in the context modeling of the prediction error. The results we obtained on a test set of several standard images are encouraging. On the average, our method achieves a compression ratio significantly better than CALIC without noticeably increasing of computational complexity.

Broadcast encryption technology enables a sender to send information securely to a group of receivers excluding specified receivers over a broadcast channel. In this paper, we propose a new key-tree structure based on Rabin cryptosystem, and an access control scheme using the structure. We show the security of the access control scheme and construct a new broadcast encryption scheme based on it. The proposed broadcast encryption scheme is a modification of the complete subtree method and it reduces the number of keys a receiver stores to one. There have been proposed some modifications of the complete subtree method which minimize the number of keys for a receiver to one, and the most efficient one among them with respect to the computational overhead at receivers is based on RSA cryptosystem. The computational overhead at receivers in our scheme is around log2e times smaller than the most efficient previously proposed one, where e is a public exponent of RSA, and the proposed scheme is the most efficient among tree based one-key schemes. This property is examined by experimental results. Our scheme achieves this reduction in the computational overhead in exchange for an increase in the size of nonsecret memory by [log n * few (e.g. eight)] bits, where n is the total number of receivers.

In this paper, we proposed a new Two-Dimensional Linear Discriminant Analysis (2DLDA) method, based on Two-Dimensional Principle Component Analysis (2DPCA) concept. In particular, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the Small Sample Size (SSS) problem, thus overcoming the traditional LDA. We combine 2DPCA and our proposed 2DLDA on the Two-Dimensional Linear Discriminant Analysis of principle component vectors framework. Our framework consists of two steps: first we project an input face image into the family of projected vectors via 2DPCA-based technique, second we project from these space into the classification space via 2DLDA-based technique. This does not only allows further reducing of the dimension of feature matrix but also improving the classification accuracy. Experimental results on ORL and Yale face database showed an improvement of 2DPCA-based technique over the conventional PCA technique.

This paper proposes a novel compressing and expanding (companding) system for OFDM wireless communications that minimizes the compression distortion and so reduces the peak power of OFDM symbols. OFDM systems suffer from large variations in instantaneous peak power. Such transients distort the signals when they are passed through a nonlinear high power amplifier (HPA) prior to transmission. Existing companding systems are far from perfect since the receiver can not accurately estimate the degree of compression applied by the transmitter and thus can not regenerate the original signal by expansion; the key problems are the band pass filter (BPF), HPA, and the noise component enhanced by the channel compensation filter. In the proposed companding system, each symbol is divided into segments, and series of consecutive segments are grouped into clusters. Each cluster is multiplied by a weight equal to the inverse of the largest instantaneous power within the cluster. The receiver estimates the weight used for each time cluster. The weights for all clusters are averaged to mitigate the weight estimation error. As a result, the proposed expander can accurately estimate the weights used and thus well suppress the compression distortion.

This paper presents a robust object tracking method under pose variation and partial occlusion. In practical environment, the appearance of objects is changed dynamically by pose variation or partial occlusion. Therefore, the robustness to them is required for practical applications. However, it is difficult to be robust to various changes by only one tracking model. Therefore, slight robustness to variations and the easiness of model update are required. For this purpose, Kernel Principal Component Analysis (KPCA) of local parts is used. KPCA of local parts is proposed originally for the purpose of pose independent object recognition. Training of this method is performed by using local parts cropped from only one or two object images. This is good property for tracking because only one target image is given in practical applications. In addition, the model (subspace) of this method can be updated easily by solving a eigen value problem. Performance of the proposed method is evaluated by using the test face sequence captured under pose, partial occlusion, scaling and illumination variations. Effectiveness and robustness of the proposed method are demonstrated by the comparison with template matching based tracker. In addition, adaptive update rule using similarity with current subspace is also proposed. Effectiveness of adaptive update rule is shown by experiment.

We propose a novel strategy to obtain a high spatio-temporal resolution video. To this end, we introduce a dual sensor camera that can capture two video sequences with the same field of view simultaneously. These sequences record high resolution with low frame rate and low resolution with high frame rate. This paper presents an algorithm to synthesize a high spatio-temporal resolution video from these two video sequences by using motion compensation and spectral fusion. We confirm that the proposed method improves the resolution and frame rate of the synthesized video.

Intercarrier interference will cause the loss of subchannel orthogonality and increase the error floor in proportion to the Doppler frequency. In this paper, we firstly analyze the generation mechanism of intercarrier interference in OFDM. Then we propose an O(N log2N) complexity ICI equalizer for OFDM systems in the presence of double selective fading which is mainly bases on FFT operation. Simulation result shows that with only 6 iterations LCD-FFT can achieve better performance than the LS-equalizer. After 10 iterations LCD-FFT performs almost the same as MMSE equalizer.

Point pattern matching (PPM) arises in areas such as pattern recognition, digital video processing and computer vision. In this study, a novel Genetic Algorithm (GA) based method for matching affine-related point sets is described. Most common techniques for solving the PPM problem, consist in determining the correspondence between points localized spatially within two sets and then find the proper transformation parameters, using a set of equations. In this paper, we use this fact that the correspondence and transformation matrices are two unitary polar factors of Grammian matrices. We estimate one of these factors by the GA's population and then evaluate this estimation by computing an error function using another factor. This approach is an easily implemented one and because of using the GA in it, its computational complexity is lower than other known methods. Simulation results on synthetic and real point patterns with varying amount of noise, confirm that the algorithm is very effective.

Kernel-based learning algorithms have been successfully applied in various problem domains, given appropriate kernel functions. In this paper, we discuss the problem of designing kernel functions for binary regression and show that using a bell-shaped cosine function as a kernel function is optimal in some sense. The rationale of this result is based on the Karhunen-Loeve expansion, i.e., the optimal approximation to a set of functions is given by the principal component of the correlation operator of the functions.

The RC mismatch among S/H stages for time-interleaved ADCs causes a phase error and a gain error and the phase error is dominant. The paper points out that clock skew and the phase error caused by the RC mismatch have similar effects on the sampling error and then can be compensated with the clock skew compensation. Simulation results agree well with the theoretical analysis. With the phase error compensation of RC mismatch, the SNDR in 14b ADC can be improved by more than 15 dB in the case that the bandwidth of S/H circuits is 3 times the sampling frequency. This paper also proposes a method of clock skew and RC mismatch compensation in time-interleaved sample-and-hold (S/H) circuits by sampling clock phase adjusting.

An unsupervised adaptive signal processing method of principal components analysis (PCA) neural networks (NN) based on signal eigen-analysis is proposed to permit the eigenstructure analysis of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals. The objective of eigenstructure analysis is to estimate the pseudo noise (PN) of DS signals blindly. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Lastly, the PN sequence can be estimated by the principal eigenvector of autocorrelation matrix. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigen-analysis method becomes inefficient when the estimated PN sequence is long. We can use an unsupervised adaptive method of PCA NN to realize the PN sequence estimation from lower SNR input DS-SS signals effectively.

This paper describes an efficient PCRD (Post-Compression Rate-Distortion) scheme for rate control of JPEG2000. The proposed method determines the rate constant in consideration of the decreasing characteristic of RD-slopes and conducts rate allocation about only coding passes excluded from the previous rate allocation. As a result, it can considerably reduce the number of operations and encoding time with nearly the same PSNR performance as the conventional rate control scheme of JPEG2000.

A hardware algorithm for computing the reciprocal of the Euclidean norm of a 3-dimensional (3-D) vector which appears frequently in 3-D computer graphics is proposed. It is based on a digit-recurrence algorithm for computing the Euclidean norm and an on-line division (on-line reciprocal computation) algorithm. These algorithms are modified, so that the reciprocal of the Euclidean norm is computed by performing on-line division where the divisor is the partial result of Euclidean norm computation. Division, square-rooting, and reciprocal square-root computation, which are important operations in 3-D graphics, can also be performed using a circuit based on the proposed algorithm.

The purpose of the study is to obtain the automatic and optimal matching between a motion-measurement device such as a data glove and an output device such as a dexterous robot hand, where there are many differences in the numbers of degree of freedom, sensor and actuator positions, and data format, by means of motion imitation by the humans. Through the algorithm proposed here, a system engineer or user need no labor of determining the values of gains and parameters to be used. In the system, a subject with data glove imitated the same motion with a dexterous robot hand which was moving according to a certain mathematical function. Autoregressive models were adapted to the matching, where each joint angle in the robot and data glove data of the human were used as object and explanatory variables respectively. The partial regression coefficients were estimated by means of singular value decomposition with a system-noise reduction algorithm utilizing statistical properties. The experimental results showed that the robot hand was controlled with high accuracy with small delay, suggesting that the method proposed in this study is proper and easy way and is adaptive to many other systems between a pair of motion-measurement device and output device.

Two schemes for fast identification of JPEG coded images are proposed in this paper. The aim is to identify the JPEG images that are generated from the same original image and have equivalent or different compression ratios. Fast identification can be achieved since the schemes work on the quantized Discrete Cosine Transform (DCT) domain. It is not required to inverse the quantization and the DCT. Moreover, only a few coefficients are commonly required for identification. The first approach can avoid identification leakage or false negative (FN), and probably result in a few false positives (FP). The second approach can avoid both FN and FP, with a slightly higher processing time. By combining the two schemes, a faster and a more perfect identification can be achieved, in which FN and FP can be avoided.

This paper presents a novel gain boosted and bandwidth enhanced CMOS Op-Amp based on the well-known folded cascode structure. In contrast with the conventional methods which increase output resistance for gain boosting, the transconductance of the circuit is increased, therefore the -3 dB frequency is the same as for folded cascode structure. With negligible extra power consumption, the unity gain bandwidth is increased considerably. In this method, a new node is created in the circuit which introduces a pole to the transfer function with a frequency lower than cascode pole; feed-forward compensation is employed to reduce the effect of this pole on the frequency response. The input common mode range is limited slightly by 0.2-0.3 V with respect to folded cascode which is insensible. HSPICE simulations using level 49 parameters (BSIM3v3) in a typical 0.35 µm CMOS technology result in three times gain boosting and 60% enhancement in unity gain bandwidth compared to folded cascode, while the power consumption is increased by 10%.

This paper describes the efficiency of VLSI architecture for UMHexagonS (hybrid Unsymmetrical cross Multi Hexagon grid Search) matching algorithm. This algorithm is used for ME (Motion Estimation) of H.264/AVC video compression standard. The UMHexagonS is called a hybrid algorithm since it uses different kinds of searching patterns. VLSI architecture based on UMHexagonS is designed to provide a good tradeoff between gate sizes and high throughput. We implemented this architecture with about 309 K gates and 1/1792 throughput [block/cycle] for a search range of 16 and 44 macro blocks using synthesizable Verilog HDL.