This paper presents two novel blind set-theoretic adaptive filtering algorithms for suppressing "Multiple Access Interference (MAI)," which is one of the central burdens in DS/CDMA systems. We naturally formulate the problem of MAI suppression as an asymptotic minimization of a sequence of cost functions under some linear constraint defined by the desired user's signature. The proposed algorithms embed the constraint into the direction of update, and thus the adaptive filter moves toward the optimal filter without stepping away from the constraint set. In addition, using parallel processors, the proposed algorithms attain excellent performance with linear computational complexity. Geometric interpretation clarifies an advantage of the proposed methods over existing methods. Simulation results demonstrate that the proposed algorithms achieve (i) much higher speed of convergence with rather better bit error rate performance than other blind methods and (ii) much higher speed of convergence than the non-blind NLMS algorithm (indeed, the speed of convergence of the proposed algorithms is comparable to the non-blind RLS algorithm).

The emergence of intelligent and sophisticated attack techniques makes web services more vulnerable than ever which are becoming an important business tool in e-commerce. Many techniques have been proposed to remove the security vulnerabilities, yet have limitations. This paper proposes an adaptive mechanism for a web-server intrusion-tolerant system (WITS) to prevent unknown patterns of attacks by adapting known attack patterns. SYN flooding attacks and their adaptive defense mechanisms are simulated as a case study to evaluate the performance of the proposed adaptation mechanism.

In this paper, we briefly describe a fast Jacket transform based on simple matrices factorization. The proposed algorithm needs fewer and simpler computations than that of the existing methods, which are RJ's [2], Lee's [7] and Yang's algorithm [8]. Therefore, it can be easily applied to develop the efficient fast algorithm for signal processing and data communications.

In this paper, we address issues in improving hands-free speech recognition performance in different car environments using multiple spatially distributed microphones. In the previous work, we proposed the multiple linear regression of the log spectra (MRLS) for estimating the log spectra of speech at a close-talking microphone. In this paper, the concept is extended to nonlinear regressions. Regressions in the cepstrum domain are also investigated. An effective algorithm is developed to adapt the regression weights automatically to different noise environments. Compared to the nearest distant microphone and adaptive beamformer (Generalized Sidelobe Canceller), the proposed adaptive nonlinear regression approach shows an advantage in the average relative word error rate (WER) reductions of 58.5% and 10.3%, respectively, for isolated word recognition under 15 real car environments.

We proposed a fuzzy control scheme to implement the cycle-to-cycle control for restoring swing phase of gait using functional electrical stimulation (FES). We designed two fuzzy controllers for the biceps femoris short head (BFS) and the vastus muscles to control flexion and extension of the knee joint during the swing phase. Control capabilities of the designed fuzzy controllers were tested and compared to proportional-integral-derivative (PID) and adaptive PID controllers in automatic generation of stimulation burst duration and compensation of muscle fatigue through computer simulations using a musculo-skeletal model. Parameter adaptations in the adaptive PID controllers did not significantly improve the control performance of the PID controllers. The fuzzy controllers were superior to the PID and adaptive PID controllers under several subject conditions and different fatigue levels. These results showed the fuzzy controller would be suitable to implement the cycle-to-cycle control of FES-induced gait.

In this paper we propose a stepped-FM array radar system that can precisely estimate the target position by combining S- and T-MUSIC and adaptive beamforming. By adopting the adaptive beamformer as a preprocessor of T-MUSIC, the proposed system can uniquely determine the direction and distance of targets. In addition, the distance estimation precision is improved by introducing beamformer.

In this paper, a pilot-assisted channel estimation using adaptive interpolation (in which, different interpolation filter tap weights is used for different symbol position) is proposed. Each set of tap weights is updated using the normalized least mean square (NLMS) algorithm, the reference signal for which is obtained by decision feedback and reverse modulation of the received data symbol. In order to reduce the number of tap weight sets and to achieve fast convergence, the conjugate centrosymmetry property of the tap weight set is used. The average bit error rate (BER) performance in a frequency-selective Rayleigh fading channel is evaluated by computer simulation. Also evaluated is the robustness against the frequency offset between a transmitter and a receiver.

Jacobian Adaptation (JA) has been successfully used in Automatic Speech Recognition (ASR) systems to adapt the acoustic models from the training to the testing noise conditions. In this work we present an improvement of JA for speaker verification, where a specific training noise reference is estimated for each speaker model. The new proposal, which will be referred to as Model-dependent Noise Reference Jacobian Adaptation (MNRJA), has consistently outperformed JA in our speaker verification experiments.

In adaptive motion estimation, spatial-temporal correlation based motion type inference has been recognized as an effective way to guide the motion estimation strategy adjustment according to video contents. However, the complexity and the reliability of those methods remain two crucial problems. In this paper, a motion vector field model is introduced as the basis for a new spatial-temporal correlation based motion type inference method. For each block, Full Search with Adaptive Search Window (ASW) and Three Step Search (TSS), as two search strategy candidates, can be employed alternatively. Simulation results show that the proposed method can constantly reduce the dynamic computational cost to as low as 3% to 4% of that of Full Search (FS), while remaining a closer approximation to FS in terms of visual quality than other fast algorithms for various video sequences. Due to its efficiency and reliability, this method is expected to be a favorable contribution to the mobile video communication where low power real-time video coding is necessary.

This paper overviews a total solution for frequency-domain blind source separation (BSS) of convolutive mixtures of audio signals, especially speech. Frequency-domain BSS performs independent component analysis (ICA) in each frequency bin, and this is more efficient than time-domain BSS. We describe a sophisticated total solution for frequency-domain BSS, including permutation, scaling, circularity, and complex activation function solutions. Experimental results of 22, 33, 44, 68, and 22 (moving sources), (#sources#microphones) in a room are promising.

A robust microphone array for speech enhancement and noise suppression is studied in this paper. To overcome target signal cancellation problem of conventional beamformer caused by array imperfections or reverberation effects of acoustic enclosure, the proposed microphone array adopts an arbitrary model of channel transfer function (TF) relating microphone and speech source. Since the estimation of channel TF itself is often intractable, herein, transfer function ratio (TFR) is estimated instead and used to form a suboptimal beamformer. A robust TFR estimation method is proposed based on signal subspace analysis technique against stationary or slowly varying noise. Experiments using simulated signal and actual signal recorded in a real room illustrate that the proposed method has high performance in adverse environment.

With reference to video motion estimation in the framework of the new H.264/AVC video coding standard, this paper presents algorithmic and architectural solutions for the implementation of context-aware coprocessors in real-time, low-power embedded systems. A low-complexity context-aware controller is added to a conventional Full Search (FS) motion estimation engine. While the FS coprocessor is working, the context-aware controller extracts from the intermediate processing results information related to the input signal statistics in order to automatically configure the coprocessor itself in terms of search area size and number of reference frames; thus unnecessary computations and memory accesses can be avoided. The achieved complexity saving factor ranges from 2.2 to 25 depending on the input signal while keeping unaltered performance in terms of motion estimation accuracy. The increased efficiency is exploited both for (i) processing time reduction in case of software implementation on a programmable platform; (ii) power consumption reduction in case of dedicated hardware implementation in CMOS technology.

This paper presents a highly parallel architecture for deblocking filter in H.264/AVC. We adopt various parallel schemes in memory sub-system and datapath. A 2-dimensional parallel memory scheme is employed to support efficient parallel access in both horizontal and vertical directions in order to speed up the whole filtering process. This parallel memory also eliminates the need for a transpose circuit. In the datapath, an algorithm optimization is performed to implement parallel filtering with hardware reuse. Pipeline techniques are also adopted to improve the throughput of filtering operations. Our design is implemented under TSMC 0.18 µm technology. Results show that the core size is 0.821.13 mm2 when the maximum frequency is 230 MHz. Compared to other existing architectures, our design has advantages in both speed and area.

This paper presents an adaptive control design for the ABR traffic congestion control in ATM networks. Firstly, we consider a control-based mathematical model to the ABR traffic congestion control problem. Then the feedback pole placement control design is applied to the ATM ABR traffic congestion control problem for the case of known delays. Finally, by using the online plant parameter estimation algorithm and modifying the controller parameters adaptively in real time, a method to treat the case of unknown time-varying delays is proposed. Several design modifications are introduced to solve practical control issues such as bounded command rate constraint, output buffer saturation and bounded values to the plant parameter estimation algorithm. Simulations are implemented to verify the proposed control design. It is shown that while considering these practical control issues, the control method satisfies the requirements of fairness to users, network efficiency, unknown time-varying delays, queue length control and good convergence performance at an acceptable computation effort.

This paper proposes a reconfigurable adaptive FEC system based on Reed-Solomon (RS) code with interleaving. In adaptive FEC schemes, error correction capability t is changed dynamically according to the communication channel condition. For given error correction capability t, we can implement an optimal RS decoder composed of minimum hardware units for each t. If the hardware units of the RS decoder can be reduced for any given error correction capability t, we can embed as large deinterleaver as possible into the RS decoder for each t. Reconfiguring the RS decoder embedded with the expanded deinterleaver dynamically for each error correction capability t allows us to decode larger interleaved codes which are more robust error correction codes to burst errors. In a reliable transport protocol, experimental results show that our system achieves up to 65% lower packet error rate and 5.9% higher data transmission throughput compared to the adaptive FEC scheme on a conventional fixed hardware system. In an unreliable transport protocol, our system achieves up to 76% better bit error performance with higher code rate compared to the adaptive FEC scheme on a conventional fixed hardware system.

In this paper, we propose a new on-line adaptive relaxation algorithm for an inverse filter in a multichannel sound reproduction system. The fluctuation of room transfer functions degrades reproduced sound in conventional sound reproduction systems in which the coefficients of the inverse filter are fixed. In order to resolve this problem, an iterative relaxation algorithm for an inverse filter performed by truncated singular value decomposition (adaptive TSVD) has been proposed. However, it is difficult to apply this method within the time duration of the sound of speech or music in the original signals. Therefore, we extend adaptive TSVD to an on-line-type algorithm based on the observed signal at only one control point, normalizing the observed signal with the original sound. The result of the simulation using real environmental data reveals that the proposed method can always carry out the relaxation process against acoustic fluctuation, for any time duration. Also, subjective evaluation in the real acoustic environment indicates that the sound quality improves without degrading the localization.

Our goal in this paper is to provide a complete detection analysis for the OS processor along with OSGO and OSSO modified versions, for M postdetection integrated pulses when the operating environment is nonideal. Analytical results of performance are presented in both multiple-target situations and in regions of clutter power transitions. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the Swerling II target fluctuation model. As the number of noncoherently integrated pulses increases, lower threshold values and consequently better detection performances are obtained in both homogeneous and multiple target background models. However, the false alarm rate performance of OSSO-CFAR scheme at clutter edges is worsen with increasing the postdetection integrated pulses. As predicted, the OSGO-CFAR detector accommodates the presence of spurious targets in the reference window, given that their number is within its allowable range in each local window, and controls the rate of false alarm when the contents of the reference cells have clutter boundaries. The OSSO-CFAR scheme is useful in the situation where there is a cluster of radar targets amongst the estimation cells.

We present a simple bit allocation scheme based on adaptive coding for MIMO-OFDM (Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing) systems with V-BLAST (Vertical-Bell laboratories LAyered Space-Time) detector. The proposed scheme controls the code rate of the channel coding and assigns the same modulation and coding to the set of selected sub-channels, which greatly reduces the feedback burden while achieving good performance. Simulation results show that the proposed scheme with minimal feedback provides significant performance improvement over other systems.

This paper attempts to use the polarimetric correlation coefficient for extraction of the polarimetric features of the urban areas and the natural distributed areas from Polarimetric Synthetic Aperture Radar (POLSAR) data. There is a possibility that the polarimetric correlation coefficient can reveal various scattering mechanisms of terrains based on the reflection symmetry property. In order to verify the capability of polarimetric correlation coefficient, we examined the behavior of this coefficient of the urban areas and the natural distributed areas with respect to the several polarimetric scattering models in the linear and circular polarization bases, and the difference of the polarimetric scattering characteristics between these two areas was derived. It was confirmed that the polarimetric correlation coefficient is useful to extract the polarimetric features from the actual L-band and X-band POLSAR data.

A method for detecting shallowly buried landmines using sequential ground penetrating radar (GPR) data is presented. After removing a dominant coherent component arising from the ground surface reflection from the GPR data, three kinds of target features related to wave correlation, energy ratio, and signal arrival time are extracted. Since the detection problem treated here is reduced to a binary hypothesis test, an approach based on a likelihood ratio test is employed as a detection algorithm. In order to check the detection performance, a Monte Carlo simulation is carried out for data generated by a two-dimensional finite-difference time domain (FDTD) method. Results given in the form of receiver operating characteristic (ROC) curves show that good detection performance is obtained even for landmines buried at shallow depths under rough ground surfaces, where the responses from the landmines and that from the ground surface overlap in time.