The audio delay is becoming an important factor in audio streaming over short-range wireless network. In this study, we propose an efficient two-level delay control method, called frame sequence adaptation and audio sampling frequency compensation, for achieving stable audio delay with a small variation. To prove the effectiveness of our scheme, we implemented and evaluated the scheme on a Bluetooth network. Experimental results show that our scheme can control audio delay robustly and remove phase shift problem in multi-channel stereophonic audio broadcasting as well.

We propose an adaptive beamforming scheme for the combination of orthogonal frequency division multiplexing (OFDM) and adaptive antenna array. The combinational scheme is characterized by the sample matrix inverse (SMI) algorithm, frequency-to-time pilot transform and pre-FFT combination. For every OFDM block containing both data and pilot symbols, we transform the frequency-domain pilot symbols to the corresponding time-domain components. One of the obvious advantages of this transform is that the time interval of the antenna weight vector update can be reduced to only one OFDM sample interval, from one OFDM block interval of the conventional beamforming scheme in which the transform is not applied. This feature can greatly accelerate the convergence of SMI beamforming. The simulation results verify that the proposed beamforming scheme is capable of improving the convergence behavior significantly.

In wireless adaptive modulation and coding (AMC) systems, modulation type of a user's connection can change and the ongoing connection might fail due to the change of modulation. In this paper, we approach the AMC-induced call admission control (CAC) problem by focusing on the guaranteed connection. Three classes of calls, new, handoff, and modulation-changed, are considered. We modify the guard-channel CAC scheme such that the modulation-changed calls, in addition to the handoff calls, are allowed to use the guard channel. Then we analyze a Markov model for the CAC scheme designed with long-term AMC in mind. The proposed approach will be an essential tool to determine the guard-channel thresholds in the wireless AMC networks.

This article presents the analysis of the Bragg scattering phenomenon which has been observed in the images of machine-planted rice paddies acquired by the JERS-1 L-band synthetic aperture radar (SAR). The simultaneous measurements of rice plants were made at the SAR data acquisition times. Large differences of 20-25 dB in image intensity between the transplanting and ripening stages are found to be dependent on the planting direction and bunch separation. This selective image enhancement is a result of the Bragg resonance backscatter due to the double-bounce of incident L-band microwave between the flooded water surface and periodically planted bunches of rice plants. Support for the idea of double-bounce scattering is provided by the decomposition analysis of L-band and X-band polarimetric Pi-SAR data; and a simple numerical simulation based on the physical optics model shows fairly good agreement with the JERS-1 SAR data. The results presented in this paper is mainly of academic interest, but a suggestion can be made on the selection of suitable microwave band for monitoring rice fields.

This paper proposes a novel pre-FFT type OFDM adaptive array antenna called "Eigenvector Combining." The eigenvector combining array antenna is a realization of a post-FFT type OFDM adaptive array antenna through a pre-FFT signal processing, so it can achieve excellent performance with less computational complexity and shorter training symbols. Numerical results demonstrate that the proposed eigenvector combining array antenna shows excellent bit error rate performance close to the lower bound just with 2 OFDM symbol-long training symbols.

The convergence speed of the conventional adaptive LMS algorithms for time delay estimation (TDE) is highly dependent on the spectral distribution of the desired random source signals of interest, thus the performance of TDE might be degraded, dramatically. To solve this problem, in this letter, a DCT-transform domain constrained adaptive normalized-LMS filtering scheme, referred to as the adaptive constrained DCT-LMS algorithm, is devised for TDE. Computer simulation results verify that the proposed scheme can be used to achieve desired performance, for input random signals with different spectral distributions; it outperforms the unconstrained DCT-LMS and time-domain constrained adaptive LMS algorithms.

In this letter we present steady-state analyses of a gradient algorithm (GA) for second-order adaptive infinite impulse response (IIR) notch filters. A method for deriving more accurate estimation mean square error (MSE) expressions than the recently proposed method is presented. The method is based on the estimation error power spectral density (PSD). Moreover, an expression for the estimation bias for the adaptive IIR notch filter with constrained poles and zeros is shown to be obtained from the estimation MSE expression. Simulations are presented to confirm the validity of the analyses.

Expressions are presented for the probability of target detection and the measurement accuracy of the detection, taking into account the effects of antenna beam-pointing error. Evaluation of these expressions requires numerical integration, which is computationally expensive. Approximate but analytic and efficient expressions are also presented. Numerical examples are given to present the relative accuracy of our analytic approximations.

Active Shape Model (ASM) has been shown to be a powerful tool to aid the interpretation of images, especially in face alignment. ASM local appearance model parameter estimation is based on the assumption that residuals between model fit and data have a Gaussian distribution. Moreover, to generate an allowable face shape, ASM truncates coefficients of shape principal components into the bounds determined by eigenvalues. In this paper, an algorithm of modeling local appearances, called AdaBoosted ASM, and a shape parameter optimization method are proposed. In the algorithm of modeling the local appearances, we describe our novel modeling method by using AdaBoosted histogram classifiers, in which the assumption of the Gaussian distribution is not necessary. In the shape parameter optimization, we describe that there is an inadequacy on controlling shape parameters in ASM, and our novel method on how to solve it. Experimental results demonstrate that the AdaBoosted histogram classifiers improve robustness of landmark displacement greatly, and the shape parameter optimization solves the inadequacy problem of ASM on shape constraint effectively.

This paper presents a method of simple adaptive control (SAC) using neural networks for a class of nonlinear systems with bounded-input bounded-output (BIBO) and bounded nonlinearity. The control input is given by the sum of the output of the simple adaptive controller and the output of the neural network. The neural network is used to compensate for the nonlinearity of the plant dynamics that is not taken into consideration in the usual SAC. The role of the neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems. Furthermore, convergence and stability analysis of the proposed method is performed. Finally, the effectiveness of the proposed method is confirmed through computer simulation.

In this study, we propose an adaptive handoff scheme with dynamic hysteresis value for cellular communications, which is based on distance between the mobile station and the serving base station. Performance is evaluated in terms of the expected number of handoffs, the expected handoff delay, standard deviation of handoff location, and the expected link degradation probability as well. Numerical results and simulations show that the proposed scheme outperforms the handoff schemes with static hysteresis levels. The effect of distance error is also discussed.

We developed a system that detects the vehicle driving immediately ahead of one's own car in the same lane and measures the distance to and relative speed of that vehicle to prevent accidents such as rear-end collisions. The system is the first in the industry to use non-scanning millimeter-wave radar combined with a sturdy stereo image sensor, which keeps cost low. It can operate stably in adverse weather conditions such as rain, which could not easily be done with previous sensors. The system's vehicle detection performance was tested, and the system can correctly detect vehicles driving 3 to 50 m ahead in the same lane with higher than 99% accuracy in clear weather. Detection performance in rainy weather, where water drops and splashes notably degraded visibility, was higher than 90%.

In this paper, Morse code is selected as a communication adaptive device for persons whose hand coordination and dexterity are impaired by such ailments as amyotrophic lateral sclerosis, multiple sclerosis, muscular dystrophy, and other severe handicaps. Morse code is composed of a series of dots, dashes, and space intervals, and each element is transmitted by sending a signal for a defined length of time. A suitable adaptive automatic recognition method is needed for persons with disabilities due to their difficulty in maintaining a stable typing rate. To overcome this problem, the proposed method combines the support vector machines method with a variable degree variable step size LMS algorithm. The method is divided into five stages: tone recognition, space recognition, training process, adaptive processing, and character recognition. Statistical analyses demonstrated that the proposed method elicited a better recognition rate in comparison to alternative methods from the literature.

A multi-stage approach -- which is fast, robust and easy to train -- for a face-detection system is proposed. Motivated by the work of Viola and Jones [1], this approach uses a cascade of classifiers to yield a coarse-to-fine strategy to reduce significantly detection time while maintaining a high detection rate. However, it is distinguished from previous work by two features. First, a new stage has been added to detect face candidate regions more quickly by using a larger window size and larger moving step size. Second, support vector machine (SVM) classifiers are used instead of AdaBoost classifiers in the last stage, and Haar wavelet features selected by the previous stage are reused for the SVM classifiers robustly and efficiently. By combining AdaBoost and SVM classifiers, the final system can achieve both fast and robust detection because most non-face patterns are rejected quickly in earlier layers, while only a small number of promising face patterns are classified robustly in later layers. The proposed multi-stage-based system has been shown to run faster than the original AdaBoost-based system while maintaining comparable accuracy.

In this letter, we propose an FFT-based SNR estimation method for wireless OFDM systems, and analyze the impact of the proposed SNR estimation method on adaptive OFDM performance in slow Rayleigh fading channels. Numerical and simulation results show that the proposed method is effective and feasible for adaptive modulation in slow Rayleigh fading channels.

In this letter, we provide a performance evaluation of a Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM) with adaptive modulation in a multipath fading channel. The MB-OFDM system in conjunction with adaptive modulation technique can be considered as one of the solutions for severely degraded channels such as those common in frequency selective fading environments.

In this paper, a chromatic adaptation model (CAM) based on the dependence on LMS cone responses of the human visual system (HVS) is proposed for TV and PC monitors under a variety of viewing conditions. We derived the proposed CAM based on Breneman's corresponding color data. The results of the experiments were carried out to assess the proposed model performance in terms of color fidelity by comparing complex images on a LCD monitor. We confirmed that the proposed model performed better to predict corresponding colors under various viewing conditions. Therefore, the reproduced colors, which are viewed in real surround viewing conditions, are perceived the same as original object colors, when the proposed CAM was applied to color display devices such as CRT, LCD, and PDP.

This paper proposes an adaptive transmission scheme for MIMO systems to provide different bit error rates and transmission rates for multimedia traffic. The adaptive transmission scheme allocates antennas, rate and power jointly according to the feedback information to satisfy the diverse QoS requirements of the multimedia traffic. Furthermore, an efficient search algorithm with low complexity is proposed for practical implementation. Simulation results show that the proposed scheme improves the spectral efficiency while guaranteeing the QoS requirements of multimedia traffic. Moreover, the proposed search algorithm achieves close optimal performance with great complexity reduction.

In wireless local area networks (WLANs), the necessity of quality-of-service (QoS) control for uplink flows is increasing because interactive applications are becoming more popular. Fairness between flows transmitted by stations with different physical transmission rates must be ensured in QoS control for link-adaptive WLANs, which are widely used nowadays. We propose a novel distributed access scheme called QC-DCA to satisfy these requirements. QC-DCA adaptively controls the parameters of carrier sense multiple access with collision avoidance (CSMA/CA). QC-DCA has two QoS control functions: guarantee and classification. QC-DCA guarantees target throughputs and packet delays by quickly adjusting CSMA/CA parameters. In QoS classification, the difference of throughputs and packet delays between different QoS classes is maintained. These two functions allow QC-DCA to suppress the unfairness caused by differences of transmission rates in the physical layer. We evaluated the throughput and delay performances of our scheme using computer simulations. The results show the viability of our scheme.

This paper presents a new statistical model-based voice activity detection (VAD) algorithm in the wavelet domain to improve the performance in non-stationary environments. Due to the efficient time-frequency localization and the multi-resolution characteristics of the wavelet representations, the wavelet transforms are quite suitable for processing non-stationary signals such as speech. To utilize the fact that the wavelet packet is very efficient approximation of discrete Fourier transform and has built-in de-noising capability, we first apply wavelet packet decomposition to effectively localize the energy in frequency space, use spectral subtraction, and employ matched filtering to enhance the SNR. Since the conventional wavelet-based spectral subtraction eliminates the low-power speech signal in onset and offset regions and generates musical noise, we derive an improved multi-band spectral subtraction. On the other hand, noticing that fixed threshold cannot follow fluctuations of time varying noise power and the inability to adapt to a time-varying environment severely limits the VAD performance, we propose a statistical model-based VAD algorithm in wavelet domain with an adaptive threshold. We perform extensive computer simulations and compare with the conventional algorithms to demonstrate performance improvement of the proposed algorithm under various noise environments.