This paper presents a new adaptive minor subspace extraction algorithm based on an idea of Peng and Yi ('07) for approximating the single minor eigenvector of a covariance matrix. By utilizing the idea inductively in the nested orthogonal complement subspaces, the proposed algorithm succeeds to relax the numerical sensitivity which has been annoying conventional adaptive minor subspace extraction algorithms for example, Oja algorithm ('82) and its stabilized version: O-Oja algorithm ('02). Simulation results demonstrate that the proposed algorithm realizes more stable convergence than O-Oja algorithm.

In this paper, we investigate the performance of maximum ratio combining (MRC) in the presence of multiple cochannel interferences over a flat Rayleigh fading channel. Closed-form expressions of signal-to-interference-plus-noise ratio (SINR), outage probability, and average symbol error rate (SER) of quadrature amplitude modulation (QAM) with M-ary signaling are obtained for unequal-power interference-to-noise ratio (INR). We also provide an upper-bound for the average SER using moment generating function (MGF) of the SINR. Moreover, we quantify the array gain loss between pure MRC (MRC system in the absence of CCI) and MRC system in the presence of CCI. Finally, we verify our analytical results by numerical simulations.

An adaptive chirp beamforming method is proposed to solve the bias problem in the direction-of-arrival (DOA) estimation of wideband chirp signals that have identical time-frequency parameters yet emanate from different directions. The proposed method, based on the steered minimum variance (STMV) method, exploits the time-frequency structure of a chirp signal to improve the DOA estimation performance by effectively suppressing the wideband chirp interferences causing the bias. Simulations are performed to demonstrate the effectiveness of the proposed method.

This paper proposes a novel MIMO system that introduces a heterogeneous stream (HTS) scheme and a blind signal detection method for mobile radio communications. The HTS scheme utilizes different modulation or coding methods for different MIMO streams, and the blind detection method requires no training sequences for signal separation, detection, and channel estimation. The HTS scheme can remove the ambiguity in identifying separated streams without unique words that are necessary in conventional MIMO blind detection. More specifically, two examples of HTS are considered: modulation type HTS (MHTS) and timing-offset type HTS (THTS). MHTS, which utilizes different modulation constellations with the same bandwidth for different streams, has been previously investigated. This paper proposes THTS which utilizes different transmission timing with the same modulation. THTS can make the blind detection more robust and effective with fractional sampling. The blind joint processing of detection and channel estimation performs adaptive blind MIMO-MLSE and is derived from an adaptive blind MLSE equalizer that employs the recursive channel estimation with the Moore-Penrose generalized inverse. Computer simulations show that the proposed system can achieve superior BER performance with Eb/N0 degradation of 1 dB in THTS and 2.5 dB in MHTS compared with the ideal maximum likelihood detection.

The adaptive predistorter and the negative feedback system are known as methods to compensate for the nonlinear distortion of a power amplifier. Although the feedback method is a simple technique, its instability impedes the capability of high-feedback gain to achieve a high-compensation effect. On the other hand, the predistorter requires a long time for convergence of the adaptive predistorters. In this paper, we propose a nonlinear distortion compensation method for a narrow-band signal. In this method, an adaptive predistorter and negative feedback are combined. In addition, to shorten the convergence time to minimize nonlinear distortion, a variable step-size (VS) method is also applied to the algorithm to determine the parameters of the adaptive predistorter. Using computer simulations, we show that the proposed scheme achieves both five times faster convergence speed than that of the predistorter and three times higher permissible delay time in the feedback amplifier than that of a negative feedback only amplifier.

We investigate a least squares (LS) based multi-step autoregressive (AR) prediction filter for delay compensation over MIMO channels. We describe the robustness of an adaptive MIMO-OFDM with that filter over mobile fading channels.

This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In this approach, the adaptive fuzzy-neural network (FNN) in AFNO is adopted on line to model the nonlinear term in the transmitter. Additionally, the master's unknown states can be reconstructed from one transmitted state using observer design in the slave end. Synchronization is achieved when all states are observed. The utilized scheme can adaptively estimate the transmitter states on line, even if the transmitter is changed into another chaos system. On the other hand, the robustness of AFNO can be guaranteed with respect to the modeling error, and external bounded disturbance. Simulation results confirm that the AFNO design is valid for the application of chaos synchronization.

This paper presents a coherent decomposition scheme for polarimetric SAR data. Coherent decomposition means the decomposition is applied to a single or a few scattering matrix data. Based on the scattering matrix acquired with an FM-CW polarimetric SAR system, we have devised a simple decomposition technique using the coherency matrix for the purpose of identifying scatterers. This paper presents the decomposition technique and some decomposition results obtained by a fully polarimetric FM-CW radar. It is shown the scattering mechanisms are well recovered and the orientation angles of wire scatterer are precisely measured.

A new adaptive model based on fuzzy integrals has been presented and used for combining three well-known methods, Eigenface, Fisherface and SOMface, for face classification. After training the competence estimation functions, the adaptive mechanism enables our system the filtering of unsure judgments of classifiers for a specific input. Comparison with classical and non-adaptive approaches proves the superiority of this model. Also we examined how these features contribute to the combined result and whether they can together establish a more robust feature.

A variable phase shifter using a movable waffle iron metal plate comprised of iron rods a quarter-wavelength in length is proposed. A study of the waffle iron structure was carried out and the design method for creating a structure that would achieve large phase changes, small loss, and good isolation between adjacent phase shifters is discussed. Experiments on 1-port and 2-port phase shifters operating in the 5 GHz band show that they not only have low loss characteristics but also wide phase changes. Furthermore, the application to phased array antennas using the proposed phase shifter and its principle are demonstrated.

A C-band polarimetric radar on Okinawa Island successfully observed large-scale bird migrations over the western Pacific Ocean. The birds generated interesting polarimetric signatures. This paper describes the signatures and speculates bird behavior.

The performance of the adaptive scan for spaceborne rain radar, which uses a quick scan for rain search followed by a normal or concentrated scan only for rainy areas, are studied through a simulation using TRMM (Tropical Rainfall Measuring Mission) Precipitation Radar (PR) data. Trade-off studies are performed to find an optimum quick-scan and rain search method to minimize rain missing and false alarm of rain area. Using the optimum method thus determined, consecutive 8-day TRMM PR data are used to statistically evaluate the performance of the adaptive scan in terms of sensitivity improvement and power consumption saving. It is shown that more than 3-dB improvement in effective signal-to-noise ratio (SNe) can be achieved for 40% of the total observations. Alternatively, about 26% power saving can be achieved if the SNe is kept the same.

Spatial filtering is a useful method to suppress undesired reflection from unwanted scatters in Radar Cross Section (RCS) measurements. Actually, it is difficult to prepare an ideal field which satisfies the far-field criterion for RCS measurements of large targets. We applied the filtering method to a bistatic RCS measurement in a near field and investigated the validity of that method by varying the scanning angular span. Electromagnetic simulations show that predicted RCS profiles from near-field data with unwanted scatters closely matched far-field reference data of the test target. In conclusion, the results show that the method is effective for bistatic RCS measurements in practical field enviroments.

Spread Spectrum (SS) has been widely used for various wireless systems such as cellular systems, wireless local area network (LAN) and so on. Using multiple antennas at the receiver, two-dimensional (2D) RAKE is realized over the time- and the space-domain. However, it should be noted that the 2D-RAKE receiver must detect the bit timing prior to the RAKE combining. In case of deep fading, it is often difficult to detect it due to low signal-to-noise power ratio (SNR). To solve this problem, we propose a new blind 2D-RAKE receiver based on the constant modulus algorithm (CMA). Since it does not need a priori bit timing detection, it is possible to compensate frequency selective fading even in very low SNR environments. The proposed method is particularly suitable for the software defined radio (SDR) architecture. The performance of the proposed method is investigated through computer simulations.

In this paper, we investigate minimum mean absolute error (mmae) predictors for lossless image coding. In some prediction-based lossless image coding systems, coding performance depends largely on the efficiency of predictors. In this case, minimum mean square error (mmse) predictors are often used. Generally speaking, these predictors have a problem that outliers departing very far from a regression line are conspicuous enough to obscure inliers. That is, in image compression, large prediction errors near edges cause the degradation of the prediction accuracy of flat areas. On the other hand, mmae predictors are less sensitive to edges and provide more accurate prediction for flat areas than mmse predictors. At the same time, the prediction accuracy of edge areas is brought down. However, the entropy of the prediction errors based on mmae predictors is reduced compared with that of mmse predictors because general images mainly consist of flat areas. In this study, we adopt the Laplacian and the Gaussian function models for prediction errors based on mmae and mmse predictors, respectively, and show that mmae predictors outperform conventional mmse-based predictors including weighted mmse predictors in terms of coding performance.

In this paper, we investigated the effect of chromaticity and luminance of surround to decide subject neutral white, and conducted a mathematical model of adapting degree for environment. Factors for adapting degree consist of two parts, adapting degree of ambient chromaticity and color saturation. These can be applied to color appearance models (CAM), actually improve the performance of color matching of CAM, hence would produce the method of image reproduction to general display systems.

We propose a new robust frequency domain acoustic echo cancellation filter that employs a normalized residual echo enhancement. By interpreting the conventional robust step-size control approaches as a statistical-model-based residual echo enhancement problem, the optimal step-size introduced in the most of conventional approaches is regarded as optimal only on the assumption that both the residual echo and the outlier in the error output signal are described by Gaussian distributions. However, the Gaussian-Gaussian mixture assumption does not always hold well, especially when both the residual echo and the outlier are speech signals (known as a double-talk situation). The proposed filtering scheme is based on the Gaussian-Laplacian mixture assumption for the signals normalized by the reference input signal amplitude. By comparing the performances of the proposed and conventional approaches through the simulations, we show that the Gaussian-Laplacian mixture assumption for the normalized signals can provide a better control scheme for the acoustic echo cancellation.

In the acoustic echo canceller (AEC), the step-size parameter of the adaptive filter must be varied according to the situation if double talk occurs and/or the echo path changes. We propose an AEC that uses a sub-adaptive filter. The proposed AEC can control the step-size parameter according to the situation. Moreover, it offers superior convergence compared to the conventional AEC even when the double talk and the echo path change occur simultaneously. Simulations demonstrate that the proposed AEC can achieve higher ERLE and faster convergence than the conventional AEC. The computational complexity of the proposed AEC can be reduced by reducing the number of taps of the sub-adaptive filter.

We derive the optimum power and rate adaptation for maximizing the spectral efficiency of Multilevel Quadrature Amplitude Modulation (MQAM) over Multiple-Input Multiple-Output (MIMO) fading channels based on imperfect channel estimation. We use Pilot Symbol Assisted Modulation (PSAM)-based Minimum Mean Square Error (MMSE) channel estimator, and show that the optimum power adaptation on each sub-channel is a generalization of water-filling. We also show that the conventional water-filling (with bias) strategy for power adaptation is a suboptimum solution of the general optimization problem and it tends to the optimal solution as the correlation coefficients between eigenvalues of the true channel matrix and its estimate tend to one.

Nowadays IEEE 802.11 wireless local area networks (WLANs) support multiple transmission rates. To achieve the best performance, transmitting stations adopt the various forms of automatic rate fallback (ARF). However, ARF suffers from severe performance degradation as the number of transmitting stations increases. In this paper, we propose a new rate adaptation scheme which adjusts the ARF's up/down threshold according to the channel contention level. Simulation result shows that the proposed scheme achieves fairly good performance compared with the existing schemes.