The Multiple Input Multiple Output (MIMO) technique is motivating the world-wide researchers to realize the next generation wireless LANs with the higher channel capacity and higher signal quality. This paper proposes and analyzes the Space Division Multiplexing Single Carrier OFDM (SDM-SCOFDM) system with adaptive modulation (AM) method over MIMO channels. The salient features of the proposed method are to enable the significant reduction of the number of required feedback adaptive modulation information (AMI) bits and the improvement of the PAPR (peak to average power ratio) performance at a cost of little degradation of channel capacity as comparing with the AM aided SDM-OFDM system. We also propose the Carrier-to-Noise Power Ratio (CNR) estimation method for SDM-SCOFDM signals over multi-path fading channels, which can be used for the assignment of the optimal modulation scheme in each transmit antenna. This paper presents the various computer simulation results to verify the proposed method under a typical wireless LAN environment.

Subcircuits designed for integrated silicon DCS1800/ PCS1900 base station receivers have been reconfigured into hybrid and single-chip Doppler radar transceivers. Radar chips have been fully integrated in 0.25 µm silicon CMOS and BiCMOS processes. These chips have been used to monitor heart and respiration activity without contact, and they have successfully detected heartbeat and respiration rate up to 1 m from the subject. This monitoring device may be useful in home monitoring, continuous monitoring, and physiological research.

In this letter, we investigate the performance of using subband adaptive loading for the combination of orthogonal frequency division multiplexing (OFDM) and adaptive antenna array. The frequency-domain adaptive loading is very effective to deal with the frequency-selective fading which is inevitable in broadband wireless communications. However, almost all of the existing adaptive loading algorithms are based on "subcarrier-to-subcarrier" mode which may results in awfully large signaling overhead, since every subcarrier needs its own signaling loop between the transmitter and receiver. We investigate the performance of the combination of OFDM and adaptive antenna array when a subband adaptive loading algorithm is used to decrease the signaling overhead. It is shown by simulation results that at the cost of some tolerable performance loss, the signaling overhead of adaptive loading can be greatly reduced.

In this paper, we examine the effect of random steering errors on the signal-to-interference-plus-noise-ratio (SINR) at the output of the recently addressed wavelet-based generalized sidelobe canceller (GSC). This new beamformer employs a set of P-regular M-band wavelet bases for the design of the blocking matrix of the GSC. We first carry out a general expression of the output SINR of the GSC with multiple interferers present. With this expression, we then examine the analysis of wavelet-based GSC by expressing the SINR in terms of parameters such as the regularity of wavelet filters, the number of bands of wavelet filters, the length of adaptive weights, and the input signal-to-noise ratio (SNR). Some simulation results verify the analytically predicted performance.

Unlike most of the previous work for smart antennas that covered each area individually (antenna-array design, signal processing and communications algorithms and network throughput), this paper may be considered as a review of comprehensive effort on smart antennas that examines and integrates antenna array design, the development of signal processing algorithms (for angle of arrival estimation and adaptive beamforming), strategies for combating fading, and the impact on the network throughput. In particular, this study considers problems dealing with the impact of the antenna design on the network throughput. In addition, fading channels and tradeoffs between diversity combining and adaptive beamforming are examined as well as channel coding to improve the system performance.

We describe elements of a fast integral equation solver for large periodic and partly periodic finite array systems. A key element of the algorithm is utilization (in a rigorous way) of a block-Toeplitz structure of the impedance matrix in conjunction with either conventional Method of Moments (MoM), Fast Multipole Method (FMM), or Fast Fourier Transform (FFT)-based Adaptive Integral Method (AIM) compression techniques. We refer to the resulting algorithms as the (block-)Toeplitz-MoM, (block-)Toeplitz-AIM, or (block-)Toeplitz-FMM algorithms. While the computational complexity of the Toeplitz-AIM and Toeplitz-FMM algorithms is comparable to that of their non-Toeplitz counterparts, they offer a very significant (about two orders of magnitude for problems of the order of five million unknowns) storage reduction. In particular, our comparisons demonstrate, that the Toeplitz-AIM algorithm offers significant advantages in problems of practical interest involving arrays with complex antenna elements. This result follows from the more favorable scaling of the Toeplitz-AIM algorithm for arrays characterized by large number of unknowns in a single array element and applicability of the AIM algorithm to problems requiring strongly sub-wavelength resolution.

Although production systems are widely used in artificial intelligence (AI) applications, they are seen to have certain disadvantages in terms of their need for special purpose assistance software to build and execute their knowledge-bases (KB), and in the fact that they will not run on any operating system (platform dependency). Furthermore, for AI applications such as learning assistance systems, there is a strong requirement for a self-adaptive function enabling a flexible change in the service contents provided, according to the user. Against such a background, a Java based production system (JPS) featuring no requirement for special purpose assistance software and no platform dependency, is proposed. Furthermore, a new self-adaptive Java production system (A-JPS) is proposed to realize the "user adaptation" requirement mentioned above. Its key characteristic is the combination of JPS with a Causal-network (CN) for obtaining a "user profile". In addition, the execution time of the JPS was studied using several benchmark problems with the aim of comparing the effectiveness of different matching algorithms in their recognize-act cycles as well as comparing their performance to that of traditional procedural programs for different problem types. Moreover, the effectiveness of the user adaptation function of the A-JPS was studied for the case of a CN with a general DAG structure, using the experimental KB of a learning assistance system.

In this article, it is shown that Unified Complex Hadamard Transform (UCHT) can be derived from Walsh functions and through direct matrix operation. Unique properties of UCHT are analyzed. Recursive relations through Kronecker product can be applied to the basic matrices to obtain higher dimensions. These relations are the basis for the flow diagram of a constant-geometry iterative VLSI hardware architecture. New Normalized Complex Hadamard Transform (NCHT) matrices are introduced which are another class of complex Hadamard matrices. Relations of UCHT and NCHT with other discrete transforms are discussed.

This paper proposes a new implementation of an adaptive noise canceller based upon a parallel block structure, which aims to raise the processing and convergence rates and to improve the steady-state performance. The procedure is as follows: First, an IIR bandpass filter with a variable center angular frequency using adaptive Q-factor control and two adaptive control signal generators are realized by the parallel block structure. Secondly, a new algorithm for adaptive Q-factor control with parallel block structure is proposed to improve the convergence characteristic. In addition, the steady-state performance of the filter is stabilized by using the variable step size parameter in adaptive control of the center frequency and the speed up of the convergence rate is achieved by adopting a normalized gradient algorithm for adaptive control. Finally, simulation results are given to demonstrate the convergence performance.

This paper presents a novel blind multiple access interference (MAI) suppression filter in DS/CDMA systems. The filter is adaptively updated by parallel projections onto a series of convex sets. These sets are defined based on the received signal as well as a priori knowledge about the desired user's signature. In order to achieve fast convergence and good performance at steady state, the adaptive projected subgradient method (Yamada et al., 2003) is applied. The proposed scheme also jointly estimates the desired signal amplitude and the filter coefficients based on an approximation of an EM type algorithm, following the original idea proposed by Park and Doherty, 1997. Simulation results highlight the fast convergence behavior and good performance at steady state of the proposed scheme.

In a high-rate indoor wireless personal communication system, the delay spread due to multi-path propagation results in intersymbol interference which can significantly increase the transmission bit error rate (BER). The technique most commonly used for combating the intersymbol interference and frequency-selective fading found in communications channels is the adaptive equalization. In this paper, we propose a novel neural detector based on self-organizing map (SOM) to improve the system performance of the receiver. In the proposed scheme, the SOM is used as an adaptive detector of equalizer, which updates the decision levels to follow the received faded signal. To adapt the proposed scheme to the time-varying channel, we use the Euclidean distance, which will be updated automatically according to the received faded signal, as an adaptive radius to define the neighborhood of the winning neuron of the SOM algorithm. Simulations on a 16 QAM system show that the receiver using the proposed neural detector has a significantly better BER performance than the traditional receiver.

Motion estimation is the key issue in video compressing. Several methods for motion estimation based on the center biased strategy and minimum mean square error trend searching have been proposed, such as TSS, FSS, UCBDS and MIBAS, but these methods yield poor estimates or find local minima. Many other methods predict the starting point for the estimation; such methods include PMEA, PSA and GPS: these can be fast but are inaccurate. This study addresses the causes of wrong estimates, local minima and incorrect predictions in the prior estimation methods. The Multiple Searching Trend (MST) is proposed to overcome the problems of ineffective searches and local minima, and the Adaptive Dilated Searching Field (ADSF) is described to prevent prediction from wrong location. Applying MST and ADSF to the listed estimating methods, such as UCBDS, a fast and accurate can be reached. For this this reason, the method is called CockTail Searching (CTS).

A simple adaptive internal model control structure is designed and tested on the real-time temperature control of a process. The design procedure remains same for both minimum and non-minimum phase systems. The effect of the process zeros on the output is compensated by using adaptive finite impulse response filters. This guarantees the stability of the closed-loop.

Matching Pursuits (MP), a technique for signal decomposition using a dictionary of functions, is applied to ground penetrating radar (GPR) signals in order to remove noise and clutter included in the signals and to extract target responses. A wave-based dictionary composed of wavefronts and resonances is employed. Noise reduction performance and the removal of ground-surface reflection are evaluated through numerical simulations. The results show that the MP approach performs well and offers an effective method for feature extraction from GPR signals.

In this paper, we propose two adaptive filtering schemes for Stereophonic Acoustic Echo Cancellation (SAEC), which are based on the adaptive projected subgradient method (Yamada et al., 2003). To overcome the so-called non-uniqueness problem, the schemes utilize a certain preprocessing technique which generates two different states of input signals. The first one simultaneously uses, for fast convergence, data from two states of inputs, meanwhile the other selects, for stability, data based on a simple min-max criteria. In addition to the above difference, the proposed schemes commonly enjoy (i) robustness against noise by introducing the stochastic property sets, and (ii) only linear computational complexity, since it is free from solving systems of linear equations. Numerical examples demonstrate that the proposed schemes achieve, even in noisy situations, compared with the conventional technique, (i) much faster and more stable convergence in the learning process as well as (ii) lower level mis-identification of echo paths and higher level Echo Return Loss Enhancement (ERLE) around the steady state.

In this paper we propose a parallel composition based adaptive notch filter for eliminating sinusoidal signals whose frequencies are unknown. The proposed filter which is implemented using second order all-pass filter and a band-pass filter can achieve high convergence speed by using the output of an additional band-pass filter to update the coefficients of the notch filter. The high convergence speed of the proposed notch filter is obtained by reducing an effect that an updating term of coefficient for adaptation of a notch filter significantly increases when the notch frequency approaches the sinusoidal frequency. In this paper, we analyze such effect obtained by the additional band-pass filter. We also present an analysis of a convergence performance of cascaded system of the proposed notch filter for eliminating multiple sinusoids. Simulation results have shown the effectiveness of the proposed adaptive notch filter.

In this paper we apply a parallel adaptive solution algorithm to simulate nanoscale double-gate metal-oxide-semiconductor field effect transistors (MOSFETs) on a personal computer (PC)-based Linux cluster with the message passing interface (MPI) libraries. Based on a posteriori error estimation, the triangular mesh generation, the adaptive finite volume method, the monotone iterative method, and the parallel domain decomposition algorithm, a set of two-dimensional quantum correction hydrodynamic (HD) equations is solved numerically on our constructed cluster system. This parallel adaptive simulation methodology with 1-irregular mesh was successfully developed and applied to deep-submicron semiconductor device simulation in our recent work. A 10 nm n-type double-gate MOSFET is simulated with the developed parallel adaptive simulator. In terms of physical quantities and refined adaptive mesh, simulation results demonstrate very good accuracy and computational efficiency. Benchmark results, such as load-balancing, speedup, and parallel efficiency are achieved and exhibit excellent parallel performance. On a 16 nodes PC-based Linux cluster, the maximum difference among CPUs is less than 6%. A 12.8 times speedup and 80% parallel efficiency are simultaneously attained with respect to different simulation cases.

We have proposed a speculative selection function for adaptive routing, which uses idle cycles of the network physical links to exchange network information between nodes, thus helps to decide the best selection. Previous study on the mesh network showed that SSR gives message selection flexibility that improves network performance by balancing the network traffic in both global and local scopes. This paper evaluates the speculative selection function on 2D torus network with simulation. The simulation compares the network throughput and latency with various traffic patterns. The visualization graphs show how the speculative selection eliminates hotspots and disperses traffic in the global scope. The simulation results demonstrate that by using speculative selection, the network performance is increased by around 7%. Compared to the mesh network, the torus's version has smaller gain due to the high performance nature of the torus network.

In this paper, a simple and practical corresponding-color reproduction model based on the chromatic adaptation of the human visual system (HVS), named a modified von Kries chromatic-adaptation model, is proposed for TV and PC monitors under a variety of viewing conditions. We derived the proposed corresponding-color reproduction model based on Breneman's corresponding-color data. The proposed model has a 1-2% less error than Fairchild's and Breneman's model, 11.5% less error than von Kries' model and 60% less error than CIECAM97s' model in terms of color reproduction errors, (). Also, these tendency is similar in color reproduction errors, . We implemented a corresponding-color reproduction system using the proposed model under a variety of viewing conditions. For the determination of viewing conditions, illuminant correlated color temperatures (CCTs) are measured by yellow/cyan sensors. These measured surround illuminant CCTs can estimate an adaptated neutral point of the HVS in TV viewing conditions. Experiments were carried out to assess the proposed model performance in terms of color fidelity by comparing complex images on a LCD monitor under illuminants from 2500 K to 7500 K. We confirmed that the implemented system using the proposed model can predict corresponding-color data very well under a variety of viewing conditions. Therefore, by applying the proposed model and system to the LCD, the reproduction colors viewed in real surrounding viewing conditions on the LCD could appear the same as the original object colors under standard viewing conditions. Furthermore, they could be applied to any other color display device such as a CRT, a PDP, and a DLP in order to get better reproduction colors.

Internet traffic engineering is much important for Internet Service Providers (ISPs) today, since it can be used to fully utilize already deployed network resources. For ISPs, the requirements for traffic engineering should be simple, easy to configure, cost-effective and efficient. Based on these considerations, we propose an algorithm called Web First Adaptive Traffic Engineering (WFATE). Since World Wide Web (WWW) services dominate most of the total Internet traffic and WWW flows are not long-lived, we only apply load balancing to WWW traffic in the algorithm. It can be shown that the number of coexistent WWW flows at an ingress node is almost certainly below a bound, and thus a forward-per-flow mechanism without keeping track of the state of each flow is feasible. This mechanism can balance traffic load at fine granularity and therefore get better performance. Through simulations and performance comparison, it is shown that WFATE is quite efficient, which can improve the network throughput averagely by 26% under the "dense source" traffic pattern and 9% under the "sparse source" traffic pattern.