An adaptive array antenna for the suppression of high-power interference in direct-sequence code-division multiple access (DS-CDMA) systems is presented. Although DS-CDMA has sufficient flexibility to support a variety of services, from voice to moving-pictures, with high levels of quality, multiple access interference (MAI) is a problem. This is particularly so of the high-power interference which accompanies high-speed transmission in DS-CDMA. While the application of adaptive array antennas is an effective way of improving signal-to-interference-plus-noise ratio (SINR), problems with this approach include large levels of power consumption and the high costs of hardware and of implementing the antennas. Therefore, our main purpose is to realize a simple configuration for an adaptive array system. In order to reduce the required amounts of processing, a common beam provides suppression of high-power interference for the low-bit-rate users; this makes per-user preparation of weights unnecessary. This approach also reduces the consumption of power by the system. Interference is cancelled by minimization of the array output power (i.e., the application of a power inversion algorithm) before despreading. The approach also allows us to improve the implementation of the antenna elements by using small auxiliary antennas. The basic performance of the system is confirmed through numerical calculation and computer simulation. Furthermore, a real-time processing unit has been developed and the effectiveness of the approach is confirmed by an experiment in a radio-anechoic chamber.

In this paper, a novel confidence scoring method that is applied to N-best hypotheses (word candidates) output from an HMM-based classifier is proposed. In the first pass of the proposed method, the HMM-based classifier with monophone models outputs N-best hypotheses and boundaries of all monophones in the hypotheses. In the second pass, an SM (Subspace Method)-based verifier tests the hypotheses by comparing confidence scores. To test the hypotheses, at first, the SM-based verifier calculates the similarity between phone vectors and an eigen vector set of monophones, then this similarity score is converted into a likelihood score with normalization of acoustic quality, and finally, an HMM-based likelihood of word level and an SM-based likelihood of monophone level are combined to formulate the confidence measure. Two kinds of experiments were performed to evaluate this confidence measure on speaker-independent word recognition. The results showed that the proposed confidence scoring method significantly reduced the word error rate from 4.7% obtained by the standard HMM classifier to 2.0%, and in an unknown word rejection, it reduced the equal error rate from 9.0% to 6.5%.

Blind separation of sources from their linear mixtures is a well understood problem. However, if the mixtures are nonlinear, this problem becomes generally very difficult. This is because both the nonlinear mapping and the underlying sources must be learned from the data in a blind manner, and the problem is highly ill-posed without a suitable regularization. In our approach, multilayer perceptrons are used as nonlinear generative models for the data, and variational Bayesian (ensemble) learning is applied for finding the sources. The variational Bayesian technique automatically provides a reasonable regularization of the nonlinear blind separation problem. In this paper, we first consider a static nonlinear mixing model, with a successful application to real-world speech data compression. Then we discuss extraction of sources from nonlinear dynamic processes, and detection of abrupt changes in the process dynamics. In a difficult test problem with chaotic data, our approach clearly outperforms currently available nonlinear prediction and change detection techniques. The proposed methods are computationally demanding, but they can be applied to blind nonlinear problems of higher dimensions than other existing approaches.

This paper derives a set of orthogonal polynomials for a complex random variable that is uniformly distributed in two dimensions (2D). The polynomials are used in a series expansion to approximate memoryless nonlinearities in digital QAM systems. We also study stochastic identification of nonlinearities using the orthogonal polynomials through analysis and simulations.

In this paper, a grey filtering approach based on GM(1,1) model is proposed. Then the grey filtering is applied to speech enhancement. The fundamental idea in the proposed grey filtering is to relate estimation error of GM(1,1) model to additive noise. The simulation results indicate that the additive noise can be estimated accurately by the proposed grey filtering approach with an appropriate scaling factor. Note that the spectral subtraction approach to speech enhancement is heavily dependent on the accuracy of statistics of additive noise and that the grey filtering is able to estimate additive noise appropriately. A magnitude spectral subtraction (MSS) approach for speech enhancement is proposed where the mechanism to determine the non-speech and speech portions is not required. Two examples are provided to justify the proposed MSS approach based on grey filtering. The simulation results show that the objective of speech enhancement has been achieved by the proposed MSS approach. Besides, the proposed MSS approach is compared with HFR-based approach in [4] and ZP approach in [5]. Simulation results indicate that in most of cases HFR-based and ZP approaches outperform the proposed MSS approach in SNRimp. However, the proposed MSS approach has better subjective listening quality than HFR-based and ZP approaches.

Transmit adaptive array requires the forward link channel state for evaluating the optimum transmit weight in which a feedback channel transports the forward link channel state to the base station. Since the feedback information limits the transmission rate of the reverse link traffic, it is necessary to keep the number of feedback bits to a minimum. This paper presents a system in which the N transmit antennas are extended to the 2N transmit antennas while the feedback channel is limited as that of N-transmit antenna system. The increased antennas can give additional diversity gain but requires higher rate of feedback bits. The limited feedback channel increases the quantization error of feedback information since the number of feedback bits assigned to each antenna is reduced. In order to overcome the limited rate of feedback channel problem, this paper proposes the transmit antenna selection schemes which can effectively use the limited feedback bits, reduce the computational complexity at the mobile station, and eventually achieve diversity gain. System performances are investigated for the case of N=4 for the various antenna selection schemes on both flat fading and multi-path fading channels.

In this paper, we discuss crosstalk equalization technique for high-speed digital transmission systems. This equalization technique makes use of the cyclostationarity of the crosstalk interferer. We first analyze the eigenstructure of the equalizer in the presence of cyclostationary crosstalk interference. It is shown that the eigenvalues of the equalizer depend upon the folded signal and interferer power spectra, and the cross power spectrum between the signal and the interferer. The expressions of the minimum mean square error (MMSE) and the excess MSE are then obtained by using the equalizer's eigenstructure. Analysis and simulation results indicate that such peculiar equalizer's eigenstructure in the presence of cyclostationary interference results in significantly different initial convergence and steady-state behaviors as compared with the stationary noise case. We also show that the performance of the equalizer varies depending on the relative clock phase of the symbol clocks used by the signal and the crosstalk interferer.

One of the biggest limitations of BP algorithm is its low rate of convergence. The Variable Learning Rate (VLR) algorithm represents one of the well-known techniques that enhance the performance of the BP. Because the VLR parameters have important influence on its performance, we use learning automata (LA) to adjust them. The proposed algorithm named Adaptive Variable Learning Rate (AVLR) algorithm dynamically tunes the VLR parameters by learning automata according to the error changes. Simulation results on some practical problems such as sinusoidal function approximation, nonlinear system identification, phoneme recognition, Persian printed letter recognition helped us better to judge the merit of the proposed AVLR method.

A novel fast calculation algorithm (FCA) for calculating the decision metric of the multiple-symbol differential detection (MSDD) considering the autocorrelation of a received sequence is proposed. In correspondence to the star quadrature amplitude modulation (QAM), the M algorithm is adopted to MSDD over Rayleigh fading channels, in order to reduce the number of search paths. The computational complexity of the decision metric can be greatly reduced by the proposed FCA and the M algorithm. Through computer simulations, it is confirmed that the symbol error rate (SER) performance of the MSDD considering autocorrelation is closer to that of the ideal coherent detection as the length of an observed sequence becomes larger over Rayleigh fading channels.

Emerging requirements for higher rate data services and better spectrum efficiency are the main issues of third-generation mobile radio systems. In particular, a new concept of burst switching has been introduced for supporting the packet data services in the CDMA-based wireless system. In the burst switching system, radio resources are allocated to users for the duration of data bursts, which is a series of packets, as opposed to the conventional packet switching scheme. To implement the burst switching scheme, three different states (active, control hold, dormant states) are defined and two transition timers are employed to release the fundamental and supplemental code channels, respectively, at certain instances. Furthermore, the system is subject to burst admission control policy, with which a burst is admitted only when the number of currently available channels is greater than the admission threshold. Since there exists a trade-off between the additional packet access delay during a burst and resource utilization depending on the time-out value of the transition timer and burst admission threshold, it is critical to understand the performance characteristics in terms of the underlying design parameters. In this paper, we develop an analytic model and present a Quasi-Birth-Death (QBD) queueing analysis for evaluating the performance of burst switching schemes. This work focuses on the trade-off studies for optimizing the time-out value of the transition timer so as to minimize the average delay performance. Theoretical performance measures are derived by means of the matrix geometric method and furthermore, some simulation results are presented to validate the proposed analytical approach.

The effect of a conductive sheet placed over a PCB with a microstrip line on electromagnetic noise shielding is investigated. As a typical conductive sheet, a copper sheet is used, and is not grounded. First, the input impedance of the microstrip line and the magnetic field when varying the distance between the PCB and the conductive sheet are measured, and the distance that does not affect the signal transmission is set at 8 mm. Second, the effect of the conductive sheet size on the magnetic field radiation is discussed by measurements and FDTD modeling, and the magnetic near-field distribution around the PCB is visualized by using the FDTD calculation. A conductive sheet whose width is larger than the PCB width should be effective for suppression of the magnetic near-field noise radiation just above a PCB.

Appropriate test signals defined by formula or generated by algorithm are used for measuring objective QoS (Quality of Services) for voice operated telecommunication devices such as telephone and speech codec (coder-decoder). However, that for measuring residual echo characteristics in hands-free telecommunications equipped with acoustic echo canceller is under study in ITU-T Recommendation G.167. This paper describes comparative assessment of test signals for measurement of residual echo characteristics. In hands-free telecommunications, acoustical echo canceller has been developed to remove a room echo signal through the loudspeaker to the microphone in the receiving end. Performance of the echo canceller system is evaluated by residual echo characteristics expressed in echo return loss enhancement (ERLE). The ERLE can be conventionally measured by putting white noise into the echo canceller system. However, white noise is not adequate as the test signal for measuring the performance of the echo canceller, since the performance may depend on the characteristics of input test signal, and the characteristics of the white noise differ from those of real voice. Therefore, this paper discusses appropriate characteristics of real voice required for objective quality evaluation of echo canceller system. The test signals used for this verification tests were real voice (RV), white noise (WN), frequency weighted noise (FWN), artificial voice (AV), and composite source signal (CSS) depending on the approximation of real voice characteristics. As the comparative assessment results, the ERLE characteristics measured by artificial voice conforming to ITU-T Recommendation P.50 having average characteristics of real voices in time and frequency domains are almost equivalent to those of real voice and best among those test signals. It is concluded that artificial voice P.50 is satisfied with measurement of residual echo characteristics.

RF noise in quarter-micron nMOSFETs is analysed on the device level based on Shockley's impedance field method. The impact of different transport models and physical parameters is discussed in detail. Well-calibrated drift-diffusion and energy-balance models give very similar results for noise current spectral densities and noise figures. We show by numerical simulations with the general-purpose device simulator DESSIS_ISE that the hot-electron effect on RF noise is unimportant under normal operating conditions and that thermal substrate noise is dominant below 0.5 GHz. The contribution of energy-current fluctuations to the terminal noise is found to be negligible. Application of noise sources generated in bulk full-band Monte Carlo simulations changes the noise figures considerably, which underlines the importance of proper noise source models for far-from-equilibrium conditions.

Changing vehicle structures and backgrounds makes it very difficult to correctly extract a license plate region from a vehicle image. In this paper, we propose a simple method to extract the license plate region using edge properties of wavelet subband. The High Frequency Subband (HFS) of an image has edge information for each direction. Edge information is concentrated in each direction of the Headlight-Radiator-Headlight (H-R-H) and the license plate region compared to other regions in the vehicle image. This paper shows a license plate region extraction method using these edge properties and our experimental results with various vehicle images.

Though, high dielectric constant material is a possible near future solution in order to suppress gate current densities of MOSFETs, the barrier height generally decreases with an increasing dielectric constant. In this paper, the injection current through gate stacks has been calculated while taking into account the electron temperature using the W.K.B. method to understand the impact of the injection current from the drain edge.

Beaulieu has proposed four signal-to-noise ratio (SNR) estimators for quadrature phase shift keying (QPSK) signaling in time domain. In this letter, we propose SNR estimators for QPSK signaling in frequency domain. A discrete Fourier transform (DFT) algorithm is used for the frequency domain analysis of the received signal. The frequency spectrum enables biased SNR estimation in the frequency domain. Circular convolution is used for robust and fast SNR estimation when the received signal exhibits a frequency offset. Simulation results show that the new estimators present good performance even when the received signal exhibits a large frequency offset.

The problem of separating blindly independent sources from a convolutive mixture cannot be addressed in its widest generality without resorting to statistics of order higher than two. The core of the problem is in fact to identify the paraunitary part of the mixture, which is addressed in this paper. With this goal, a family of statistical contrast is first defined. Then it is shown that the problem reduces to a Partial Approximate Joint Diagonalization (PAJOD) of several cumulant matrices. Then, a numerical algorithm is devised, which works block-wise, and sweeps all the output pairs. Computer simulations show the good behavior of the algorithm in terms of Symbol Error Rates, even on very short data blocks.

This paper concerns recognizing 3-dimensional object using proposed multi-layer block model. In particular, we aim to achieve desirable recognition performance while restricting the computational load to a low level using 3-step feature extraction procedure. An input image is first precisely partitioned into hierarchical layers of blocks in the form of base blocks and overlapping blocks. The hierarchical blocks are merged into a matrix, with which abundant local feature information can be obtained. The local features extracted are then employed by the kernel based support vector machines in tournament for enhanced system recognition performance while keeping it to low dimensional feature space. The simulation results show that the proposed feature extraction method reduces the computational load by over 80% and preserves the stable recognition rate from varying illumination and noise conditions.

In this paper, the power plane resonance problem in a multi-layered PCB is numerically analyzed by applying the alternating-direction implicit (ADI) FDTD method. This method is extremely suitable for analyzing the power plane resonance problems having locally fine structures of two closely located planes. This paper also analyzes the effect of the decoupling capacitor, which is one of the solutions for reducing the resonance problem. The results of the ADI-FDTD agree well with those of the conventional FDTD and the analytic solutions, and the computational CPU time is reduced to about a half of that of the conventional FDTD.

Impact ionization and thermionic tunnelling as two possible breakdown mechanisms in scaled pseudomorphic high electron mobility transistors (PHEMTs) are investigated by Monte Carlo (MC) device simulations. Impact ionization is included in MC simulation as an additional scattering mechanism whereas thermionic tunnelling is treated in the WKB approximation during each time step in self-consistent MC simulation. Thermionic tunnelling starts at very low drain voltages but then quickly saturates. Therefore, it should not drastically affect the performance of scaled devices. Impact ionization threshold occurs at greater drain voltages which should assure a reasonable operation voltage scale for all scaled PHEMTs.