In multiple-input multiple-output (MIMO) wireless systems, the receiver must extract each transmitted signal from received signals. Iterative signal detection with belief propagation (BP) can improve the error rate performance, by increasing the number of detection and decoding iterations in MIMO systems. This number of iterations is, however, limited in actual systems because each additional iteration increases latency, receiver size, and so on. This paper proposes a convergence acceleration technique that can achieve better error rate performance with fewer iterations than the conventional iterative signal detection. Since the Log-Likelihood Ratio (LLR) of one bit propagates to all other bits with BP, improving some LLRs improves overall decoder performance. In our proposal, all the coded bits are divided into groups and only one group is detected in each iterative signal detection whereas in the conventional approach, each iterative signal detection run processes all coded bits, simultaneously. Our proposal increases the frequency of initial LLR update by increasing the number of iterative signal detections and decreasing the number of coded bits that the receiver detects in one iterative signal detection. Computer simulations show that our proposal achieves better error rate performance with fewer detection and decoding iterations than the conventional approach.

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.

In this letter, we propose a shared adaptive packet loss concealment scheme for the high quality guaranteed Internet telephony service which connects multiple users. In order to recover packet loss efficiently in the all-IP based convergence environment, we provide a robust signal recovery scheme which is based on the shared adaptive both-side information utilization. This scheme is provided according to the average magnitude variation across the frames and the pitch period replication on the 1-port gateway (G/W) system. The simulated performance demonstrates that the proposed scheme has the advantages of low processing times and high recovery rates in the all-IP based ubiquitous environment.

FeedForward (FF-) Blind Source Separation (BSS) systems have some degree of freedom in the solution space. Therefore, signal distortion is likely to occur. First, a criterion for the signal distortion is discussed. Properties of conventional methods proposed to suppress the signal distortion are analyzed. Next, a general condition for complete separation and distortion-free is derived for multi-channel FF-BSS systems. This condition is incorporated in learning algorithms as a distortion-free constraint. Computer simulations using speech signals and stationary colored signals are performed for the conventional methods and for the new learning algorithms employing the proposed distortion-free constraint. The proposed method can well suppress signal distortion, while maintaining a high source separation performance.

CORDIC (COordinate Rotation DIgital Computer) is a well known algorithm using simple adders and shifters to evaluate various elementary functions. Thus, CORDIC is suitable for the design of high performance chips using VLSI technology. In this paper, a complete analysis of the computation error of both the (conventional) CORDIC algorithm and the CORDIC algorithm with expanded convergence range is derived to facilitate the design task. The resulting formulas regarding the relative and absolute approximation errors and the truncation error are summarized in the tabular form. As the numerical accuracy of CORDIC processors is determined by the word length of operands and the number of iterations, three reference tables are constructed for the optimal choice of these numbers. These tables can be used to facilitate the design of cost-effective CORDIC processors in terms of areas and performances. In addition, two design examples: singular value decomposition (SVD) and lattice filter for digital signal processing systems are given to demonstrate the goal and benefit of the derived numerical analysis of CORDIC.

Spatial multiplexing (SM) offers a linear increase in transmission rate without bandwidth expansion or power increase. In SM systems, the LMMSE receiver establishes a good tradeoff between the complexity and performance. The performance of the LMMSE receiver would be degraded by MIMO channel estimation errors. This letter focus on obtaining the asymptotic convergence of output interference power and SIR performance for the LMMSE receiver with channel uncertainty. Exactly matched simulation results verify the validity of analysis in the large-system assumption. Furthermore, we find that the analytical results are also valid in the sense of average results for limited-scale system in spite of the asymptotic assumption used in derivation.

An iterative reconstruction algorithm of accelerating the estimation of the complex relative permittivity of a cylindrical dielectric object based on the multigrid optimization method (MGOM) is presented. A cost functional is defined by the norm of a difference between the scattered electric fields measured and calculated for an estimated contrast function, which is expressed as a function of the complex relative permittivity of the object. Then the electromagnetic inverse scattering problem can be treated as an optimization problem where the contrast function is determined by minimizing the cost functional. We apply the conjugate gradient method (CGM) and the frequency-hopping technique (FHT) to the minimization of the cost functional, and also employ the multigrid method (MGM) with a V-cycle to accelerate the rate of convergence for getting the reconstructed profile. The reconstruction scheme is called the multigrid optimization method. Computer simulations are performed for lossy and inhomogeneous dielectric circular cylinders by using single-frequency or multifrequency scattering data. The numerical results demonstrate that the rate of convergence of the proposed metod is much faster than that of the conventional CGM for both noise-free and noisy cases.

We compare the convergence performance of different numerical schemes for computing the fundamental matrix from point correspondences over two images. First, we state the problem and the associated KCR lower bound. Then, we describe the algorithms of three well-known methods: FNS, HEIV, and renormalization. We also introduce Gauss-Newton iterations as a new method for fundamental matrix computation. For initial values, we test random choice, least squares, and Taubin's method. Experiments using simulated and real images reveal different characteristics of each method. Overall, FNS exhibits the best convergence properties.

Over the years, many improvements and refinements to the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning extension for the backpropagation learning algorithm and its variants is proposed. The new method initially puts pressure on artificial neural networks in order to get all outputs for all training patterns into the correct half of the output range, instead of mainly focusing on minimizing the difference between the target and actual output values. The upper bound of the penalty values is also controlled. The technique is easy to implement and computationally inexpensive. In this study, the new approach is applied to the backpropagation learning algorithm as well as the RPROP learning algorithm. The superiority of the new proposed method is demonstrated though many simulations. By applying the extension, the percentage of successful runs can be greatly increased and the average number of epochs to convergence can be well reduced on various problem instances. The behavior of the penalty values during training is also analyzed and their active role within the learning process is confirmed.

The Feedback-Guided Dynamic Loop Scheduling (FGDLS) algorithm [1] is a recent dynamic approach to the scheduling of a parallel loop within a sequential outer loop. Earlier papers have analysed convergence under the assumption that the workload is a positive, continuous, function of a continuous argument (the iteration number). However, this assumption is unrealistic since it is known that the iteration number is a discrete variable. In this paper we extend the proof of convergence of the algorithm to the case where the iteration number is treated as a discrete variable. We are able to establish convergence of the FGDLS algorithm for the case when the workload is monotonically decreasing.

A parallel notch filter (PNF) for eliminating a sinusoidal signal whose frequency and phase are unknown, has been proposed previously. The PNF achieves both fast convergence and high estimation accuracy when the step-size for adaptation is appropriately determined. However, there has been no discussion of how to determine the appropriate step-size. In this paper, we derive the convergence condition on the step-size, and propose an adaptive algorithm with variable step-size so that convergence of the PNF is automatically satisfied. Moreover, we present a new filtering structure of the PNF that increases the convergence speed while keeping the estimation accuracy. We also derive a variable step-size scheme for the new PNF to guarantee the convergence. Simulation results show the effectiveness of the proposed method.

This letter develops convergence analysis of normalized sign-sign algorithm (NSSA) for FIR-type adaptive filters, based on an assumption that filter tap weights are Gaussian distributed. We derive a set of difference equations for theoretically calculating transient behavior of filter convergence, when the filter input is a White & Gaussian process. For a colored Gaussian input and a large number of tap weights, approximate difference equations are also proposed. Experiment with simulations and theoretical calculations of filter convergence demonstrates good agreement between simulations and theory, proving the validity of the analysis.

Color CRTs (Cathode Ray Tubes) are still evolving in competition with other display devices in the growing TV markets, with continuing demands for enhanced performance and lower cost. In response to these trends, we have developed a new self-converging system of CRT with simple structure. It offers advantages in terms of high resolution for HDTV and large deflection angle for short depth TV sets. The system realizes less spot distortion at the screen periphery of the CRT and lower horizontal dynamic focus voltage than those in a conventional self-converging system, while keeping the cost just as low. In the system, a uniform horizontal deflection field and a newly-developed magnet lens are utilized. The uniform field reduces the spot distortion in exchange for occurrences of raster distortion and convergence error, both of which can be corrected by the newly-developed magnet lens without additional circuit modifications. As a core part of the new system, the lens power of the newly-developed magnet lens varies along the horizontal axis in order to simultaneously achieve convergence and correct the pincushion distortion of the raster. Furthermore, countermeasures for magnet-related issues are taken from the viewpoints of real operation and mass production. The system with the new DY was evaluated in experiments using 86 cm CRTs (16 : 9), and it has been found that the system realizes substantially smaller spot distortions as well as favorable convergence and raster performances, with a drawback of decrease in horizontal deflection sensitivity. The spot oblateness, defined as horizontal spot diameter divided by vertical spot diameter, has decreased from 2.65 to 1.70 accompanying a 15% reduction of horizontal spot sizes at the corners of the screen with 30% decreased dynamic focus voltages and 10% decreased horizontal deflection sensitivity.

This paper presents a theoretical convergence analysis of a CORDIC-based adaptive ARMA lattice filter. In previous literatures, several investigation methods for adaptive lattice filters have been proposed; however, they are available only for AR-type filters. Therefore, we have developed a distinct technique that can reveal the convergence properties of the CORDIC ARMA lattice filter. The derived technique provides a quantitative convergence analysis, which facilitates an efficient hardware design for the filter. Moreover, our analysis technique can be applied to popular multiplier-based filters by slight modifications. Hence, the presented convergence analysis is significant as a leading attempt to investigate ARMA lattice filters.

This paper depicts the future R&D direction and the importance of SoC (System-on-Chip) based on a forecast of the Consumer Electronics trend in the Digital Convergence Era. Real-life examples of Samsung Electronics in order to solidify the competitiveness of its set products are presented.

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.

In this paper, we describe a development of a Bluetooth Access Point for the WAN connection of home network devices. Especially, users can access the PSTN at home instead of expensive CDMA network through the AP, using the 'one-phone,' which is the Bluetooth enabled cellular phone. The one-phone service becomes a convergence of wired and wireless communication through the AP.

We have developed an experimental approach that allows us to estimate the performance of a large-scale enterprise network to update routing information. This approach was applied to the integration of the UFJ Bank network system on January 15, 2002. The main characteristic of this approach is the application of a formula that represents the delays in updating routing information that accompany reductions in CPU resources. This procedure consists of two steps: one is to estimate the reduction in the availability of CPU resources caused by forwarding of data packets at a router, and the other is to estimate the levels of CPU resources required for replying to a query about a new route and subsequently updating the routing information. These steps were applied to estimate the performance of the network in terms of routing information convergence. The results of our experiments on the network showed that updating the routing information was possible as long as the average level of CPU utilization during any five-minute period at the routers was less than 40%. We were able to apply this guideline and thus confirm the stability of the UFJ Bank network.

BGP might experience a lengthy path exploration process to reach the convergence after the routing changes. found that the BGP rate-limiting timer--MinRouteAdvertisementInterval (MRAI) has an optimal value Mo that achieves the best trade-off between the stability and the convergence speed. In this paper, with the aid of a timed BGP model, we investigate the effects of MRAI and its optimal value Mo for the BGP convergence process. We find that an adequately long MRAI timer can batch-remove candidate paths and ensure the routing stability in the convergence process. There exists a minimal MRAI Ms that achieves the effect, which is also the upper bound of Mo and provides an approximation of Mo. We calculate the approximations of Ms for different settings and estimate the optimal MRAI for the Internet. According to the results, the optimal MRAI for the Internet might be 5-10 times less than the current default value used in the Internet. The simulations taken with SSFNet and the experiments conducted over the Planet-Lab demonstrate the correctness of our analysis.

The paper first researches the properties of neural networks in the framework of the dual linear programming theory, then discusses the variation range of a Hessian matrix associated to dual linear programming problems. By means of eigenvalues method, a Lipschitz constant based formula for determining the algorithm step-size is presented. Two examples are given to show that the proposed formula is efficacious.