نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال پنجم شماره 1 (پیاپی 9، بهار 1386)

In recent years there has been a growing interest to employ non-linear predictive techniques and models in speech coding to further reduce bit-rate and therefore channel bandwidth. Usually neural nets are used for this purpose that result in an additional up to 3dB reduction in the excitation signal energy. Non-linear prediction can also be performed based on Volterra series expansion wherein the expansion is usually limited to first and second terms, for simplicity (quadratic prediction). Early studies have shown that employing Volterra filters results in a much higher reduction in excitation signal energy (6 to 10 dB), as compared with neural nets. But, because of instability, this reduction can not be materialized in terms of bit-rate reduction or signal to noise improvement. This instability in the decoder is triggered by computational errors (i.e. due to quantization of the excitation signal) and high sensitivity of algorithms to these errors. In the original work, presented here, the instability in the codec is studied in both forward and backward prediction schemes using LS and LMS algorithms respectively. It is shown that stability can be obtained at the cost of losing most of saving in excitation signal energy where final reduction level is as much as for neural nets. With forward prediction, after stabilizing, in spite of a small increasing in the operational complexity for 20 to 45% of frames including the quadratic term will be beneficial. So a scheme is developed to perform non-linear prediction only on these frames. This algorithm results in an improvement of up to 4 dB in final signal to noise ratio. Sequential backward quadrant prediction, although much more interesting from implementation point of view, does not lead to an appreciable better performance over linear prediction.

In this paper we propose a new Gaussian-based analytical method for performance analysis of regular LDPC codes iterative decoding on AWGN channel. The proposed method has good accuracy and low complexity in comparison with current methods. Based on our developed analytical equations, we present an error propagation model for the iterative decoder of LDPC codes which can be used as a simple tool for convergence analysis of LDPC codes on the AWGN channel.

In this paper, the parabolic equation method is applied to analyze radio wave propagation through window structures. By this method, a typical window propagation situation is simulated for different window sizes and frame types. The simulation results are represented for both normal and oblique incident cases of uniform and non-uniform plane wave. Results from the implementation of the parabolic equation method show good agreement with FDTD reported simulations. Base on this study, as the parabolic equation method needs less memory size and CPU time against FDTD method, it can be used as an efficient algorithm to analyze this kind of problems.

Capacity of wireless communication systems can be increased significantly by using arrays of antenna at the transmitter and receiver. In these so called multiple input multiple output (MIMO) communication systems, the algorithms used for detection of transmitted symbols are based on perfect channel state information (CSI) at the receiver side. The optimum detection approach in the sense of symbol error rate (SER) is Maximum likelihood (ML) detector. However, in the case of imperfect channel knowledge, the performance of this type of detection method degrades and symbol error rate (SER) increases. In this manuscript, we have briefly addressed the effect of imperfect channel knowledge on the performance of MIMO communication systems. Then, an analytical approach is proposed to cope with the destructive effect of CSI uncertainty on the ML detection algorithm and the performance of our proposed method is verified via computer simulations.

The presence of nonlinear and unbalance loads in a three-phase network causes harmonics generation and dissipation in power network. One of the usual methods used for decreasing and eliminating these effects is the application of active and passive filter. The passive filter is designed for a particular kind of frequency and therefore eliminates a particular harmonics. Its weakness, however, is the possibility of its resonance with the equivalent network impedance and the large size of its elements. The active filter helps to remove the above problems. Moreover, this filter causes the harmonics to be rejected individually or all together and prohibits the occurrence of resonance with the network. One of the problems of these filters is the limited dynamics response that considers the steady state of harmonics. In this paper, unlike the previous methods on single phase analysis, the inverter used in active filter is analyzed more precisely, i.e., the simultaneous three- phase analysis. The ohmic effect of phase-inductances is also taken into account. The inverter control system makes use of two internal and external loops. The external loop produces suitable signal for on/off switching through sliding mode control. The internal loop utilizes energy feedback to adjust the capacitor voltages. This new method effectively improves the speed of dynamic filter response in comparison with the previously reported methods and is able to quickly compensate harmonics and load unbalancing.

Appropriate routing in data transfer is a challenging problem that can lead to improved performance of networks in terms of lower delay in delivery of packets and higher throughput. Considering the highly distributed nature of networks, several multi-agent based algorithms, and in particular ant colony based algorithms, have been suggested in recent years. However, considering the need for quick optimization and adaptation to network changes, improving the relative slow convergence of these algorithms remains an elusive challenge. Our goal here is to reduce the time needed for convergence and to accelerate the routing algorithm’s response to network failures and/or changes by imitating pheromone propagation in natural ant colonies. More specifically, information exchange among neighboring nodes is facilitated by proposing a new type of ant (assistant ants) to the AntNet algorithm. This method is an extension of authors’ earlier work by allowing intermediate nodes, in addition to destination nodes, to produce assistant ants. The resulting algorithm, the “modified AntNet,” is then simulated via NS2 on NSF and NttNet network topologies. The network performance is evaluated under various conditions. Statistical analysis of results confirms that the new method can significantly reduce the average packet delivery time and rate of convergence to the optimal route when compared with standard AntNet.

Decision making is finding the best compromised solution from all feasible alternatives. Multi-criteria decision making is one of the most applied branches of decision making. Many methods have been presented for solving MCDM problems ever since. Among these methods, simple additive weighting, SAW, is the most commonly used method. In this paper, two methods are proposed for solving MCDM problems based on artificial neural networks. This paper shows an application of soft computing techniques in classic problems, such as decision making. Herein, two methods are presented based on both supervised and unsupervised neural networks. The results of the methods have been compared with SAW.