An attenuated iterative reliability-based majority-logic (AIML) decoding algorithm for low-density parity-check (LDPC) codes is proposed, which pertains to hybrid decoding schemes. The algorithm is devised based on the orthogonal check-sums of one-step majority-logic (OSMLG) decoding algorithm in conjunction with certain of reliability measures of the received symbols. Computation of reliability measure of the syndrome sum is refined by introducing an attenuation factor. Simulation results show that, in binary-input additive white Gaussian noise (BI-AWGN) channel, the AIML decoding algorithm outperforms other popular iterative reliability-based majority-logic (IML) decoding algorithms with a slight increase in computational complexity. Within maximum iteration number of 5, the AIML algorithm can achieve almost identical error performance to sum-product algorithm (SPA). No error floor effect can be observed for the AIML algorithm down to the bit error rate (BER) of 10-8, while error floor appears for SPA around the BER of 10-7 even with maximum iteration number of 100. Furthermore, the inherent feature of parallel procession for AIML algorithm enforces the decoding speed in contrast to those serial decoding schemes, such as weighted bit-flipping (WBF) algorithm.

Wireless sensor networks (WSNs) have been paid more attention in recent years. However, energy efficiency is still a troublesome issue in real WSN applications. In this paper, we studied the performance of a virtual multiple-input multiple-output (VMIMO)-based communications architecture for WSN applications. By analyzing the bit error rate (BER) of each cooperative branch, we presented the closed-form expressions for optimal transmitting power (TP) scheme in K×1 VMIMO cluster-based system. Then, the impact of the number of cooperating nodes on energy efficiency with energy-per-useful-bit (EPUB) metric was studied. Performance enhancement of the strategy with optimal TP assignment was verified by extensive simulations under different scenes. A thorough explanation of optimally choosing the number of cooperating nodes was also delivered by the aid of simulation verifications.

A wideband rectangular patch antenna resonating at 3.5 GHz and 8 GHz frequencies is developed on a flexible substrate, which can be used for wearable applications. The proposed antenna gives a wide impedance bandwidth of 116%, operating from 2.5 GHz to 9.5 GHz, covering most of the ultra-wideband (UWB) operating frequency range. A two-element multiple-input multiple-output (MIMO) system is developed using the proposed antenna, and the mutual coupling between the two antennas for various separations and frequencies is analyzed by using artificial neural networks (ANNs). The neural structure is trained by using different ANN algorithms and a comparative study is made between them. It is shown that, quasi-Newton (QN) and quasi-Newton multi layer perceptron (QN-MLP) algorithms are better in terms of training, testing errors, and correlation coefficient.

The ridge waveguide is useful in various microwave applications because it can be operated at a lower frequency and has lower impedance and a wider mode separation than a simple rectangular waveguide. An accurate model is essential for the analysis and design of ridge waveguide that can be obtained using electromagnetic simulations. However, the electromagnetic simulation is expensive for its high computational cost. Therefore, artificial neural networks (ANNs) become very useful especially when several model evaluations are required during design and optimization. Recently, ANNs have been used for solving a wide variety of radio frequency (RF) and microwave computer-aided design (CAD) problems. Analysis and design of a double ridge waveguide has been presented in this paper using ANN forward and inverse models. For the analysis, a simple ANN forward model is used where the inputs are geometrical parameters and the outputs are electrical parameters. For the design of RF and microwave components, an inverse model is used where the inputs are electrical parameters and the outputs are geometrical parameters. This paper also presents a comparison of the direct inverse model and the proposed inverse model.

For an odd prime p, a new sequence family of period pm-1, size (M-1)pmr is proposed using multiplicative and additive characters. The upper bound for the maximum magnitude of nontrivial correlations of the sequence family is derived using well-known character sums. The upper bound is shown to be (r+1)pm+3, which meets the Welch bound asymptotically.

Induction cooking has several advantages compared to traditional heating system; however, to obtain best efficiency, it is essential to have an inductor giving homogeneous temperature on the pan bottom. For this aim, we propose a structure of inductor with four throats containing coils and optimize their distribution. In this paper, first we model magneto-thermal phenomenon of the system by a finite element method (FEM) for the mean to determine the distribution of temperature on the pan bottom by taking the nonlinearity of system. This study shows that a temperature distribution is not homogeneous. Second, with the aim to have homogeneous temperature distribution on the pan bottom, the optimal determination of throats distribution and their dimensions is obtained by genetic algorithms (GAs). The optimized structure permits to satisfy our aim.

White noise deconvolution has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. Using the Kalman filtering method, the time-varying optimal distributed fusion white noise deconvolution estimator is presented for the multisensor linear discrete time-varying systems. It is derived from the centralized fusion white noise deconvolution estimator so that it is identical to the centralized fuser, i.e., it has the global optimality. It is superior to the existing distributed fusion white noise estimators in the optimality and the complexity of computation. A Monte Carlo simulation for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.

Atmospheric ice accretion on transmission lines is of great danger to the security of service of electrical power system. This paper reviews the progress in research dealing with the formation of ice accretions on transmission lines and the effects of ice on the mechanical and electrical performance of transmission lines. The results show that ice accretions on transmission lines can be categorized into five types: glaze, hard rime, soft rime, hoar frost, and snow and sleet. In all types of ice accretions, glaze grown in a wet regime is of the greatest danger to the transmission lines. Meteorological conditions, terrain and geographic conditions, and some other factors significantly influence the ice accumulation speed and the ice amount. Drastic decrease of mechanical property and electric property as a result of severe icing is the main reason for ice accidents. The amount of ice, the asymmetrical ice accretion, and the asynchronous ice shedding can considerably change the conductor strain, conductor sag, variation amount of the span, displacement of the insulator string, and the tension difference. The amount and type of ice, the uniformity of ice accumulation, and the conductivity of freezing water have significant influence on the flashover voltage of ice-covered insulators.

The switched reluctance motor (SRM) is applied in various industrial applications due to its profitable advantages. However, the robustness speed of SRM is one of the major drawbacks, which greatly affects the performance of motor. Thus, the aim of this paper is to control the speed of SRM using H-infinity control strategy. This H-infinity control technique is stronger against robustness. In the proposed speed controller, the rotor position of the SRM is applied to the controller. The speed variation of the rotor is determined from the reference speed and applied to the controller as input. Then, the speed variation and the corresponding sensitivity function are determined. The sensitivity function determination is based on the input weight of the controller. The weight adjustment process is repeated until a stable speed condition is achieved. Then, the output of the proposed control technique is compared with the existing control technique and the robustness is analyzed. Here, the existing control techniques considered are proportional-integral (PI) controller and fuzzy logic controller (FLC)-based PI gain tuning. The proposed control strategy is simulated in MATLAB working platform and the control performance is analyzed.

This paper presents a new sensorless vector controlled induction motor drive robust against rotor resistance variation. Indeed, the speed and rotor resistance are estimated using extended Kalman filter (EKF). Then, we introduce a new fuzzy logic speed controller based on learning by minimizing cost function. This strategy is based on a topology control self-organized and an algorithm for modifying the knowledge base of fuzzy corrector. The learning mechanism addresses the consequences of corrector rules, which are modified according to the comparison between the current speed of machine and an output signal or a desired trajectory. Thus, fuzzy associative memory is constructed to meet the criteria imposed in problems either control or pursuit. The consequent algorithm updating consists of a regulator mechanism allowing a fast and robust learning without unnecessarily compromising the control signal and steady-state performance. The performance of this new strategy is satisfactory, even in the presence of noise or when there are variations in the parameters of induction motor drive.