Electrical and Electronics

Power electronic devices are non-linear loads that create harmonic distortion and can be susceptible to voltage dips if not adequately protected. The non linear nature of these switching devices causes harmonic current injection into the ac mains; there by polluting the power quality (PQ) at the point of common coupling (PCC).This power quality (PQ) improvement is achieved by using multi-pulse converters in THREE-PHASE ac-dc converters (ADCs).The proposed multi-pulse ac-dc converter is based on autotransformer configurations and passive tuned filters. The proposed ac–dc converter is able to eliminate lower order harmonics in the ac supply current. The resulting supply current is near sinusoidal in shape with low total harmonic distortion and a nearly unity power factor. The proposed multi-pulse ac-dc converter is designed and the simulation model is developed in MATLAB. It improves the power quality at the ac mains and meets IEEE-519 standard requirements at varying loads.

Several approaches have been introduced in literature for active noise control (ANC) systems. Since FxLMS algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. In this paper, modification is in done the existing FxLMS algorithm that provides a new structure for improving the tracking performance and convergence rate based on the secondary path modeling technique. The convergence rate is improved by the dynamically varying the step size of the error signal. It is also implemented using FPGA.

An efficient approach for producing electricity from the abundant energy of the sun is discussed. The Nantenna Electromagnetic Collectors (NEC) [1] devices target mid-infrared wavelengths, where conventional photovoltaic (PV) solar cells are inefficient and where there is an abundance of solar energy. The initial concept of designing NEC was based on scaling of radio frequency antenna theory. This NEC is basically a Nano antenna which collects the sun radiation and thermal radiation and converts it into electrical signal. The aim is to realize a low-cost device that will collect and convert this radiation into electricity, which will lead to a wide spectrum, high conversion efficiency, and low-cost solution to complement conventional photovoltaic (PV) solar cells.

Fourth-Generation (4G) wireless networks will be heterogeneous, integrating different networks to provide continuous access for mobile users with multi mode access capability. The Fourth Generation communications systems is all about a global wireless communications system and defines a cost effective, simple, operable and personalized according to the users’ needs concept. The vertical handoff requirements are potential of the network, network cost, handoff latency, network conditions, power consumption and user’s preference. These must be considered during vertical handoff. In this research paper a new vertical handoff decision based on ARM & AI Techniques is designed. A refined, intelligent Associative Rule Mining (ARM) and Artificial Intelligence (AI) based technique is used to execute the vertical handoff intelligent mechanism in fourth generation wireless networks to produce an effective service which reduces the handoff delay and complexity.

Advancement in the field of embedded system and VLSI has induced the researcher in designing low power embedded systems and VLSI circuit design. The embedded systems are mostly batteries operated in nature. The power loss during static, dynamic and switching characteristics are tabulated. The switching nature in cmos constitutes a large value of power loss during the switching condition. Many research papers have been proposed in reducing the switching loss, and low power estimation, this paper clearly demonstrates the comparison of them. The main features of the dominated design techniques and methodologies of transistor level, gate level, RTL level, behavior level and system level are reviewed. The corresponding advantages and drawbacks, as well as comparisons between the techniques and the methodologies are also presented. The low-power design process such as transistor level, gate level, RTL level, behavior level and system-level models are explained.

The range of brightness levels and Histogram shows how individual brightness levels are occupied in any image, for image contrast measurement .Low-level image processing is one of the most important issues of image enhancement. To control and enhance the contrast, Histogram Equalization (HE) is the simple and effective technique, but this approach causes some unnatural look in output image. For best effect the brightness of input image must be retained. Fuzzy techniques can manage the vagueness and ambiguity efficiently. Fuzzy logic is a powerful tool to represent and process human knowledge in form of fuzzy if-then rules. In this work we are using fuzzy membership function in the proposed algorithm. The proposed concept in this paper is named as Brightness Preservation by Fuzzy based Multi-peak Generalized Histogram Equalization (BPFMGHE) and it is simulated by MATLAB; this technique first combined the global histogram equalization of image with local information and then after calculating noise free generalised multi-peak histogram by equalization, the image was again decomposed into several sub-images, and then applied the fuzzy membership function dependent HE process to each of them to preserve image brightness.The distribution of grey level is in complete control with the given method and image improvement is effective. Image is improved and brightness is preserved effectively with the discussed process.

Digital image processing is now increasingly being used in electron accelerators to characterize electron beam. Measurement of electron beam parameters like beam size, beam centroid with high accuracy is required to optimize accelerator performance. Measurement accuracy of these parameters using digital image processing is limited by the noise present in the images. Reduction of noise without altering the features present in the image is a desired goal of image processing. In this paper we evaluate the noise reduction capability of median, mean, gaussian and wiener filters from digital images of electron beam image. The images were collected from Transport Line-1 in Indus Accelerator Complex at Raja Ramanna Centre for Advanced Technology (RRCAT), Indore, India. We also evaluate the effect of these filters on the measurement accuracy of beam parameters like beam size and beam centroid. It has been observed that performance of median filter for noise reduction is better than mean, gaussian and wiener filter. Median filter also creates less distortion in beam size and centroid of the beam in comparison with other filters.

Selection of feature extraction method is most important factor in achieving high recognition performance in automatic pattern recognition systems. Similarly selection of suitable classifier also plays a very important role for the same. Plenty of feature selection methods and classifiers are existing in computer domain and choice of each of these mainly depends on task in hand. This paper presents an efficient and novel method for recognition of handwritten numerals using bar codes. Handwritten numerals are scan converted to binary images and normalized to a size of 30 x 30 pixels. The features are extracted using barcodes and are classified successfully using the statistical technique.

Variation in load frequency is an index for normal operation of power systems. When load Perturbation takes place anywhere in any area of the system, it will affect the frequency at other areas also. To control load frequency of power systems various controllers are used in different areas, but due to non-linearity's in the system components and alternators, these controllers cannot control the frequency quickly and efficiently. The simple neural networks can alleviate this difficulty. This paper deals with the Artificial Neural Network ( ANN) is applied to self tune the parameters of Proportional-Integral-Derivative(PID) ontroller. The single, Two Area non-reheat system has been considered for simulation of the proposed self tuning ANN based PID controller. In the PID controller parameters are continuously adjusted according to the change in area-control error (ACE). Simulations of the networks are carried out for different load changes 1% and change of 1% in governor time constant and turbine time constant parameters. The proposed method for simulation results are obtained by the other controllers of PI and PID compared highlighting the performance of PID-ANN controller. The simulation works developed by MATLAB- SIMULINK Environment. The simulink results are obtained by qualitatively and quantitatively .The qualitative comparison is used for the Integral Square Error (ISE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE) is minimized in single and multi area power system. Therefore the Comparison of responses with conventional integral controller(PI) & PID controller show that the neural-network controller (ANN-PID) has quite satisfactory generalization capability, feasibility and reliability, as well as accuracy in both single and multi area power system.

An efficient VLSI implementation of Adaptive Rank Order Filter (AROF) and Adaptive Median Filter (AMF) is proposed in this paper. Impulse noise is introduced in digital images during image acquisition and transmission. The noise reduction algorithms remove noise without degrading image information. Linear filters tend to blur an image; hence they are not commonly used. Non-linear filters provide more satisfactory results in comparison to linear filters. The proposed paper adapts the filter based on the level of noise intensity in the image. AMF provides better filtering properties than standard median filters for images corrupted with 60% noise density. AROF provides better filtering properties than it is possible with AMF for images corrupted with higher noise densities (>60%). The VLSI architecture for AROF and AMF implements pipelining with parallel processing in order to speed up the filtering process. The performance of the proposed algorithm is compared with Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF).