Solar water pumping is a promising renewable alternative to conventional water pumping for large, medium and small power applications. This system is also useful in remote areas where electricity is not available and it is very expensive to lay long transmission line. This paper deals with basics of solar powered water pumping system and the comparison of performance between AC motor and PMDC motor pumping systems. In this work, the MATLAB based modelling of a PV array, single-phase induction motor and permanent magnet DC motor are studied and developed. The photovoltaic is used to feed power to the water pumping systems. The performance characteristics of single-phase induction motor and permanent magnet direct current motor are obtained by using MATLAB simulation and are validated with experimental results. From the experimental results, it is found that the permanent magnet direct current motor is best suitable for photovoltaic water pumping than the single-phase induction motor in low power applications.

In general Wireless Sensor Networks (WSN’s), improving the network reliability by using cooperative communication to select the relay node with low overhead is a challenging task. Also the design of routing scheme based on link quality is a critical problem. Due to the resource constraints, connections between the nodes are changeable one. It leads to the network to create a various routes which are necessary. By using the established path, it delivers the data to the base station in a reliable manner. This paper concentrates on a cross layer approach by using various cooperative metric such as Packet Delivery Rate (PDR), Packet Reception Rate (PRR), Required Number of Packet transmission (RNP), Average number of packet sent/resent and Expected Transmission Time (ETX). These parameters are taken as a fuzzy parameter that exploits the cooperative diversity at the routing layer. We propose a Fuzzy based High Quality Link Set Routing (FHQLSR) which selects the high quality relay node and evaluates the quality of link in each node by using RNP and ETX parameters. This FHQLSR scheme is used to find the effective relay node that forward packets to the base station via high quality link and also find the optimal path using Improved Dijikstra’s Algorithm (IDA) in the multi hop networks. By considering routing and link quality parameters, the FHQLSR scheme improves the overall reliability, scalability and stability of the network.

The emission of pollutants from coal based thermal generating stations into the atmosphere such as oxides of carbon (COx), oxides of Nitrogen (NOx) and oxides of sulphur (SOx) causes green house effect and leads to the global warming. Since coal based thermal power generation is the major source of electricity in all the developing countries, it is the challenge for the power engineers to solve the multi objective optimization problems such as emission and economic dispatch problems. This paper presents an efficient Bacteria Foraging Algorithm for the economic and emission dispatch problems. The approach utilizes the natural selection of global optimum bacterium having successful foraging strategies as the cost function. The proposed algorithm is applied for solving economic dispatch, emission dispatch, combined economic and emission dispatch and emission constrained economic dispatch of IEEE-30 bits system with six thermal generators. The performance of the proposed algorithm is compared with the conventional method, real coded GA, hybrid GA and PSO methods and it is observed that this method is reliable and may replace effectively the conventional practices presently performed in different central load dispatch centers. The comparison of results shows that the proposed method was indeed capable of obtaining higher quality solutions efficiently for the above problems. Copyright (C) 2012 Praise Worthy Prize S.r.l. - All rights reserved.

Water pumping using AC motors has become one of the most feasible photovoltaic (PV) applications. Moreover, PV pumping is getting more attention in recent days mainly in remote areas where connection to the grid is technically not possible. Power generation by Photovoltaic is reliable. The operation and maintenance costs are low. The induction motor is more rugged, and maintenance-free motor. In this paper, a PV fed water pumping system using single phase induction motor has been considered. The modeling of PV cell, Boost converter, inverter and single phase induction motor have been studied and developed. The overall AC pumping system fed by PV cell has been simulated using MATLAB and the results are obtained. The results show the performance of a single-phase induction motor drive supplied by a photovoltaic generator.

Photovoltaic (PV) powered water pumps are basically similar to any other pumping system. Just as wind turbine pumps, PV pumps use intermittent power. When there is enough sunlight, the system functions well. The simplest PV water pumping system consists of a PV array, boost converter, DC motor or PMDC motor and a pump. This paper deals with the simulation of PV based DC motor and PMDC motor pumping systems, comparison of them and proving a simple but efficient photovoltaic water pumping system. Motors and centrifugal pumps operate at variable speed in a photovoltaic pumping system. From the comparison, it is suggested that PV fed Permanent Magnet Direct Current Motor pumping system is better.

Short Term Load Forecasting (STLF) is an important tool for successful planning and operation of power generating stations. This paper proposes neural network algorithm for STLF using Functional Link Neural Network (FLN) with Slope Parameter. This Neural Network Algorithm includes peak and minimum loads and load factors as additional inputs for the final forecast. The Proposed Functional Link Network with Slope Parameter (FLNSP) is tested for the Tamilnadu state, (India) grid data and the results are compared with the Conventional Back Propagation (CBP) method. Simulation results indicate that the proposed forecasting techniques are effective.

A study on ultrasound kidney images using proposed dominant Gabor wavelet is made for classifying a few important kidney categories. Three kidney categories, namely, normal (NR), medical renal diseases (MRD) and cortical cyst (CC) are considered for the analysis. Of the 30 Gabor wavelets, a unique dominant Gabor wavelet is determined by maximizing the similarity between original pre-processed image and reconstructed Gabor image. The dominant Gabor features “\({\mu_{mn}^D }\) ” and “\({AAD_{mn}^D }\) ” are then evaluated to characterize the tissues of kidney region and compared with the Gabor features derived by considering all Gabor wavelets individually and as a whole using the resultant classification efficiency. The results obtained show that the proposed dominant Gabor wavelet features provide the classification efficiency of 86.66% for NR, 76.66% for MRD and 83.33% for CC, while individual wavelet features offer less than 70%, 63.33% and 66% for NR, MRD and CC. The overall classification efficiency improves by 18.89% with dominant Gabor features when compared to the classification efficiency obtained by considering all the Gabor wavelets features. The outputs of the proposed technique are validated with medical experts to assess the actual efficiency. The overall discriminating ability of the systems is also evaluated with performance evaluation measures, F-score and ROC. It has been observed that the dominant Gabor wavelet improves the classification efficiency appreciably and explores the possibility of implementing a computer-aided diagnosis system exclusively for ultrasound kidney images.

This paper presents a clustering routing protocol for event-driven WSNs with Reduction of Reporting node in each cluster. We demonstrate that decreasing the number of reporting nodes in each cluster; increase the number of reports that need to be sent to the sink in order to achieve the energy efficient and desired information reliability. The algorithm also aims at even energy dissipation among the nodes in the network by alternating the possible routes to the Sink and autonomous selection of energy efficient cluster head. This helps to balance the load on sensor nodes while avoiding congested links at the same time. Moreover, the algorithm proposes using an energy efficient approach by choosing high energy values of a node stored in buffer table for each round. We discuss the implementation of our protocol, and present its performance evaluation through Network Simulator.

TCP (Transmission Control Protocol) (1) was designed to provide reliable end-to-end delivery of data over unreliable networks.In theory, TCP should be independent of the technology of the underlying infrastructure.In particular, TCP should not care whether the Internet Protocol (IP) is running over wired or wireless connections.In practice, it does not matter because most TCP deployments have been carefully designed based on assumptions that are specific to wired networks.Ignoring the properties of wireless transmission can lead to TCP implementations with poor performance.In wireless and Ad hoc networks, the principal problem of TCP lies in performing congestion control in case of losses that are not induced by network congestion.Since bit error rates are very low in wired networks, nearly all TCP versions nowadays assume that packets losses are due to congestion.Consequently, when a packet is detected to be lost, either by timeout or by multiple duplicated acknowledgements (ACK), TCP slows down the sending rate by adjusting its congestion window.Unfortunately, wireless networks suffer from several types of losses that are not related to congestion, making TCP not adapted to this environment.Numerous enhancements and optimizations have been proposed over the last few years to improve TCP performance over wireless and Ad hoc Networks.These improvements include infrastructure based WLANs (2), (3), ( 4), (5), mobile cellular networking environments ( 6), ( 7), and satellite networks (8), (9).It is noted that the following TCP versions: Tahoe, Reno, Newreno, and Vegas perform differently in Ad hoc networks (10).However, all these versions suffer from the

This paper presents a clustering routing protocol for event- driven WSNs with Reduction of Reporting node in eachcluster. We demonstrate that decreasing the number of reporting nodes in each cluster; increase the number of reports that need to be sent to the sink in order to achieve the energy efficient and desired information reliability. The algorithm also aims at even energy dissipation among the nodes in the network by alternating the possible routes to the Sink and autonomous selection of energy efficient cluster head. This helps to balance the load on sensor nodes while avoiding congested links at the same time. Moreover, the algorithm proposes using an energy efficient approach by choosing high energy values of a node stored in buffer table for each round. We discuss the implementation of ourprotocol, and present its performance evaluation through NS2.

Rate based transport protocol determines the rate of data transmission between the sender and receiver and then sends the data according to that rate. To notify the rate to the sender, the receiver sends ACKplusRate packet based on epoch timer expiry. In this paper, through detailed arguments and simulation it is shown that the transmission of ACKplusRate packet based on epoch timer expiry consumes more energy in network with low mobility. To overcome this problem, a new technique called Dynamic Rate Feedback (DRF) is proposed. DRF sends ACKplusRate whenever there is a change in rate of (plus or minus) 25 percent than the previous rate. Based on ns2 simulation DRF is compared with a reliable transport protocol for ad hoc network (ATP)

Summary In image authentication watermarking, hidden data is inserted into an image to detect any accidental or malicious image alteration. In the literature, quite a small number of cryptography based secure authentication methods are available for binary images. In a cryptography based authentication watermarking, a message authentication code (or digital signature) of the whole image is computed and the resulting code is inserted into the image itself. This paper proposes a new authentication watermarking method for binary images. The main idea is to use the prioritized sub-blocks by pattern matching scheme to embed the code. Shuffling is applied before embedding to equalize the uneven embedding capacity. It detects any alteration while maintaining good visual quality for all types of binary images. The security of the algorithm lies only on the secrecy of a secret or private keys used.

The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. The proposed method distinguishes three kidney categories namely normal, medical renal diseases and cortical cyst. For the each pre-processed ultrasound kidney image, 36 features are extracted. Two types of decision support systems, optimized multi-layer back propagation network and hybrid fuzzy-neural system have been developed with these features for classifying the kidney categories. The performance of the hybrid fuzzy-neural system is compared with the optimized multi-layer back propagation network in terms of classification efficiency, training and testing time. The results obtained show that fuzzy-neural system provides higher classification efficiency with minimum training and testing time. It has also been found that instead of using all 36 features, ranking the features enhance classification efficiency. The outputs of the decision support systems are validated with medical expert to measure the actual efficiency. The overall discriminating capability of the systems is accessed with performance evaluation measure, f-score. It has been observed that the performance of fuzzy-neural system is superior compared to optimized multi-layer back propagation network. Such hybrid fuzzy-neural system with feature extraction algorithms and pre-processing scheme helps in developing computer-aided diagnosis system for ultrasound kidney images and can be used as a secondary observer in clinical decision making.

A higher order spline interpolated contour obtained with up-sampling of homogenously distributed coordinates for segmentation of kidney region in different classes of ultrasound kidney images has been developed and presented in this paper. The performance of the proposed method is measured and compared with modified snake model contour, Markov random field contour and expert outlined contour. The validation of the method is made in correspondence with expert outlined contour using maximum coordinate distance, Hausdorff distance and mean radial distance metrics. The results obtained reveal that proposed scheme provides optimum contour that agrees well with expert outlined contour. Moreover this technique helps to preserve the pixels-of-interest which in specific defines the functional characteristic of kidney. This explores various possibilities in implementing computer-aided diagnosis system exclusively for US kidney images. Keywords—Ultrasound Kidney Image – Kidney Segmentation – Active Contour – Markov Random Field – Higher Order Spline Interpolation

The transmission control protocol (TCP) was designed to provide reliable end to end delivery of data over unreliable networks. Ignoring the properties of wireless adhoc networks can lead to poor TCP implementation. Therefore several transport layer protocol have been designed exclusively for adhoc network such as ATCP, ATP, TPA etc. Out of these protocols ATP finds to be more suitable for adhoc network. In ATP, the performance of sending TCP is concentrated but the performance and load of the intermediate node is not taken in to account while designing it. In this paper a simple approach is proposed to address the intermediate node problem by making it to operate in three layers rather than operating in four layers. This enhanced ATP is called as PATPAN.

Different transport layer protocols are suitable for different applications. If the ad hoc network is fairly closed and consists of nodes from single organizations that are communicating with themselves, compatibility with TCP may not be required, and thus ATP (a reliable transport protocol for ad hoc network) is the good choice for this scenario. In ATP, the performance of sending TCP is concentrated but the performance and load of the intermediate node is not taken in to account. Traditional transport layer protocols are designed to operate in end to end basis but ATP is designed such that it operates on semi node to node basis. In this paper, three simple approaches is proposed to make ATP to perform end to end rather than node to node without sacrificing the performance The proposed system is implemented in java and its performance is compared with ATP protocol.

This paper aims to extract potential features that provide tissue characteristics of kidney region in ultrasound images for classifying the disorders objectively. The acquired images are pre-processed to retain the pixels-of- interest. Based on the scale space representation the evaluation of multi-scale differential principal curvature features is made. The various dissimilarity metrics are determined to measure the extent of isolation between the features of different kidney categories. The results obtained show that all the extracted features are highly significant in classifying normal and abnormal kidneys. The possibility of discriminating the disorders with few features is also been explored. The analysis also indicates the prospect of implementing a computer-aided diagnosis system for ultrasound kidney images. The realization of such system will act as a secondary observer and simplifies the procedure of pathology identification.

A study on ultrasound kidney images using proposed dominant Gabor wavelet is made for the automated diagnosis and classification of few important kidney categories namely normal, medical renal diseases and cortical cyst. The acquired images are initially preprocessed to retain the pixels of kidney region. Out of 30 Gabor wavelets, a unique dominant Gabor wavelet is determined by estimating the similarity metrics between original and reconstructed Gabor image. The Gabor features are then evaluated for each image. These derived features are mapped onto 2D feature space using k-mean clustering algorithm to group the data of similar class. The decision boundaries are formulated using linear discriminant function between the data sets of three kidney categories. A k-NN classifier module is used to identify the query input US kidney image category. The results show that the proposed dominant Gabor wavelet provides the classification efficiency of 87.33% for NR, 76.66% for MRD and 83.33% for CC. The overall classification efficiency improves by 18.89% compared to the classifier trained with features obtained by considering all the Gabor wavelets. The outputs of the proposed decision support systems are validated with medical expert to measure the actual efficiency. Also the overall discriminating ability of the systems is accessed with performance evaluation measure – f-score. It has been observed that the dominant Gabor wavelet improves the classification efficiency appreciably. Hence, the proposed method enhances the objective classification and explores the possibility of implementing a computer-aided diagnosis system exclusively for ultrasound kidney images.

The objective of this work is to provide a set of most significant content descriptive feature parameters to identify and classify the kidney disorders with ultrasound scan. The ultrasound images are initially pre-processed to preserve the pixels of interest prior to feature extraction. In total 28 features are extracted, the analysis of features value shows that 13 features are highly significant in discrimination. This resultant feature vector is used to train the multilayer back propagation network. The network is tested with the unknown samples. The outcome of multi-layer back propagation network is verified with medical experts and this confirms classification efficiency of 90.47%, 86.66%, and 85.71% for the classes considered respectively. The study shows that feature extraction after pre-processing followed by ANN based classification significantly enhance objective diagnosis and provides the possibility of developing computer-aided diagnosis system