Pprasanna Murali Krishna ¹, M. V. Subramanyam ², K. Satya Prasad ³

Affiliations
1 Jawaharlal Technological University, kakinada - 533003, Andhra Pradesh, IN
2 Santhiram Engineering College, Nandyal - 518501, Andhra Pradesh, IN
3 Electrinics and Communication Engineering Department, Jawaharlal Technological University, kakinada - 533003, Andhra Pradesh, IN

Abstract

Background/Objectives: The objective of this work is to improve efficient resource sharing in a Peer to Peer based Wireless Mesh Network using Hybrid Swarm Intelligence approach. Methods/Analysis: It is difficult to maintain a stable Distributed Hash Table (DHT) in a wireless environment due to frequent node mobility, multi-hop nature, link quality etc., The QoS parameters such as Packet Delivery Ratio (PDR), End to End Delay, Network Load, No. of hops to look up etc are severely affected by node mobility when structured peer to peer algorithm such as chord is applied in a muli-hop environment like wireless mesh network. The proposed method takes link quality, End to End delay, PDR, Query response time into consideration to improve the performance of chord. We have employed meta-heuristic algorithms such as Particle Swarm Optimization (PSO), FireFly algorithm (FF), a hybrid of PSO-FF to improve the performance of chord when applied over a multi-hop environment. Findings: The simulations are conducted when nodes are static and mobile. The performance of CHORD/PSO-FF is compared with CHORD/PSO and CHORD/FF and results showed improved performance in both the cases. Applications/Improvements: This hybrid approach improved the performance of chord protocol in a wireless mesh network when nodes are static and dynamic.

Keywords

Chord, FireFly Algorithm, Hybrid PSO FF, Particle Swarm Optimization, QoS Parameters, Wireless Mesh Network.

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A. Ranga Nayakulu ¹, K. Satya Prasad ²

Affiliations
1 KITS, Markapur – 523316, Andhra Pradesh, IN
2 JNTUCEK, Kakinada - 533003, Andhra Pradesh, IN

Abstract

Low power VLSI is key technology area enabling battery powered applications. The research work given here presents clock gating scheme based on signal range comparison for low power VLSI. The work at first stage is applied to FIR architectures. Further in second stage to study the usability of the proposed technique both in ASIC and FPGA applications, circuit level simulation is also carried out. Low power validation of the proposed clock gating scheme at circuit level is simulated at 130 nm technology using SPICE tools. Analyses are carried out to study the effectiveness of the clock gating scheme with respect to the presence of information in given 2's complement signal. In final stage to demonstrate an application for the clock gating scheme, the FIR filter is extended towards realizing the real time signal correlator consisting a Finite State Machine (FSM). All the blocks of filters and correlator are simulated initially through Modelsim and results are verified. Xilinx ISE tools are used to verify the synthesis aspects. Power analysis is carried out using Xilinx X power tool. The transposed FIR is more suitable for VLSI implementation and demonstrates power saving of 47% when compared with non clock gating based scheme. In circuit simulation results tt is observed that 21% of power saving is possible a teach subword register stage with the proposed clock gating scheme under 50% of probability for NO-Information (NOI) input signal conditions. The subword clock gated correlator results show power saving of 34% under given signal conditions. The research demonstrates improved clock gating mechanism suitable for most of DSP applications. The proposed clock gating scheme can be more effective in the context when signal is with less amplitude or for narrow band signal applications. The work finds application is several future low power VLSI applications.

Keywords

Correlator, Dynamic Power, Subword Register, SPICE Power Analysis.

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Balamuralikrishna Potti ¹, M. V. Subramanyam ², K. Satya Prasad ³

Affiliations
1 JNTUK, Kakinada - 533 003, Andhra Pradesh, IN
2 Department of ECE, Santhiram Engineering College, Nandyal, Kurnool - 518 501, Andhra Pradesh, IN
3 Department of ECE, University College of Engineering, JNTUK, Kakinada - 533 003, Andhra Pradesh, IN

Abstract

Background/Objectives: The objective of this work is to mitigate starvation in Wireless Mesh Networks (WMNs) being deployed in today's LAN, WAN and Internet topologies by employing a novel optimization method. Methods/Analysis: The QoS performance of WMNs is severely affected by a problem called starvation where nodes that are one-hop away from the gateway monopolize the channel so that far away nodes get starved of channel access. We propose a hybrid genetic algorithmic approach to mitigate starvation in WMNs by dynamic adjustment of contention window of mesh nodes optimally. In this approach, Genetic Algorithm incorporated with Gravitational Search Algorithm is used. Findings: Simulations are conducted using the proposed method for multimedia traffic with AODV as the routing protocol. The performance of the proposed method is compared with priority-based method, pure GA optimization, Fair Binary Exponential Back-off algorithm (FBEB) and IEEE 802.11. The local search capability of GSA incorporated in our proposed method improves the throughput by 24.64% than priority-based method and by 3.56% than pure GA optimization. In our approach, we observed a significant decrease in end-to-end delay compared to pure GA optimization. Improvement in fairness is found along one-hop, two-hop and three-hop nodes when compared with FBEB. The FBEB algorithm adjusts the CW size by indirectly estimating the traffic in communication medium leading to lesser throughput whereas our proposed method changes the CW size dynamically based on QoS parameters of network nodes leading to improvement in throughput. The proposed method increases throughput by 5.10% than IEEE 802.11 and by 1.25% than FBEB at one-hop. The proposed method increases throughput by 66.67% than IEEE 802.11 and by 22.61% than FBEB at two-hop. Application/Improvement: Our hybrid Genetic optimization method improves QoS performance of Wireless Mesh networks by avoiding throughput imbalances among users and reducing end-to-end delay effectively.

Keywords

Contention Window, Genetic Algorithm, Gravitational Search Algorithm, Starvation, Wireless Mesh Networks.

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K. Sudharani ¹, T. C. Sarma ², K. Satya Prasad ³

Affiliations
1 VNR Vignana Jyothi IET, Hyderabad - 500090, Telangana, IN
2 NRSA, Hyderabad - 500037, Telangana, IN
3 JNTU, Kakinada - 533003, Andhra Pradesh, IN

Abstract

Background: A tumor is a gathering of tissues that grows in a disordered manner that normalizes growth. Brain tumor detection in MRI, CT, PET scan is most interesting area in the medical image field. The main objective is developing a novel technique i.e., histogram based region related detection of brain tumor. Method: An automatic algorithm for detection of brain tumor and its tumor segmentation using MRI T1 weighted, MRI flair images is presented. The proposed algorithm divides the brain into four regions Top half, bottom half, right-side half and left side half. It utilizes pixel intensity levels obtained from the each region histogram of an image for the segmentation as the result is more useful to analyze the raw image. The mathematical descriptions like statistical parameters of the proposed approach are presented in detail. The proposed algorithm reduces misclassification errors where the minimal dissimilarity within each object by its own cannot guarantee the desirable result and a comparison is made with the existing techniques like entropy and moments thresholding. Findings: Brain tumor is effectively detected and located in the brain by dividing the whole brain image into four regions. After dividing the brain into four halves histogram is applied individual part of the divided region. The histogram is the no of the amount of the pixel intensity. A performance evaluation is also done by checked the results by reference/ground truth MRI images through which sensitivity and accuracy of the proposed algorithm can be determined. The performance measures Sensitivity, Specificity, Accuracy and Similarity index obtained from the proposed method are 91.429%, 86.667%, 90%, 92.754%, respectively. The statistical parameters reveal the algorithm stability and reliability. The results obtained by this algorithm are included in the study and found comparatively better than the results obtained with Entropy and Moments Thresholding techniques. Applications: The proposed histogram based brain tumor detection and analysis efficiently dealt with detection of brain tumor and image classification procedure. Doctors practice the information obtained from the algorithm results to verify the most suitable course of treatment.

Keywords

Entropy, Histogram, Morphology, Moments, Segmentation.

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K. Rajesh Babu ¹, P.V. Naganjaneyulu ², K. Satya Prasad ³

Affiliations
1 Department of Electronics and Communication Engineering, KL University, Guntur − 522502, IN
2 MVR College of Engineering and Technology, Vijayawada Rural, Paritala − 521180, Andhra Pradesh, IN
3 Department of ECE, Jawaharlal Nehru Technological University, Kakinada − 533003, Andhra Pradesh, IN

Abstract

Objective: Normally MRI scan or CT helps to view the biology of brain. The segmentation methods are used to identify the tumor size and location. Methods/Analysis: Some of the segmentation methods are the Histogram-based segmentation and the Region-based segmentation (e.g.: Edge Detection method) which have the drawbacks in detection of size of the tumor and region. We are using the clustering based segmentation algorithms in this project. The run time and efficiency are the parameters used for comparison. Findings: These clustering algorithms like K-means, Fuzzy C and Pillar means are compared to each other for better performance by calculating the run time and efficiency of algorithms. This attempt improves the efficiency and computing time. Application/Improvements: It may help pathologists to identify the exact size and region easily

Keywords

Fuzzy C, K-means, Pathologists, Pillar means, Segmentation

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