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Ramesh, R.
- FIR Filter Implementation Using Modified Distributed Arithmetic Architecture
Abstract Views :491 |
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Authors
M. Yazhini
1,
R. Ramesh
1
Affiliations
1 Department of Electronics and Communication Engineering, Saveetha Engineering College, Tamil Nadu, 602105, IN
1 Department of Electronics and Communication Engineering, Saveetha Engineering College, Tamil Nadu, 602105, IN
Source
Indian Journal of Science and Technology, Vol 6, No 5 (2013), Pagination: 4485-4491Abstract
In this project use Distributed Arithmetic (DA) technique for FIR filter. In this technique consist of Look Up Table (LUT), shift register and accumulator. Based on this technique multipliers in FIR filter are removed. Multiplication is performed through shift and addition operations. The LUT can be subdivided into a number of LUT to reduce the size of the LUT for higher order filter. Each LUT operates on a different set of filter taps. Analysis on the performance of various filter orders with different address length are done using Xilinx synthesis tool. The proposed architecture provides less latency and less area compared with existing structure of FIR filter.Keywords
FIR, Distributed Arithmetic, LUTReferences
- Kyung-Saeng K, Lee K (2003). Low-power and area efficient FIR filter implementation suitable for multiple tape, Very Large Scale Integration (VLSI) Systems, vol 11, No 1.
- Meyer-Base U (2004). Digital Signal Processing with Field Programmable Gate Arrays, 2nd Edn., Chapter 2, 60-66.
- Meyer-Base U (2004). Digital Signal Processing with Field Programmable Gate Arrays, 2nd Edn., Chapter 3, 112-113.
- Meher P K (2006). Hardware efficient systolization of DA-based calculation of finite digital convolution of finite digital convolution, IEEE Transactions on Circuit and Systems II: Express Briefs, vol 53(8), 707-711.
- Meher P K, Chandrasekaran S et al. (2008). FPGA realization of FIR filters by efficient and flexible systolization using distributed arithmetic, IEEE Transactions on Signal Processing, vol 56(7), 3009-3017.
- Modeling and Simulation of Gate Engineered Gate All-Around MOSFET for Bio-Molecule Detection
Abstract Views :142 |
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Authors
Affiliations
1 Department of Electronics and Instrumentation Engineering, JJ College of Engineering and Technology, Ammapettai, Poolangulathupatti (Post), Trichy - 620 009, Tamil Nadu, IN
2 Department of ECE, SASTRA University, Tirumalaisamudram, Thanjavur - 613 401,Tamil Nadu, IN
1 Department of Electronics and Instrumentation Engineering, JJ College of Engineering and Technology, Ammapettai, Poolangulathupatti (Post), Trichy - 620 009, Tamil Nadu, IN
2 Department of ECE, SASTRA University, Tirumalaisamudram, Thanjavur - 613 401,Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 43 (2016), Pagination:Abstract
Objectives: In this paper, modeling and simulation of a triple material gate stack gate all around MOSFET biosensor for the detection of biomolecules under dry environment conditions has been carried out. Method: A nanocavity is formed in the proposed device and its surface potential values are estimated by solving 2D Poisson and Schrödinger equations using Leibmann’s iteration method. The surface potential values are obtained for both the presence and absence of biomolecules. To the best of our knowledge, the effect of engineering on the gate stack gate all around MOSFET biosensor characteristics has been studied for the first time and its characteristics such as sensitivity and drain current are obtained. Findings: It is found that the implementation of gate engineering in the gate stack gate all around MOSFET improves the characteristics of the device. Improvements: The proposed device may be improved by implementing the device as an optical biosensor for detection of biomolecules.Keywords
Bio-Molecule Detection, Gate All Around MOSFET, Gate Engineering, Modelling, Simulation.- Bio-Inspired Computational Algorithms for Improved Image Steganalysis
Abstract Views :172 |
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Authors
Affiliations
1 Department of ECE, SRM University, SRM Nagar, Potheri, Kattankulathur, Chennai - 603203, Tamil Nadu, IN
2 Department of ECE, Saveetha Engineering College, Saveetha Nagar, Thandalam, Chennai - 602105, Tamil Nadu, IN
1 Department of ECE, SRM University, SRM Nagar, Potheri, Kattankulathur, Chennai - 603203, Tamil Nadu, IN
2 Department of ECE, Saveetha Engineering College, Saveetha Nagar, Thandalam, Chennai - 602105, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 10 (2016), Pagination:Abstract
Acquiring the best image features that best distinguishes a stego and clean image is a challenge in image steganalysis. Though higher order models acquire all these features, they pose problems due to computational complexity in terms of time and space. This demands optimization of the feature sets. Compared to the existing statistical feature optimization techniques, genetic algorithm based optimization techniques are evolving to be more promising. The existing deterministic methods of optimization have the limitation of converging into local minima as compared to the evolutionary methods which tend to converge to the global minima. Objectives: This paper intends to review the various genetic algorithm based feature optimization techniques applicable for image steganalysis of JPEG images and identify the best algorithm that converges to global minima. Method/Analysis: The methods analysed include the stochastic (metaheuristic) algorithms that make use of the random behaviour of plants and animals. The Antlion behaviour based optimization technique (ALO) has been implemented and analysed for JPEG stego images. The movement of ants are modelled as random walk and the traps built by antlions are assumed proportional to their fitness. The antlions shoot sand outwards to pull the ants inside the pits. This causes sliding down of the ants into the pits to the most minimum position. The coding of the optimization is implemented in Matlab with images taken from the standard BOSS database. Findings: The feature set after feature extraction has a dimension of 2000 × 48600 with 1000 cover and 1000 clean images. Considering these vectors as the initial positions of the ants in the Ant Lion Optimizer, for a payload of 0.5 in embedding logic the classification accuracies are studied. The convergence of this optimizer is proved according to the convergence curve for 300 iterations. After optimization, the reduced feature set is used to classify the image as cover or stego image. SVM, MLP and the fusion classifiers - Bayes, Decision template and Dempster Schafer are used. For low levels of embedding changes, the classification by MLP and Fusion schemes is good. For medium and high levels of embedding changes, the classification by Fusion schemes alone is good. It has been identified that the proposed steganalyser gives best results for Bayes fusion classification (69%) scheme when Antlion behaviour is used as optimizer. Applications/Improvements: This research has implemented a novel method of image feature optimization that improves steganalysis. The optimized feature set is 100 times less in dimension assuring reduced computational complexity in time and space. Improved version of this research may include a different selection mechanism or using a different optimization function.Keywords
Bio-Inspired Algorithms, Evolutionary Algorithm, Fusion Classifiers, Stochastic Optimization, Swarm- Quarter Plane ARMA Model for Analysis and Classification of Histopathology Images: Application to Cancer Detection
Abstract Views :145 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, SRM University, Chennai, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Saveetha Engineering College, Thandalam, Tamil Nadu, IN
1 Department of Electronics and Communication Engineering, SRM University, Chennai, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Saveetha Engineering College, Thandalam, Tamil Nadu, IN