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Srividhya, V.
- Power Optimization for Dual-Clock FIFO with Closedown Able and Reinstate Able Clock Domains
Authors
1 Vellore Institute of Technology, School of Electronics Engineering, Vellore-632014, Tamil Nadu, IN
2 Vellore Institute of Technology, School of Electronics Engineering, Vellore-632014, Tamil Nadu, IN
3 VLSI Design is with Vellore Institute of Technology, School of Electrical Sciences, Vellore-632014, Tamil Nadu, IN
Source
Programmable Device Circuits and Systems, Vol 2, No 8 (2010), Pagination: 49-56Abstract
This paper implements a scalable, and power efficient dual-clock first-input first-out (FIFO) architecture which is useful for transferring data between modules operating in different clock domains. This architecture supports correct operation in applications where multiple clock cycles of latency exist between the data producer, FIFO, and the data consumer; and with arbitrary clock frequency changes, halting, and restarting in either or both clock domains. A dual port RAM is used as the storage element which increases memory density and improves FIFO size scalability. The architecture includes configurable logic to make it suitable for many environments, and also enables complete clock halting during idle times to achieve high energy efficiency. The address pointers are transformed to gray code representation before being passed across the clock boundary and these are then converted back to binary format in the clock domain. The skew control block which includes reconfigurable delays, is inserted to balance the timing between signals. The architecture demonstrated is implemented using verilog HDL and can be implemented to cell design using TSMC 180 nanometer. The design uses a globally asynchronous and locally synchronous (GALS) array of processors. This architecture achieves 620-MHz operation and 4.17-mW power dissipation while performing simultaneous FIFO READ and WRITE operations using TSMC 180nm technology. This dual-clock FIFO architecture is well suited for many dual-clock applications and achieves high energy efficiency, good scalability and area utilization, at high clock rates.Keywords
Dual Clock FIFO, AsAP (Asynchronous Array of Simple Processors).
- Design of Novel Ultra-Low Leakage CMOS Sleepy Stack Structure for circuits with Low Leakage Power Consumption
Authors
1 Vellore Institute of Technology, School of Electronics Engineering, Vellore-632014, Tamil Nadu, IN
2 VLSI Design is with Vellore Institute of Technology, School of Electrical sciences, Vellore-632014, Tamil Nadu, IN
Source
Programmable Device Circuits and Systems, Vol 1, No 6 (2009), Pagination: 129-135Abstract
Leakage power consumption of current CMOS technology is already a great challenge. International Technology Roadmap for Semiconductors projects that leakage power consumption may come to dominate total chip power consumption as the technology feature size shrinks. Leakage is a serious problem particularly for CMOS circuits in nano scaletechnology. In this paper a novel ultra-low leakage CMOS circuit structure called the "sleepy stack” is proposed. Unlike many other previous approaches, sleepy stack can retain logic state during sleep mode while achieving ultra-low leakage power consumption. The sleepy stack CMOS circuit structure is applied to generic logic circuits. Although the sleepy stack incurs some delay and area overhead, the sleepy stack technique achieves the lowest leakage power consumption among known state-saving leakage reduction techniques, thus, providing circuit designers with new choices to handle the leakage power problem.
Keywords
Complementary Metal–Oxide–Semiconductor (CMOS), Dual Vth, Low-Leakage Power Dissipation, Transistor Stacking.- High-Speed 4 BIT Flash ADC Using CMOS Latched Comparator with Current Steering Logic SR Latch
Authors
1 Vellore Institute of Technology, School of Electronics Engineering, Vellore-632014, Tamil Nadu, IN
2 VLSI Design is with Vellore Institute of Technology, School of Electrical Sciences, Vellore-632014, Tamil Nadu, IN
3 Chettinadu College of Engineering and Technology, Karur, Tamil Nadu, IN
Source
Programmable Device Circuits and Systems, Vol 1, No 7 (2009), Pagination: 168-174Abstract
This paper describes a latched comparator for 4-bti flash DCs. A modified CMOS Latched comparator with Current Steering Logic SR latch is proposed to reduce the delay and improve the speed of operation. Current Steering Logic SR Latch (CSL-SR) architecture is used to achieve high speed of operation, high gain bandwidth, reduced power consumption and less area. The evaluation speed of an amplifier is proportional to the evaluation chain conductivity and is inversely proportional to the capacitance. Since the number of transistors in the evaluation path is less, which eads to less delay and reduced power consumption. Performance results are obtained for an operating frequency of 3GHz in 0. 18μm TSMC technology using Cadence Spectre. The proposed comparator has 66.67% reduction in delay and reduced power consumption by 35. 9% compared to the existing CMOS Latched comparator.
Keywords
CMOS Latched, Current Steering Logic, 4-Bit Flash ADC.- Performance Evaluation of Scalable Ad-Hoc Network Using Dynamic Address Routing (DART)
Authors
1 CEG, Anna University, Chennai, Tamil Nadu, IN
2 Vellore Institute of Technology, School of Electronics Engineering, Vellore-632014, Tamil Nadu, IN
3 Vellore Institute of Technology, School of Electrical Sciences, Vellore-632014, Tamil Nadu, IN
Source
Networking and Communication Engineering, Vol 2, No 2 (2010), Pagination: 9-19Abstract
In this paper we develop a scalable network layer routing protocol for mobile ad hoc networks and also compared the Dynamic Address Routing (DART) performances like overhead and throughput with respect to network size and dataflow with the existing Protocol. Dynamic Address Routing (DART) addresses this scalability problem by separating the address of a node into two separate numbers: a) a unique and static node identifier, serving the same purpose as today's IP addresses and b) a dynamic routing address, which indicates the node's current position in the network topology. The use of dynamic routing addresses creates an opportunity for route aggregation which in the case of DART, greatly improves scalability. The paper describes the method of address allocation, which executes locally on each node, and relies only on routing updates from immediate neighbors to select an available and accurate routing address. Dynamic Address Routing (DART) does not require any geographical location information, nor does it make any assumption as to the underlying medium. Wireless Omni directional links, as well as directional and even wired links are supported equally well. In addition, nodes participating in a DART network do not require any manual network configuration, making Dynamic Address Routing (DART) a strong candidate for future mesh networking applications in additional to current ad hoc networking applications. The simulations for different scenarios are run and compared graphs are obtained. The analysis of these graphs shows the increase in number of Flows and network size the reduction in the overhead of the network. It is also shown by the graphs that the overall throughput of the network is increased.Keywords
Dynamic Address Routing (DART), Scalability, Wireless Ad Hoc Routing Protocol, Temporally Ordered Routing Algorithm (TORA).- Resource Reservation Based on Mobility Prediction in Personal Communication Systems
Authors
1 VIT University, Vellore, Tamil Nadu, IN
Source
Networking and Communication Engineering, Vol 2, No 3 (2010), Pagination: 109-116Abstract
IEEE 802.11 Mobility of the users in Personal Communication systems gives rise to the problem of mobility management. Predictive reservation allows the reservation of resources for an ongoing call in the next cell, so that the call is sustained when the Mobile Station (MS) moves to the next cell. Mobility management covers the methods for storing and updating the location information. of the mobile users served by them. Mobility prediction thus becomes an inevitable process in mobility management. Mobility prediction is defined as the prediction of the mobile user’s next movement where the user is traveling between the cells of the network. By using the predicted movement, the system can effectively allocate resources to the most probable-to move cell instead of blindly allocating resources in the entire neighborhood of the cell. Mobility prediction based on data mining method to predict the mobile user’s next movement is implemented in this project. The method is based on mining the User Actual Paths to discover the regularities in the patterns, extracting mobility rules from these patterns and finally, the matching rule, having the highest confidence plus support value corresponding to the current trajectory of the user, is used to predict the mobile user’s next cell movement. Through accurate prediction, the system can reserve resources in an efficient manner, thus leading to improved resource utilization. The performance of the method is evaluated through simulation. The results obtained in each phase leading to more accurate prediction of the mobile user’s next cell movement have been presented.
Keywords
Mobile Station, Mobility Prediction, Mining, Simulation.- Energy Resourceful Distance based Clustering and Routing Algorithm with Competent Channel Allocation Scheme for Heterogeneous Wireless Sensor Networks
Authors
1 School of Electronics Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 37 (2016), Pagination:Abstract
Objectives: To implement an effective spectrum access technique using cognitive radio technology with distance based clustering and routing algorithm with in wireless sensor networks and test this algorithm with different scenarios by varying position of the base station. Methods: Network region is divided into parts for allocating the spectrum and topology control, by distance based multi hop clustering and routing algorithm which decides cluster forming strategy on the basis of distance from the base station and route the data with less hops. Findings: The simulation shows, the proposed algorithm was able to provide a better Network Life time with the same amount of initial energy. Also, election of cluster heads on the basis of distance has helped in increasing the stability. And, with different efficient distance thresholds deciding single hop and multi hop communication. Application/Improvements: The proposed algorithm increases the scalability of the network, in comparison to the existing algorithms, by an average of 29%, when the base station is kept at the corner and, by 42.5%, when it is kept away from the corner.Keywords
Cognitive Radio, Clustering, Energy efficient, Routing, Stability, Wireless Sensor Network (WSN).Full Text
- Ethylene Gas Measurement for Ripening of Fruits Using Image Processing
Authors
1 EEE/CSE Department, Center for Electronics, Automation and Industrial Research (CEAIR), Dr. M.G.R. Educational & Research Institute, Maduravoyal, Chennai, Tamil Nadu, IN
2 EEE Department, Meenakshi College of Engineering, Maduravoyal, Chennai – 560064, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 31 (2016), Pagination:Abstract
Objective of the Work: The highlight of this research work is to discover the ethylene gas level used for ripening of fruits by detecting ethylene gas (C2H4 in ppm) level employing soft sensor built using image processing and Artificial Neural Networks (ANN) algorithms. Methods/Statistical Analysis: The proposed method relies on the color which denotes the various stages in ripening and in turn indicates the amount of ethylene gas required. The changes in color, texture, intensity variation, mean, variance and standard deviation extracted from the images are the features which enable the personnel to determine the amount of ethylene gas. The Feed Forward Neural Network (FFNN) is used for ethylene gas estimation. This is made possible using Back Propagation Algorithm (BPA) for training the FFNN. As a part of image processing the intensity values in color images and its variation are tracked by dithering which is used as a unique feature input to train the FFNN. Major Findings: The novelty of the proposed method depends on the FFNN estimating the ethylene gas needed for ripening process in a feed forward fashion thereby providing the precision and recall values spontaneously for every instance. Application/Improvements: Earlier a circuit with capacitance model is used to generate ethylene gas for this purpose. Nearly 51 images are considered for training and testing respectively. Testing and confirmation result shows the required precision and recall level are in range of 80 to 89% and 100% respectively.Keywords
Back Propagation Algorithm, Ethylene Gas, Feed Forward Neural Network Feature Extraction, Image Processing.Full Text
- Sentiment Analysis on Good Service Tax (GST) Using Twitter Data
Authors
1 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore-43, IN
Source
Software Engineering, Vol 10, No 3 (2018), Pagination: 57-60Abstract
The social media has increased the opportunity to explore and track the response of new reforms and policies in India. Social Media helps in the analysis of stock market data, new product launch and movie release. One of the social networking websites is Twitter and it is the ninth largest website.
With Twitter, the registered users can search for the latest information on the topics of their interest. Since lakhs of tweets are shared on a real-time basis by the members every day, it has got more than 328 million active users a month. Twitter is the best source for the analysis of opinion and sentiment on movie reviews, product reviews and current issues in the world. Twitter is used widely as a forum for understanding the sentiments of Indians towards recently launched Goods and Services Tax by the Indian Government on 1st July 2017.
Sentiment analysis extracts positive and negative opinions from the twitter dataset and R Studio provides the best environment for this Twitter sentiment analysis. Data is written into text files as the input dataset so that Twitter data could be accessed from Twitter API. Sentiment analysis is performed on the input dataset that initially performs data cleaning by removing the stop words and then by classifying the tweets as positive and negative by considering the polarity of words. Finally, positive, negative and neutral is generated, comparing the polarity of the tweets.
Keywords
Twitter Data, Word Cloud, Sentiment Analysis, Social Media R-Studio.- Emotion Classification of Twitter Data Using Lexicon Based Approach
Authors
1 Department of Computer Applications, K.S Rangasamy College of Arts & Science for Women, Tiruchengode - 637215, IN
2 Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore - 43, IN
Source
Software Engineering, Vol 10, No 4 (2018), Pagination: 69-71Abstract
The main aim of sentiment analysis is to determine the attitude of a speaker or a writer with respect to some topic. The sentiment classification has been classified into two types which are emotional classification and polarity classification. This research work has been done by using emotional classification, which is used to classify the emotions such as joy, fear, disgust, anger, sad and surprise. These six types of emotions are classified using twitter dataset. The classified emotions are visualized using graph. The work is focused on analyzing the tweets of people for Donald Trump and Hillary Clinton and classifies the sentiment from tweets.
Keywords
Emotion, Sentiment Analysis, Naive Bayesian Algorithm, Lexicon, Word Cloud, Visualization.- Automated Summarization of Restaurant Reviews Using Hybrid Approaches
Authors
1 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 4 (2022), Pagination: 2690-2696Abstract
The arena of automatic text summarization incorporates the paramount and relevant information from a large document. This research paper attempts at representing two hybrid models for automatic text summarization. Extractive summarization followed by an abstractive summarization, is the strategy which is adopted in this paper to produce an informative and concise summary. The LexRank algorithm is used for extractive summarization, while BART (Bidirectional and Auto Regressive Transformers) and T5 (Text-To-Text Transfer Transformer) are used for abstractive summarization. BART and T5 are advanced per-trained models based on Transformer. The Transformer-based Per-trained models are causing a stir in the deep learning world. The first hybrid model is constructed using LexRank with BART (LRB) and the second hybrid model is constructed with LexRank with T5 (LRT). This specific approach will result in the generation of the extractive summary using the LexRank algorithm. The resulted output of LexRank is used as the input for BART and T5. The efficiency of two hybrid models is analyzed using qualitative and quantitative methods. The human-generated summary is used to evaluate the quality of the models, while the ROUGE score provides a quantitative assessment of their performance. Thus, this work may be concluded in the precision that, the LRT hybrid model is more effective than LRB hybrid model.Keywords
BART, T5, LexRank, ROUGE Score, Extractive Summarization, Abstractive SummarizationReferences
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