Yeshwantrao Chavan College of Engineering

About: Yeshwantrao Chavan College of Engineering is a based out in . It is known for research contribution in the topics: Inverter & Microstrip antenna. The organization has 632 authors who have published 586 publications receiving 4037 citations.

Improved fused floating point add-subtract unit for FFT implementation

Abstract: This paper describes the design and implementation of user defined fused floating-point arithmetic operations that can be used to implement Radix 2 Fast Fourier Transform (FFT) for complex numbers used in Digital Signal Processing (DSP-C) processors. The design is implemented and simulated by targeting Xilinx vertex 5 FPGA device. This paper describes the optimization of fused floating point modules in terms of area, delay, power and energy. Here we have achieved reduction in area (in terms of LUT required) by 27.09%, reduced delay by 17.10%, reduction in power consumption by 11 % and energy is reduced by 26.22% as compared to discrete implementation.

Comprehensive Energy Audit of Food Processing Industry

Abstract: From past few decades power engineers gives more stress on energy conservation along with increase in the energy generation to meet the consumer demand which then automatically reduces the excess energy consumption without affecting the production. Energy conservation is an effective tool used to find out the gap between the actual energy requirement to run a load and the measured value of actual energy consumed by the load. This gap analysis can be done by referring the standards set by the Bureau of Energy Efficiency. In every energy intensive sector there is always an opportunity to save the energy. In most of the cases energy may save just by tuning the system up-to its set standards but in few cases energy conservation may possible by overhauling, upgrading or replacing the existing system. Management always prefers the recommendations or proposals given by auditor which attracts zero, low and high investment. Energy audit is not a fault finding technique, it is a tool to identify the gap between the actual parameters at which the machine or the system is running and the standards set by the BEE and the manufacturer. This tool is helpful to run a system or machine at its optimum efficiency.

Implementation of adaptive filter for channel estimation

Abstract: Adaptive Channel Estimator is a most important research topic in wireless communication. Here we have to reduce the error from the received signal which may be corrupted by the environmental reason or due to the multipath fading. In this paper we are concentrated more on the hardware implementation of Adaptive filter using LMS algorithm which has been synthesized within FPGA. In this paper our main aim is to implement the LMS core in FPGA by using VHDL code and observe its synthesis and simulation result.

A review on method of stream data classification through tree based approach

Abstract: Today, rapid growth in hardware technology has provided a means to generate huge volume of data continuously. Most of the real time data stream application such as network monitoring, stock market and URL filtering we found that the volume of data is so large that it may be impossible to store the data on disk. Furthermore, even if the data can be stored on the disk, the volume of the incoming data may be so large that it may be difficult to process any particular record more than once. These large volumes of data need to be mined for getting interesting patterns and relevant information out of it. Consequently, we need further enhanced technique for, data stream classification while dealing with various challenges which are not solved by traditional data mining methods such as large volume, concept drift, and concept evolution.

Semantic Video Concept Detection using Novel Mixed-Hybrid-Fusion Approach for Multi-Label Data

Abstract: The performance of the semantic concept detection method depends on, the selection of the low-level visual features used to represent key-frames of a shot and the selection of the feature-fusion method used. This paper proposes a set of low-level visual features of considerably smaller size and also proposes novel ‘hybrid-fusion’ and ‘mixed-hybrid-fusion’, approaches which are formulated by combining early and late-fusion strategies proposed in the literature. In the initially proposed hybrid-fusion approach, the features from the same feature group are combined using early-fusion before classifier training; and the concept probability scores from multiple classifiers are merged using late-fusion approach to get final detection scores. A feature group is defined as the features from the same feature family such as color moment. The hybrid-fusion approach is refined and the “mixed-hybrid-fusion” approach is proposed to further improve detection rate. This paper presents a novel video concept detection system for multi-label data using a proposed mixed-hybrid-fusion approach. Support Vector Machine (SVM) is used to build classifiers that produce concept probabilities for a test frame. The proposed approaches are evaluated on multi-label TRECVID2007 development dataset. Experimental results show that, the proposed mixed-hybrid-fusion approach performs better than other proposed hybrid-fusion approach and outperforms all conventional early-fusion and late-fusion approaches by large margins with respect to feature set dimensionality and Mean Average Precision (MAP) values.