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FPGA – Based Electro Cardio Graphy Signal Analysis System using Least Square Linear Phase Finite Impulse Response Filter

Proposed design for analyzing electrocardiography (ECG) signals. This methodology employs high pass least-square linear phase finite impulse response (FIR) filtering technique to filter out the baseline wander noise embedded in the input ECG signal to the system. Discrete wavelet transform (DWT) was utilized as a feature extraction methodology to extract the reduced feature set from the input ECG signal. The design uses back propagation neural network classifier to classify the input ECG signal. The system is simulation on xilinx system generator.

Published by: P. Purani, M. Sasikala, J. Sharmila Devi, A. Yasodha, S. V Sathyah, S. Jeya Anusuya

Author: P. Purani

Paper ID: V4I2-1210

Paper Status: published

Published: March 14, 2018

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Research Paper

Integrated Redevelopment of Foreshore Estate and Srinivasapuram

In Chennai there are constraints on the availability of unused land within the city limits, coupled with fast growing demand for built spaces and sky rocketing prices. On the other hand, that there are thousands of aging buildings which are dilapidated and under used blighted urban areas which have reached a stage where it is not possible to carry out structural repairs and rehabilitation as the same are not economically viable. Therefore, redevelopment has become a necessity as in time these areas become more dangerous and unfit for habitation. One such potential project is the Integrated Redevelopment of Foreshore Estate, where a large land parcel available for re-development, right on the shoreline. Foreshore Estate is an ideal location from where one could easily reach the most sought-after locations like Adyar(which is a large residential neighborhood in south Chennai with very high property values), Besant Nagar, R A Puram, Alwarpet and Mylapore(it is a cultural hub and neighborhood in the southern art of the city of Chennai .it is also known for its tree-lined avenue), MRC Nagar ( it is a commercial hub of the city which also include residential areas), as well as the best of the hotels in the city, besides critical facilities like the airport, sea port and railway station within 10 to 30 minutes. The idea is to develop a mix of high-end residential units, a fair amount of commercial space and also allot sufficient space for development of low-cost housing units, as the re-settlement of earlier tenements also has to be addressed. Tamilnadu Slum Clearance Board (TNSCB) and Tamilnadu Housing Board (TNHB) together hold 51.01 acres of land (23.5 and 27.51 acres respectively) in Srinivasapuram and Foreshore Estate area, facing the Marina Beach. Projected as the “Integrated Development in Foreshore Estate” the scheme envisions malls, luxury hotels and high-end residential complexes for private parties and senior Government officials and resettling the 3576 slums and 2000 housing board tenements.

Published by: Niranjan, Praveen, Sneha Feroz, Richard. A, Sathyaseelan. R. K, Asfiya Khatoon, Nirupama. K. V

Author: Niranjan

Paper ID: V4I2-1180

Paper Status: published

Published: March 10, 2018

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Research Paper

Analysis of Different Classifier for the Detection of Double Compressed AMR Audio

An digital audio can be easily recorded by handheld devices such as digital voice recorders and Smartphone. A nd these audio are using as evidence in courts in many cases . One of the most important problem is that many audio often contains content forgery. Here we analyze the authenticity of AMR audio . A AMR is a audio codec for speech compression ,these format is widely used in today's handheld devices such as digital audio recorder or in Smartphone etc . Designing hand-crafted features is a challenging and time-consuming problem. In this paper, instead of manually extracting the features, we investigate how to use deep learning techniques in this audio forensics problem.. For an audio clip with many frames, the features of all the frames are aggregated and classified by classifier . Here we use three classifier and compare them .Instead of hand-crafted features, we used the SAE to learn the optimal features automatically from the audio waveforms. Audio frames is the input to feature extractor and the last hidden layer’s output constitutes the features of a single frame. At last the features of all the frames are aggregated and classified by either UBM-GMM or SVM or Bayesian classifier . when comparing and analyzing these three classifier the SVM and Bayesian classifier shows high degree of accuracy for detecting the authenticity of an audio .

Published by: Fathima Najiya P, Vipin Kishnan. C. V

Author: Fathima Najiya P

Paper ID: V4I2-1170

Paper Status: published

Published: March 10, 2018

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Research Paper

Synthesis of Stannic Oxide and its Application as Adsorbent to Study Adsorption of Copper (II) and its Separation from other Elements

A method has been developed for the quantitative adsorption, of Cu (II)and its separation from other elements.Spectrophotometric determination of Cu(II) has been carried by employingtetraethylenepentamine (TEPA)as a spectrophotometric reagent. Stannic oxide has been prepared by co-precipitation method which is nanomaterial. Stannic oxide is found to be stable towards acids, bases and most of the common chemical reagents. The effects of different parameters such as pH, time of contact, amount of stannic oxide in the adsorption of 50.0µg of Cu (II) has been studied. It has been observed that 250 mg of stannic oxide is sufficient for maximum adsorption (94.34 ± 1.34%) of 50.0µg Cu (II) at pH 5.0 and contact time of 5.0 minutes. Under the optimum conditions of adsorption, effect of various anions and cations in the adsorption of Cu (II) also has been studied. Interfering cations has been masked by using suitable masking agents so as to make the process more selective. Distribution co-efficient values has been determined for various cations under experimental conditions which enable to predict affinity of various cations for the adsorption on stannic oxide.

Published by: Dr. S. D. Ajagekar

Author: Dr. S. D. Ajagekar

Paper ID: V4I2-1169

Paper Status: published

Published: March 10, 2018

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Research Paper

Intelligent Voice Controlled Robot using HMM Recognizer

The Robots are used more commonly in all the fields. In this paper we are implementing systematicway for the voice base robotic vehicle, whichconsistshardware as well as software interfacing with clarity of voice commands spoken by human being, this voice commands are recognized by electronic circuit containing HM2007 voice recognition IC When we come to the recognition of speech, many recognizers are available for improvement of voice recognition. The main task is to find sequence of commands given by human being. The purpose of this paper is to recognize voice commands given to the robotic vehicle with the combination of techniques to enhance control of robotic vehicle.

Published by: Shubham Thakare, Swapnil Pathak, Anmol M. Borkar, Shubham Meshram, Amar Gupta, M. M. Baig

Author: Shubham Thakare

Paper ID: V4I2-1159

Paper Status: published

Published: March 10, 2018

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Research Paper

Causes of Stress among the Nurses Working in Intensive Care Units 2015

Stress is everyday life’s reality. Everyone is struck by it in one way or the other. No one is safe from it. This study aimed to assess factors that can cause stress among nurses working in critical care unit and to find the relation between these factors and demographic data, it is a descriptive study done in Khartoum, hospital and Omdurman hospital June -July 2015. 56 nurses enrolled in the study, selected randomly. data were collected after applied self-administer questionnaire .in the results most of the respondents strongly agreed that lack of practice and a shortage of staff and prolonged period of working without break, and lack of support and improper communications between their college and improper environments all these factors cause stress for nurses in the critical care unit. Conclude: majority of the nurses had severe stress with different stress factors and highly significant association between these factors and the demographic variable which support our hypothesis

Published by: Manal Bilal Mohamed

Author: Manal Bilal Mohamed

Paper ID: V4I2-1148

Paper Status: published

Published: March 10, 2018

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