2010 3rd International Conference on Emerging Trends in Engineering and Technology最新文献_第7页

Finger-Knuckle-Print Region of Interest Segmentation Using Gradient Field Orientation & Coherence 基于梯度场定向和相干性的手指指关节指纹感兴趣区域分割

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.68

H. B. Kekre, V. Bharadi

引用次数: 17

Unmanned Air Vehicle Collision Avoidance System and Method for Safety Flying in Civilian Airspace 无人机在民用空域安全飞行的避碰系统与方法

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.162

T. Karthick, S. Aravind

引用次数: 2

Graph-Based Algorithms for Text Summarization 基于图的文本摘要算法

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.104

Khushboo Thakkar, R. Dharaskar, M. Chandak

引用次数: 70

Action Estimation from Human Activity Monitoring Data Using Soft Computing Approach 基于软计算方法的人体活动监测数据动作估计

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.149

M. Nii, Kazuki Nakai, T. Fujita, Yutaka Takahashi

引用次数: 13

Fractional-Order PID Controller Design for Speed Control of DC Motor 直流电机转速控制的分数阶PID控制器设计

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.123

V. Mehra, S. Srivastava, P. Varshney

引用次数: 43

Group Communication Scheme for Mobile Networks with Mobile Router 基于移动路由器的移动网络组通信方案

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.67

Rama Mohan Babu K.N., Prathima Mabel J., K.N. Balasubramanya Murthy, Mamatha

引用次数: 3

Face Normalization: Enhancing Face Recognition 人脸规范化:增强人脸识别

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.83

S. Chaudhari, Archana Kale

引用次数: 11

Design and VLSI Implementation of Interpolators/Decimators for DUC/DDC DUC/DDC插补/抽取器的设计与VLSI实现

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.72

Y. N. Santhosh, Namita Palacha, C. Raj

{"title":"Design and VLSI Implementation of Interpolators/Decimators for DUC/DDC","authors":"Y. N. Santhosh, Namita Palacha, C. Raj","doi":"10.1109/ICETET.2010.72","DOIUrl":"https://doi.org/10.1109/ICETET.2010.72","url":null,"abstract":"In this paper, we implemented a DUC/DDC for DSBSC modulation technique, which is used for power line communication system. The input signal is ranging from 300-4000 Hz is sampled at 64 KHz is fed to the series of two stage CIC interpolation filters. A CIC filters up-samples the input signal by a factor 12. The up-sampled signal is now given to the multiplier as the first input. Variable DDS is used to generate HF carrier frequencies in the range of 20-512 KHz is given as a second input to the multiplier will produce the DUC signal. Here 48 KHz carrier is selected. This DUC output is given as an input for DDC. In DDC, the incoming signal is multiplied with DDS which generates frequency of same as in DUC. The output of the multiplier is down-sampled by factor 12 by two stage CIC decimation FIR filter. The input signal of DUC and the output of DDC are compared. These filters are designed using Matlab simulink and developed Verilog code. Simulation is performed using ModelSim XE 6.3c and functional verification is carried out using Xilinx ISE 10.1 and FPGA implementation on Spartan-3. Here our main aim is to implement both DUC and DDC in a single FPGA. Integrating both blocks in same IC such a way that it has to perform both down conversion and up conversion. This is designed for reduce the power consumption of the blocks and also to reduce the cost. The design used 23% of flip-flop’s, 20% of look up tables, 30% of slices out of total available in Spartan-3 FPGA board. Design used minimum 10.184ns period of clock and minimum input arrival time before clock is 8.568ns and Maximum output required time after clock is 6.216ns. The maximum operating frequency of the design is 98.191MHz. The total power consumed by the design is 42mW at 26.30C.","PeriodicalId":175615,"journal":{"name":"2010 3rd International Conference on Emerging Trends in Engineering and Technology","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122813774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Text-Dependent Multilingual Speaker Identification for Indian Languages Using Artificial Neural Network 基于人工神经网络的印度语文本依赖多语说话人识别

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.23

Rajesh Ranjan, S. Singh, A. Shukla, R. Tiwari

{"title":"Text-Dependent Multilingual Speaker Identification for Indian Languages Using Artificial Neural Network","authors":"Rajesh Ranjan, S. Singh, A. Shukla, R. Tiwari","doi":"10.1109/ICETET.2010.23","DOIUrl":"https://doi.org/10.1109/ICETET.2010.23","url":null,"abstract":"In this paper an attempt is made to develop speaker identification system which is used to determine the identity of an unknown speaker among several speakers of known speech characteristics, from a sample of his or her voice. Every speaker has different individual characteristics embedded in his /her speech utterances. These characteristics can be extracted from utterances and different neural network models are used to get the desired results. To evaluate speech characteristics from utterances they are stored in digitized form. Speech features namely LPC, RC, APSD, Number of zero crossing and Formant frequencies are extracted from speech signal and formed speech feature vectors. These data features are fed into Artificial Neural Network using back propagation learning algorithm and clustering algorithm for training and identification processes of different speakers. The database used for this system consists of 20 speakers including both male and female from different parts of India and languages are Hindi, Sanskrit, Punjabi and Telugu. The average identification rate 83.29% is achieved when the network is trained using back propagation algorithm and it is improved by about 9% and reached up to 92.78% when using clustering algorithm.","PeriodicalId":175615,"journal":{"name":"2010 3rd International Conference on Emerging Trends in Engineering and Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115961843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Pipelined Recursive Modified Euclidean Algorithm for RS Decoder RS译码器的流水线递归改进欧几里得算法

2010 3rd International Conference on Emerging Trends in Engineering and Technology Pub Date : 2010-11-19 DOI: 10.1109/ICETET.2010.39

Mansi C. Shirwadkar, U. Ghodeswar

引用次数: 0