2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)最新文献_第3页

Reduction of mutual coupling in antenna arrays using periodic structures 利用周期结构减少天线阵列中的相互耦合

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7807772

Prakash K Kuravatti, T. Rukmini

引用次数: 4

A MIMO antenna: Study on reducing mutual coupling and improving isolation 一种MIMO天线:减少相互耦合和提高隔离的研究

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7808131

Pratima Nirmal, A. Nandgaonka, Sanjay L. Nalbalwa

引用次数: 15

Mathematical analysis for maximization of cell capacity using partial frequency reuse in LTE network LTE网络中部分频率复用最大化小区容量的数学分析

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7807826

D. C. Shah, Nikita Dighe, Akshay Malhotra

{"title":"Mathematical analysis for maximization of cell capacity using partial frequency reuse in LTE network","authors":"D. C. Shah, Nikita Dighe, Akshay Malhotra","doi":"10.1109/RTEICT.2016.7807826","DOIUrl":"https://doi.org/10.1109/RTEICT.2016.7807826","url":null,"abstract":"The huge increase of high bandwidth demanding applications like interactive gaming, video streaming etc. over wireless channel and exponential growth of average mobile user population raise need of higher spectral density and cell capacity. The 4th Generation (4G) wireless network standardized as Long Term Evolution (LTE) utilizes advantages Orthogonal Frequency Division Multiple Access (OFDMA), where each cell of the network uses same frequency band, i.e Frequency Reuse equal to `1'. This leads to very high inter-cell interference (ICI) for the users at the edges of the cell and results in drastic decrease in cell capacity. To overcome ICI, Partial Frequency Reuse (PFR) method is used. Our paper presents intense mathematical calculations for optimization of cell capacity considering higher number of users near cell center. The cell partitioning radius is the variable parameter considered for mathematical integration operation for optimization problem. Assuming initial stationary position of a user nearby base station we use optimization for percentage of bandwidth reassigned to the cell-center region from partial frequency reuse region. As presented in this paper the variable user position from the base station is considered later to evaluate the optimization problem completely for demonstration of tradeoff between cell capacity and cell partitioning radius. We have simulated the rigorous mathematical expressions using Matlab scripting.","PeriodicalId":6527,"journal":{"name":"2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89845811","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: 0

Data driven prognosis approach for safety critical systems 安全关键系统的数据驱动预测方法

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7808123

Venkatesh Kulkarni, Manju Nanda

{"title":"Data driven prognosis approach for safety critical systems","authors":"Venkatesh Kulkarni, Manju Nanda","doi":"10.1109/RTEICT.2016.7808123","DOIUrl":"https://doi.org/10.1109/RTEICT.2016.7808123","url":null,"abstract":"Safety critical systems are being developed to improve the performance and cost effectiveness. The safety critical system are used in various domain such as aerospace domain, military, defense etc. In an aerospace domain there are many parameters affects the system environmental conditions, or hazards which cause many faults in the system which leads to failure. It is necessary to know before the system fails, so that necessary remedies can take to prevent the failure. The tool/software is needed to monitor the health management of safety critical systems. In this paper a prognostic technique is being used to mitigate the system failure. There are many techniques for the prognosis such as data driven technique, model based technique, and hybrid technique. This paper proposes implementation of the artificial neural network [ANN] based prognosis illustrates the use of data driven technique. The novelty of the proposed algorithm is that it uses formal techniques to develop a robust & reliable prognostics algorithm. The approach developed will be demonstrated for gyro sensor a critical component in the aerospace domain. The ANN can train and classify real data from the gyro sensors, and it is implemented using high level interpreted language GNU-Octave. The cost function/error function is calculated for the trained ANN data and it is being observed that the values are converging to the minimum value. At last the system is classified as healthy, partially healthy, and unhealthy state of the system.","PeriodicalId":6527,"journal":{"name":"2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89180512","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: 1

Design and implementation of Hodgkin and Huxley spiking neuron model on FPGA 霍奇金和赫胥黎脉冲神经元模型在FPGA上的设计与实现

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7808078

J. Kumar, Shanmukha, Murali, Jayanth Kumar, R. Bhakthavatchalu

{"title":"Design and implementation of Hodgkin and Huxley spiking neuron model on FPGA","authors":"J. Kumar, Shanmukha, Murali, Jayanth Kumar, R. Bhakthavatchalu","doi":"10.1109/RTEICT.2016.7808078","DOIUrl":"https://doi.org/10.1109/RTEICT.2016.7808078","url":null,"abstract":"The rudimentary cells of the central nervous system are the neurons which are connected to each other. An ordinary neuron consists of three different parts Dendrites, Soma and Axon. Each part is having its role in transferring the information. The connection between the neurons can be either Dendrite-Axon or Dendrite-Dendrite or Axon-Axon. Dendrites have the pivotal role in collecting the signals from other neurons and transmitting them to soma which implies that the dendrites act as an input device to the neuron. Soma performs a Non-linear operation, i.e. if input exceeds a certain threshold, an output signal is generated. The Axon performs the role of an output device which takes the processed signal from soma and transmitting it to the other neurons. This is the basic function of a biological neuron. A biological neuron model which is also known as Spiking Neuron Model is a mathematical description of properties of neuron that is to be designed accurately to describe and predict the biological processes. So there comes the concept of modelling and analysis of neurons. Modelling and analysis of neurons was performed by different researchers on First, Second and Third generation of neurons. The Third generation of neurons are also called as spiking neurons. The objective of this work is to implement different types of spiking neuron models developed by Hodgkin and Huxley which is a biological model. The spiking neuron model simulations are done in MATLAB and they are modelled using digital logic circuits in Verilog Hardware Description Language (HDL) and simulated in ModelSIM RTL simulator. These models are then implemented in Xilinx FPGA and checked for the functionality.","PeriodicalId":6527,"journal":{"name":"2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81103246","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: 5

Real time application of Raspberry Pi in compression of images 树莓派在图像压缩中的实时应用

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7807990

S. Sahitya, H. Lokesha, L. K. Sudha

引用次数: 9

A review paper on awareness statistics on plagiarism among research scholars 研究学者抄袭意识统计综述

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7807774

Shikha Pandey, H. Sharma, Arpana Rawal

引用次数: 1

ECG denoising using weiner filter and adaptive least mean square algorithm 采用维纳滤波和自适应最小均方算法对心电信号进行去噪

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7807781

Bharati Sharma, R. Suji

引用次数: 14

FPGA implementation of combined compression & denoising scheme for remote sensing images 遥感图像压缩降噪组合方案的FPGA实现

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7807850

P. Sendamarai, M. N. Giriprasad

引用次数: 4

An analysis of BER comparison of various digital modulation schemes used for adaptive modulation 自适应调制中各种数字调制方案的误码率比较分析

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) Pub Date : 2016-05-20 DOI: 10.1109/RTEICT.2016.7807820

M. Rajesh, B. Shrisha, N. Rao, H. Kumaraswamy

{"title":"An analysis of BER comparison of various digital modulation schemes used for adaptive modulation","authors":"M. Rajesh, B. Shrisha, N. Rao, H. Kumaraswamy","doi":"10.1109/RTEICT.2016.7807820","DOIUrl":"https://doi.org/10.1109/RTEICT.2016.7807820","url":null,"abstract":"In this paper, a comparative analysis of BER performance of various higher order digital modulation techniques for channel utilization is proposed and the analysis are carried out with SNR as the reference parameter. This performance analysis yields to adaptive modulation technique which can be used to attain high spectral efficiency by modulating the signal at higher order modulation schemes based on channel state information (CSI) from the receiver. Signal over AWGN channel is assumed for the analysis. To achieve lower BER values for a specific SNR, techniques to alter signal characteristics like pulse shaping filters, matching filters and convolution encoding are applied and analysis are carried out. An observation from the above analysis produces lower BER. For 256-QAM at SNR of 20, BER of 4.76×10-3 and grey coded BER of 3.83×10-4 is obtained. Highest possible modulation technique is selected with lower BER obtained. Thus, spectral efficiency which is the prime objective of the proposed paper is achieved. The above results are computed and simulated using MATLAB.","PeriodicalId":6527,"journal":{"name":"2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79358899","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: 17