iosr-JECE   Volume-13 ~ Issue-3 ~ May-June 2018

Abstract: In the design modelling of a DFT interface, the controlling and access operation defines the performance of BIST interfacing. In a DFT application, data stored in the BIST units are mapped to given query input and an output is developed as a match signal to which as decision is made. in the process of DFT operation, to obtain a faster matching and low power consumption, a new search approach and pattern alignment logic is defined. To improve the storage capacity of a DFT unit, a multi page interface is proposed. To the defined unit a new fault tolerance approach is integrated for a reliable, low power and fast processing DFTapplication.

Index Terms - DFT unit, low power, fast search, high volume, fault tolerant.

[1]. A. V. Aho and M. J. Corasick, "Efficient string matching: An AID to bibliographic search," Commun. ACM, vol. 18, no. 6, pp. 333–340, 1975.
[2]. Derek Pao1, Wei Lin2,1, and Bin Liu, "Pipelined Architecture for Multi-String Matching", IEEE Computer Architecture Letters Vol. 7, 2008.
[3]. M. Alicherry, M. Muthuprasanna, and V. Kumar, "High speed address matching for network IDS/IPS," in Proc. IEEE Int. Conf. Netw. Protocols (ICNP), 2006, pp. 187–196.
[4]. B. Brodie, R. Cytron, and D. Taylor, "A scalable architecture for high-throughput regular-expression address matching," in Proc. 33rd Int. Symp. Comput. Arch. (ISCA), 2006, pp. 191–122.
[5]. Z. K. Baker and V. K. Prasanna, "High-throughput linked-address matching for intrusion detection systems," in Proc. Symp. Arch. For Netw. Commun. Syst. (ANCS), Oct. 2005, pp. 193–202.

Abstract: The advancement in technology such as digital imaging, data storage and widespread use of internet has resulted in the explosion of the collection of images which has motivated the creation of an efficient image retrieval system. The Content based image retrieval has proved to be an efficientimage retrieval system for retrieving the database images that exhibit similarity to the query image presented by the user. The retrieval process is carried out by low level feature extraction from the image such as colour, texture and shape. This paper gives an idea about Content based image retrieval system(CBIR), literature survey of CBIR techniques and challenges faced by CBIR system.

[1]. K.Jenni, S.Mandala&M.S.sunar, " Content Based Image Retrieval using Color String Comparison," Elsevier , Procedia Computer Science, Vol. 50, pp. 374-379, 2015.
[2]. Y. Mistry, D.T. Ingole& M.D. Ingole, "Content based image retrieval using hybrid features and various distance metric," Journal of Electric Systems and Information Technology, Vol. 18, pp. 335-342, 2016

[3]. Hassan Farsi, SajadMohammadzadeh, "Color and Texture based image retrieval using Hadamard matrix in discrete wavelet transform," IET Image Process 2013, Vol 7, Iss 3, pp 212-218 doi: 10.1049/iet-ipr.20122.0203,2013
[4]. I.J.Sumana, M.M.Islam&D.Zhang, "Content based image retrieval using curvelet transform," IEEE 10th workshop on Multimedia Signal Processing, pp.11-16, 2008.
[5]. K.Seetharaman,S.Sathiamoorthy,"Color image retrieval using statistical model and radial basis function neural network", Egyptian Informatics Journal ,2014

Abstract: In this project, the Electromyogram (EMG) response is fed to control an automation system which is obtained from human arm response. The raw EMG signal is obtained from that part of the human muscle where the vibration generates responses within a suitable voltage range. This is fed to the automation system by proper conversion and filtering circuits to obtain the necessary pulse required to control the automated system. The raw EMG signal obtained in the range of few mV is amplified for further processing. To keep the response in the operating range we set the frequency bandwidth of a standard EMG response. For obtaining the EMG signals, surface electrode method is widely popular as it is non invasive and it can be conducted without any medical supervision. In this paper we are introducing an automated system which is controlled by the Electromyogram signal generated by contraction and relaxation of muscles, fed to the system wirelessly using a RF module, processed using Arduino

[1]. Dr. Scott Day, Important factors in surface EMG measurement.
[2]. Mohan C, Vinod Kumar Giri, DC motor control using EMG signal for prosthesis, IJECT vol.2, Issue 2,June 2011.
[3]. Chun Sing Louis Tsui, P. Jia, J. Q. Gan, H. Hu and K. Yuan, "EMG -based hands-free wheelchair control with EOG attention shift detection," in IEEE International Conference on Robotics and Biomimetics (ROBIO), Sanya, 2007.
[4]. Shital B. Sonone, G.D. Dalvi , Real time control of Robotic Arm using EMG signals,2016.
[5]. T. Tsuj, O. Fukuda, H. Shigeyoshi and M. Kaneko, "Bio-mimetic impedance control of an EMG-controlled prosthetic hand," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000),Takamatsu, 2000