B. Pon Pragadesh Kumar ¹, N. R. Raajan ², S. Karthik ²

Affiliations
1 Department of Electronics and Communication Engineering,School of Electrical & Electronics Communication, SASTRA University,Thanjavur,Tamil Nadu- 613401, IN
2 Department of Electronics and Communication Engineering,School of Electrical & Electronics Communication,SASTRA University,Thanjavur,Tamil Nadu- 613401, IN

Abstract

ne of the most efficient way to utilize the bandwidth in wireless communication systems is Full- duplex communication. It is proposed for full duplex wireless communication over a single channel. The signals are transmitted and received at a same time and same frequency. It creates the large self-interference over the transmitted and received signals and lead to large loss of data. We used different values of phase offset to reduce the self-interference between the transmitted and received signals. The full duplex communication system is designed for input as image and by using wavelet transform. The performance of full-duplex communication system using various modulations like 16-QAM, QPSK and 64-QAM along with Haar, Daubechies2 and Daubechies 4 wavelets over different values of phase offset are evaluated by bit error rate.

Keywords

BER, Full-duplex Communication, OHWDM, QAM, QPSK, Wavelet Transform

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S. Karthik ¹

Affiliations
1 Centre for Maritime Research, AMET University, Chennai, IN

Abstract

Autonomous Underwater Vehicles (AUVs) have gained more popularity in recent years for military as well as civilian applications. One potential application of AUVs is for the purpose of undersea surveillance. As research into underwater surveillance using AUVs progresses, issues arise as to how an AUV acquires acts on, and shares information about the underwater battle space. These issues naturally touch on aspects of vehicle autonomy and underwater communications, and need to be resolved through a spiral development process that includes experiments at sea. This paper presents an implementation of swarm network communication for AUVs which is used to transfer the data, to communicate with one another to perform tasks as an intelligent group including surveillance. When a task identified from a seafloor, a single AUV could follow that task and report the whereabouts. Other vehicles in the swarm could track additional individuals, produce detailed maps of the area, detect AUV updates to a distant command post. As individual AUV leave the swarm to fulfill their assigned tasks, the swarm could autonomously reorient itself. This reduces the inspection duration and inspection cost for underwater vehicle. This paper has described the on-board signal processing including a navigation and network communication which were successfully implemented. The vehicle is designed and simulation is studied in computer analysis as per the required parameters and condition. The Simulation of swarm network communication and navigation multi-path trajectory is performed.

Keywords

Communication, Intelligent, Navigation, Swarm Network, Underwater Vehicle

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P. Amudha ¹, S. Karthik ², S. Sivakumari ³

Affiliations
1 Department of CSE, Faculty of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Mettupalayam Road, Coimbatore - 641 108, Tamilnadu, IN
2 Department of CSE, SNS College of Technology, SNS Kalvi Nagar, Sathy Main Road, NH-209, Vazhiyampalayam, Saravanampatti Post, Coimbatore - 641035, Tamil Nadu, IN
3 Department of CSE, Avinashilingam Institute for Home Science and Higher Education for Women, Mettupalayam Road, Coimbatore - 641108, Tamil Nadu,, IN

Abstract

Background: Recently network security is achieved using intrusion detection, in which data mining techniquesare used as a new methodology. The vital features considered is one of the major aspects that affect the efficiency of the Intrusion Detection System (IDS). Methods: The key idea of this work is to propose a feature selection method to discover useful features and to classify user behaviour patterns of system features from the network traffic data using classification approaches. In the process of selecting significant features, the dimensions of data is reduced and the features are sorted by finding the accuracy of each attribute and then selects the best vital features among them based on its accuracy value. Also this work aims to choose a hybrid classifier model (ABC-SVM) based on Artificial Bee Colony (ABC) and Support Vector Machine (SVM) algorithms to construct a perfect IDS using KDDCup'99 dataset. Results: The result analysis indicates that the features selected improve the accuracy rate of ABC-SVM than using all features. Also the hybrid algorithm is better than other traditional algorithms with respect to the performance measures such as detection rate, specificity and training time.

Keywords

Artificial Bee Colony, Classification, Data Mining, Intrusion Detection, Network Security, Support Vector Machine

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V. Jean Shilpa ¹, P. K. Jawahar ¹, S. Karthik ²

Affiliations
1 Department of ECE, B.S. Abdur Rahman University, Chennai - 600048, Tamil Nadu, IN
2 Department of ECE, SRM University, IN

Abstract

Objectives: A hardware-software co-design of Least Mean Square (LMS) adaptive FIR filter utilized for the real time noise cancellation purpose has been designed and demonstrated. Method/Analysis: This LMS adaptive filter which serves as a better choice in filtering real-time signals with noise, is being designed as an IP for various different users to make the filter reconfigurable to their designs. Fascinating feature about creating an IP on Zedboard which belong to the Zynq series of FPGA boards is to design the LMS filter in Simulink and convert to RTL (Register Transfer Level) logic by employing HDL coder. This process simplifies the task of either writing an HDL code or designing circuits using transistors, which is time-consuming and cumbersome. Findings: To estimate the performance the IP designed is profiled with the Simulink of MATLAB to estimate the functions, which takes more time for execution in the Simulink design of LMS adaptive filter. Profiling the application, it has been found out that LMS algorithm block for the calculation of adaptive coefficients takes around 311.63ns time/call of the execution time. Hence only this block is imported to FPGA as a soft-core block and interconnected with the hardcore ARM cortex A9 processor block which achieves hardware-software co-design of LMS adaptive filter. Application: This method simplifies the process of hardware-software co-design process, which can be applied to any complex design that can be generated from MATLAB/Simulink and generate the same design as an application to run on an advanced FPGA board.

Keywords

Field Programmable Gate Array (FPGA), IP (Intellectual Property), LMS (Least Mean Square) Algorithm, Simulink, ZedBoard (Zynq Evaluation and Development Board).

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