Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

Design of power electronic transformer based variable speed wind energy conversion system

Abstract: The increasing energy demand makes the engineers switch over to renewable energy sources. Wind energy is one of the most vital and encouraging sources of renewable energy all over the world since it is considered to be non-polluting and economically affordable. For large variation in wind speed, the output of wind turbine is variable in both voltage and frequency, which requires a regulation at its output side to get a constant voltage and frequency. This project focuses on a wind energy conversion system using Power Electronic Transformer (PET) and permanent magnet synchronous generator. The single phase Power electronic transformer eliminates the bulky AC line frequency transformer used in the conventional PMSG wind energy systems. This proposed transformer is a high-frequency power electronic converter. Therefore, the weight and size of the transformer are considerably reduced. It has bi-directional power flow capability. The number of switches used in this proposed PET is less. The proposed wind energy conversion system model is designed and simulated using LabVIEW and the converter is simulated using Multisim 13.0 package. The wind energy conversion system and power electronic transformer is co-simulated by the LabVIEW-Multisim software. The constant frequency nature of the proposed PET helps in the integration of wind energy conversion system with the grid.

Implementation of physical downlink control channel (PDCCH) FOR LTE using FPGA

Abstract: LTE is accepted worldwide as the Long Term Evolution Perspective for today's 3G and 4G networks. The downlink physical channels of LTE include 3 data channels (PDSCH, PMCH and PBCH) and 3 control channels (PDCCH, PCFICH and PHICH). Control channels are inevitable for the transmission of both downlink and uplink data channels. Control information for one or multiple user equipments are contained in Downlink Control Information (DCI) and transmitted through PDCCH. This paper deals with transmitter architecture for PDCCH comprising of Scrambling, Modulation, Layermapping, Precoding and Resource element mapping and receiver architecture comprising of Demapping from resource elements, Removal of parity bits, Decoding, Delayermapping, Demodulation and Descrambling as described in LTE specifications. Modelsim 6.4a is used for simulation and implementation done in Xilinx Spartan 3E kit.

Control and tracking of robotic manipulator using PID controller and hardware in Loop Simulation

Abstract: In this fast growing world, automation is one of the key area where engineers put their efforts to improve the comfort level of human. Industrial automation involves the use autonomous robots to do the hazardous tasks. Broadly industrial robots are categorized as Autonomous Unmanned Vehicles and manipulators. Manipulator is a device used to manipulate materials without direct contact mostly aims at replacing the human arm. The control of such manipulators requires position tracking by certain control strategy. The main aim of the control is to ensure that the robot will maintain a desired dynamic behavior. The robot manipulator control using PID controller has a great importance in the Industrial automation. PID control scheme is one of the most general control types and probably the most commonly used. It provides quick response, good control of system stability and low steady-state error. Here, a PID controller is designed so that it attempts to correct the error between a measured process variable and a desired set point by calculating the error and then outputting a corrective action that can adjust the process accordingly. This is illustrated using Hardware in the Loop Simulation for real time simulation.

MixNet: A Robust Mixture of Convolutional Neural Networks as Feature Extractors to Detect Stego Images Created by Content-Adaptive Steganography

Abstract: Digital steganography, the modern version of the ancient art of data hiding is a prevalent tool for covert communication. Steganalysis, at least as old as steganography comes handy to unearth such hidden channels. The illegal act of information hiding through steganography of digital images can be overcome effectively only by using intelligent steganalytic techniques. In this paper, a novel MixNet framework comprising of six Convolutional Neural Networks (CNNs) is proposed as feature extractors for accomplishing generic steganalysis of spatial content-adaptive algorithms with better detection accuracy. Since the spatial content-adaptive algorithms embed secret bits in the hard to model components of the image like edges or textures, inputs to the CNNs are initially filtered using high pass filters to obtain the embedded content in the form of noise residual. Hierarchical features extracted from these networks are then concatenated and used to train Support Vector Machine classifier. Experimentation is performed using the benchmark BOSSbase v1.01 cover images and stego images are created with three state-of-the-art algorithms HUGO-BD, S-UNIWARD and WOW at five relative payloads 0.1–0.5 bits per pixel (bpp). The experimental results show that the proposed MixNet outperforms the compared related works in literature and proves the robustness of MixNet in detecting content-adaptive steganography.