REMOTE CONTROLLED METAL DETECTING ROBOT

The aim of this project is to control the operation of movement of a remote controlled robot to detect metals and to view the images surrounding the robot.

The main objectives of this project are to use radio frequency bands.

Ø  The movement of robot is controlled by the transmission of signals through air.

Ø  The receiver senses these signals from the air..

Ø  This mini project makes use of the transmitter and receiver at 433MHz that is available at low cost hence making it very complicated.

Ø  The Radio Frequency based control proves to be more advantageous compared to the Infrared Red based control that limits the operating range to only a few meters of distance.

Ø  When the robot is moving on a surface, the system produces a beep sound when metal is detected. This beep sound will be transmitted to remote place. Simultaneously the images around the robot will be transmitted to remote place. User can monitor the images and metal detection alarms on Television.

DOWNLOAD FULL PROJECT DETAILS

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Data Allocation Using Genetic Algorithm for MPSoC Systems with Hybrid Scratch-Pad Memory-paper presentation

Data Allocation Using Genetic Algorithm for MPSoC Systems with Hybrid Scratch-Pad Memory

Zhi Chen, Meikang Qiu,JianweiNiu2, Zhonghai Lu, Yongxin Zhu

Dept. of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA

School of Computer Science and Engineering, Beihang University, Beijing100191, China

Dept. of Electronic Systems, KTH Royal Institute of Technology, Stockholm, Sweden

School of Microelectronics, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract

Hybrid on-chip memory architecture has gathered muchattention recently. However, if not well managed and optimized, the memory access will be a major energy and time consumer for this architecture. This paper presents a novel hybrid Scratch-Pad Memory (SPM) comprising SRAM and Magnetic RAM(MRAM) for MultiProcessor System-on-Chip(MPSoC) architectures. To reduce memory access costs and write activities on MRAMs, we propose a novel adaptive genetic algorithm to efficiently allocate data to each memory unit of the hybrid SPM. Experimental results show that the proposed genetic algorithm can reduce write operations to MRAMs by 28.33% and energy consumption by 15.32% on average.