International Journal of Sensor Networks and Data Communications

Power line communication (PLC) has been used to transmit and receive digital data via power line in wide application. However, power lines weren’t built for data transmission originally. In practice, data transmission is commonly subjected to switching noise by the multi-path effect. The objective of this research is to establish a platform for easily tune the controller for motor control system under the PLC environment. We build up a dynamic model to simulate the power line environment for communication and propose a frequency-shift keying (FSK) technique to modulate and demodulate the signal for the purpose of network control in the disturbed feedback control loop. A modified proportional-integralderivative controller (PID controller) on positioning control a DC servo motor is proposed to against noise corruption while achieving tracking performance.

Real time detection of emotional state has multiple applications for security, safety and identification of dangerous situations. Traditionally electroencephalogram (EEG) based emotion studies are conducted in controlled lab environment with multi-channel systems and large signal processing power. In order to be useful in real world situation the system for emotion detection has to be miniature, portable and working in real time supported by calculations that can be provided by a processor power of a mobile phone. Here we present our results on real time fear detection using portable single electrode EEG system conducted on 10 subjects. We studied possibility of translation of the markers previously identified for complex multi-electrode system - ratios of slow waves to fast waves into real time portable system. It was demonstrated using Student’s t-test that the average value of the monitored parameter during normal state was significantly higher than that of during a scary stimulus with a P value of (0.027) ~ 0.03 for Theta/ Beta. The framework for portable fear detection together with the markers discussed in this study can enable many applications important for the soldiers in the battlefield or police officers while being under attack as an indicator that help is urgently needed.

There is a need for security to be provided for the automobiles due to the alarming rate of theft action across the globe. A system that reduce or even eliminate the menace on automobiles need to be developed. The design of automobile immobilizer using mobile phone is efficient in transmitting signal through the mobile phone that has both F (fast) and M (meter) buses network configurations to various destinations of users where communication network is available across the globe. The work was achieved with the aid of a specially designed microcontroller (PIC18F2550) that communicates directly with the mobile phone and some components like Schmitt Inverting Gate (74HC14N), Regulator LM7805 and LM317, NPN Transistor BC337, 12 Volt DC Relays and Liquid crystal display panel (LCD). It imbibed the operation of Short Message Service (SMS) and Communication is being established through a 15characters code (xxxxxxxxxxxxxxx) which is secretive and privately used by the owner to enables and disables such automobile.

Friction stir welding (FSW) is an innovative solid state joining technique and has been employed in aerospace, rail, automotive and marine industries for joining aluminum, magnesium, zinc and copper alloys. In this process, parameters play a major role in deciding the weld quality these parameters. Using predictive modelling for mechanical properties of FSW not only reduce experiments but also is created standard model for predict outcomes. Therefore, this paper is undertaken to develop a model to predict the microstructure and mechanical properties of FSW. The proposed model is based on Ring Probabilistic logic Neural Network (RPLNN) and optimize it utilizing Genetic Algorithms (GA). The simulation results show that performance of the RPLNN algorithm with utilizing Genetic Algorithm optimizing technique compared to real data is reliable to deal with function approximation problems, and it is capable of achieving a solution in few convergence time steps with powerful and reliable results.