As the energy demand and the environmental problems increase, the natural energy sources have become very important as an alternative to the conventional energy sources. The renewable energy sector is fast gaining ground as a new growth area for numerous countries with the vast potential it presents environmentally and economically. Solar energy plays an important role as a primary source of energy, especially for rural area. This paper aims at the development of process to track the sun and attain maximum efficiency using Arduino uno and LabVIEW for real time monitoring. The project is divided into two stages, which are hardware and software development. In hardware development, four light dependent resistor (LDR) has been used for capturing maximum light source. Two DC motors have been used to move the solar panel at maximum light source location sensing by LDR. The GUI is constructed by using LabVIEW. The performance of the system has been tested and compared with static solar panel. This paper describes the design of a low cost, solar tracking system.

The design, analysis and implementation of a robot arm system, which is expressed towards its performance with an analytical model by using LabVIEW and embedded system tools was presented in this article. Mathematical modeling of kinematics plays an essential role in design and implementation of a robot arm control. The projected work was focused to control the end effector of the robot arm to achieve any accessible point in an amorphous region using LabVIEW, ARM (Advanced RISC (Reduced Instruction Set Computing) Machine) microcontroller and Dexter ER2 robotic arm. LabVIEW uses analytical method to design the inverse kinematic model of the robot arm. The inverse kinematic model and robot arm control was implemented using LabVIEW and ARM microcontroller. LabVIEW uses the parallel communication to send the joint angles of the robot arm to the ARM. ARM microcontroller uses five PWM (Pulse Width Modulation) signals in order to control the robot arm, which was geared up with servo motors. Robot arm was controlled manually through the LabVIEW GUI (Graphical User Interface) controls. The present paper discussed about the mechanical configuration, analytical modeling, software and hardware of the above said work.

Internet of Things (IoT) provides information services based on daily usage that depend on the sensors of devices and network platform. An increase in the number of connected devices through internet, increases the demand for the number of low-latency services. In this research, Open Cloud Computing Interface (OCCI) technique is used in IoT architecture that helps to encompass application level interface. The OCCI-based architecture is proposed to manage and store the data, by introducing the resource analyzer, edge devices and monitoring manager that helps to transfer the data effectively. The monitoring manager, edge devices and broker schedule the data to minimize the traffic in the IoT. Representational State Transfer (REST) methods using Hyper Text Transfer Protocol (HTTP) for communication are presented in this technique. Simulation result showed the effectiveness of the proposed OCCI architecture. Arduino and Raspberry Pi 3 are the two major hardware used in this technique. The result of the OCCI-based architecture uploaded to the ThingSpeak server, which is the external server. Several parameters such as Temperature, Round Trip Time (RTT), latency, Clock difference and frequency are evaluated in this work. Round Trip Time reduced to 0.96 seconds by reducing the delay in the system.

In this work, a new methodology based on artificial neural networks (ANN) has been developed to study the low-velocity impact characteristics of woven glass epoxy laminates of EP3 grade. To train and test the networks, multiple impact cases have been generated using statistical analysis of variance (ANOVA). Experimental tests were performed using an instrumented falling-weight impact-testing machine. Different impact velocities and impact energies on different thicknesses of laminates were considered as the input parameters of the ANN model. This model is a feed-forward back-propagation neural network. Using the input/output data of the experiments, the model was trained and tested. Further, the effects of the low-velocity impact response of the laminates at different energy levels were investigated by studying the cause-effect relationship among the influential factors using response surface methodology. The most significant parameter is determined from the other input variables through ANOVA.

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