Demonstration of Artificial Neural Network

Artificial neural networks (ANN) is referred as the neural networks are the signal processing and in information model which is based on the biological neuron. An artificial neural network (ANN) consists of a bundle of simple processing units which communicate by sending signals to each other over a large number of weighted connections. A set of processing unit is called as neuron. A neural network is made up of an interconnection of nonlinear neuron. The purpose of this work is to examine Neural Networks (NN) and their emerging applications in the field of engineering.The paper presented the basic study of the artificial neural network and its characteristics and its applications

PERFORMANCE ANALYSIS OF LTE NETWORK USING RUNTIME PRE-CODING ALGORITHM

LTE (Long Term Evolution) meets the requirement of high data rates, improved end user performance, better spectrum utilization, no interference and many more. All this is possible because of its features, one of which is multipoint transmission with single user as well as multi user. But whenever user receives signal from number of transmitting a points, it becomes difficult to calculate the precoders and corresponding filters. The paper describes one of such scenario where user receives signal from multiple eNodeBs. We have implemented a runtime algorithm to estimate the pre-coders and its corresponding filters for system level simulation of LTE network using Matlab

DESIGNING OF TEMPERATURE & HUMIDITY MONITORING EMBEDDED SYSTEMS

The places such as weather forecasting system, nuclear radiation measurement, greenhouses, agro-automation systems require real-time monitoring of environmental parameters like temperature and humidity. So a low-cost, low-power temperature and humidity sensor interfacing with embedded systems using PIC microcontroller and PLC is designed. The paper is analyzing the operating mechanism of DHT11 temperature and humidity combined sensor; where it features temperature and humidity sensor complex with calibrated digital signal output. The DHT11 sensor interfacing with controller is programmed, then the temperature and humidity acquisition program porting to embedded platform. Meanwhile, the data through human machine interface is intuitive feedback to the user

ENVIRONMENT MONITORING SYSTEM IN CHEMISTRY LABORATORY

The impact of air quality has to be taken into consideration especially when dealing with different chemical gases in an enclosed Chemistry Laboratory. Thus the monitoring of the environment or the air quality in the Chemistry Laboratory is essential to detect the amount of concentration of gases in the air so as to develop appropriate strategies to reduce the adverse effect of air quality on the health of people working in the laboratory. The Environment Monitoring System is designed using the Wireless Sensor Network technology. Wireless Sensor Network along with Internet of Things allows the use of various sensors to detect the environmental condition and to collect the sensor data thus permitting data integration. The system designed to monitor the environmental conditions in the Chemistry Laboratory is divided into three parts:1] Sensor node: That collects and transmits the sensor data to the central repository or the Sink node 2] Sink node: It is the core node in the network performing important functions like data storage, data collaboration, computing and data integration 3] Web Interface: Development of a Web application so as to provide access to the remote user to the sensor data and also for visualization of data in a systematic manner for further analysis. The system consists of sensor nodes designed using the Atmega328 microcontroller along with a nRF24l01 module for wireless communication and various analog sensors. The base station is designed by using the open source hardware Raspberry Pi, nRF24l01 module and analog sensors. A Web Server Interface is created to access the sensor data for the user

Radial Basis Function and Neural Networks

In the paper, artificial neural networks and their various concepts in pattern recognition and signal have explain. Demand for Neural network is increasing in coming days so that interconnection between neuron to neuron is visualized effectively and it is directly related to the human brain contributing nervous system. Neural Network is a optimized problem solving technique The paper will demonstrate the Artificial Neural Network using SPSS 16.0 software. The paper will discuss the input layers, output layers and hidden layers and also interconnection of them. By observing the neural information obtained in results the paper will design a model. Designing of Neural Network in SPSS 16.0 is easier to understand so that neural paper will be convenient to design. In the paper Radial Basis Function is used to design a Neural Network. Radial Basis Function (RBF) is a hierarchy based way to design a Neural Network. Upper layer, lower layer concepts are included in the Radial Basis Function. The paper will be working on Artificial Neural Network (ANN) and will obtain a solution to the engineering problem

TESTING OF WIRELESS SCENARIO FOR DIFFERENT ENVIRONMENT

Wireless sensor Networks are widelyused. Before actual deployment of wireless scenario in real time field, testing of the scenario is very important. With the help of Qualnet simulator the different geographical environment can be created which will support to reduce real time problemsfacing afterimplementationof wireless sensor networks. This paper deals with the testing of wireless scenario for different geographical environment. Scenario considered withnine nodes with different routing protocols anda variety ofenvironment. The parameters considered are average end to end throughput, average end to end delay, total data received and average jitter