Smart Antennas Implementation for MIMO

MIMO systems place the same requirements on the RF link as do the receive diversity systems that are in place for current cellular networks, that is, there must be de-correlation between the channels received at the antenna. This de-correlation is provided by space diversity when achieved by the separation of the antennas, or by the use of polarization diversity when implemented by the use of orthogonal antenna elements. However, for dual-pole antennas, cross-polar discrimination and port-to-port isolations can affect the diversity or MIMO performance of the system by introducing correlation between the channels. MIMO systems employing smart antennas are a promising candidate for future mobile communications due to their tremendous spectral efficiency. RF engineers have to find new antenna solutions for MIMO applications, especially the integration of MIMO antennas into small handsets is a challenging task. Smart antenna systems may revolutionize future communications systems. So far, only the spectrum, the time and the code domain are exploited for communications systems. The resources spectrum and code are very limited. Smart antennas exploit the spatial domain, which has been almost completely unused so far. For multiplex transmission within one communications link, i.e. a parallel transmission of several data streams at the same time and frequency only separated by the spatial domain, multiple transmit and multiple receive antennas (multiple input multiple output - MIMO) are required. MIMO systems promise to reach very large data rates and therewith high spectral efficiencies. The proposed research work states smart antennas for mimo’s and related for wireless systems. Keywords:MIMO,SISO,DIVERSIT

Design of Uniform and Nonuniform Circular Arrays Comparison with FFA and RLS

Multiple antennas can be arranged in various geometrical configurations to form antenna array with high directive radiation pattern. Linear antennas are limited in their steering capability. The circular arrays ar   more popular in recent years over other array geometries because they have the capability to perform the scan in all the directions and considerable change in the beam pattern which provide 3600 total coverage. Circular arrays are less sensitive to mutual coupling as compared to linear and rectangular arrays since they do not have edge elements. They can be used for beam forming in the azimuth plane for example at the base stations of the mobile radio communication systems as the components for signal processing. FFA design method of circular apertures for narrow beam width and low side lobes has been reported by Taylor. It includes the development of continuous circular aperture distributions, which contain only two independent parameters, A & , where A is related to the design of side lobe level and  is a number controlling the degree of uniformity of the side lobes

WIRELESS INDUSTRIAL PARAMETER MONITORING USING RASPBERRY PI 3

This paper proposes an advanced system for process management via a credit card sized single board computer called raspberry pi based multi parameter monitoring hardware system designed using that measures and controls various global parameters. The system comprises of a single master and slave with wireless mode of communication and a raspberry pi system that can either operate on windows or Linux operating system. The parameters that can be tracked are current, voltage, temperature & light intensity. The master board use raspberry Pi, LM35 & LDR Sensors, Water level sensor(IC CD4066) ZIGBEE and Wi-Fi . From slave board the data is sent to the master and from master the data is sent to personal computer. We can monitor the data through Personal computer, display device (16x2 LCD) and simultaneously we will get email alerts when the parameter readings exceed the limit

Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques

A crucial area of research that can reveal numerous useful insights is emotional recognition. Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-based categorization problem that is at the core of emotion recognition in text documents.This research propose novel technique in linguistic based emotion detection by social media using metaheuristic deep learning architectures. Here the input has been collected as live social media data and processed for noise removal, smoothening and dimensionality reduction. Processed data has been extracted and classified using metaheuristic swarm regressive adversarial kernel component analysis. Experimental analysis has been carried out in terms of precision, accuracy, recall, F-1 score, RMSE and MAP for various social media dataset