The road and rail accidents are increasing with the advancement of human errors. Majority of the level crossing positions are not guided by any accident preventive mechanism and even not accompanied by a skilled person. This paper presents an automatic rail gate control system at level crossing positions and accident prevention mechanism. Two vibration sensors are used to control the open and close state of the gate at level crossing position. An ultrasonic sensor is positioned to detect an unauthorized object on the track. Open and Close status of the gate and unauthorized object on the track will be communicated with the central control room using wireless communication protocol. The experimental results proved the proposed mechanism is a prudent approach to safeguard the human and to curtail the train accidents.

This paper considered the secured level crossing Signaling system and Train tracking system. This paper adopted a switching logic methodology to meet the challenges of the tracking system. This work also focused to map the train on the display screen. If the train moving close to the level crossing system the signaling system causes to release the green signal that causes to close the rail gate to avoid the unauthorized entry on the rail track. The open state of the gate is influenced by the signaling system by means of a 'Red'colour signal. The train is allowed to move on the track only by considering the green signal. The rail track is organized with four stop positions. Stop 1, stop 2, stop 3 and stop 4. The locations of the train at various stop positions are sensed by using IR sensors. The detected signal is transmitted to the control room using RF transmitter operating at 433 MHZ. An Atmel micro controller is used to regulate the entire process to meet the desired state of the work. This proposed methodology had been successfully implemented on the 30 feet length of the scaled model of the rail track in the laboratory. The system results progressive response while tracking the position of the train. The results are recorded and analyzed. The proposed system may keep alert the monitoring mechanism. So, the collision of the train and unexpected human errors can be minimized.

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The forming of adaptive beam can improve the throughput of the system to a great extent by means of matching the parameters of transmitters to that of the wireless channels that are time-variant. The quality of the channel state is very crucial to the adaptive forming. The Multiple-Input Multiple-Output (MIMO) systems are known to provide some very significant gains in the spectral efficiency as well as its reliability. This has been based on an assumption that the transmitter and the receiver will have knowledge of the coefficients of the channels. In reality, however, they will have to be estimated or sometimes predicted. There are some popular methods that are used for the estimation of the channel which is made by means of using the pilot symbols and also the Space-Time Block Codes (STBCs). Both these methods will not avail the time-learning channels even during the transmission of some meaningful data. In this work, a light weight neural network is proposed for the channel selection. The proposed Artificial Neural Network (ANN) is duly optimized with the Particle Swarm Optimization (PSO) and the Bacterial Foraging Optimization (BFO)-based algorithms for enhancing the predictions. The method is popular and is robust adaptive as a beamforming technique and optimized weights will be used for training the ANN effectively. The results when compared prove the advantages of the techniques proposed.