Detection of GSM Based Accident Location, Vehicle Theft and Fuel Theft Using ARM Cortex M-3 Microcontroller

In Today's world the amount of vehicle theft, fuel theft and accident of vehicles are increasing day by day. As per the survey made, each year more than a million vehicles are stolen in the U.S (one vehicle every 30 seconds). Vehicle theft occurs not only in metropolitan areas but also it can occur in seedy areas of town. To overcome this limitation, an automotive localization system using GPS and GSM services for the detection of accident location, fuel theft and vehicle theft using ARM Cortex M-3 is proposed. Here, the Vehicle tracking and locking system installed in the vehicle, to track the place and locking engine motor. The place of the vehicle identified using Global Positioning system (GPS) and Global system mobile communication (GSM). These systems constantly watch a moving Vehicle and report the status on demand. When the theft identified, the responsible person send SMS to the ARM Cortex M-3 controller, then controller issue the control signals to stop the engine motor. Authorized person need to send the password to controller to restart the vehicle and open the door which provides more secured, reliable and low cost. The proposed model shows better in its performance

Machine learning based fusion algorithm to perform multimodal summarization

Video summarization is a rapidly growing research field which finds its application in various commercial and personal interests due to the massive surge in the amount of video data available in the modern world. The proposed approach uses ResNet-18 for feature extraction and with the help of temporal interest proposals generated for the video sequences, generates a video summary. The ResNet-18 is a convolutional neural network with eighteen layers. The existing methods don’t address the problem of the summary being temporally consistent. The proposed work aims to create a temporally consistent summary. The classification and regression module are implemented to get fixed length inputs of the combined features. After this, the non-maximum suppression algorithm is applied to reduce the redundancy and remove the video segments having poor quality and low confidence-scores. Video summaries are generated using the kernel temporal segmentation (KTS) algorithm which converts a given video segment into video shots. The two standard datasets TVSum and SumMe are used to evaluate the proposed model. It is seen that the F-score obtained on TVSum and SumMe datasets are 56.13 and 45.06 respectively

Huffman coding: Energy efficient algorithm in wireless networks

In the proposed Huffman LEACH model, energy consumption increases exponentially with distance and there are no maximum limits. The transmit power level of a sensor node can only be adjusted to discrete values that may result in one transmit power level for various distances. The resulting energy consumption for two links of different distances can be equivalent. The number of clusters generated in LEACH does not converge to a fixed value which shortens the lifespan of the network. The energy consumption of the wireless sensor network is inversely proportional to the lifetime of the wireless sensor network. Here, we assume that the network lifetime is defined as the time from the deployment of the WSN till the first gateway dies. Hence, network lifetime can be maximized by using the parameter discussed in this paper. If we can minimize the energy consumption of the CH nodes, then energy consumption of the sensor nodes can be minimized if we can minimize their relative distance from their corresponding CH’s. The experimental result of proposed model says better with its energy faster than the nodes with lower data transmission rate when compared with the existing WSN models