Close Circuit Security System Using At89c51

The purpose of this project is to provide a field that’s depending on less manual operations because everyone is interested in automated systems. To face new challenges in the present day situation automated systems are more accurate, flexible and reliable. Due to these reasons every field prefers automated control systems. Especially in electronics automated systems are doing better job. The ideal system to protect your property is CCTV (Closed Circuit Television) Not only does it act a visual deterrent but the video or digital recording provides an invaluable method of recording crime, violence or anti-social behaviour. CCTV systems offer such a wide area of applications and benefits 24-hours a day. Systems can aid the monitoring of stock, personnel, visitors, access control and prevent health and safety incidences

Monitoring Indoor People Presence in Buildings Using Low-Cost Infrared Sensor Array in Doorways

We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people’s direction, which allows monitoring users’ occupancy in any space of the building and also respects people privacy. The device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor. The computed information is transferred through a Z-Wave network. On-field evaluation of the algorithm has been conducted by placing the device on top of doorways in offices and laboratory rooms. To evaluate the performance of the algorithm in varying ambient temperatures, two groups of stress tests have been designed and performed. These tests established the detection limits linked to the difference between the average ambient temperature and perturbation. For an in-depth analysis of the accuracy of the algorithm, synthetic data have been generated considering temperature ranges typical of a residential environment, different human walking speeds (normal, brisk, running), and distance between the person and the sensor (1.5 m, 5 m, 7.5 m). The algorithm performed with high accuracy for routine human passage detection through a doorway, considering indoor ambient conditions of 21–30 °C

Human identification via unsupervised feature learning from UWB radar data

This paper presents an automated approach to automatically distinguish the identity of multiple residents in smart homes. Without using any intrusive video surveillance devices or wearable tags, we achieve the goal of human identification through properly processing and analyzing the received signals from the ultra-wideband (UWB) radar installed in indoor environments. Because the UWB signals are very noisy and unstable, we employ unsupervised feature learning techniques to automatically learn local, discriminative features that can incorporate intra-class variations of the same identity, and yet reflect differences in distinguishing different human identities. The learned features are then used to train an SVM classifier and recognize the identity of residents. We validate our proposed solution via extensive experiments using real data collected in real-life situations. Our findings show that feature learning based on K-means clustering, coupled with whitening and pooling, achieves the highest accuracy, when only limited training data is available. This shows that the proposed feature learning and classification framework combined with the UWB radar technology provides an effective solution to human identification in multi-residential smart homes

Target Tracking with Binary Sensor Networks

Binary Sensor Networks are widely used in target tracking and target parameter estimation. It is more computationally and financially efficient than surveillance camera systems. According to the sensing area, binary sensors are divided into disk shaped sensors and line segmented sensors. Different mathematical methods of target trajectory estimation and characterization are applied. In this thesis, we present a mathematical model of target tracking including parameter estimation (size, intrusion velocity, trajectory, etc.) with line segmented sensor networks. Software simulation and hardware experiments are built based on the model. And we further analyze how the quantization noise affects the results

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

The development of pervasive communication devices and the emergence of the Internet of Things (IoT) have acted as an essential part in the feasibility of smart city initiatives. Wireless sensor network (WSN) as a key enabling technology in IoT offers the potential for cities to get smatter. WSNs gained tremendous attention during the recent years because of their rising number of applications that enables remote monitoring and tracking in smart cities. One of the most exciting applications of WSNs in smart cities is detection, monitoring, and tracking which is referred to as object tracking sensor networks (OTSN). The adaptation of OTSN into urban cities brought new exciting challenges for reaching the goal of future smart cities. Such challenges focus primarily on problems related to active monitoring and tracking in smart cities. In this paper, we present the essential characteristics of OTSN, monitoring and tracking application used with the content of smart city. Moreover, we discussed the taxonomy of OTSN along with analysis and comparison. Furthermore, research challenges are investigated concerning energy reservation, object detection, object speed, accuracy in tracking, sensor node collaboration, data aggregation and object recovery position estimation. This review can serve as a benchmark for researchers for future development of smart cities in the context of OTSN. Lastly, we provide future research direction

Embedded sensor speed and width estimation

The goal of this report is to provide a novel system of estimating width and velocity of an object passing perpendicularly through a sensor field. By fixing four sensors on an axis at known angles the distance and velocity of an object can be estimated from the times of detection of each sensor using a system of equations. To develop a prototype of this system the scenario was modeled and simulated, a processor and sensors were selected, and algorithms for detection and estimation were developed. The goal of the report was to develop a self-contained sensor network and computing platform that would correctly estimate the speed of the object in miles per hour within 15% accuracy, and to estimate the width of the object within 20% accuracy. Due to prototype design errors, these requirements were not met, however useful algorithms and simulations were developed to lead towards successful future work.Electrical and Computer Engineerin