Low Power System Design for Emerging Pervasive Platform using MATLAB

Personal computing devices have gone through more than four decades of evolution. The form factor of computing devices has gone down dramatically, while computation workload for each computing device has increased significantly. Moreover, people spend much more time interacting with computing devices than before. In short, everyone has a pocket supercomputer.

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An Event Reporting and Early-Warning Safety System based on the Internet of Things for Underground Coal Mines: A Case Study using MATLAB

Fatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner's localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster analysis for outlier detection, spatiotemporal statistical analysis, and an RSS range-based weighted centroid localization algorithm for improving safety management and preventing accidents in underground coal mines. The proposed platform seamlessly integrates monitoring, analyzing, and localization approaches using the Internet of Things (IoT), cloud computing, a real-time operational database, application gateways, and application program interfaces. The prototype has been validated and verified at the operating underground Hassan Kishore coal mine. Sensors for air quality parameters including temperature, humidity, CH4, CO2, and CO demonstrated an excellent performance, with regression constants always greater than 0.97 for each parameter when compared to their commercial equivalent. This framework enables real-time monitoring, identification of abnormal events (>90%), and verification of a miner's localization in the harsh environment of underground mines. The main contribution of this study is the development of an open source, customizable, and cost-effective platform for effectively promoting underground coal mine safety. This system is helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility associated with safety in coal mines.

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Design of a Transmitter for Ultra Wideband Radio using MATLAB

Ultra Wideband Radio (UWB) is an upcoming alternative for wireless communications. Since the Federal Communication Commission in the USA allowed UWB for unlicensed usage in April 2002, more and more companies have started developing UWB systems. The major difference with UWB compared to other RF systems is that UWB sends information with pulses instead of using a carrier wave. The technique is from the nineteenth century and was first developed by Heinrich Hertz (1857-1894), which led to transatlantic communications 1901. This Master thesis presents a proposal of a transmitter for Ultra Wideband Radio using multiple bands. The proposed transmitter is implemented on system level in Simulink, Matlab. The frequency generation in the transmitter is also implemented at component level in a 0.13 um IBM process. The thesis begins with an introduction of UWB theory and techniques.

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Smart Control for Home Water Heater Saving using MATLAB

Existing gas or electric water heaters can become inefficient through the overheating of water and through parasitic heat loss. These inefficiencies are able to be solved by monitoring when a home uses hot water. AquAdapt is a smart sensor which is capable of attaching to any existing residential gas or electric water heater. By constantly monitoring the temperature change of a home water heater, the first law of thermodynamics can be used to relate temperature change to the amount of hot water leaving the water heater. Utilizing this information, a schedule can be generated to optimize the heating of home hot water. Regulating the on-off state of a water heater based on the household’s learned usage pattern, allows AquAdapt to reduce residential water heating energy consumption by up to 33%. With a final product cost of $60, the return on investment of AquAdapt is estimated to be 8 months.

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Aperture Coupled Microstrip Antenna Design and Analysis using MATLAB

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Obstacle Recognition based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System using MATLAB

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

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