Dynamic Recofiguration Techniques for Wireless Sensor Networks

The need to achieve extended service life by battery powered Wireless Sensor Networks (WSNs) requires new concepts and technqiues beyond the state-of-the-art low-power designs based on fixed hardware platforms or energy-efficient protocols. This thesis investigates reconfiguration techniques that enable sensor hardware to adapt its energy consumption to external dynamics, by means of Dynamic Voltage Scaling (DVS), Dynamic Modulation Scaling (DMS), and other related concepts. For sensor node-level reconfiguration, an integration of DVS and DMS techniques was proposed to minimize the total energy consumption. A dynamic time allocation algorithm was developed, demonstrating an average of 55% energy reduction. For network-level reconfiguration, a node activation technique was presented to reduce the cost of recharging energy-depleted sensor nodes. Network operation combined with node activation was modeled as a stochastic decision process, where the activation decisions directly affected the energy efficiency of the network. An experimental test bed based on the Imote2 sensor node platform was realized, which demonstrated energy reduction of up to 50%. Such energy saving can be effectively translated into prolonged service life of the sensor network

Online circuit breaker monitoring system

Circuit breakers are used in a power system to break or make current flow through power system apparatus. Reliable operation of circuit breakers is very important to the well-being of the power system. Historically this is achieved by regular inspection and maintenance of the circuit breakers. An automated online circuit breaker monitoring system is proposed to monitor condition, operation and status of high and medium voltage circuit breakers. By tracking equipment condition, this system could be used to perform maintenance only when it is needed. This could decrease overall maintenance cost and increase equipment reliability. Using high accurate time synchronization, this system should enable development of system-wide applications that utilize the data recorded by the system. This makes possible tracking sequence of events and making conclusions about their effect on-line. This solution also enables reliable topology analysis, which can be used to improve power flow analysis, state estimation and alarm processing

A Novel Electrocardiogram Segmentation Algorithm Using a Multiple Model Adaptive Estimator

This thesis presents a novel electrocardiogram (ECG) processing algorithm design based on a Multiple Model Adaptive Estimator (MMAE) for a physiological monitoring system. Twenty ECG signals from the MIT ECG database were used to develop system models for the MMAE. The P-wave, QRS complex, and T-wave segments from the characteristic ECG waveform were used to develop hypothesis filter banks. By adding a threshold filter-switching algorithm to the conventional MMAE implementation, the device mimics the way a human analyzer searches the complex ECG signal for a useable temporal landmark and then branches out to find the other key wave components and their timing. The twenty signals and an additional signal from an animal exsanuinaiton experiment were then used to test the algorithm. Using a conditional hypothesis-testing algorithm, the MMAE correctly identified the ECG signal segments corresponding to the hypothesis models with a 96.8% accuracy-rate for the 11539 possible segments tested. The robust MMAE algorithm also detected any misalignments in the filter hypotheses and automatically restarted filters within the MMAE to synchronize the hypotheses with the incoming signal. Finally, the MMAE selects the optimal filter bank based on incoming ECG measurements. The algorithm also provides critical heart-related information such as heart rate, QT, and PR intervals from the ECG signal. This analyzer could be easily added as a software update to the standard physiological monitors universally used in emergency vehicles and treatment facilities and potentially saving thousands of lives and reducing the pain and suffering of the injured

Bayesian Models Applied to Cyber Security Anomaly Detection Problems

Cyber security is an important concern for all individuals, organisations and governments globally. Cyber attacks have become more sophisticated, frequent and dangerous than ever, and traditional anomaly detection methods have been proved to be less effective when dealing with these new classes of cyber threats. In order to address this, both classical and Bayesian models offer a valid and innovative alternative to the traditional signature-based methods, motivating the increasing interest in statistical research that it has been observed in recent years. In this review we provide a description of some typical cyber security challenges, typical types of data and statistical methods, paying special attention to Bayesian approaches for these problems

Solar Energy for Poverty Alleviation in China Environmental Effectiveness and Economic Impact

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A Framework for Recommending Signal Timing Improvements Based on Automatic Vehicle Matching Technologies

Continuously monitoring and automatically identifying existing problems in traffic signal operation is a challenging and time-consuming task. Although data are becoming available due to the adoption of emerging detection technologies, efforts on utilizing the data to diagnose signal control are limited. The current practices of retiming signals are still periodic and based on several days of aggregated turning movement counts. This dissertation developed a framework of automatic signal operation diagnosis with the aim to support decision-making processes by assessing the signal control and identifying the signal retiming needs. The developed framework used a combination of relatively low-cost data from Wi-Fi sensors and historical signal timing records from existing signal controllers. The development involved applying multiple data matching and filtering algorithms to allow the estimation of travel times of vehicular traversals. The Travel Time Index (TTI) was then used as a measure to assess the traffic conditions of various movements. Historical signal timing records were also analyzed, and an additional signal-timing measure, referred to as the Max-out Ratio (MR), was proposed to evaluate the frequency in which the green time demand of a phase exceeded its preset value. Thresholds for the TTI and MR variables were used as a basis for the diagnosis. This diagnosis first identified the needs for assigning additional green times for individual signal phases. Further assessments were then made to determine whether or not the cycle length for the entire intersection or capacity was sufficient. The developed framework was implemented in a real-world signalized intersection and proved to be capable of identifying retiming needs, as well as providing support for the retiming process. Compared to field observations, the diagnosis results were able to reflect the signal operations of most of the movements during various time periods. Moreover, the flexibility of the developed framework allows users to select different thresholds for various movements and times of day, and thus customize the analysis to agency needs