Recently, by increasing the productivity of industrial manufacture, industrial robots have played a key role in many fields of industry (e.g. automobile production, food production, etc.) However, there are two problems rarely mentioned in this field. First, compared with automatization in other fields, industrial robots are programmed manually by a human operator. Second, because of the physical difference between robots and difference of operating platform, there doesnât exist a general method to define the skill (motion records) of robots and make it possible to reuse the skills between robots. In this work, we are trying to propose a skill definition of transfer system which combine the strengths of traditional DMP algorithm and deep learning method. Specifically, in our method, a set of motion primitive bases are generated from motion records in different robots. Skills are re-defined by the linear coefficient of the primitive bases and transferred based on motion primitive bases translation between different platforms. Experiment shows that our method can successfully transfer skills between different models with less space requirement.

Electricity is a critical component in our daily life. Because it is almost always available, we take it for granted. However, given the proper conditions, blackouts do happen every once in a while and can cause discomfort at a minimum, and a catastrophe in rare circumstances. The largest blackouts typically include cascading failures, which are sequences of interdependent outages. Although timely and effective operator intervention can often prevent a cascade from spreading, such interventions require ample situational awareness. The goals of this dissertation are twofold: to provide power system operators with insight into the risk of blackouts given the space of potential initiating outages, and to evaluate control systems that might mitigate cascading failure risk. Accordingly, this dissertation proposes a novel method to estimate cascading failure risk. It is shown that this method is at least two orders of magnitude faster in estimating risk, compared with a traditional Monte-Carlo simulation in two test systems including a large-scale real power grid model. This method allows one to find critical components in a system and suggests ideas for how to reduce blackout risk by preventive measures, such as adjusting initial dispatch of a system. In addition to preventive measures, it is also possible to use corrective control strategies to reduce blackout sizes. These methods could be used once the system is under stress (for example if some of the elements are overloaded) to stop a potential cascade before it unfolds. This dissertation focuses on a distributed receding horizon model predictive control strategy to mitigate overloads in a system, in which each node can only control other nodes in its local neighborhood. A distributed approach not only needs less communication and computation, but is also a more natural fit with modern power system operations, in which many control centers manage disjoint regional networks. In addition, a distributed controller may be more robust to random failures and attacks. A central controller benefits from perfect information, and thus provides the optimal solution. This dissertation shows that as long as the local neighborhood of the distributed method is large enough, distributed control can provide high quality solutions that are similar to what an omniscient centralized controller could achieve, but with less communication requirements (per node), relative to the centralized approach.

The optic nerve is a vital bundle of axons which carries all visual information from the retina posterior to the brain for higher order processing. The optic nerve and eye orbit are affected by many devastating diseases including optic neuritis, glaucoma and anterior ischemic optic neuritis. This dissertation addresses the use of magnetic resonance imaging for investigating anatomical and microstructural changes in the optic nerve in healthy controls and disease cohorts. We propose a fully automated pipeline for segmentation of the optic nerve and other eye-orbit structures. This pipeline is applied to large-scale disease cohort to search for correlations between morphological changes and functional visual measures. We introduce a clinically viable advanced MRI sequence for accurate visualization of the optic nerve and sub-arachnoid cerebrospinal fluid. We develop and improve upon an algorithm to automatically estimate optic nerve and surrounding cerebrospinal fluid radius along the length of the optic nerve. We perform a short- and long-term reproducibility study on young healthy controls for algorithm evaluation and publicly release this data for the standardized comparison of future proposed algorithms. We apply this validated automatic radius estimation algorithm to a clinical population of patients with multiple sclerosis to detect differences in patientsâ eyes with and without a history of optic neuritis. Finally, we utilize a simulation framework to numerically optimize quantitative magnetization transfer imaging sampling patterns to move towards reducing scan times and increasing clinical viability of quantitative magnetization transfer imaging for microstructural characterization of tissue.

Alpha, heavy-ion, neutron, and proton experimental results from 130-nm to 28-nm technology nodes are establish single-event upset cross section trends in soft and hardened flip-flop designs. Trends show that at any LET value soft flip-flops show a decreasing single-event upset cross section with decreasing feature size. Hardened redundant storage node flip-flops show similar cross sections across technologies if the redundant storage node transistor spacing is held constant. Technology computer aided design (TCAD) simulations are used to show there are many competing mechanisms that influence flip-flip single-event upset cross sections as technology feature sizes decrease.

<p> This thesis demonstrates the benefits of using Graph Signal Processing (GSP) techniques for an intelligent taxis transportation system. Graph Signal Processing, an application arising to handle multiple source signals on a graph, has developed into an active field of research during the last several years due to its ability to analyze enormous datasets or dynamic data that usually pose a challenge to researchers. One of the most significant operations of Graph Signal Processing that arises in many areas is noise reduction. </p><p> This thesis introduces a possible method of using Graph Signal Processing and its operations to analyze signals in a network of taxi stand locations. Two examples are given using real data of taxis' and stands' locations in San Francisco where the number of taxis around these stands is the detected signal. The results showed the effectiveness of using Graph Fourier Transform to detect the anomalies in the signals which represent unusual transportation activities or driver distributions within the taxi network. </p><p> Signal denoising is addressed by using four techniques which are often based on the signal filtering methods. The first technique used the low pass filter, followed by a harmonic decline filter, and then standard and modified Kalman filters, including the case for uncertain observation or process noise between the standard and adaptive Kalman filters. The results are compared with the other filters.</p>

<p> This project report presents a fast-acquisition all-digital delay-locked loop (ADDLL) using a starting-bit prediction algorithm for the successive-approximation register (SBP-SAR). The SBP effectively eliminates the harmonic lock and the false lock. The ADDLL design allows wide clock frequency range operation. The algorithm and the digital circuit are digitally simulated and the performance and the advantage over the Conventional-SAR are shown. </p>

<p> With advancements in technology and people being more conscious about their health than ever, technical advancements in the field of medicine are inevitable. People are switching towards wearable devices to keep track of their health and fitness related data. This project looks into the development of a heart rate monitor to keep track of the user&rsquo;s heart rate. The report presents the development of a heart rate monitor interfaced with a Bluetooth module to wirelessly view related data on a smartphone or any other Bluetooth compatible device. The heart rate monitor developed uses the photoplethysmography (PPG) principle to calculate the heart beats per minute. The PPG module developed is then interfaced with an Arduino Uno board responsible for calculating the beats per minute. It then transfers the serial data to a Bluetooth module, which transmits the data to another Bluetooth compatible device. The results show that the heart rate is successfully transferred over Bluetooth and could be helpful in emergency or monitoring situations.</p>

Hardware implementations of Object-Tracking Algorithms are susceptible to radiation-induced soft errors. The thesis analyzes the results of fault emulation experiments conducted on register-transfer level on a field programmable gate array (FPGA) implementation of object tracking. Typical single event effect (SEE) induced faults were injected to core modules within the object tracking system. The results indicate that injected faults can cause observable errors in tracking system outputs, which are defined as values exceeding a selected threshold. The level of degradation is related to the fault injection location as well as the type of faults. Under the worst-case experiments, the output error rate was more than 88%. The cross-layered reliability analysis between circuit and algorithm is significant to algorithms optimization and selective circuit hardening.

The total ionizing dose (TID) response of HfO2-SiO2/SiGe pMOS FinFETs under different irradiation biases has been evaluated. Negative bias irradiation leads to the worst-case degradation. We attribute this result to an increase in density of additional radiation-induced holes that become trapped in the HfO2 under negative bias, and additional electron trapping under positive bias in the HfO2, as compared with the 0 V irradiation case. When devices are exposed to negative bias-temperature stress, we find similar values of Ea for oxide-trap charge buildup, and a reduced Ea for interface-trap buildup, for Si0.55Ge0.45 pMOSFETs with high-k gate stacks, compared to control Si devices with SiO2 gate dielectrics. The low-frequency 1/f noise of these devices was also investigated. The magnitude of noise is unaffected by negative-bias-temperature stress (NBTS) for temperatures below ~ 250 K, but increases significantly at higher temperatures. The noise is described well by the Dutta-Horn model before and after NBTS. The noise is attributed to oxygen-vacancy and hydrogen-related defects in the SiO2 and HfO2 (especially at the higher measuring temperatures) and/or hydrogen-dopant interactions in the SiGe layer of the device (especially for lower measuring temperature).

<p> The need to conserve water has increased with adverse weather conditions due to global warming. The proposed embedded system model provides an economical method in preventing unmonitored water loss from storage facilities. The model consists of an ultrasonic level controller that is interfaced with a microcontroller. Using sonar, the delay between the transmitted and received signals is used for finding the liquid level. When the level of a liquid falls below a certain point, the pump automatically starts and continues pumping until the required liquid level is reached. The pump operates using the signals received from a microcontroller and uses a relay mechanism. A step down transformer, a bridge rectifier, a capacitive filter, and a voltage regulator convert the AC to DC voltage level of +5v to operate both the microcontroller and Liquid Crystal Display (LCD). Results show the level measurement is unaffected by the physical properties of a liquid.</p>