No abstract

No abstract

Abstract:The increasing integration of large solar PV and wind farms into the power grid has fueled, over the past two decades, growing demands for high-power, high-voltage, utility-scale inverters. Multilevel inverters have emerged as the industry's choice for megawatt-range inverters because of their reduced voltage stress, capability for generating an almost-sinusoidal voltage, built-in redundancy and other benefits. This paper presents a novel switched-source multilevel inverter (SS MLI) architecture. This new inverter shows superior capabilities when compared to existing topologies. It has reduced voltage stress on the semiconductor, uses fewer switches (i.e., reduced size/weight/cost) and exhibits increased efficiency. The proposed SS MLI is comprised of two voltage sources (V 1 , V 2 ) and six switches. It is capable of generating five-level output voltage in symmetric mode (i.e., V 1 = V 2 ) and seven-level output voltage in asymmetric mode (i.e., V 1 = V 2 ). We present simulations results (using MATLAB R /Simulink R ) for five-and seven-level output voltages, and they strongly support the validity of the proposed inverter. These positive results are further supported experimentally using a laboratory prototype.

Link travel times are crucial information for advanced traveler information system and traffic management applications. However, current systems for estimating them have many limitations. In order to improve travel time estimation, a novel framework for vehicle re-identification via signature matching is proposed. Individual vehicles are matched between well-separated stations in a road transportation network using signatures captured by roadway embedded sensors. Statistical signal processing techniques are used to develop robust signature pre-processing algorithms that support the subsequent signature matching problem, which is formulated using techniques from communication theory. A probabilistic modeling of the observed matching assignments is used to devise a travel time estimation algorithm that is robust to potential misidentifications. The proposed method is tested under real traffic scenarios and satisfactory link travel time measures are reported.

Kenneth Connor is a professor in the Department of Electrical, Computer, and Systems Engineering (ECSE) where he teaches courses on electromagnetics, electronics and instrumentation, plasma physics, electric power, and general engineering. His research involves plasma physics, electromagnetics, photonics, biomedical sensors, engineering education, diversity in the engineering workforce, and technology enhanced learning. He learned problem solving from his father (ran a gray iron foundry), his mother (a nurse) and grandparents (dairy farmers). He has had the great good fortune to always work with amazing people, most recently professors teaching circuits and electronics from 13 HBCU ECE programs and the faculty, staff and students of the SMART LIGHTING ERC, where he is Education Director. Dr. Dianna Newman, University at Albany/SUNY Dr. Dianna Newman is a research professor at the Evaluation Consortium at the University at Albany/SUNY. Her major areas of study are program evaluation with an emphasis in STEM related programs. She has numerous chapters, articles, and papers on technology-supported teaching and learning as well as systems-change stages pertaining to technology adoption. He teaches courses in both analog and digital electronic circuit design and instrumentation, with a focus on wireless communication. He has more than 15 years experience in the development and delivery of synchronous and asynchronous web-based course supplements for electrical engineering courses. Dr. Astatke played a leading role in the development and implementation of the first completely online undergraduate ECE program in the State of Maryland. He has published over 50 papers and presented his research work at regional, national and international conferences. He also runs several exciting summer camps geared towards middle school, high school, and community college students to expose and increase their interest in pursuing Science Technology Engineering and Mathematics (STEM) fields. Dr. Astatke travels to Ethiopia every summer to provide training and guest lectures related to the use of the mobile laboratory technology and pedagogy to enhance the ECE curriculum at five different universities. AbstractThis paper presents findings from a multi-year project that is initiating experimental centric approaches to learning in electrical engineering courses at 13 Historically Black Colleges and Universities. The tool supporting to experimental student-centered learning at these institutions was an Analog Discovery Board (ADB). The content or setting of use reflect introductory, circuits, and supporting electrical engineering courses. The students were 1st, 2nd, and 3rd year undergraduates enrolled in EE courses; the unique audience represents students enrolled in HBCU colleges. In this paper, the authors discuss how integration of the innovative Mobile Studio concept was used to increase the amount of student-centered learning and document its impact on student outcomes. The authors begin with an overview of theories that inspired the...

No abstract

An estimation method known as least absolute shrinkage and selection operator (LASSO) or ℓ1-regularized LS estimation has been found to perform well in a number of applications. In this paper, we use the majorize-minimize method to develop an algorithm for minimizing the LASSO objective function, which is the sum of a linear LS objective function plus an ℓ1 penalty term. The proposed algorithm, which we call the LASSO estimation via majorization-minimization (LMM) algorithm, is straightforward to implement, parallelizable, and guaranteed to produce LASSO objective function values that monotonically decrease. In addition, we formulate an extension of the LMM algorithm for reconstructing ground penetrating radar (GPR) images, that is much faster than the standard LMM algorithm and utilizes significantly less memory. Thus, the GPR specific LMM (GPR-LMM) algorithm is able to accommodate the big data associated with GPR imaging. We compare our proposed algorithms to the state-of-the-art ℓ1-regularized LS algorithms using a time and space complexity analysis. The GPR-LMM greatly outperforms the competing algorithms in terms of the performance metrics we considered. In addition, the reconstruction results of the standard LMM and GPR-LMM algorithms are evaluated using both simulated and real GPR data.

No abstract