This short review does not permit a detailed description of the work being carried out but it may indicate that in writing programs of this kind, students learn how to overcome some of the difficulties faced by design...
This paper presents an analysis of computer networks courses offered by universities and colleges in the departments of computerscience, electricalengineering, or information science. The results are based on the in...
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This paper presents an analysis of computer networks courses offered by universities and colleges in the departments of computerscience, electricalengineering, or information science. The results are based on the information collected from course web sites from twenty-seven universities and colleges in computerscience, electricalengineering and information science departments, primarily within the United States. The data analyzed include the course titles, course structure, textbooks used, major topics and how they are covered, projects, and laboratory exercises, if any. We found that the courses can be divided into three categories: those that cover the general topics of computer networks using some practical examples, those that specifically discuss Internet protocols, and those that work through a set of programming projects after students have had a previous network course.
engineering educators have always been concerned with teaching good methodology so that students learn how to efficiently approach problems in design, analysis, and manufacturing. But the definition of "good meth...
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engineering educators have always been concerned with teaching good methodology so that students learn how to efficiently approach problems in design, analysis, and manufacturing. But the definition of "good methodology" has changed with the advent in engineering of computer-aided design (CAD). Curriculums need to be updated so that students learn methods appropriate for the environments in which they will work. Though interest in incorporating computer aids into courses is high, a number of challenges are encountered in implementing such changes. Teaching CAD tools takes time;what can be left out of courses to make room? Required computing resources are costly to acquire and to maintain. Faculty members, especially those having less computer sophistication, are sometimes hesitant to take on the additional burden of making their classes dependent on CAD. Several years ago, the circuits and computer hardware courses at the University of Michigan's Department of electricalengineering and computerscience were restructured. A uniform set of electronic design automation (EDA) tools was incorporated, beginning early in the undergraduate curriculum. The ability to teach good engineering in the Department has been significantly strengthened by these changes. This article describes the program and the details which have made it successful. They include: using a consistent set of well-supported commercial tools throughout the curriculum;providing adequate computing resources through a tuition surcharge for engineering students;and offering department-wide support through CAD short courses and consulting hours.
This paper describes an interdisciplinary project for a freshman course designed for electricalengineering, computerengineering, and computerscience majors. The project uses LEGO building sets and a microcontroller...
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This paper describes an interdisciplinary project for a freshman course designed for electricalengineering, computerengineering, and computerscience majors. The project uses LEGO building sets and a microcontroller in the design, implementation, and documentation of a sequence of increasingly complex tasks. Students learn interdisciplinary team skills, and are introduced to computer hardware concepts and the C programming language. Project tasks, costs, and suggested equipment are discussed in detail.
In the United States, more than 10% of traditional electricalengineering programs have combined with computerscience into a single department \Historically computerscience programs emerged from mathematics or elect...
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Research has demonstrated the positive influence of Undergraduate Research Experience (URE) programs in science, Technology, engineering, and Mathematics (STEM) on students' educational journey and their developme...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inher...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memris...
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Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.
This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a mo...
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This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a modelbased reinforcement learning(MBRL)algorithm is combined with Lyapunov approach,which determines the safe region of states and *** obtain near optimal control policy,the control performance is safely improved by approximate dynamic programming(ADP)using data sampled from the region of attraction(ROA).Moreover,to enhance the control robustness against parameter uncertainty in the inverter,a Gaussian process(GP)model is adopted by the proposed algorithm to effectively learn system dynamics from *** simulations validate the effectiveness of the proposed algorithm.
Numbers of students with disabilities are growing recently around the whole Europe. Young people that are confronted with those special challenges are more and more interested in education at Higher Education Institut...
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Numbers of students with disabilities are growing recently around the whole Europe. Young people that are confronted with those special challenges are more and more interested in education at Higher Education Institutions, even more they are more and more interesting also in mobility what is a new motivation and challenge for them, their families and institutions. Different Higher Education Institutions have different support systems, different organizational rules and approaches to the teaching in the case of students with disabilities, but some common points (models) exists. Mostly all this rules have a background in the European and national legislation, but practical solutions (details) are different from the institution to the institution. In our paper we will present procedures from the University of Maribor - Faculty of electricalengineering and computerscience, as well as some available data.
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