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IEEE Annual Conference on Wireless and Microwave Technology (WAMICON)

作者： Jiachen Guo Pingzhu Gong Kenle Chen Department of Electrical and Computer Engineering (ECE) University of Central Florida (UCF) Orlando U.S. Department of ECE UCF Orlando U.S.

ISBN: (数字)9798331509606

ISBN: (纸本)9798331509613

This paper presents the first-ever wideband loadmodulated double balanced amplifier (LMDBA) with an intrinsic isolation to load. Building upon the foundational concept of the generic load modulated balanced amplifier (LMBA), by setting the control amplifier (CA) to balanced topology, the LMDBA can inherit the load-mismatch tolerance characteristic of balanced amplifiers, eliminating the need for a magnetic circulator. To demonstrate the principle, a wideband RF-input LMDBA is developed using GaN devices and commercial branchline quadrature hybrid couplers from 1.9-2.7 GHz. The experimental results exhibit an efficiency of 54-75 % at peak power and 45-60 % at 10-dB output back-off (OBO) under the matched condition of load across the band. An efficiency up to 70.2 % at peak power and up to 54.2 % at 10-dB OBO are measured under 2:1 VSWR of load. Additionally, the PA's linearity profiles (AMAM and AMPM) can be well maintained against load mismatch compared to matched condition without reconfiguration. In modulated measurement, the efficiency and linearity performance can well maintained under various load as well.

关键词： Wireless communication Power measurement Linearity Modulation Prototypes Power amplifiers Couplers Topology Transistors Wideband

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Technoeconomic Analysis of Photovoltaic Systems for Promoting Clean and Affordable Energy in Vulnerable Communities

Technoeconomic Analysis of Photovoltaic Systems for Promotin...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Elizabeth A. Trader Alejandro Aparcedo Thomas Wahl Kelly A. Stevens Christopher T. Emrich Zhihua Qu Bo Tu Siddiqur Rahman Herbert Longenecker Kristopher O. Davis Mengjie Li Department of Electrical and Computer Engineering University of Central Florida (UCF) Orlando FL USA Resilient Intelligent and Sustainable Energy Systems (RISES) Cluster UCF Orlando FL USA Florida Solar Energy Center (FSEC) UCF Cocoa FL USA Department of Civil Environmental and Construction Engineering UCF Orlando FL USA National Center for Integrated Coastal Research UCF Orlando FL USA School of Public Administration UCF Orlando FL USA Department of Materials Science and Engineering UCF Orlando FL USA

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

Extreme weather events resulting from climate change can have diverse impacts on the energy sector. Factors such as extreme heat, cold spells, strong winds, or flooding can lead to heightened energy demand, decreased energy production, and infrastructure failures, causing power outages. Underserved communities, with lower infrastructure investments, are disproportionately affected by these outages. To tackle the social and technical challenges arising from these phenomena, we propose a novel transdisciplinary framework. This framework integrates perspectives from engineering, economics, climatology, and policy studies to explore solutions for delivering clean, affordable, and resilient energy systems to vulnerable communities. Our approach focuses on expanding the use of solar energy production coupled with increased storage capacities in areas where power outages and social vulnerability intersect. This paper focuses on the techno-economic component of the framework and describes the process of determining which technological solutions are economically feasible for a given area.

关键词： Social factors Climate change Photovoltaic systems Solar power generation Power system reliability Economics Technology management Energy management Government policies

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UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning

arXiv

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arXiv 2022年

作者： Karim, Md. Nazmul Rizve, Mamshad Nayeem Rahnavard, Nazanin Mian, Ajmal Shah, Mubarak Department of Electrical and Computer Engineering UCF United States Center for Research in Computer Vision UCF United States Department of Computer Science and Software Engineering UWA Australia

Supervised deep learning methods require a large repository of annotated data;hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization performance of deep neural networks. To combat label noise, recent state-of-the-art methods employ some sort of sample selection mechanism to select a possibly clean subset of data. Next, an off-the-shelf semi-supervised learning method is used for training where rejected samples are treated as unlabeled data. Our comprehensive analysis shows that current selection methods disproportionately select samples from easy (fast learnable) classes while rejecting those from relatively harder ones. This creates class imbalance in the selected clean set and in turn, deteriorates performance under high label noise. In this work, we propose UNICON, a simple yet effective sample selection method which is robust to high label noise. To address the disproportionate selection of easy and hard samples, we introduce a Jensen-Shannon divergence based uniform selection mechanism which does not require any probabilistic modeling and hyperparameter tuning. We complement our selection method with contrastive learning to further combat the memorization of noisy labels. Extensive experimentation on multiple benchmark datasets demonstrates the effectiveness of UNICON;we obtain an 11.4% improvement over the current state-of-the-art on CIFAR100 dataset with a 90% noise rate. Our code is publicly available. © 2022, CC BY.

关键词： Deep neural networks

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When teaching breaks down: Teachers rationally select what information to share, but misrepresent learners' hypothesis spaces 40

When teaching breaks down: Teachers rationally select what i...

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40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018

作者： Aboody, Rosie Velez-Ginorio, Joey Santos, Laurie R. Jara-Ettinger, Julian Department of Psychology Yale University New HavenCT06520 United States Department of Electrical Engineering UCF OrlandoFL32816 United States Department of Computer Science Yale University New HavenCT06520 United States

ISBN: (纸本)9780991196784

Although we possess intuitions about pedagogy from early in life, adults commonly fail to teach effectively in real-world situations. Why might adults struggle in more complex teaching tasks? Here we develop a simple teaching task where adults fail to teach naïve learners, despite reporting high confidence that they taught effectively. Using a formal model of a rational teacher, we analyze the sources of our adult teachers' failures. Our model-based analyses reveal that teachers successfully provided high-quality examples, but failed to address hypotheses that naïve learners find plausible. We validate these results in a second experiment, where we find that constraining learners' hypothesis space increases their performance in the task. Our findings help bridge the gap between children's teaching proficiency in constrained tasks, and adults' teaching failures in more naturalistic tasks. © 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.

关键词： Computation theory

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Broadband Substrate Integrated Waveguide Slotted Array Antenna at mm-Wave Bands

Broadband Substrate Integrated Waveguide Slotted Array Anten...

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IEEE MTT-S International Microwave Workshop Series on 5G Hardware and System Technologies (IMWS-5G)

作者： Asim Ghalib Mohammad S. Sharawi Hussein Attia Raj Mittra Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Electrical and Computer Engineering Department University of Central Florida (UCF) Orlando Florida USA

ISBN: (纸本)9781538611982

In this paper, a novel substrate integrated waveguide (SIW) based on a longitudinal slot array is designed with the help of a Genetic Algorithm (GA). The GA is used to find the optimum location of the slots on the surface of SIW. The antenna is radiating towards broad side with a side lobe level (SLL) of at least 10 dB. The antenna is optimized to provide a 3 GHz of BW ranging from 27 GHz to 30 GHz covering future 5G bands. The maximum gain of the antenna is 9.18 dB with a size of 62×7.23 mm 3 .

关键词： Antenna radiation patterns Antenna arrays Slot antennas Substrates Genetic algorithms Broadband antennas

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Implementation of Feeding Task via Learning from Demonstration

Implementation of Feeding Task via Learning from Demonstrati...

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IEEE International Conference on Robotic Computing (IRC)

作者： Nabil Ettehadi Aman Behal Nabil Ettehadi is with the Department of Electrical and Computer Engineering University of Central Florida (UCF) Orlando FL USA the ECE Dept. and the NanoScience Technology Center University of Central Florida (UCF) Orlando FL USA

In this paper, a Learning From Demonstration (LFD) approach is used to design an autonomous meal-assistant agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of Gaussian Mixture Model (GMM) are learned using Gaussian Mixture Regression (GMR) and Expectation Maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot's end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.

关键词： Task analysis Trajectory Robot kinematics Probabilistic logic Hidden Markov models Optimization

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Interpreting encoding and decoding models

arXiv

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arXiv 2018年

作者： Kriegeskorte, Nikolaus Douglas, Pamela K. Department of Psychology Department of Neuroscience Department of Electrical Engineering Zuckerman Mind Brain Behavior Institute Columbia University New YorkNY United States Center for Cognitive Neuroscience University of California Los Angeles CAModeling Simulation Computer Science UCF United States

Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their results requires care. Decoding models can help reveal whether particular information is present in a brain region in a format the decoder can exploit. Encoding models make comprehensive predictions about representational spaces. In the context of sensory experiments, where stimuli are experimentally controlled, encoding models enable us to test and compare brain-computational theories. Encoding and decoding models typically include fitted linear-model components. Sometimes the weights of the fitted linear combinations are interpreted as reflecting, in an encoding model, the contribution of different sensory features to the representation or, in a decoding model, the contribution of different measured brain responses to a decoded feature. Such interpretations can be problematic when the predictor variables or their noise components are correlated and when priors (or penalties) are used to regularize the fit. Encoding and decoding models are evaluated in terms of their generalization performance. The correct interpretation depends on the level of generalization a model achieves (e.g. to new response measurements for the same stimuli, to new stimuli from the same population, or to stimuli from a different population). Significant decoding or encoding performance of a single model (at whatever level of generality) does not provide strong constraints for theory. Many models must be tested and inferentially compared for analyses to drive theoretical progress. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain

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66-3: Submillisecond-Response 10-Megapixel 4K2K LCoS for Microdisplay and Spatial Light Modulator

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SID Symposium Digest of Technical Papers 2019年第1期50卷

作者： Jhou-Pu Yang Huang-Ming Philip Chen Yuge Huang Yao-Chung Chang Fan-Wei Lai Shin-Tson Wu Cynthia Hsu Richard Tsai Ron Hsu Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University (NCTU) Hsinchu Taiwan 30010 ROC College of Optics and Photonics University of Central Florida (UCF) Orlando FL 32816 USA Jasper Display Corporation (JDC) 7F-16 81 Shui-Li Rd Hsinchu Taiwan 30059 ROC

The NCTU PCU-3-02 10-megapixel 4K2K (max. 4160x2464) LCoS panels with LCM-1107 nematic LC were assembled and evaluated. The results of both 1.2-inch 4K2K (~4,000 PPI) amplitude type LCoS microdisplay and 0.7-inch 4K2K (~7,000 PPI) phase-only type LCoS spatial light modulator (SLM) can achieve 1~2 ms response time at T=45 °C. Meanwhile, the phase linearity, precision and accuracy of 4K2K LCoS-SLM were assessed in this work. These new LCoS schemes will potentially be utilized in color-sequential, holographic, virtual and augmented reality display.

关键词： Liquid-crystal on silicon 10-megapixel 4K2K panel Spatial light modulators Digital holography

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Influencing sense of community in a STEM living-learning community

Influencing sense of community in a STEM living-learning com...

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2010 ASEE Annual Conference and Exposition

作者： Dagley-Falls, Melissa Georgiopoulos, Michael Young, Cynthia UCF College of Engineering and Computer Science University of Central Florida United States UCF School of Electrical Engineering and Computer Science University of Central Florida United States Department of Mathematics UCF College of Sciences University of Central Florida United States

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Excel in mathematics: Applications of calculus

Excel in mathematics: Applications of calculus

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2010 ASEE Annual Conference and Exposition

作者： Young, Cynthia Georgiopoulos, Michael Crouse, Tace Geiger, Cherie Islas, Alvaro Hagen, Scott Dagley-Falls, Melissa Ramsey, Patricia Lancey, Patrice Department of Mathematics College of Sciences UCF United States School of Electrical Engineering and Computer Science UCF United States Machine Learning Laboratory UCF United States Faculty Center for Teaching and Learning University of Central Florida United States Department of Chemistry College of Sciences UCF United States Civil Environmental and Construction Department UCF United States Coastal Hydroscience Analysis Modeling and Predictive Simulations Laboratory UCF United States Academic Affairs College of Engineering and Computer Science UCF United States Institutional Research University of Central Florida United States Department of Operational Excellence and Assessment Support University of Central Florida United States

Nationally only 40% of the incoming freshmen STEM majors are successful in earning a STEM degree [1]. The University of Central Florida (ucf) EXCEL program is an NSF funded STEP (Science, Technology, engineering and Mathematics Talent Expansion Program) whose goal is to increase the number of ucf STEM graduates. One of the activities that EXCEL has identified as essential in retaining students in science and engineering disciplines is the development and teaching of special courses at the freshman level, called Applications of Calculus I and Applications of Calculus II, or Apps I and Apps II, respectively. In Apps I and II, science and engineering professors are asked to lecture twice, as guest lectures, and to demonstrate to students where calculus topics appear in upper level science and engineering classes as well as where faculty use calculus in their research programs. This paper outlines the process used in producing the educational materials for Apps I and II courses (textbook, presentations slides, in-class homework assignments, demos) and it also discusses the assessment results pertaining to this specific EXCEL activity. © American Society for engineering Education, 2010.

关键词： Calculations

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