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Office Hours, Demographic Groups and COVID

Office Hours, Demographic Groups and COVID

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2023 ASEE Annual Conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Huang, Heidi Yan, Kevin DeOrio, Andrew Department of Electrical Engineering and Computer Science University of Michigan United States

The COVID-19 pandemic has had a significant impact on student life and academic performance. Moreover, different demographic groups have experienced the pandemic very differently. An important factor in the student learning process and therefore academic performance is student help seeking behaviors in office hours. In this paper, we examine the ways in which the pandemic has affected student use of office hours. We examined office hours usage trends with data from three large computing courses. We augmented this dataset with demographic information from university records. Each entry in our dataset contains information about the time and date of an office hours encounter, the instructor, the student, and the student's gender and underrepresented minority (URM) status. In total, our dataset comprised 33,136 unique encounters. Our research question examines whether the onset of the pandemic affected the way that students use office hours and if these patterns varied with demographics. Our results suggest the impact of the pandemic on office hours usage patterns was small. We were surprised to find that the factor with the largest magnitude was not the onset of the pandemic, but rather a student's gender. Men averaged 31.49% fewer office hours encounters compared to women, regardless of pandemic onset. Additionally, we found that the relationship between underrepresented-minority status and office hours encounters was not statistically significant. © American Society for engineering Education, 2023.

关键词： Students

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Optical DACs for Ultra-High-Speed Green Photonic Interconnects 23

Optical DACs for Ultra-High-Speed Green Photonic Interconnec...

引用

23rd International Conference on Transparent Optical Networks, ICTON 2023

作者： Nazarathy, Moshe Tomkos, Ioannis Technion Israel Institute of Technology Faculty of Electrical & Computer Engineering Haifa3200 Israel University of Patras Electrical and Computer Engineering Department Patras26504 Greece

ISBN: (纸本)9798350303032

We review the principles of operation and latest conceptual advances in optical DACs for next-gen Ultra-High-Speed (UHS) photonic interconnects. Optical DACs 'done right' enable UHS optical transmitters at high-resolution, high photonic-efficiency and high energy-efficiency. An oDAC is defined as a Transmit-Optical-Sub-Assemby (TOSA) unit generating a constellation of C=2B levels, comprising a collection of B modulators with static or slowly-adjustable 'glue optics';the admissible drives of the B modulators are specified as either two-level (NRZ, OOK) or 4-level (unipolar or bipolar PAM4);neither higher-order electrical driving-DACs, nor power-hungry digital encoders at baudrate are *** model and compare serial (segmented-MZM) vs. multi-parallel oDAC architectures and their win-win combination. We present analytical models of serial and multi-parallel oDACs based on the code matrix concept. We introduce suitable metrics for oDACs characterisation. A 'perfect' oDAC features a max-full-scale (inherently optical-loss-free) uniform constellation (having equispaced levels), minimizing BER. We prove that for coherent (COH) detection links it is possible to design 'perfect' oDACs in the optical-field domain, but for a direct-detection link there exists no C>4 'perfect' optical-power domain oDAC. We show how oDACs are assembled as building blocks in complete IQXY optical transmitters for coherent transmission links, at unprecedented performance, energy-efficiencies, reconfigurability. © 2023 IEEE.

关键词： Quadrature amplitude modulation

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Reflectance-based Code Optimization for Motion Deblurring

Reflectance-based Code Optimization for Motion Deblurring

引用

2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Lee, Jaelin Jeon, Byeungwoo Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350320213

We propose a method for determining a code set based on the reflectance of the scene in the coded exposure photography technique using multiple color LEDs with same power. Coded exposure imaging affects image brightness, which affects motion deblurring performance. Thus, the performance of the motion deblurring depends on the duty cycle of the code. Previously, since the image was modulated with a single code, the duty cycle of the code considered only the overall brightness of the scene. The coded imaging with multiple color LEDs can consider the brightness of each color channel, allowing the code set to be further optimized based on the reflectance of each color in the scene. We demonstrated the effectiveness of our method by comparing it with conventional methods and confirmed that the average PSNR increases by 2.2dB. © 2023 IEEE.

关键词： code optimization coded exposure photography motion deblurring Poisson noise

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NERF-SOS: ANY-VIEW SELF-SUPERVISED OBJECT SEGMENTATION ON COMPLEX SCENES 11

NERF-SOS: ANY-VIEW SELF-SUPERVISED OBJECT SEGMENTATION ON CO...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Fan, Zhiwen Wang, Peihao Jiang, Yifan Gong, Xinyu Xu, Dejia Wang, Zhangyang Department of Electrical and Computer Engineering University of Texas Austin United States

Neural volumetric representations have shown the potential that Multi-layer Perceptrons (MLPs) can be optimized with multi-view calibrated images to represent scene geometry and appearance without explicit 3D supervision. Object segmentation can enrich many downstream applications based on the learned radiance field. However, introducing hand-crafted segmentation to define regions of interest in a complex real-world scene is non-trivial and expensive as it acquires per view annotation. This paper carries out the exploration of self-supervised learning for object segmentation using NeRF for complex real-world scenes. Our framework, called NeRF with Self-supervised Object Segmentation (NeRF-SOS), couples object segmentation and neural radiance field to segment objects in any view within a scene. By proposing a novel collaborative contrastive loss in both appearance and geometry levels, NeRF-SOS encourages NeRF models to distill compact geometry-aware segmentation clusters from their density fields and the self-supervised pre-trained 2D visual features. The self-supervised object segmentation framework can be applied to various NeRF models that both lead to photo-realistic rendering results and convincing segmentation maps for both indoor and outdoor scenarios. Extensive results on the LLFF, BlendedMVS, CO3Dv2, and Tank & Temples datasets validate the effectiveness of NeRF-SOS. It consistently surpasses 2D-based self-supervised baselines and predicts finer object masks than existing supervised counterparts. Code is available at: https://***/VITA-Group/NeRF-SOS. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Geometry

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A DC-Link Ripple Current Reduction Method Based on Variable Switching Sequences for Three-Level NPC Inverters 6

A DC-Link Ripple Current Reduction Method Based on Variable ...

引用

6th IEEE Conference on Energy Conversion, CENCON 2023

作者： Bae, Yun-Jae Lee, Kyo-Beum Ajou University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350325096

This paper presents a DC-link ripple current reduction method based on variable switching sequences for three-level neutral-point-clamped (NPC) inverters. Capacitors are used to maintain the stable DC-link voltage and current in the DC-link of a three-level NPC inverter. In addition, an allowable ripple current is a key factor affecting capacitor design. The ripple current in the DC-link increases the capacitor temperature and reduces lifespan, which can lead to decreased reliability of power conversion systems. The proposed method divides the voltage modulation region into two areas. An optimized switching scheme generates a new switching sequence by selecting the voltage vector that minimizes the DC-link current based on the voltage modulation region. The effectiveness of the proposed method is verified with simulation results. © 2023 IEEE.

关键词： Vector spaces

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Lens-Less Acoustic Tweezers Based on Spiral-Arm Vortex-Beam Transducers Capable of Levitating, Trapping, and Manipulating Large and Heavy Particles 22

Lens-Less Acoustic Tweezers Based on Spiral-Arm Vortex-Beam ...

引用

22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Lee, Jaehoon Esfahani, Kianoush Sadeghian Barekatain, Matin Kim, Eun S. University Of Southern California Department Of Electrical And Computer Engineering United States

ISBN: (纸本)9784886864352

This paper presents the lens-less acoustic tweezers (18 × 18 × 1 mm3 in size) based on a spiral-arm vortex-beam acoustic transducer (SVAT) capable of trapping large and heavy particles. Moreover, the tweezers is shown to levitate a particle (with a levitation force of up to 21.3μN) from the tweezers' surface and trap the particle at a distance determined by the voltage applied to the tweezers. By changing the amplitude of the applied sinusoidal voltage, the tweezers is shown to selectively trap particles of various sizes and densities as well as to change the z-position of the trapped particle. Also, the trapped particle can be (1) manipulated in a 3-dimensional (3D) space by moving the tweezers and (2) pushed to the liquid-air interface by increasing the applied voltage. © 2023 IEEJ.

关键词： Acoustic trapping Acoustic tweezers Logarithmic spiral Spiral arms vortex bean transducer Vortex beam transducer

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Analysis of correlations between electric arc furnace model coefficients

Analysis of correlations between electric arc furnace model ...

引用

2023 IEEE International Conference on Environment and electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Klimas, Maciej Grabowski, Dariusz Silesian University of Technology Department of Electrical and Computer Engineering Gliwice Poland

ISBN: (纸本)9798350347432

Electric arc furnaces (EAFs) are the largest loads in power systems and are characterized by a significant negative influence on power quality. The modeling of EAFs provides the knowledge necessary for the proper design of power systems and power quality improvement systems. This paper introduces a new EAF model based on the Random Differential Equation resulting from an extension of the power balance arc model with stochastic processes. The proposed solution considers correlations between the coefficients of the equation, resulting in improved accuracy and simplification of the model. The developed solution is based on real measurement data originating from the melting stage of a large industrial EAF. © 2023 IEEE.

关键词： Power quality

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Robust RF Data Normalization for Deep Learning

Robust RF Data Normalization for Deep Learning

引用

2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Sharifzadeh, Mostafa Benali, Habib Rivaz, Hassan Concordia University Department of Electrical and Computer Engineering MontrealQC Canada

ISBN: (纸本)9798350346459

Radio frequency (RF) data contain richer information compared to other data types, such as envelope or B-mode, and employing RF data for training deep neural networks has attracted growing interest in ultrasound image processing. However, RF data is highly fluctuating and additionally has a high dynamic range. Most previous studies in the literature have relied on conventional data normalization, which has been adopted within the computer vision community. We demonstrate the inadequacy of those techniques for normalizing RF data and propose that individual standardization of each image substantially enhances the performance of deep neural networks by utilizing the data more efficiently. We compare conventional and proposed normalizations in a phase aberration correction task and illustrate how the former enhances the generality of trained models. © 2023 IEEE.

关键词： Deep neural networks

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A Deep Learning Framework for Denoising MRI Images using Autoencoders 5

A Deep Learning Framework for Denoising MRI Images using Aut...

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5th International Conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Zein, Mohammad El Laz, Wissam El Laza, Malak Wazzan, Taha Kaakour, Ibrahim Adla, Yasmine Abu Baalbaki, Jad Diab, Mohammad O. Sabbah, Maher Zantout, Rached Rafik Hariri University Department of Electrical and Computer Engineering Mechref Lebanon

ISBN: (纸本)9798350338492

Magnetic Resonance Imaging (MRI) has become an indispensable tool in the medical field for diagnosing and monitoring various diseases and conditions. However, the quality of MRI images can be degraded by noise, which can lead to inaccurate interpretations and diagnoses. In recent years, machine learning techniques have shown great potential in enhancing the accuracy of image denoising, especially in the medical domain. In this study, we propose a novel deep learning model based on autoencoders for denoising MRI images. We employed various preprocessing techniques such as data augmentation, resizing, normalization, and conventional denoising methods on our MRI images. Our model comprises a convolutional neural network autoencoder (CCNAE), which we fine-tuned by testing different parameters and layers to achieve optimal performance. Our results demonstrate a validation loss of approximately 0.0001, indicating a substantial improvement in denoising performance. Our work represents an important step towards developing an efficient and effective method for denoising MRI images without compromising critical data or time. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Usability of Pre-trained Diffusion Models in Generating Novel Datasets and Its Performance Evaluation 23

Usability of Pre-trained Diffusion Models in Generating Nove...

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7th International Conference on Innovation in Artificial Intelligence, ICIAI 2023

作者： Hossain, Md. Yearat Rakib, Md. Mahbub Hasan Nijhum, Ifran Rahman Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (纸本)9781450398398

Even though sophisticated deep learning methods are getting better and better day by day, still they rely on a large number of datasets. But it is not always possible to acquire large datasets for all kinds of problems. Though diffusion models are now popular for their creative applications, it is already proven that they can generate better realistic-looking synthetic images compared to Generative Adversarial Networks (GAN). GANs are a popular option for image synthesis that helps the data sampling process for datasets that have low amounts of data or imbalanced data. In our work, we have experimented with a pre-trained text-to-image generation diffusion model for generating datasets for two different classes of problems. These problems are two common problems that can get benefitted from deep learning-based solutions but the lack of datasets hampers the process. We used the diffusion model to generate synthetic images and used those images as the training and validation data for the problems we tried to solve. Then we tested the models with manually collected real-world data and demonstrated the performance of such a method comparatively. From our experiments, we found that the diffusion model can generate realistic images and is up to 50 times faster in data generation compared to the manual human process. Also, in our testing, we found that the Convolutional Neural Networks trained with these synthetic data can achieve up to 80% and 89% accuracy scores. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Large dataset

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