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TechRxiv 2023年

作者： Xiao, Yi Deng, Shan Zhao, Zijian Faris, Zubair Xu, Yixin Huang, Tzu-Jung Narayanan, Vijaykrishnan Ni, Kai Microsystems Engineering Ph.D. program Rochester Institute of Technology RochesterNY14623 United States Department of Computer Science and Engineering Penn State University University ParkPA16802 United States Department of Electrical and Microelectronic Engineering Rochester Institute of Technology RochesterNY14623 United States

In this work, we propose a 2TnC ferroelectric random access memory (FeRAM) cell design to realize the quasi-nondestructive readout (QNRO) of ferroelectric polarization (PFE) in a capacitor, which can relax the endurance requirement of the ferroelectric thin film and exploits the benefits of both FeRAM and ferroelectric FET (FeFET). We demonstrate that: i) QNRO sensing of PFE is conducted successfully in experiment with a ON/OFF ratio (ION/IOFF)>103, ION>10 µA, and read endurance>106 cycles, which can relax the FeRAM endurance requirement by 106x;ii) optimization of the cell performance can be realized by tuning the metal-ferroelectric-metal capacitor (MFM) capacitor to read transistor area ratio and read transistor threshold voltage (VTH);iii) the proposed cell structure is 3D-compatible, enabling integration of highly dense memory solution;iv) the proposed cell structure also enables compute-in-memory (CIM) applications of FeRAM, which has not been widely explored. With this technology, storage and memory-centric computing can be enabled. © 2023, CC BY.

关键词： Cytology

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EllipsoNet: Deep-learning-enabled optical ellipsometry for complex thin films

arXiv

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arXiv 2022年

作者： Wang, Ziyang Lin, Yuxuan Cosmi Zhang, Kunyan Wu, Wenjing Huang, Shengxi Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Electrical Engineering The Pennsylvania State University University ParkPA16802 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States

Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational ellipsometry approach based on a conventional tabletop optical microscope and a deep learning model called EllipsoNet. Without any prior knowledge about the multilayer substrates, EllipsoNet can predict the complex refractive indices of thin films on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers-Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials within complex photonic structures or optoelectronic devices. © 2022, CC BY.

关键词： Refractive index

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A Novel Resource Allocation in Software-Defined Networks for IoT Application

A Novel Resource Allocation in Software-Defined Networks for...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Alexandre Ladeira de Sousa Ogobuchi Daniel OKey Renata Lopes Rosa Muhammad Saadi Demostenes Zegarra Rodriguez Department of Computer Science Federal University of Lavras Lavras Minas Gerais Brazil Graduate Program in Information Engineering Federal University of ABC Santo Andre Sao Paulo Brazil Department of Electrical Enginnering University of Central Punjab Punjab Pakistan Computer Science Department Federal University of Lavras Lavras Minas Gerais Brazil

The rapid proliferation of Internet of Things (IoT) devices has posed significant challenges for network resource allocation and management. In this paper, we propose a novel methodology for efficient resource allocation in Software-Defined Networks (SDNs) to meet the unique requirements of IoT applications. Our approach leverages the flexibility and programmability of SDN to dynamically allocate network resources based on the specific needs of IoT devices. Our proposal combines reinforcement learning with dynamic resource allocation, demonstrating its effectiveness in optimizing resource allocation and enhancing network performance. The results highlight the potential of our approach to addressing the resource challenges in SDN-based IoT environments. The proposed method obtains results superior to 30% of throughput in comparison to a traditional resource allocation in SDN to IoT applications.

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Unified Approach to Video-Based AI Inference Tasks in Augmented Reality Systems Assisted by Mobile Edge Computing

Unified Approach to Video-Based AI Inference Tasks in Augmen...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

In the rapidly evolving field of Augmented Reality (AR), delivering real-time, immersive experiences places a significant demand on computational resources, particularly in the context of video-based Artificial Intelligence (AI) inference tasks. Some solutions explorate optimize the tasks using the potential of Mobile Edge Computing (MEC), in which MEC brings the advantages of reduced latency and network congestion, which are critical for AR systems. In this work, we designed a novel algorithm named AR-MEC Optimization for the distribution of AI inference tasks among edge servers while considering several factors including task complexity, server capacity, and network conditions. Our approach not only ensures low-latency responses but also improves overall system efficiency and the user’s Quality of Experience (QoE). Experimental results demonstrate that our unified approach significantly outperforms traditional cloud-based methods in terms of response time and computational efficiency, thereby highlighting the potential of MEC-assisted AR systems for real-world applications.

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Growing Our PEARLS and ASSETS Through a Support Ecosystem for Low-Income Academically Talented Students

Growing Our PEARLS and ASSETS Through a Support Ecosystem fo...

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Frontiers in Education (FIE) Conference

作者： Carla Lopez del Puerto Manuel Jimenez Monica Alfaro Carmen Bellido Matias J. Cafaro Civil Engineering Department Univ. of Puerto Rico Mayaguez Mayaguez PR Electrical & Computer Eng. Department Univ. of Puerto Rico Mayaguez Mayaguez PR Biology Department Univ. of Puerto Rico Mayaguez Mayaguez PR Teacher Preparation Program Univ. of Puerto Rico Mayaguez Mayaguez PR

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research to practice paper describes two NSF-funded projects: The “program for engineering Access, Retention, and LIATS Success“ (PEARLS) and the “Academic and Socioemotional Support Ecosystem for Talented low-income STEM Students“ (ASSETS). These initiatives aim to support low-income, academically talented students pursuing STEM degrees, offering academic, socio-emotional, and financial assistance. Both projects provide mentoring, workshops, mental health services, and scholarships. Outcomes have shown increased engagement, retention, and graduation rates among participants compared to non-participants. Retention and persistence rates among PEARLS participants reached 97% and 96.3% respectively, showcasing the model effectiveness in keeping students focused on their studies. Graduation rates saw remarkable improvements, with on-time graduation rates soaring to 4.17 times that of non-participating students. The paper presents a robust framework for an integrated support ecosystem, emphasizing scalability and replicability across diverse institutions. By addressing the challenges faced by underprivileged students during environmental crises, this framework aims to foster equity and inclusion in higher education, ensuring all students have the support they need to succeed.

关键词： engineering profession Scalability Scholarships Conferences Ecosystems Education Mental health Mentoring Replicability STEM

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Carrier confinement and alloy disorder exacerbate Auger-Meitner recombination in AlGaN ultraviolet light-emitting diodes

arXiv

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arXiv 2024年

作者： Pant, Nick Bushick, Kyle McAllister, Andrew Lee, Woncheol Van de Walle, Chris G. Kioupakis, Emmanouil Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Materials Science & Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering & Computer Science University of Michigan Ann ArborMI48109 United States Materials Department University of California Santa BarbaraCA93106 United States

The quantum efficiency of AlGaN ultraviolet light-emitting diodes (LEDs) declines (droops) at increasing operating powers due to Auger-Meitner recombination (AMR). Using first-principles density-functional theory, we show that indirect AMR mediated by electron-phonon coupling and alloy disorder can induce bulk C coefficients as large as ∼10-31 cm6/s. Furthermore, we find that the confinement of carriers by polarization fields within quantum wells severely relaxes crystal-momentum conservation, which exacerbates the rate of AMR over radiative recombination by an order of magnitude relative to the bulk. This results in a striking decrease in quantum efficiency at high power. Suppressing polarization fields and jointly increasing the well width would greatly mitigate AMR and efficiency droop. Copyright © 2024, The Authors. All rights reserved.

关键词： Aluminum gallium nitride

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Fast non-iterative algorithm for 3D point-cloud holography

arXiv

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arXiv 2023年

作者： Ersaro, Nathan Tessema Yalcin, Cem Murray, Liz Kabuli, Leyla Waller, Laura Muller, Rikky Graduate Program in Bioengineering University of California Berkeley University of California San Francisco BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States

Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm's relative computation speed advantage was found to increase with SLM pixel count, reaching >100,000x at 512x512 array format. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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DYNAMIC PROBABILISTIC RISK ASSESSMENT FOR ELECTRIC GRID CYBERSECURITY RISK ASSESSMENT

DYNAMIC PROBABILISTIC RISK ASSESSMENT FOR ELECTRIC GRID CYBE...

引用

2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2021

作者： Diao, Xiaoxu Zhao, Yunfei Smidts, Carol Lei, Hangtian Johnson, Brian K. Nuclear Engineering Program Department of Mechanical and Aerospace Engineering Ohio State University 201 W 19th Ave ColumbusOH43210 United States Department of Electrical and Computer Engineering University of Idaho 875 Perimeter Drive MS 1023 MoscowID83844 United States

Electric grids have been evolving with fast adoption of advanced sensing, communication, and computation technologies. While improving the efficiency and safety of electric grids, these technologies may introduce unanticipated vulnerabilities into the systems and increase the opportunities of cyberattacks to cause physical damage and economic losses. For such critical infrastructure as electric grids, it is important to understand their cybersecurity risk posture. In this research, we propose a modularized dynamic probabilistic risk assessment framework for electric grid cybersecurity risk assessment. The framework comprises a physics model for simulating electric power system dynamics, a state estimator for observing system state, a protection system module for controlling circuit breakers, a failure module for considering natural component failures, an attacker module for simulating attacker actions, and an operator module for modeling operator response to abnormal events. The attacker module can be configured so that the attacker attacks the system at a specific point, for example, altering sensor signals or corrupting control signals. Based on a specific configuration of the different modules and accordingly the interactions between the modules, simulations can be run to obtain possible evolution sequences of the system under study. We demonstrate the proposed framework using an IEEE 14-bus system by investigating different module configurations. © 2021 PSA. All Rights Reserved.

关键词： Risk assessment

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Hanyu Zheng Quan Liu You Zhou Ivan I. Kravchenko Yuankai Huo Jason Valentine Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA Department of Computer Science Vanderbilt University Nashville TN USA Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN USA Department of Mechanical Engineering Vanderbilt University Nashville TN USA

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac... 详细信息

ISBN: (纸本)9781957171258

关键词： Image analysis Image resolution Machine vision Neural networks Optical systems Signal processing

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