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

作者： Park, Mingyo Wang, Jialin Ansari, Azadeh Georgia Institute Of Technology School Of Electrical And Computer Engineering AtlantaGA United States

ISBN: (纸本)9784886864352

This work presents an 18 GHz thin-film bulk acoustic wave resonator (FBAR) based on epitaxial Aluminum Scandium Nitride (Al1-xScxN), and epitaxial metal electrodes, optimized for simultaneously achieving high electromechanical coupling coefficient (kt2) and high conductor quality factor (Q). Here, we utilize 40 nm-thick epi-metal electrodes to obtain the highest {kt2 achievable for 120 nm-thick Al0.8Sc0.2N piezo films. Our 40 nm-thick epi-Molybdenum (Mo) exhibits sheet resistance (Rsh) 25 times smaller than sputtered-Mo films of similar thickness, thus increasing the conductor Q by 25×. We report on the first demonstration of an 18 GHz all-epi Mo/AlScN/Al FBAR, with a QBode of 110 and kt2 of 6%. © 2023 IEEJ.

关键词： 5G mobile communication Aluminum Scandium Nitride and Epitaxial metal Films bulk acoustic resonators Ku-band millimeter-wave acoustic filters

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Distributed Functional Compression for Independent Component Analysis in Wireless Networks

Distributed Functional Compression for Independent Component...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Alamoudi, A. Saidutta, Y. Fekri, F. University of Jeddah College of Engineering Department of Electrical and Electronic Engineering Jeddah Saudi Arabia Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States

ISBN: (纸本)9798350351255

In this paper, we consider distributed Independent Component Analysis (ICA) in wireless networks, where data from several geographically distributed wireless nodes (nodes) must be transmitted to a central server (server) to extract original sources through ICA. However, transmitting the vast amount of data over wireless channels to the server poses significant challenges due to limited bandwidth and privacy concerns. Our research addresses how to encode node data to meet channel rate constraints while providing maximally relevant information for ICA. Particularly, we propose a distributed functional compression framework for learning ICA over orthogonal AWGN channels. The framework leverages the Information Bottleneck (IB) principle to encode and compress the data to meet the channel rate constraint while maximally preserving the functionally relevant information for ICA. We train both neural encoders at the nodes and a neural decoder at the server in an unsupervised manner using the IB principle. We consider ICA for both linear and nonlinear mixing setups. Compared to the state-of-the-art, over real dataset, our proposed framework demonstrates a remarkable improvement of up to approximately 43% in accurately estimating the source signals in ICA while meeting the channels' rate constraints. Finally, we propose a three-stage training algorithm, where the raw sensory data never leaves the nodes either for training or inference, to reduce the communication overhead. We show that our proposed training algorithm notably reduces channel use compared to the traditional cloud-based method, where the observed data from the nodes are compressed and transmitted to the cloud for learning ICA. © 2024 IEEE.

关键词： Channel coding

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Detection and Remediation of Internal Resonance Problems in Integral Equations

Detection and Remediation of Internal Resonance Problems in ...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Bibby, Malcolm M. Peterson, Andrew F. School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781733509633

In this paper we demonstrate that residual error estimators can be used to detect internal resonance problems arising from the electric and magnetic field integral equations for perfectly conducting targets. In addition, a new approach to constructing a 'combined' field equation is introduced. When solved, the resulting over-determined system produces a different type of residual that yields insight into the solution accuracy. © 2023 ACES.

关键词： Computational electromagnetics

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Ferroelectric Nonvolatile Capacitive Synapse for Charge Domain Compute-in-Memory

Ferroelectric Nonvolatile Capacitive Synapse for Charge Doma...

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2024 Symposium on Materials, Devices, and Systems for Neuromorphic Computing and Artificial Intelligence Hardware, held at the 245th ECS Meeting

作者： Phadke, O. Kim, T.-H. Luo, Y.-C. Yu, S. Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781623326784

The ferroelectric field effect transistor (FeFET) can be configured in nvCAP mode to enable a small-signal capacitive readout. Operating the FeFET in nvCAP mode for Charge-domain Compute-in-Memory provides benefits such as reduced power consumption and suppressed read disturb. In this review article, working of FeFET in nvCAP mode is described. Further, the TCAD study results are summarized to optimize the FeFET structure for maximizing the device performance in nvCAP mode, where the gate area and the overlap region decides the ON and OFF state capacitance. Next, the reliability aspects of nvCAP are discussed. The FeFET in nvCAP demonstrates an initial endurance of 106 P/E cycles, which can be further extended by 100× by performing a recovery operation. The nvCAP device shows a retention of at least 1 day at 850C for the fresh, fatigued and recovered state of the device, making it a suitable candidate for Compute-in-Memory. © The Electrochemical Society.

关键词： Ferroelectricity

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A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 657-675页

作者： Dhawan, Sanjeev Singh, Kulvinder Batra, Amit Choi, Anthony Choi, Ethan Department of Computer Science and Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Electrical and Computer Engineering Mercer University MaconGA United States

The increasing number of electronic transactions on the Internet has given rise to the design of recommendation systems. The main objective of these systems is to give recommendations to the users about the items (i.e., movies, music, books, etc.) they are likely to purchase in near future. These recommendations are on the foundation of the response provided by the other individuals about these products in the terms of ratings, which are in the form of a five-star rating system. Plenty of algorithms have been proposed in the past for building recommender systems. The most popular of these algorithms comprise content-centered and collaborative filtering recommendation methodologies. Recently, matrix factorization has appeared as a technique in recommender systems. Several algorithms centered on matrix factorization have been suggested in the recent past. In this paper, an improved alternating least squares algorithm has been proposed for building more efficient and accurate recommender systems which improves the performance of matrix factorization. The performance of the model has been evaluated using metrics like root-mean-square-error and execution time. However, in general, the time complexity of both algorithms is O(n^3), where n is the number of users or items. This is because both IALS and ALS involve iteratively solving a system of linear equations, and the time complexity of solving a system of linear equations is O(n^3). However, IALS can achieve significant performance improvements over ALS, particularly for large datasets, due to its incorporation of stochastic gradient descent and parallelization techniques. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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AdaFedProx: A Heterogeneity-Aware Federated Deep Reinforcement Learning for Medical Image Classification

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1432-1441页

作者： Sahoo, Pranab Tripathi, Ashutosh Saha, Sriparna Mondal, Samrat Singh, Jyoti Prakash Sharma, Bhisham Computer Science and Engineering Department Indian Institute of Technology Patna India Electrical and Electronics Engineering Department The Rajiv Gandhi Institute of Petroleum Technology India Indian Institute of Technology Computer Science and Engineering Department Patna India National Institute of Technology Computer Science and Engineering Department Patna India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India

In the realm of smart healthcare, vast amounts of valuable patient data are generated worldwide. However, healthcare providers face challenges in data sharing due to privacy concerns. Federated learning (FL) offers a privacy-preserving solution by enabling collaborative model training without direct access to patient data. This decentralized approach utilizes data from diverse sources, resulting in a globally learned model with satisfactory performance across individual sites. However, federated training encounters challenges such as "system heterogeneity"in client specifications and "data heterogeneity"in imbalanced data distribution. Standard FL methods become unstable and require extensive hyperparameter tuning for optimal performance when facing heterogeneous clients, posing challenges in real-world applications. This research introduces a novel heterogeneity-aware federated learning approach named AdaFedProx to address the performance degradation resulting from heterogeneity. AdaFedProx utilizes a reinforcement learning-driven strategy, introducing a tailored proximal term into the objective function to incorporate partial client information in heterogeneous settings. Dynamically determining the proximal term based on individual client states, including critical characteristics like data distribution, system specification, and performance feedback, empowers AdaFedProx to make regularized decisions throughout the federated training. AdaFedProx consistently outperforms four state-of-the-art methods on three medical datasets across diverse heterogeneity settings (0%, 50%, and 75% straggler). The code can be found at https://***/Pranabiitp/AdaFedProx. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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An In-Context Learning Approach for LLM-Enabled Multi-Robot Task Allocation

An In-Context Learning Approach for LLM-Enabled Multi-Robot ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Huibo Chen, Shengkang Yin, Huan Zhang, Fumin School of Electrical and Computer Engineering Hong Kong University of Science and Technology SAR Hong Kong Hong Kong School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781665481090

In-context learning for large language models (LLMs) is employed to design a novel hybrid optimization framework for multi-robot task allocation. Results show that this approach can improve existing approaches, such as the market based scheme or the speedup and slow-down (SUSD) scheme by leveraging the contextual understanding ability of a LLM. Incorporating LLM removes the need for detailed information regarding the utility function for multi-robot task allocation. LLM helps the algorithm to escape local optima without relying on any human feedback. However, in-context learning is necessary to allow the LLM to agree with the underlying optimization algorithms. This paper proposes an method to instigate in-context learning for LLMs and demonstrate convergence to consensus between an LLM and the underlying optimization algorithms. The proposed method reduces the need for expert intervention and enhances the applicability and scalability for complex task allocation problems. Experimental results verify that the hybrid approach can improve the effectiveness of task allocations compared with recent SUSD-only methods. © 2024 IEEE.

关键词： Multipurpose robots

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Hierarchical Federated ADMM

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 11-15页

作者： Azimi-Abarghouyi, Seyed Mohammad Bastianello, Nicola Johansson, Karl H. Fodor, Viktoria KTH Royal Institute of Technology School of Electrical Engineering and Computer Science and Digital Futures Stockholm10044 Sweden

In this letter, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multipliers (ADMM), leveraging a network architecture consisting of a single cloud server and multiple edge servers, where each edge server is dedicated to a specific client set. Within this framework, we propose two novel FL algorithms, which both use ADMM in the top layer: one that employs ADMM in the lower layer and another that uses the conventional gradient descent-based approach. The proposed framework enhances privacy, and experiments demonstrate the superiority of the proposed algorithms compared to the conventional algorithms in terms of learning convergence and accuracy. Additionally, gradient descent on the lower layer performs well even if the number of local steps is very limited, while ADMM on both layers lead to better performance otherwise. © 2019 IEEE.

关键词： Federated learning

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Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

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Australian Journal of electrical and Electronics engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sumit Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). ©, Engineers Australia.

关键词： Energy utilization

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Experimental assessment of the optical transfer function for quantitative oblique back-illumination microscopy

Experimental assessment of the optical transfer function for...

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2024 Label-free Biomedical Imaging and Sensing, LBIS 2024

作者： Li, Zhenmin Filan, Caroline Guang, Zhe Casteleiro Costa, Paloma Robles, Francisco E. School of Electrical and Computer Engineering Georgia Institute of Technology United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology United States Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University United States

ISBN: (纸本)9781510669673

Quantitative oblique back illumination microscopy (qOBM) is a recently developed phase imaging modality that enables 3D quantitative phase imaging and refractive index (RI) tomography of thick scattering samples. The approach uses four oblique illumination images (acquired in epi-mode) at a given focal plane to obtain cross sectional quantitative information. In order to quantify the information, qOBM uses a deconvolution algorithm which requires an estimate of the angular distribution of light at the focal plane to obtain the system's optical transfer function (OTF). This information is obtained using Monte Carlo numerical simulations which uses published scattering parameters of tissues. While this approach has shown robust results with high quantitative fidelity, the reliance on available published scattering parameters is not optimal. Here we present an experimental approach to measure the angular distribution of the back-scattered light at the focal plane. The approach simultaneously obtains information from the imaging plane and the Fourier plane to provide insight into the overall angular distribution of light at the focal plane. Together with the pupil function, given by the known numerical aperture of the system, this approach directly yields the OTF. A theoretical analysis and experimental results will be presented. This approach has the potential to widen the utility of qOBM to also include tissues and samples whose scattering properties are not well documented in the literature. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Refractive index

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