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Optimum classical beam-position sensing

Physical Review Applied 2024年第4期22卷 L041004页

作者： Wenhua He Christos N. Gagatsos Dalziel J. Wilson Saikat Guha Wyant College of Optical Sciences Department of Electrical and Computer Engineering Program in Applied Mathematics

Beam-displacement measurements are widely used in optical sensing and communications; however, their performance is affected by numerous intrinsic and extrinsic factors, including beam profile, propagation loss, and receiver architecture. Here we present a framework for designing a classically optimal beam-displacement transceiver, using quantum estimation theory. We consider the canonical task of estimating the position of a diffraction-limited laser beam after passing through an apertured volume characterized by Fresnel-number product DF. As a rule of thumb, higher-order Gaussian modes provide more information about beam displacement, but are more sensitive to loss. Applying quantum Fisher information, we design mode combinations that optimally leverage this trade-off, and show that a greater than tenfold improvement in precision is possible, relative to the fundamental mode, for a practically relevant DF=100. We also show that this improvement is realizable with a variety of practical receiver architectures. Our findings extend previous works on lossless transceivers, may have immediate impact on applications, such as atomic force microscopy and near-field optical communication, and pave the way towards globally optimal transceivers using nonclassical laser fields.

关键词： Classical optics Metrology Quantum Fisher information Quantum optics Quantum parameter estimation Devices Wireless communication networks Atomic force microscopy

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 31

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Chen, Yuzong Abouelhamayed, Ahmed F. Dai, Xilai Wang, Yang Andronic, Marta Constantinides, George A. Abdelfattah, Mohamed S. Cornell University Computer Systems Lab United States Systems and Networking Research Group Microsoft Research United States Imperial College London Department of Electrical and Electronic Engineering United Kingdom

ISBN: (纸本)9798331506476

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD1, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types;our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with © 2025 IEEE.

关键词： Integrated circuit design

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Using Random Forest and Linear SVM for Dyslexia Classification on Hebb-Williams Mazes 59

Using Random Forest and Linear SVM for Dyslexia Classificati...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： Kaushik, Padmanabh Yu, Yih-Choung Gabel, Lisa Lafayette College Department of Electrical and Computer Engineering EastonPA18042 United States Lafayette College Department of Psychology and Neuroscience Program EastonPA18042 United States

ISBN: (纸本)9798331513269

Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite normal level of education and intelligence. Studies have shown that early detection of dyslexia is vital for improving learning abilities in young children. Many virtual platforms exist for diagnosing and rehabilitating dyslexia;however, most require tests that measure reading skills. Since developing reading capabilities can delay the detection of dyslexia, a gaming platform based on Hebb-Williams mazes has been developed. This platform does not rely on reading skills and can diagnose dyslexia in young children. This paper presents a machine learning driven approach using two algorithms - Random Forest and Linear SVM - to classify reading abilities based on data from participants performing virtual maze tasks, which are indicative of symptoms of dyslexia. Results from this study indicate that it is possible to predict dyslexia with up to 95% accuracy based on a participant's performance in virtual gaming environments. © 2025 IEEE.

关键词： Adversarial machine learning

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Symmetric Series-Capacitor Buck in 48V-to-12V Regulated Conversion for High-Performance Server Boards

Symmetric Series-Capacitor Buck in 48V-to-12V Regulated Conv...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Xu, Xinmiao Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798350376067

This paper presents an extended-conversion-ratio modulation for a two-phase symmetric series-capacitor buck (SSCB) converter. With the proposed scheme, highly efficient and regulated 48V-to-12V conversion can be realized with the SSCB topology. A versatile regulated first stage streamlines power delivery architectures for server boards in data centers, because regulated 12-V buses not only serve loads requiring well-controlled 12-V input but also simplify design and optimization of a 12V-to-1V second stage for processors. Based on this idea, a highly efficient and regulated power architecture is proposed for 48-V server boards. A prototype is built with gallium nitride (GaN) transistors and minimal high-frequency loop inductance, operating with a relatively wide input voltage range from 40 V to 60 V and generating a regulated 12-V output. In 48V-to-12V regulated conversion, the GaN-based prototype achieves 98.88% peak efficiency, 98.11% full-load efficiency (including gating loss) and 787 W/inch2 power density with 80-A output current, which is beyond the performance of state-of-the-art commercially available solutions. © 2024 IEEE.

关键词： Gallium nitride

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Explainability Spectrum Analysis of Weather Sequences in Short-Term Load Forecasting 14th

Explainability Spectrum Analysis of Weather Sequences in Sho...

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14th International Conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Kontogiannis, Dimitrios Bargiotas, Dimitrios Daskalopulu, Aspassia Tsoukalas, Lefteri H. Department of Electrical and Computer Engineering University of Thessaly Volos Greece Center for Intelligent Energy Systems Purdue University West Lafayette United States

ISBN: (纸本)9783031674259

Short-term load forecasting tasks utilize a plethora of weather sequences in order to study meaningful features that describe complex relationships between environmental parameters and the variable of load, as well as the occurrence of impactful events, thus reinforcing the predictive power of estimators. It is evident that the inclusion of those additional features increases the input dimensions and decreases the overall explainability of forecasting models, since not all of the extracted features are equally compatible with the assumptions of the regressors, resulting in models where the justification of predictive potency, the connection between the variance of the target variable and the variance of predictors, as well as the explanation of the computational flow become increasingly difficult due to ambiguity. Therefore, this analysis investigates the representativeness of weather variables and examines the explainability of interpretable short-term load forecasting structures that utilize environmental sequences towards the selection of important feature subsets that maximize the explained variance of load time series as well as the seasonal, trend and residual load components during training. This methodology considers linear regression and prominent tree-based estimators coupled with model-dependent feature importance frameworks utilizing game theoretic, permutative and impurity-based approaches for the derivation of optimal feature sets and feature set combinations in terms of explained variance scores. Additionally, two structural approaches of explainable feature set generation from the joint feature importance scores through the utilization of hierarchical and majority inclusion are compared, their execution behavior towards explainability maximization is examined, and the main observations from each stage of this analysis are discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Weather forecasting

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A Novel Rotor Position Synchronization Method for Central Converter Multi-Machine Drives 4

A Novel Rotor Position Synchronization Method for Central Co...

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4th International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： De Andrade Lima, Claudio Cale, James State University Electrical and Computer Engineering Departament Colorado Fort Collins United States Colorado State University Systems Engineering Department Fort Collins United States

ISBN: (纸本)9798350391183

The aerospace industry is undergoing a paradigm shift towards More Electric Aircraft. Safe and reliable electromechanical drives are a key component of this transition. Previous research has shown a potential rotor position synchronization control for central-converter, multi-machine systems applied to aerospace actuators. This paper proposes a novel rotor position synchronization method referred to as the 'voltage-boost' method. The method is based on in-line induced voltages through compact split-core three-phase transformers and low-power auxiliary converters. The paper describes the principle of operation of the method along with practical installation advantages, such as a non-invasive deployment. Simulation results show that the method is capable of achieving position synchronization under different load torque distributions, maintaining reasonable position error, and achieving a fast response. Additionally, a real aerospace typical load profile is utilized to verify the method's robustness under realistic operating conditions. © 2024 IEEE.

关键词： Aerospace industry

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Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

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Journal of Modern Power systems and Clean Energy 2022年第2期10卷 471-481页

作者： Shengyuan Liu Tianhan Zhang Zhenzhi Lin Yilu Liu Yi Ding Li Yang the School of Electrical Engineering Zhejiang UniversityHangzhou 310027China the Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA the School of Electrical Engineering Shandong UniversityJinan 250061China Electrical and Electronics Systems Research Division Oak Ridge National LaboratoryOak RidgeTN 37830USA

Controlled islanding plays an essential role in preventing the blackout of power *** there are several studies on this topic in the past,no enough attention is paid to the uncertainty brought by renewable energy sources(RESs)that may cause unpredictable unbalanced power and the observabilit>T of power systems after islanding that is essential for back-up black-start ***,a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming(MISOCCP)is proposed to address these ***,the uncertainty of RESs is characterized by their possibility distribution models with chance constraints,and the requirements,e.g.,system observability,for rapid back-up black-start measures are also ***,a law of large numbers(LLN)based method is em-ployed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex ***,case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed *** results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

关键词： Controlled islanding second-order cone chance-constrained programming renewable energy source(RES) black-start observability.

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A large-area bionic skin for high-temperature energy harvesting applications

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Nano Research 2023年第7期16卷 10245-10255页

作者： Zhaojun Liu Bian Tian Yao Li Jiaming Lei Zhongkai Zhang Jiangjiang Liu Qijing Lin Chengkuo Lee Zhuangde Jiang State Key Laboratory for Mechanical Manufacturing Systems Engineering School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117576Singapore Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China

For the large amount of waste heat wasted in daily life and industrial production,we propose a new type of flexible thermoelectric generators(F-TEGs)which can be used as a large area bionic skin to achieve energy harvesting of thermal *** reference to biological structures such as pinecone,succulent,and feathers,we have designed and fabricated a biomimetic flexible TEG that can be applied in a wide temperature range which has the highest temperature energy harvesting capability *** laminated free structure of the bionic F-TEG dramatically increases the efficiency and density of energy *** F-TEGs(single TEG only 101.2 mg in weight),without an additional heat sink,demonstrates the highest output voltage density of 286.1 mV/cm^(2)and the maximum power density is 66.5 mW/m^(2) at a temperature difference of nearly 1000℃.The flexible characteristics of F-TEGs make it possible to collect the diffused thermal energy by flexible attachment to the outer walls of high-temperature pipes and vessels of different diameters and *** work shows a new design and application concept for flexible thermal energy collectors,which fills the gap of flexible energy harvesting in high-temperature environment.

关键词： bionic structure high-temperature flexible thermoelectric generators(TEGs)

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Data Fusion-based Hybrid Framework for Cyber-physical Security of the Smart Grid

Data Fusion-based Hybrid Framework for Cyber-physical Securi...

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2024 Resilience Week, RWS 2024

作者： Boamah, Sharon A. Mathieu, Reynold Faillace, Victor Agnew, Dennis McNair, Janise Bretas, Arturo University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States Sandia National Laboratory Distributed Systems Group United States

ISBN: (纸本)9798350388985

The integration of information communication technology with the power grid exposes it to cyber threats. The network state estimation process provides stability to the smart grid. The communication network plays a major role in ensuring the successful transmission of state information. However, these network measurements are vulnerable to malicious attacks. This subsequently affects the network measurement such as associated high transmission delays and packet losses affecting the reliability of the smart grid. In this work, we propose a hybrid physics-based data-driven model that uses data fusion from the state-of-the-art physics-based Network State Estimation model and a data-driven model to detect false data injection attacks in the communication network layer of the smart grid. The performance of the data fusion method is evaluated and the simulation results show that the proposed model outperforms the standalone approaches in the detection of bad data. This shows that the proposed scheme is able to improve the cyber-physical security of the communication network layer of the smart grid. © 2024 IEEE.

关键词： Cyber attacks

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Foundry Manufactured 6-bit Resolution, 150µm Long Slow-Light Electro-Optic Modulator for On-Chip Photonic Tensor Computing

Foundry Manufactured 6-bit Resolution, 150µm Long Slow-Ligh...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Zhang, Meng Begovic, Amir Yin, Dennis Gangi, Nicholas Gu, Jiaqi Huang, Rena Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering 110 8th Street TroyNY12180 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States

We demonstrate 6-bit DAC resolution with an ultra-compact slow-light electro-optic modulator. The 10× modulation length reduction enables 31× compute density, 1.17× energy efficiency, and 36.1× energy efficiency per unit area for photonic tensor computing. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Light modulation

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