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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Yajie Sheng Bin Chen Yi Lei Jingxin Deng Jiwei Xu Mengfan Fu Qunbi Zhuge Shen Li Department of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Centre of Optics Photonics and Lasers (COPL) Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： AWGN channels Wireless communication Optical fibers Probabilistic logic Optical fiber communication Encoding Complexity theory Gain Photonics Signal to noise ratio

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Enhancing Phishing Detection with AI: A Novel Dataset and Comprehensive Analysis Using Machine Learning and Large Language Models

Enhancing Phishing Detection with AI: A Novel Dataset and Co...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Robin Chataut Yusuf Usman Chowdhury Mohammad Abid Rahman Sohan Gyawali Prashnna K Gyawali Department of Computer Science Texas Christian University Fort Worth TX USA School of Computing and Engineering Quinnipiac University Hamden CT USA LANE Department of Computer Science West Virginia University Morgantown WV USA Department of Technology Systems East Carolina University Greenville NC USA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Phishing emails are a significant threat to organizations, with over $90 \%$ of cyber attacks starting from a malicious email. Despite built-in security measures, relying solely on these defenses can leave organizations vulnerable to cybercriminals who exploit human nature and the lack of tight security. Phishing emails, designed to deceive recipients into disclosing personal and financial information, represent a significant cybersecurity challenge. This paper introduces a comprehensive dataset curated explicitly for detecting phishing emails, featuring a collection of authentic and phishing emails. The dataset includes a broad spectrum of phishing techniques, such as sophisticated social engineering tactics, impersonation of reputable entities, and using urgent or threatening language to manipulate recipients. Phishing emails were collected to cover various scenarios, including financial fraud, account verification, and malware dissemination attempts. Our analysis involves a range of classical machine learning models alongside exploratory analysis with LLMs. The performance of these models was rigorously evaluated to furnish a comparative analysis of their detection capabilities. The dataset, one of the largest of its kind, offers a significant resource for researchers and cybersecurity professionals aiming to advance phishing detection methods. The dataset used in this research is publicly available, enabling further exploration and replication of the findings by the research community [1].

关键词： Analytical models Phishing Large language models Finance Machine learning Organizations Mobile communication Malware Electronic mail Fraud

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Ultra-broadband external-cavity quantum cascade laser synchronization for mode-hop free tuning 24

Ultra-broadband external-cavity quantum cascade laser synchr...

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Novel In-Plane Semiconductor Lasers XXIV 2025

作者： Kosan, Nicholas Meyer, Zane Nowicki, Keith Silver, Joel Peterson, Kristen Reyes-Newell, Adriana Applegate, Jeff Rafkin, Scot Wysocki, Gerard Department of Electrical and. Computer Engineering Princeton University PrincetonNJ08544 United States Planetary Science Directorate Southwest Research Institute BoulderCO80302 United States Southwest Sciences Inc. Santa FeNM87505 United States BAE Systems BoulderCO80301 United States Northern Arizona University United States

ISBN: (纸本)9781510685185

The LASVEGAS (Laser Absorption Spectrometer for Volatile and Evolved GASes) instrument is a prototype ultrabroadband external-cavity (EC) quantum cascade laser (QCL) -based spectrometer under development for potential extraterrestrial mission, where simultaneous broad spectral coverage and fine resolution are highly desirable. This is accomplished with an array of EC-QCLs with unique spectral coverage, allowing efficient scaling of the spectral emission range over hundreds of cm-1. To date, the tuning range of the instrument exceeds 600 cm-1 within a span bounded approximately by 944 cm-1 and 1930 cm-1. To access local mode hop free tuning, we leverage a simulation of the EC geometry in order to synchronize the parameters of the EC. While specific mode hop free scans are limited by the dynamic range of the laser drive current and are limited to the scale of 1.5 cm-1, mode hop free sections of adjacent scans can be stitched together. The simulation is used to coordinate the position of the stages in the EC, controlling the EC "air" length and the grating angle. © 2025 SPIE.

关键词： Quantum cascade lasers

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A Composite T60 Regression and Classification Approach for Speech Dereverberation

arXiv

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arXiv 2023年

作者： Li, Yuying Liu, Yuchen Williamson, Donald S. The Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States The Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States The Department of Computer Science and Engineering The Ohio State University ColumbusOH43065 United States

Dereverberation is often performed directly on the reverberant audio signal, without knowledge of the acoustic environment. Reverberation time, T60, however, is an essential acoustic factor that reflects how reverberation may impact a signal. In this work, we propose to perform dereverberation while leveraging key acoustic information from the environment. More specifically, we develop a joint learning approach that uses a composite T60module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation. The reverberation time module provides key features to the dereverberation module during fine tuning. We evaluate our approach in simulated and real environments, and compare against several approaches. The results show that this composite framework improves performance in environments. © 2023, CC BY.

关键词： Deep neural networks

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Identifying Supports and Barriers in engineering Students’ Processes Toward Career Attainment

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Biomedical engineering Education 2023年第1期4卷 33-56页

作者： Desing, Renee M. Shermadou, Amena S. Nocera, Tanya M. Delaine, David A. Atiq, S. Zahra Ortiz-Rosario, Alexis Childers, Rachel C. Lewis, Racheida S. School of Civil and Construction Engineering Oregon State University Corvallis USA School of Engineering Education Purdue University West Lafayette USA Department of Biomedical Engineering The Ohio State University Columbus USA Department of Engineering Education The Ohio State University Columbus USA Department of Computer Science and Engineering The Ohio State University Columbus USA School of Electrical and Computer Systems Engineering University of Georgia Athens USA

Evidence shows that biomedical engineering students face unique challenges in entering the workforce compared to peers in other engineering majors. The purpose of this study is to explore the factors impacting undergraduate engineering students’ career attainment, or the acquisition of employment in a chosen field, and how the students experienced these factors in their pathways towards post-graduate employment. By studying differences in processes towards career attainment, there is an opportunity to promote equity and better support the inclusion and persistence of women and racially minoritized groups in engineering, particularly their readiness to enter the engineering workforce. We sought to answer two research questions: (RQ1) What contextual factors are identified by engineering students as supports and barriers to their attainment of a career in the engineering field? (RQ2) How are engineering students experiencing inequities in their processes toward career attainment? We conducted six focus groups with undergraduate engineering students at a large Midwestern University. Participants were purposefully sampled based on demographics from four engineering disciplines: biomedical engineering and the three preferred majors for students transferring out of biomedical engineering at our university, chemical engineering, materials science and engineering, and mechanical engineering. We used social cognitive career theory (SCCT) to inform our data collection and analysis and interpret the findings. The transcripts were analyzed by developing a codebook containing theory-driven codes from SCCT and emergent codes from the data. We identified five themes representing the contextual factors impacting engineering students’ processes toward career attainment: implications of (1) interpersonal relationships; (2) institutional infrastructure; (3) academics; (4) social identity; and (5) out-of-class experiences. We also found that these contextual factors may act as either a

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Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification

arXiv

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arXiv 2024年

作者： Alharbi, Fadi Vakanski, Aleksandar Zhang, Boyu Elbashir, Murtada K. Mohammed, Mohanad College of Engineering Department of Computer Science University of Idaho MoscowID83844 United States College of Computer and Information Sciences Department of Information Systems Jouf University Sakaka Aljouf72441 Saudi Arabia School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Scottsville3209 South Africa

Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration poses significant challenges due to the high dimensionality, data complexity, and distinct characteristics of various omics types. This study addresses these challenges and evaluates three graph neural network architectures for multi-omics (MO) integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN) for classifying 31 cancer types and normal tissues. To address the high-dimensionality of multi-omics data, we employed LASSO (Least Absolute Shrinkage and Selection Operator) regression for feature selection, leading to the creation of LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOTGN models. Graph structures for the networks were constructed using gene correlation matrices and protein-protein interaction networks for multiomics integration of messenger-RNA, micro-RNA, and DNA methylation data. Such data integration enables the networks to dynamically focus on important relationships between biological entities, improving both model performance and interpretability. Among the models, LASSO-MOGAT with a correlation-based graph structure achieved state-of-the-art accuracy (95.9%) and outperformed the LASSO-MOGCN and LASSO-MOTGN models in terms of precision, recall, and F1-score. Our findings demonstrate that integrating multi-omics data in graph-based architectures enhances cancer classification performance by uncovering distinct molecular patterns that contribute to a better understanding of cancer biology and potential biomarkers for disease progression. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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SBQ: Exploiting Significant Bits for Efficient and Accurate Post-Training DNN Quantization

SBQ: Exploiting Significant Bits for Efficient and Accurate ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jiayao Ling Gang Li Qinghao Hu Xiaolong Lin Cheng Gu Jian Cheng Xiaoyao Liang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China The Key Laboratory of Cognition and Decision Intelligence for Complex Systems CASIA Beijing China AiRiA Nanjing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Post-Training Quantization is an effective technique for deep neural network acceleration. However, as the bit-width decreases to 4 bits and below, PTQ faces significant challenges in preserving accuracy, especially for attention-based models like LLMs. The main issue lies in considerable clipping and rounding errors induced by the limited number of quantization levels and narrow data range in conventional low-precision quantization. In this paper, we present an efficient and accurate PTQ method that targets 4 bits and below through algorithm and architecture co-design. Our key idea is to dynamically extract a small portion of significant bit terms from high-precision operands to perform low-precision multiplications under the given computational budget. Specifically, we propose Significant-Bit Quantization (SBQ). It exploits a product-aware method to dynamically identify significant terms and an error-compensated computation scheme to minimize compute errors. We present a dedicated inference engine to unleash the power of SBQ. Experiments on CNNs, ViTs, and LLMs reveal that SBQ consistently outperforms prior PTQ methods under 2~4-bit quantization. We also compare the proposed inference engine with state-of-the-art bit-operation-based quantization architectures TQ and Sibia. Results show that SBQ can achieve the highest area and energy efficiency.

关键词： Quantization (signal) Accuracy Heuristic algorithms Europe computer architecture Artificial neural networks Energy efficiency Inference algorithms Engines Faces

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A K -Band Four-Beam Transmitter with Decoupled Phase/Gain Control and Enhanced Power Back off Efficiency for SATCOM

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IEEE Transactions on Microwave Theory and Techniques 2024年第11期72卷 6443-6459页

作者： Gao, Huiyan Li, Nayu Xie, Xinhong Wang, Shaogang Lu, Hang Kuan, Yen-Cheng Yu, Xiaopeng Song, Chunyi Jane Gu, Qun Xu, Zhiwei Institute of Marine Electronics and Intelligent Systems Ocean College Zhejiang University Zhoushan316021 China Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China Donghai Laboratory Zhoushan316021 China National Yang Ming Chiao Tung University International College of Semiconductor Technology Hsinchu30010 Taiwan Institute of VLSI Design and Research College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

This article presents a 17.7-20.2 GHz eight-element four-beam RF-beamforming transmitter in 65-nm CMOS for satellite communication (SATCOM). The transmitter utilizes an analog scheme in the variable-gain amplifier (VGA) to achieve dB-in-linear gain control with a high dynamic range (DR), together with a current combining crossbar for a compact footprint. The vector-modulation-based (VM-based) phase shifter (PS) adopts the cancellation-based Gm units for low switching variation. Furthermore, eight PSs of each beam are split into two groups, with each group sharing the same tunable I/Q generator for low power consumption. Considering the increased peak-to-average power ratio (PAPR) caused by high-order modulation schemes and multibeam operation, the transmitter incorporates compact transformer-based Doherty power amplifiers (DPAs) with adaptive bias control to improve the power efficiency at back-off. The chip achieves a 31-dB gain-tuning range with a 0.283-dB step and a 360° phase-shifting range with a calibrated 7-bit resolution. The measured root-mean-square (RMS) phase/gain errors induced by the VGA/PS are below 2.05°/0.147 dB, respectively, which decouples phase/gain control. The transmitter achieves a maximum output 1-dB gain compression point (OP1dB) of 17.9 dBm and a maximum saturated power (Psat) of 18.9 dBm. The output element exhibits 29.3% drain efficiency (DE) at 6-dB power back off (PBO) from Psat , which is 1.56/3.12 times over the normalized class-B/class-A implementations, demonstrating Doherty PBO efficiency enhancement. The chip occupies an area of 8.59 × 4.17 mm2 and consumes 398 mW per element at OP1dB. Additionally, a 16-element linear array has been developed to showcase the capabilities of the multibeam operation and gain tapering. © 1963-2012 IEEE.

关键词： Efficiency

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Enhanced Distributed Self-Healing System for Electrical Distribution Networks Using ADMM

Enhanced Distributed Self-Healing System for Electrical Dist...

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IEEE General Meeting Power& Energy Society

作者： Juan Camilo López E. T. Marco Gerards Johann L. Hurink Marcos J. Rider Department of Systems and Energy School of Electrical and Computer Engineering University of Campinas (UNICAMP) Campinas SP Brazil Faculty of Electrical Engineering Mathematics and Computer Science University of Twente (UT) Enschede Overijssel the Netherlands

Unlike centralized versions, a distributed self-healing system (SHS) for electrical distributed systems is less vulnerable to single-point failures (or attacks), requires less information from the agents, and is more scalable. However, optimality is challenging to achieve because binary variables are used in the modelling of the distributed service restoration problem. To deal with this challenge, this paper proposes an enhanced alternating direction method of multipliers (ADMM)based algorithm used to developed a fully distributed SHS in electrical distribution networks. Hereby, three ADMM-based heuristics are executed in parallel to improve the chances of obtaining a feasible solution. However, if none of the heuristics converge within given reasonable time, the proposed distributed SHS uses a basic restoration plan that is feasible in terms of topology and operational constraints. Results using the IEEE 123node system show that the proposed distributed SHS is reliable and it always provides a feasible solution.

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RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Li, Heng Li, Haojin Chen, Jianyu Fu, Huazhu Liu, Jiang The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Institute of High Performance Computing Agency for Science Technology and Research Singapore

Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced in clinical settings due to the scarcity of annotated data resulting from the professional and private nature of medical data. Despite the existence of decent solutions, many of them are hindered in clinical settings due to limitations in data collection and computational complexity. To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain. A frequency filter-based data augmentation strategy is first proposed to promote domain variability within a single-source domain by introducing variations in frequency space and blending homologous samples. Then Gaussian filter-based structural saliency is also leveraged to learn robust representations across augmented samples, further facilitating the training of generalizable segmentation models. To validate the effectiveness of RaffeSDG, we conducted extensive experiments involving out-of-domain inference on segmentation tasks for three human tissues imaged by four diverse modalities. Through thorough investigations and comparisons, compelling evidence was observed in these experiments, demonstrating the potential and generalizability of RaffeSDG. The code is available at https://***/liamheng/NonIID_Medical_Image_Segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Inference engines

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