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arXiv 2024年

作者： Wu, Zheyu Liu, Ya-Feng Chen, Wei-Kun Masouros, Christos The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China The Department of Electronic and Electrical Engineering University College London Torrington Place LondonWC1E 7JE United Kingdom

Both dual-functional radar-communication (DFRC) and massive multiple-input multiple-output (MIMO) have been recognized as enabling technologies for 6G wireless networks. This paper considers the advanced waveform design for hardware-efficient massive MIMO DFRC systems. Specifically, the transmit waveform is imposed with the quantized constant-envelope (QCE) constraint, which facilitates the employment of low-resolution digital-to-analog converters (DACs) and power-efficient amplifiers. The waveform design problem is formulated as the minimization of the mean square error (MSE) between the designed and desired beampatterns subject to the constructive interference (CI)-based communication quality of service (QoS) constraints and the QCE constraint. To solve the formulated problem, we first utilize the penalty technique to transform the discrete problem into an equivalent continuous penalty model. Then, we propose an inexact augmented Lagrangian method (ALM) algorithm for solving the penalty model. In particular, the ALM subproblem at each iteration is solved by a custom-built block successive upper-bound minimization (BSUM) algorithm, which admits closed-form updates, making the proposed inexact ALM algorithm computationally efficient. Simulation results demonstrate the superiority of the proposed approach over existing state-of-the-art ones. In addition, extensive simulations are conducted to examine the impact of various system parameters on the trade-off between communication and radar performances. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

arXiv

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arXiv 2024年

作者： Roodini, Bijan Khajehpiri, Boshra Moghaddam, Hamid Abrishami Forouzanfar, Mohamad Department of Systems Engineering École de technologie supérieure Université du Québec MontréalQC Canada Laboratory Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran Iran Montréal Canada

Background: Precise estimation of cardiac patients’ current and future comorbidities is an important factor in prioritizing continuous physiological monitoring and new therapies. Machine learning (ML) models have shown satisfactory performance in short-term mortality prediction of patients with heart disease, while their utility in long-term predictions is limited. This study aims to investigate the performance of tree-based ML models on long-term mortality prediction and the effect of two recently introduced biomarkers on long-term mortality. Methods: This study utilized publicly available data from the Collaboration Center of Health Information Application (CCHIA) at the Ministry of Health and Welfare, Taiwan, China1. The data pertained to patients admitted to the cardiac care unit for acute myocardial infarction (AMI) between November 2003 and September 2004. Included in the dataset were patients over 20 years old diagnosed with type 1 AMI. Exclusion criteria encompassed patients with missing data for brachial pre-ejection period (bPEP) and brachial ejection time (bET), as well as those with atrial fibrillation or extremity amputations, resulting in a final cohort of 139 AMI patients. We collected and analyzed mortality data up to December 2018. All patients had provided informed consent, in accordance with the Declaration of Helsinki. Medical records were used to gather demographic and clinical data, including age, gender, body mass index (BMI), percutaneous coronary intervention (PCI) status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction (STEMI), and non-ST segment elevation myocardial infarction (NSTEMI). Using medical and demographic records as well as two recently introduced biomarkers, bPEP and bET, collected from 139 patients with acute myocardial infarction, we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality w

关键词： Adaptive boosting

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Robust Co-Optimization of Distribution Network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

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Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training

arXiv

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arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou The Department of Electrical and Computer Engineering National University of Singapore Singapore The Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Fortemedia Singapore138637 Singapore Institute for Infocomm Research A*STAR Singapore The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The University of Bremen 28359 Germany

Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in such cases, AV-ASD systems without a selective listening ability are short of effectively filtering out disruptive voice components from mixed audio inputs. In this paper, we propose a Multi-modal Speaker Extraction-to-Detection framework named ‘MuSED’, which is pre-trained with audio-visual target speaker extraction to learn the denoising ability, then it is fine-tuned with the AV-ASD task. Meanwhile, to better capture the multi-modal information and deal with real-world problems such as missing modality, MuSED is modelled on the time domain directly and integrates the multi-modal plus-and-minus augmentation strategy. Our experiments demonstrate that MuSED substantially outperforms the state-of-the-art AV-ASD methods and achieves 95.6% mAP on the AVA-ActiveSpeaker dataset, 98.3% AP on the ASW dataset, and 97.9% F1 on the Columbia AV-ASD dataset, respectively. We will publicly release the code in due course. © 2024, CC BY.

关键词： Extraction

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Efficient Error-Bounded Lossy Compression for CPU Architectures

Efficient Error-Bounded Lossy Compression for CPU Architectu...

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International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication systems (MASCOTS)

作者： Griffin Dube Jiannan Tian Sheng Di Dingwen Tao Jon C. Calhoun Franck Cappello Department of Computer Science Northwestern University Evanston IL Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

Modern HPC applications produce increasingly large amounts of data, which limits the performance of current extreme-scale systems. Lossy compression, helps to mitigate this issue by decreasing the size of data generated by these applications. SZ, a current state-of-the-art lossy compressor, is able to achieve high compression ratios, but its prediction/quantization methods contain RAW dependencies that prevent parallelizing this step of the compression. Recent work proposes a parallel dual prediction/quantization algorithm for GPUs which removes these dependencies. However, some HPC systems and applications do not use GPUs, and could still benefit from the fine-grained parallelism of this method. Using the dual-quantization technique, we implement and optimize a SIMD vectorized CPU version of SZ (vecSZ), and create a heuristic for selecting the optimal block size and vector length. We propose a novel block padding algorithm to decrease the number of unpredictable values along compression block borders and find it reduces the number of prediction outliers by up to 100%. We measure performance of our vecSZ against an CPU version of SZ using dual-quantization, pSZ, as well as SZ-1.4. Using real-world scientific datasets, we evaluate vecSZ on the Intel Skylake and AMD Rome architectures. vecSZ results in up to 32% improvement in rate-distortion and up to 15× speedup over SZ-1.4, achieving a prediction and quantization bandwidth in excess of 3.4 GB/s.

关键词： Analytical models Quantization (signal) Computational modeling Rate-distortion computer architecture Bandwidth Prediction algorithms

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Impact of Techno-Economic Parameters on Optimal Sizing of EV Fast Charging Stations

Impact of Techno-Economic Parameters on Optimal Sizing of EV...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

As the adoption for electric vehicles (EV) increases, the need for proper charging infrastructure also increases. As such, it is necessary to ensure proper charging infrastructure to meet the charging demand. Hence, studies concerning the planning of EV charging stations which include the sizing of distributed energy resources (DERs) in the station to reduce the peak demand on the grid. In this paper, EV charging stations were designed keeping in mind factors such as the EV charging load profile (on demand charging versus deferred charging), uncertainties in the load profile as well as the tariff rates. A sensitivity analysis was also carried out in order to examine the impact of several techno-economic parameters on the overall cost of the system.

关键词： Vehicle-to-grid Costs Uncertainty Sensitivity analysis Tariffs Vehicular ad hoc networks Transportation

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Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Multi-User Communications

Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Mul...

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IEEE Conference on Vehicular Technology (VTC)

作者： Shupei Zhang Yutong Zhang Boya Di Hongliang Zhang School of Electronics Engineering and Computing Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering Princeton University NJ USA

ISBN: (纸本)9781665454698

Holographic multiple-input multiple-output (HMIMO) with a spatially continuous aperture is a promising solution for future radio access to handle the explosively increasing data demands. As a key enabler of HMIMO, the reconfigurable refractive surface (RRS) can serve as an antenna array with numerous programmable radiation elements. In this paper, we consider a multi-user system with an RRS-aided base station (BS) where the transmit signal is refracted by the RRS towards the users. A beamforming scheme is developed via codebook design and beam training. A larger codebook size implies more codewords, each corresponding to a directional beam. When the codebook size increases, the directivity of the refracted beam is enhanced, bringing a higher data rate. However, it also leads to an exponential growth of the training overhead. To achieve the critical tradeoff between the data rate and overhead, we evaluate the system throughput and model the relation between the codebook size of the RRS and the throughput mathematically. The optimal codebook size is then derived given different user distributions. Simulation results verify our theoretical analysis and show the influence of both codebook size and RRS size on the throughput.

关键词： Training Vehicular and wireless technologies Array signal processing Simulation System performance Transmitting antennas Throughput

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

SSRN

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SSRN 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spa-tial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Dispersion compensating fiber with Brillouin fiber laser generation

Dispersion compensating fiber with Brillouin fiber laser gen...

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Communication and Information Technology (AICCIT), Al-Sadiq International Conference on

作者： Hasanain Naser Abd Ali Sulaiman Wadi Harun Norhana Arsad Taj-Aldeen Naser Abdali Ahmed Salih Al-Khaleefa Mustafa H Hassan Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi Malaysia Department of Electrical Engineering Photonics Engineering Laboratory Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Department of Electronic Computer Center University of Misan Misan Iraq Department of Physics Faculty of Education University of Misan Misan Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

Short linewidth light lasers are essential for a wide range of applications, including quantum computing, spectroscopy, and sensing. Stimulated Brillouin scattering is an intriguing method for achieving extremely coherent light laser output. The demonstration of an unstable pulsed Brillouin erbium fiber laser (BFL) operating at kHz is shown here. The BFL works at 1550.1 nm, which is 0.09 nm higher than the Brillouin pump wavelength since the dispersion compensating fiber was pumped over the 24.77 mW threshold. It has an extreme power of 0.7 dBm, a side-mode suppression ratio of 29 dB, and a pump power of 59.12 mW at 980 nm. Due to the intrinsic instability in relaxation oscillation, which generates the nonlinear self-pulsing mechanism in the BEFL cavity, the BEFL produced a pulse train running at 200.9 kHz with a 24.77 mW pump. The pulse frequency was raised to 3.02 MHz. The pump power has been increased to 59.12 mW. Yet, as the pump power was raised further, more than one pulse was produced. This is the first example of generating kHz pulses in a BFL cavity. The laser system is straightforward, small, and all-fiber. These are the attempts of experimental work to realize the BFL with Q-switched multi-wavelength output using a specific length of DCF to generate the pulses.

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A single magnetic tunnel junction for the implementation of a spiking neuron

A single magnetic tunnel junction for the implementation of ...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Rodrigues, Davi R. Moukhader, Rayan Pontlevy, Adrien Hamadeh, Abbass Zeng, Zhongming Carpentieri, Mario Finocchio, Giovanni Politecnico di Bari Department of Electrical and Information Engineering Bari70125 Italy University of Messina Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences MessinaI-98166 Italy Technische Universität Kaiserslautern Fachbereich Physik and Landesforschungszentrum Optimas Kaiserslautern67663 Germany Suzhou Institute of Nano-Tech and Nano-Bionics Cas Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou215123 China

ISBN: (纸本)9798350338362

We propose a magnetic tunnel junction device to perform bio-realistic neuron firing for spiking neural networks. Spiking neural networks is the third generation of artificial neural networks and promises significantly reduced energy consumption, especially for sparse time-dependent data, by mimicking the biological brain. Magnetic tunnel junctions allow for drop-in replacement in current SNN CMOS-based hardware implementations to increase scalability and efficiency. The magnetization dynamics in the magnetic tunnel junction fully emulates the features of the Huxley-Hodgkin model of the neuron. Hence, the device allows for the implementation of biologically plausible algorithms. The device also emulates the simpler and commonly used Leaky-Integrate and Fire model. © 2023 IEEE.

关键词： Neural networks

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