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CPT:A Configurable Predictability Testbed for DNN Inference in AVs

Tsinghua Science and Technology 2025年第1期30卷 87-99页

作者： Liangkai Liu Yanzhi Wang Weisong Shi Department of Computer and Information Science University of DelawareNewarkDE 19713USA Department of Electrical&Computer Engineering Northeastern UniversityBostonMA 02115USA

Predictability is an essential challenge for autonomous vehicles(AVs)’*** neural networks have been widely deployed in the AV’s perception ***,it is still an open question on how to guarantee the perception predictability for AV because there are millions of deep neural networks(DNNs)model combinations and system configurations when deploying DNNs in *** paper proposes configurable predictability testbed(CPT),a configurable testbed for quantifying the predictability in AV’s perception *** provides flexible configurations of the perception pipeline on data,DNN models,fusion policy,scheduling policies,and predictability *** top of CPT,the researchers can profile and optimize the predictability issue caused by different application and system *** has been open-sourced at:https://***/Torreskai0722/CPT.

关键词： predictability deep neural network autonomous driving

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High Step-up DC-DC Converter Using Voltage Lift Techniques for Renewable Applications

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CSEE Journal of Power and Energy Systems 2025年第2期11卷 739-749页

作者： Saed Mahmoud Alilou Mohammad Maalandish Ataollah Samadian Pouya Abolhassani Seyed Hossein Hosseini Mohammad-Hassan Khooban Faculty of Electrical&Computer Engineering University of TabrizTabrizEast Azerbaijan 51666Iran Engineering Faculty Near East UniversityNorth CyprusMersin 10Turkey Aarhus University AarhusMidtjylland 8000Denmark

This paper presents a non-isolated DC-DC boost converter using voltage lift techniques. The proposed structure offers continuous input current. Substantial voltage gain along-side low voltage stress across semiconductors in this topology contribute to utilizing an MOSFET with lower RDS-ON and devices with low nominal voltage values. Consisting of a switch makes the control procedure of the converter uncomplicated. Voltage analysis for the converter has been done in continuous and discontinuous conduction modes (CCM)-(DCM). Furthermore, current calculation, design process and comparison study are provided. Dynamic performance of the proposed circuit is scrutinized by applying the state space average technique and small signal model. In order to accredit performance of the converter, an archetype with 26V input and 222V output voltages and approximately 196W power level at 50 kHz switching frequency has been built and tested.

关键词： High voltage gain non-isolated DC-DC converter voltage lift

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YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

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computers, Materials & Continua 2025年第2期82卷 3399-3417页

作者： Honglin Wang Yangyang Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.

关键词： Forest fire detection YOLOv5 lightweight small object detection

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Enhanced Multi-Scale Object Detection Algorithm for Foggy Traffic Scenarios

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computers, Materials & Continua 2025年第2期82卷 2451-2474页

作者： Honglin Wang Zitong Shi Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical&Computer Engineering University of Illinois at Urbana ChampaignUrbanaIL 61801USA

In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.

关键词： Deep learning object detection foggy scenes traffic detection YOLOv8

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DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

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computers, Materials & Continua 2025年第5期83卷 1929-1949页

作者： Honglin Wang Yaolong Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV *** address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object *** leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small *** the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere *** components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference ***,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object *** results on the VisDrone 2019 dataset demonstrate the effectiveness *** to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter *** results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.

关键词： YOLOv8 UAV-based object detection AFPN small-object detection head SPPELAN DualConv loss function

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A Multifunctional Hydrogel with Multimodal Self-Powered Sensing Capability and Stable Direct Current Output for Outdoor Plant Monitoring Systems

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Nano-Micro Letters 2025年第4期17卷 1-24页

作者： Xinge Guo Luwei Wang Zhenyang Jin Chengkuo Lee Department of Electrical&Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore Center for Intelligent Sensors and MEMS(CISM) National University of Singapore5 Engineering Drive 1Singapore 117608Singapore NUS Graduate School-Integrative Sciences and Engineering Program(ISEP) National University of SingaporeSingapore 119077Singapore Research Center for Sustainable Urban Farming(SUrF) National University of SingaporeSingapore 117558Singapore

Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability *** systems facilitate informed decisions that enhance efficiency in broader environmental *** outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex ***,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by *** can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous ***,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption *** potential extends to acting as other self-powered environmental *** multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.

关键词： Self-powered sensor Hydrogel Energy harvester Outdoor farming Self-sustainable IoT

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Wood Gasification in Catastrophes: Electricity Production from Light-Duty Vehicles

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Energy engineering 2025年第4期122卷 1265-1285页

作者： Baxter L.M.Williams Henri Croft James Hunt Josh Viloria Nathan Sherman James Oliver Brody Green Alexey Turchin Juan B.Garcia Martinez Joshua M.Pearce David Denkenberger Department of Mechanical Engineering University of CanterburyChristchurch8041New Zealand Alliance to Feed the Earth in Disasters(ALLFED) LafayetteCO 80026USA Department of Electrical&Computer Engineering Western UniversityLondonON N6A 5B9Canada Ivey Business School Western UniversityLondonON N6A 5B9Canada

Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical *** alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use,such as decentralized electricity generation from wood *** study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification.A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical *** vehicle successfully produced wood chips suitable for gasification with an energy return on investment(EROI)of 3.7 and sustained a stable output of 20 kW electrical *** analyses suggest such solutions could provide electricity to the critical water sanitation sector,equivalent to 4%of global electricity demand,if production of woodchippers was increased *** research could investigate the long-term durability of modified vehicles and alternative electricity generation,and quantify the scalability of wood gasification in GCIL *** work provides a foundation for developing resilient,decentralized energy systems to ensure the continuity of critical services during catastrophic events,leveraging existing vehicle infrastructure to enhance disaster preparedness.

关键词： Global catastrophic infrastructure loss decentralized energy systems wood gasification energy resilience

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Technical and economic feasibility assessment for hybrid energy system electricity and hydrogen generation: A case study

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Global Energy Interconnection 2025年第1期8卷 62-81页

作者： Paul C.Okonkwo Samuel Chukwujindu Nwokolo El Manaa Barhoumi Ibrahim B.Mansir Usman Habu Taura Barun Kumar Das Ahmed Bahgat Radwan Wilfred Emori Ephraim Bonah Agyekum Khalid Al Kaaf Mechanical&Mechatronics Engineering Department Dhofar UniversitySalalah 211the Sultanate of Oman Department of Physics Faculty of Physical SciencesUniversity of CalabarCalabar PMB 1115Nigeria Electrical&Computer Engineering Department Dhofar UniversitySalalah 211the Sultanate of Oman Department of Mechanical Engineering Prince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Centre for Energy Research and Training Ahmadu Bello UniversityPMB 1045ZariaNigeria Oil&Gas Research Center Sultan Qaboos UniversityMuscat 123the Sultanate of Oman Department of Mechanical Engineering Rajshahi University of Engineering&TechnologyRajshahi 6204Bangladesh Center for Advanced Materials Qatar UniversityDoha 2713Qatar School of Materials Science and Engineering Sichuan University of Science and EngineeringZigong 643000PR China Department of Nuclear and Renewable Energy Ural Federal University Named after the First President of Russia BorisEkaterinburg 620002Russia Western Caspian University Baku AZ1001Azerbaijan Department of Civil and Environmental Engineering Dhofar UniversitySalalah 211the Sultanate of Oman

Hydrogen is emerging as a promising alternative to fossil fuels in the transportation *** study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al Buraymi,and Salalah,using Hybrid Optimization of Multiple Electric Renewables(HOMER)*** hybrid energy systems,photovoltaic-wind turbine-battery,photovoltaic-battery,and wind turbine-battery were analyzed for each *** indicated that Duqm offers the lowest net present cost(NPC),levelized cost of energy,and levelized cost of hydrogen,making it the most cost-effective ***,Sensitivity analysis showed that as the life of electrolyzer increases during operation,the initial capital expenditure is distributed over a longer operational period,leading to a reduction in the *** so,renewable energy systems produced no emissions which supports Oman’s mission *** comprehensive analysis confirms the feasibility of establishing a hydrogen refueling station in Duqm,Oman,and highlights advanced optimization techniques’superior capability in designing cost-effective,sustainable energy systems.

关键词： Cities Economic indicator Hydrogen production Optimization Solar

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Deep Neural Network Ensembles for the Detection of Alzheimer's Disease Using Imbalanced Clock Drawing Test Images

Deep Neural Network Ensembles for the Detection of Alzheimer...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Sai Santosh Reddy Danda Alper Kursat Uysal Tian Qin Anu Kumar Varshitha Sannareddy Mengyao Hu Richard Gonzalez Yi Lu Murphey Department of Electrical&Computer Engineering University of Michigan-Dearborn Dearborn USA Department of Computer Engineering Alanya Alaaddin Keykubat University Alanya Turkiye School of Public Health The University of Texas Health Science Center at Houston Houston United States Department of Psychology and Statistics University of Michigan Ann Arbor MI USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

This paper explores innovative machine learning technologies for the detection of Alzheimer's Disease (AD) from large and imbalanced clock drawing test (CDT) images. We present a deep ensemble learning framework, 2Level MM-MD, which includes two-level ensemble of MultiModel deep neural networks (DNN) trained on MultiData blocks. The data blocks are generated by applying partition based and bootstrap statistical data resampling methods to the large but imbalanced CDT image data. To produce a more robust system that is better adapted to imbalanced CDT data, the 2Level MM-MD ensemble framework attempts to leverages both 1) the richness of large data blocks generated using effective data re-sampling schemes, and 2) multiple deep neural network systems that are trained on the resembled data blocks through transfer learning to produce a more robust system that is better adapted to more generalized CDT image domains. Three deep neural network models (EfficientNet, ResNet101, and ViT), along with six different sampling methods are used to generate re-balanced data blocks, and five different decision schemes are implemented and evaluated. The ensemble systems are trained and evaluated using a large CDT image dataset from Rounds 1 to 9 of the National Health and Aging Trends Study (NHATS), which contains over 47,723 CDT images, where 92% are class 0 (no dementia) and 8% are class 1 (probable dementia). The top classification system is a 2Level MM-MD system E5 with a two-level decision rule SoM. The system generated 0.67 Recall and 0.35 F1-Score on the test data. This represents a 123% improvement in Recall and a 25% improvement in F1-Score compared to the best baseline system, which is a classifier trained using EfficientNet with the training data balanced by oversampling the minority class.

关键词： Transfer learning Training data Artificial neural networks Aging Sampling methods Probabilistic logic Data models Ensemble learning Alzheimer's disease Clocks

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On chip chiral and plasmonic hybrid dimer or tetramer:Generic way to reverse longitudinal and lateral optical binding forces

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Chinese Physics B 2022年第5期31卷 305-316页

作者： Sudipta Biswas Roksana Khanam Rumi Tasnia Rahman Raima Saikat Chandra Das M R C Mahdy Department of Electrical&Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh

For both the longitudinal binding force and the lateral binding force,a generic way of controlling the mutual attraction and repulsion(usually referred to as reversal of optical binding force)between chiral and plasmonic hybrid dimers or tetramers has not been reported so *** this paper,by using a simple plane wave and an onchip configuration,we propose a possible generic way to control the binding force for such hybrid objects in both the near-field region and the far-field *** also investigate different inter-particle distances while varying the wavelengths of light for each inter-particle distance throughout the *** of all,for the case of longitudinal binding force,we find that chiral-plasmonic hybrid dimer pairs do not exhibit any reversal of optical binding force in the near-field region nor in the far-field region when the wavelength of light is varied in an air ***,when the same hybrid system of nanoparticles is placed over a plasmonic substrate,a possible chip,it is possible to achieve a reversal of the longitudinal optical binding ***,for the case of lateral optical binding force,we investigate a setup where we place the chiral and plasmonic tetramers on a plasmonic substrate by using two chiral nanoparticles and two plasmonic nanoparticles,with the setup illuminated by a circularly polarized plane *** applying the left-handed and the right-handed circular polarization state of light,we also observe the near-field and the far-field reversal of lateral optical binding force for both *** far as we know,so far,no work has been reported in the literature on the generic way of reversing the longitudinal optical binding force and the lateral optical binding force of such hybrid *** a generic way of controlling optical binding forces can have important applications in different fields of science and technology in the near future.

关键词： longitudinal binding force plasmonics chirality optical force

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