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IEEE Symposium on Systems and Information engineering Design, SIEDS

作者： Lizaveta Babior Iman Sayyadzadeh Department of Computer Science & Engineering University of California San Diego La Jolla CA USA Rady School of Management University of California San Diego La Jolla CA USA

ISBN: (数字)9798331535759

ISBN: (纸本)9798331535766

Dijkstra’s shortest path algorithm is a fundamental graph search method widely used in domains such as navigation, robotics, gaming, and network routing. However, its performance can degrade on large, complex graphs. This paper summarizes and evaluates several optimization techniques that enhance Dijkstra’s algorithm, including heuristic-guided search (Greedy Best-First and A * ), hierarchical preprocessing (Contraction Hierarchies), and a hybrid Genetic Algorithm approach. We describe the integration of each technique with the base algorithm and conduct comparative efficiency analysis using a real-world road network dataset. Performance is evaluated through key metrics: runtime, memory usage, and vertices explored for each algorithm. Our results show that heuristic methods drastically reduce search exploration time, while a Contraction Hierarchies approach achieves millisecond query speeds. The genetic algorithm finds high-quality paths but incurs significantly higher computational cost. We discuss the trade-offs of each enhancement in terms of optimality, speed, and resource usage. While our optimizations were tested on road networks, we discuss their potential application to different graph types and problem domains. Future research should explore these optimizations in dynamic environments with real-time graph changes, such as traffic updates. These findings provide practical guidance on choosing appropriate pathfinding optimizations for various applications and graph sizes.

关键词： Navigation Roads Heuristic algorithms Routing Real-time systems User experience Computational efficiency Optimization Robots Genetic algorithms

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IndiText Boost: Text Augmentation for Low Resource India languages

arXiv

引用

arXiv 2024年

作者： Litake, Onkar Yagnik, Niraj labhsetwar, Shreyas Department of Computer Science and Engineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States

Text Augmentation is an important task for low-resource languages. It helps deal with the problem of data scarcity. A data augmentation strategy is used to deal with the problem of data scarcity. Through the years, much work has been done on data augmentation for the English language. In contrast, very less work has been done on Indian languages. This is contrary to the fact that data augmentation is used to deal with data scarcity. In this work, we focus on implementing techniques like Easy Data Augmentation, Back Translation, Paraphrasing, Text Generation using LLMs, and Text Expansion using LLMs for text classification on different languages. We focus on 6 Indian languages namely: Sindhi, Marathi, Hindi, Gujarati, Telugu, and Sanskrit. According to our knowledge, no such work exists for text augmentation on Indian languages. We carry out binary as well as multi-class text classification to make our results more comparable. We get surprising results as basic data augmentation techniques surpass LLMs. © 2024, CC BY.

关键词： Classification (of information)

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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Mixed-Integer Optimal Control With Disjunctive Characteristic Time Constraints for Energy-Optimal Speed Planning

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IEEE Transactions on Transportation Electrification 2025年

作者： Dong, Shiying Chen, Hong Luo, Xi Meng, Xiangyu Huang, Yanjun Liu, Qifang Gao, Bingzhao Jilin University Department of Control Science and Engineering Changchun China Tongji University College of Automotive Studies Shanghai China Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States

This paper considers the energy optimization problem with multiple time window constraints, which is motivated by the eco-driving of connected and automated vehicles (CAVs) in urban traffic. Timely crossing of signalized intersections defines complex spatio-temporal constraints, and multiple available open time windows introduce the discontinuous feasible domains of crossing times. The CAVs should select an open time-window for each intersection, determine the crossing times, and optimize the speed profiles accordingly, which turns out to be a hard-to-solve optimization problem. To address this challenge, this paper proposes a unifying approach that transforms the energy optimization problem into a novel mixed-integer optimal control problem with characteristic time constraints (MIOCP-CT). This approach facilitates the simultaneous optimization of open time windows, crossing times, and speed profiles within a single optimization problem, which can be solved by our proposed novel numerical solution procedure. The application of the proposed method in the eco-driving of CAVs approaching multiple signalized intersections demonstrates its performance within real-world urban driving cycles. The experimental results of the hardware-in-the-loop (HiL) demonstrate a superior energy-saving performance of 5.83% compared to existing methods, while also achieving real-time feasibility within 100 milliseconds. © 2015 IEEE.

关键词： Mixed-integer linear programming

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MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule

arXiv

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arXiv 2024年

作者： Cai, Guohui Zhang, Ruicheng He, Hongyang Zhang, Zeyu Ergu, Daji Cao, Yuanzhouhan Zhao, Jinman Hu, Binbin Liao, Zhibin Zhao, Yang Cai, Ying College of Computer Science and Artificial Intelligence Southwest Minzu University Chengdu China School of Intelligent System Engineering Sun Yat-sen University Guangzhou China Department of Computer Science University of Warwick United Kingdom Australian National University CanberraACT2601 Australia School of Computer Science and Technology Beijing Jiaotong University Beijing China University of Toronto Toronto Canada University of Adelaide Adelaide Australia La Trobe University Melbourne Australia

Pulmonary nodules are critical for early lung cancer diagnosis, but traditional CT imaging methods suffer from low detection rates and poor localization. Small nodule detection is challenging due to subtle differences in density and issues like occlusion. Existing methods such as FPN, with its fixed feature fusion and limited receptive field, struggle to effectively overcome these issues. To address these challenges, our paper proposed three key contributions: Firstly, we proposed MSDet, a multiscale attention and receptive field network for detecting tiny pulmonary nodules. Secondly, we proposed the extended receptive domain (ERD) strategy to capture richer contextual information and reduce false positives caused by nodule occlusion. We also proposed the position channel attention mechanism (PCAM) to optimize feature learning and reduce multiscale detection errors, and designed the tiny object detection block (TODB) to enhance the detection of tiny nodules. Experiments on the LUNA16 dataset show an 8.8% improvement in mAP over YOLOv8, achieving state-of-the-art performance. The code is available at https://***/CaiGuoHui123/MSDet. © 2024, CC BY-NC-SA.

关键词： Lung cancer

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The Impact of COVID-19 on Global GMAT Test-Taking Patterns: A Statistical Analysis 13th

The Impact of COVID-19 on Global GMAT Test-Taking Patterns...

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13th International Conference on Health Information science, HIS 2024

作者： Xu, Puti Yin, Jiao Cao, Jinli Zhang, Yanchun Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC3011 Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua321004 China Department of New Networks Peng Cheng Laboratory Shenzhen518055 China

ISBN: (纸本)9789819655960

The COVID-19 pandemic has profoundly impacted the education sector globally, yet its effects on GMAT test-taking patterns remain underexplored. This study systematically analyzes changes in the number of examinees and mean total scores during the pandemic, examining the association between COVID-19 metrics and these changes. Data from GMAT test reports and COVID-19 statistics across various continents were analyzed using the Wilcoxon signed-rank test and the Spearman correlation coefficient. Results indicate a significant decrease in examinee numbers in the early stages of the pandemic, with a negative correlation between new COVID-19 cases/deaths and annual examinee numbers. This trend is more pronounced among males than females. Mean total scores declined significantly during the early stage of the outbreak, yet no significant correlation was found between COVID-19 metrics and changes in scores. These findings highlight the pandemic’s substantial impact on GMAT testing due to exam cancellations and socio-psychological factors at the early stage. As restrictions eased, the effects diminished, suggesting no sustained changes in academic performance. This study enhances the understanding of the pandemic’s impact on GMAT dynamics, providing decision support and scenario foresight for policymakers to be more prepared for similar challenges in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Statistical tests

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Optimization and Application of a Pathology computer Assisted Microscope (PathCAM) for Real-Time Slide Digitization and Analysis

Optimization and Application of a Pathology Computer Assiste...

引用

Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Cooper, Max S. Ashman, Kimberly Maira, Cooper Halat, Shams Sholl, Andrew Wenk, Carola Fox, Sharon Summa, Brian Brown, J. Quincy Department of Biomedical Engineering Tulane University New OrleansLA70118 United States Department of Computer Science Tulane University New OrleansLA70118 United States Department of Pathology and Lab Medicine Tulane University New OrleansLA70112 United States Southeast Louisiana Veterans Healthcare System Pathology and Laboratory Medicine Service New OrleansLA70119 United States LSU Health Sciences Center Department of Pathology New OrleansLA70112 United States Delta Pathology Group Touro Infirmary New OrleansLA70115 United States

We describe the development and optimization of PathCAM, a system for real-time slide digitization during clinical glass slide review, and initial real-time "expert-in-the-loop" applications including assess... 详细信息

关键词： Real time systems

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Motion Correction Image Reconstruction using NeuralCT Improves with Spatially Aware Object Segmentation 7

Motion Correction Image Reconstruction using NeuralCT Improv...

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7th International Conference on Image Formation in X-Ray Computed Tomography

作者： Chen, Zhennong Gupta, Kunal Contijoch, Francisco Department of Bioengineering University of California La Jolla San DiegoCA United States Department of Computer Science Engineering University of California La Jolla San DiegoCA United States Department of Radiology University of California La Jolla San DiegoCA United States

ISBN: (纸本)9781510656697

NeuralCT [1] has been recently proposed as an implicit neural representation-based image reconstruction that can produce time-resolved images from CT sinograms and reduce motion artifacts, even when undergoing complex motions. NeuralCT does not require the prior motion model or estimation of object motion. Instead, it utilizes a network to implicitly represent the time-varying object boundary by singed distance function and optimizes the network via differentiable rendering. In this work, we modify the NeuralCT framework to reconstruct scenes that have multiple moving objects with distinct attenuation levels. We show that the performance of NeuralCT reconstruction depends on the quality of the initialization of the network (in this case, object segmentation in motion corrupted FBP image). We show how spatially aware object segmentation can improve motion-corrected reconstruction in moving objects with multiple attenuation levels despite high angular motion and complex topological changes. © 2022 SPIE.

关键词： Image reconstruction

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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ARCH-COMP22 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics 9th

ARCH-COMP22 Category Report: Continuous and Hybrid Systems w...

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9th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2022

作者： Geretti, Luca Sandretto, Julien Alexandre Dit Althoff, Matthias Benet, Luis Collins, Pieter Duggirala, Parasara Sridhar Forets, Marcelo Kim, Edward Mitsch, Stefan Schilling, Christian Wetzlinger, Mark Department of Computer Science University of Verona Verona Italy ENSTA Paris Institut Polytechnique de Paris Palaiseau France Technische Universität München Munich Germany Mexico Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Computer Science University of North Carolina at Chapel Hill United States Universidad de la República Montevideo Uruguay Computer Science Department Carnegie Mellon University PittsburghPA United States Aalborg University Aalborg Denmark

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2022. This year, 6 tools Ariadne, CORA, DynIbex, JuliaReach, Kaa and KeYmaera X (in alphabetic order) participated. These tools are applied to solve reachability analysis problems on six benchmark problems, two of them featuring hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools. © 2022, EasyChair. All rights reserved.

关键词： Hybrid systems

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