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2024 Multimedia university engineering Conference, MECON 2024

作者： Qamar, Muhammad Salman Haq, Ihsan Ul Munir, Muhammad Fahad Roslee, Mardeni Bin Awan, Adnan Anwar Waseem, Athar International Islamic University Department of Electrical and Computer Engineering Islamabad Pakistan Multimedia University Centre for Wireless Technology Faculty of Engineering Cyberjaya63100 Malaysia School of Engineering California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798331530747

This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. The proposed methodology is precisely crafted to optimize energy consumption (EC) while preserving the overall effectiveness of WSNs. LEACH (Low Energy Adaptive Cluster Hierarchy) is a cluster-based protocol where non-cluster head nodes deactivate their RF until their allocated time slots. Nevertheless, a drawback of traditional LEACH is its random rotation of local Cluster Heads (CH), leading to unequal cluster distribution. To overcome this issue, we proposed a novel hybrid technique called LEACH-D protocol and Artificial Neural Network (ANN). The LEACH-D algorithm enhances the duration of transmission tasks while ensuring uniform battery energy consumption. On the other hand, the ANN plays a pivotal role in coordinating optimal routing decisions and CH placement, refining data aggregation and transmission mechanisms to restrain idle listening. The findings indicate that the suggested approach manifests as substantial reductions in energy consumption and prolonged network lifespan of WSN. © 2024 IEEE.

关键词： Sensor nodes

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A Lightweight Drone Simulator 10

A Lightweight Drone Simulator

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10th International Conference on software Defined Systems, SDS 2023

作者： Blake Putnam, Evan Nihal Senarath, Dylan Ramirez Urquijo, Giselle Tunc, Cihan Bryce, Renee California State Polytechnic University Humboldt Department of Computer Science ArcataCA United States California State Polytechnic University Pomona Department of Electrical and Computer Engineering PomonaCA United States University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350382235

Drones are valuable assets across industries for a variety of purposes, including but not limited to surveillance, transportation, delivery, smart agriculture, etc. However, drone-related studies may require significant costs to purchase, configure, and deploy to study algorithms and how drones interact with each other. Alternatively, drone simulators can provide a lower-cost option to investigate drones and related algorithms. Nevertheless, drone simulators on the market today tend to be computationally heavy, have a steep learning curve, and may be overwhelming for abstract problem-solving studies. There is a need for an inexpensive and easy testing alternative for researchers and for high-level algorithm development. In this paper, we present a lightweight and easy-to-use drone simulator that allows the definition of multiple drones with their sensors, actuators, speed, and battery limit as well as their communication among themselves. The simulator also allows the definition of the simulation world using small blocks that can be defined using different parameters. We presented two scenarios, such as package delivery and survey area coverage, to show the capabilities of the simulator. The simulator information can be achieved from our group website. © 2023 IEEE.

关键词： Drones

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AI Ethics in Healthcare-A Survey 23

AI Ethics in Healthcare-A Survey

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23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Singh, Bhavraaj Rahim, Muhammad Abdul Basit Ur Hussain, Shahid Rizwan, Muhammad Abid Zhao, Jie California State University Department of Computer Science Long BeachCA United States Penn State University Department of Computer Science Behrend PA United States Florida Polytechnic University Department of Computer Science LakelandFL United States Penn State University Department of Computer Science and Software Engineering Behrend PA United States

ISBN: (纸本)9798350359398

In the realm of healthcare, the exponential growth of Artificial Intelligence has precipitated a need to scrutinize its ethical implications. This research undertakes a comprehensive survey to unravel the intricate tapestry of AI ethics within the healthcare landscape. Objective is to delineate the multifaceted challenges that arise from the symbiotic relationship between AI and healthcare and proposing viable solutions for mitigation. A pivotal focus of this study is to bridge the divide between medical practitioners and AI developers, thus addressing a conspicuous research gap. This gap pertains to fostering seamless collaboration between these stakeholders, ensuring that AI systems align with the actual requirements of healthcare providers. The paper explores strategies to establish an effective dialogue, facilitating the design and implementation of ethically sound AI applications. The paper also delves into the moral conundrums engendered by AI's lack of emotional intelligence in sensitive healthcare contexts. The absence of human emotional comprehension has, in certain instances, led to grievous outcomes, necessitating a nuanced approach to machine autonomy. This study advocates for an equilibrium where intelligent machines operate under prudent human oversight, striking a harmonious balance between precision and compassion. Furthermore, the research evaluates prevailing systems and their attendant challenges, emphasizing the advantages of integrating ethically guided, intelligent systems. The paper contemplates governance structures, protocols and strategies to counteract biases inherent in AI algorithms. By dissecting the principles of fairness, accountability and transparency, this study paves the way for a cogent framework that governs AI deployment within healthcare. In essence, this paper charts an uncharted course through the unexplored terrain of AI ethics in healthcare. It not only recognizes the inherent challenges but also underscores the imperative

关键词： Data privacy

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Accelerated Image Stitching Via Parallel Computing for UAV Applications

Accelerated Image Stitching Via Parallel Computing for UAV A...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Ramirez, Rick Korah, John Bhandari, Subodh Nguyen, Tu Chen, Yuqi Le, Du D. Computer Science Department California State Polytechnic University PomonaCA91768 United States Aerospace Engineering Department California State Polytechnic University PomonaCA91768 United States Computer Science Department University of California IrvineCA92697 United States Informatics Department University of California IrvineCA92697 United States Mathematics and Computer Science Department Mt. San Antonio College CA91789 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In this work, we formulate an accelerated image fusion algorithm for Unmanned Aerial Vehicle (UAV) application which is based on image stitching using invariant features. By utilizing parallel computing techniques for distributed and shared memory architectures, we have improved both the run time of the feature-based image stitching procedure, and the quality of the results when compared to sequential processing. The images used to generate the panorama were obtained from a UAV at a frame rate of 30Hz and a resolution of 3840×2160. As the UAV traverses across the landscape, a video frame buffer is collected and sent to a multi-core Central Processing Unit (CPU) where individual CPU cores perform image fusion in parallel. Our algorithm can be applied to any multi-core CPU (or cluster of multi-core CPU architectures) with support for Graphics Processing Unit (GPU) accelerations. The procedure was tested on various hardware platforms including edge computing devices, ground stations, and high-performance computing environments. The results show that as we increase the number of available CPU cores, there is a decrease in the runtime by as much as 7x under the distributed memory design. There is also a decrease in runtime during the feature detection stage of the fusion process under the shared memory design by a factor of 10x. Finally, by incorporating GPU acceleration at various stages of the stitching procedure, we can decrease the runtime by another factor of 2x. Furthermore, as we increase the number of threads utilized in the shared memory design, we find that there is a significant improvement in the quality of the panorama. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Graphics processing unit

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Optimizing Weighted Ensemble ML IDS Using Parallelization and Batch Learning in Healthcare Networks

Optimizing Weighted Ensemble ML IDS Using Parallelization an...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Ward, Blake Gonzalez, Daniel Alt, Jake Fang, Dongfeng Xu, Shengjie The Department of Computer Science and Software Engineering College of Engineering California Polytechnic State University San Luis ObispoCA93407 United States The Department of Cyber Intelligence and Information Operations College of Applied Science and Technology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9798350350548

The increasing adoption of IoT devices in healthcare necessitates the development of lightweight and fast intrusion detection systems (IDS). This paper proposes a weighted ensemble ML IDS with parallelized batch learning to optimize runtime while maintaining ML performance. The proposed method is compared to other solutions, evaluating ML performance and runtime across three different datasets. Experimental results demonstrate that although batch learning introduces feedback delay that slightly degrades ML performance, parallel computing significantly reduces runtime. © 2024 IEEE.

关键词： Network intrusion

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Task Allocation Optimization Strategy in UAV-enabled Mobile Edge Computing System

Task Allocation Optimization Strategy in UAV-enabled Mobile ...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Yang, Sanhuan Su, Zhou Xu, Qichao Xing, Rui Fang, Dongfeng Shanghai University School of Mechatronic Engineering and Automation Shanghai China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China California Polytechnic State University San Luis Obispo Department of Computer Science and Software Engineering United States

ISBN: (纸本)9798350333336

Unmanned Aerial Vehicles (UAVs) have been widely used as aerial computing devices in Internet of Things (IoT) to support time-sensitive and compute-intensive applications and services, e.g. automatic driving, data analysis, etc. In this paper, a UAV-enabled mobile edge computing (MEC) system is considered for computing offloading services, in which the offloaded tasks from ground devices are processed by UAVs. However, when multiple computing tasks exist, the task allocation for devices should be carried out at the lowest computing cost. To this end, we first propose a non-cooperative game model-based power allocation (NGPA) scheme, and the optimal power allocation strategy of devices is determined to minimize transmission energy consumption. Next, we model the problem of task allocation as a bilateral matching between devices and UAVs to minimize the overall computing cost. Then, a bilateral matching mechanism based on improved Hungarian algorithm is provided to obtain the optimal solution. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes on computing cost. © 2023 IEEE.

关键词： Mobile edge computing

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A Systematic Review on Pattern-based GUI Testing of Android and Web Apps: state-of-the-Art, Taxonomy, Challenges and Future Directions 25

A Systematic Review on Pattern-based GUI Testing of Android ...

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25th International Multi Topic Conference, INMIC 2023

作者： Kousar, Ambreen Khan, Saif Ur Rehman Hussain, Shahid Abdul Basit Ur Rahim, M. Wang, Wen-Li Ibrahim, Naseem Comsats University Islamabad Dept of Computer Science Islamabad Pakistan Shifa Tameer-e-Millat University Dept. of Software Engineering Islamabad Pakistan Penn State University Behrend School of Computer Science and Software Engineering Pennsylvania16563 United States California State University Dept. of Computer Engineering and Computer Science Long Beach United States Penn State Erie Dept. of Computer Science and Software Engineering United States

ISBN: (纸本)9798350317701

Graphical User Interface (GUI) designing and testing of different applications is significant from a user point of view. GUI testing ensures that the user interface of apps is visually appealing, user-friendly, and enhances user satisfaction. Pattern-Based GUI Testing (PBGT) is a new model-based testing approach that aims to enhance reusability while reducing the workload associated with modeling user interfaces and testing applications. The objective of conducting a Systematic Literature Review (SLR) on pattern-based GUI testing is to consolidate existing knowledge and provide insights for improving the effectiveness of PBGT methods. The objectives of conducting an SLR are the identification of current state-of-the-art approaches and tools in the targeted research context. To accomplish the mentioned objectives, A hybrid methodology combining Kitchenham's SLR and PRISMA guidelines was used to conduct this SLR. We performed a keyword-based search on well-known databases IEEE, ACM, science Direct, Wiley, and Springer Link to determine the potential studies published in the last 10 years. Following a comprehensive systematic analysis, we identified a total of twenty-four studies employing varied approaches for pattern-based GUI testing. Additionally, we identified 11 tools that have been used and proposed in PBGT for Android and web apps and made a taxonomy of tools. We also identified challenges and research directions. The implications of this study are significant. The current study is helpful for researchers intending to work in pattern-based GUI testing. The comprehensive knowledge about the current state-of-the-art Tools, techniques, and approaches will help practitioners to effectively apply them in industry and enable researchers to extend their work in this context in future research. © 2023 IEEE.

关键词： Graphical user interfaces

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SSA: a novel method for Single-cell and Spatial transcriptomics Alignment 16th

SSA: a novel method for Single-cell and Spatial transcriptom...

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16th International Conference on Bioinformatics and Computational Biology, BICOB 2024

作者： Tran, Bang Tran, Dao Nguyen, Tin Department of Engineering & Computer Science California State University 6000 J St SacramentoCA95819 United States Department of Computer Science and Software Engineering 345 W Magnolia Ave AuburnAL36849 United States

Single-cell RNA sequencing (scRNA-seq) provides expression profiles of individual cells but fails to preserve crucial spatial information. On the other hand, Spatial Transcriptomics technologies are able to analyze specific regions within tissue sections, but lack of the capability to examine in single-cell resolution. To overcome these issues, we present Single-cell and Spatial transcriptomics Alignment (SSA), a novel technique that employs an optimal transport algorithm to assign individual cells from a scRNA-seq atlas to their spatial locations in actual tissue based on their expression profiles. SSA has demonstrated superior performance compared to existing methods SpaOTsc, Tangram, Seurat and DistMap using 10 semi-simulated datasets generated from a high-resolution spatial transcriptomics human breast cancer dataset with 100,064 cells. This advancement provides a refined tool for researchers to delve deeper in understanding of the relationship between cellular spatial organization and gene expression. © 2024, EasyChair. All rights reserved.

关键词： Tissue

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Alignment efficient image-sentence retrieval considering transferable cross-modal representation learning

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Frontiers of computer science 2024年第1期18卷 77-91页

作者： Yang YANG Jinyi GUO Guangyu LI Lanyu LI Wenjie LI Jian YANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Computing Hong Kong Polytechnic UniversityHong Kong 100872China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing 210094China 14th Research Institute of China Electronics Technology Group Corporation Nanjing 210094China guangyu.li2017@***

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from another modality in *** basic assumption behind these methods is that parallel multi-modal data(i.e.,different modalities of the same example are aligned)can be obtained in *** other words,the image-sentence cross-modal retrieval task is a supervised task with the alignments as ***,in many real-world applications,it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs,leading the non-parallel multi-modal data and existing methods cannot be used *** the other hand,there actually exists auxiliary parallel multi-modal data with similar semantics,which can assist the non-parallel data to learn the consistent ***,in this paper,we aim at“Alignment Efficient Image-Sentence Retrieval”(AEIR),which recurs to the auxiliary parallel image-sentence data as the source domain data,and takes the non-parallel data as the target domain *** single-modal transfer learning,AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel ***,AEIR learns the image-sentence consistent representations in source domain with parallel data,while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial ***,we can effectively learn the consistent representations for target domain considering both the structure and semantic ***,extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.

关键词： image-sentence retrieval transfer learning semantic transfer structure transfer

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Exploring Extended Reality to Optimize Human-Cobot Interaction 25

Exploring Extended Reality to Optimize Human-Cobot Interacti...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Javier Gonzalez-Sanchez Rafael Guerra-Silva Computer Science and Software Engineering California Polytechnic State University San Luis Obispo California USA Industrial Technology and Packaging California Polytechnic State University San Luis Obispo California USA

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

We explore the integration of extended reality technologies for advancing human-robot collaboration in manufacturing environments. Utilizing a commercial XR headset, the Meta Quest 3, we capture real-time hand and virtual object movements to intuitively control a Universal Robotics e-Series cobot. Our primary objective is to assess whether this interaction paradigm is intuitive for operators while effectively leveraging the cobot's capabilities. Data is streamed from the headset to a broker using MQTT. A local computing module subscribes to the data, translates coordinates into the robot's workspace, and generates control instructions for the cobot. Preliminary findings suggest that XR-based interaction can provide an immersive and efficient framework for human-robot collaboration. These results set the stage for further exploration of multi-user and multi-robot scenarios.

关键词： Headphones Hands Protocols Extended reality Tracking Robot kinematics Human-robot interaction Collaborative robots Real-time systems Motion capture

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