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arXiv 2024年

作者： Meilander, Jeff Herman, Chloe Manley, Andrew Augustine, Georgia Birdsell, Dawn Bolyen, Evan Celona, Kimberly R. Coffey, Hayden Cocking, Jill Donoghue, Teddy Draves, Alexis Erickson, Daryn Foley, Marissa Gehret, Liz Hagen, Johannah Hepp, Crystal Ingram, Parker John, David Kadar, Katarina Keim, Paul Lloyd, Victoria Osterink, Christina Queeney, Victoria Ramirez, Diego Romero, Antonio Ruby, Megan C. Sahl, Jason W. Soloway, Sydni Stone, Nathan E. Trottier, Shannon Van Orden, Kaleb Painter, Alexis Wallace, Sam Wilcox, Larissa Wood, Colin V. Yancey, Jaiden Gregory Caporaso, J. Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States Department of Biological Sciences Northern Arizona University FlagstaffAZ United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Genetics Core Facility Office of the Vice President for Research Northern Arizona University FlagstaffAZ United States Pathogen and Microbiome Division Translational Genomics Research Institute FlagstaffAZ United States Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States

Human excrement composting (HEC) is a sustainable strategy for human excrement (HE) management that recycles nutrients and mitigates health risks while reducing reliance on freshwater, fossil fuels, and fertilizers. We present a comprehensive microbial time series analysis of HEC and show that the initial gut-like microbiome of HEC systems transitions to a microbiome similar to soil and traditional compost in fifteen biological replicates tracked weekly for one year. © 2024, CC BY.

关键词： Anthropogenic

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Leveraging Machine Learning for Gait Phase Classification with Varied Training Methods

Leveraging Machine Learning for Gait Phase Classification wi...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Amal Mekni Jyotindra Narayan Hassène Gritli Higher Institute of Information and Communication Technologies University of Carthage RISC Laboratory (LR16ES07) Borj Cedria Tunis Tunisia Department of Computing Imperial College London UK Chair of Digital Health Universität Bayreuth Germany Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Accurate gait phase recognition is crucial for real-time analysis and intervention in rehabilitation, biomechanics, and prosthetics. However, achieving this is challenging due to the diverse machine learning (ML) training methods. This study employs ML algorithms to classify gait phases, focusing on stance and swing phases, utilizing open-source data from $\mathbf{1 0 0}$ participants ($41.91 \pm 5.3$ yrs). The classification algorithms considered are k-Nearest Neighbor (k-NN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayesian (NB) algorithms. The study evaluates these algorithm performances using two training methods with five and ten lower body movements: one randomly selects $80 \%$ of stance and swing phase data for training, while the other divides data by participants, allocating $80 \%$ for training and $20 \%$ for testing. When assessing accuracy with five movements, RF achieved $99.8 \%$ for both training methods. With ten movements, RF achieved a high accuracy of $99.9 \%$ using the second method. Notably, the performance of all ML algorithms exhibited improvement when considering data from ten movements versus five. Additionally, it was observed that the second training method proved more effective with five-movement data compared to data involving ten movements. This comprehensive evaluation highlights the potential of machine learning algorithms for accurate gait phase recognition in diverse applications with varied training methods.

关键词： Training Radio frequency Support vector machines Machine learning algorithms Accuracy Real-time systems Classification algorithms

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Optimized efficient attention-based network for facial expressions analysis in neurological health care

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Computers in Biology and Medicine 2024年 179卷 108822-108822页

作者： Munsif, Muhammad Sajjad, Muhammad Ullah, Mohib Tarekegn, Adane Nega Cheikh, Faouzi Alaya Tsakanikas, Panagiotis Muhammad, Khan Sejong University Seoul143-747 Korea Republic of Digital Image Processing Lab Department of Computer Science Islamia College Peshawar25000 Pakistan Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Institute of Communication and Computer Systems National Technical University of Athens Athens15773 Greece Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and *** code and data used in this work are available at: https://***/munsif200/Neurological-Health-Care. © 2024 Elsevier Ltd

关键词： Feature extraction

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Convolutional neural network regression for low-cost microalgal density estimation

TechRxiv

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TechRxiv 2022年

作者： Nguyen, Linh Nguyen, Dung K. Nghiem, Truong X. Nguyen, Thang Institute of Innovation Science and Sustainability Federation University Australia ChurchillVIC3842 Australia College of Agriculture and Science Life Chonnam National University Buk-gu Gwangju61186 Korea Republic of School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States School of Engineering & Computing Sciences Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States

Density of microalgae is critical information for production of algae in a closed cultivation system since it can be used to optimally control their growth rate, biomass concentration and quality of the products. Given advancement in image processing techniques and thanks to low-cost camera sensors, image based methods are increasingly widely utilized to indirectly estimate the density. Advantages of the image based techniques include being less invasive and more nondestructive and biosecured. However, most of the existing techniques rely on averaging all pixels of a microalgae image, which may eliminate crucial information of their spatial correlation. Therefore, in this work we propose to exploit a convolutional neural network (CNN) to efficiently extract information from the microalgae images, which are then employed to regress the density. Interestingly, the proposed deep CNN regression model accepts the whole color image as its input while the density is calculated in the output. The proposed CNN regression architecture was validated in real-world experiments where the microalgal strain Chlorella vulgaris was cultured and their images were captured by our low-cost camera sensor system. The obtained results demonstrate that the averaged estimation accuracy of the proposed model is 99.45% (± 0.68%) while the R2 value between the density predictions and the ground truths is 0.9997, which is highly accurate and practical. © 2022, CC BY.

关键词： Microalgae

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Hybrid K-Means and Firefly Algorithm-Based Load Balancer for Dynamic Task Scheduling in Fog computing for Postoperative Healthcare systems

Hybrid K-Means and Firefly Algorithm-Based Load Balancer for...

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Advanced computing Technologies (ICoACT), International Conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan G. Yuyaraj C. Tharun Kumar Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

In fog computing for postoperative healthcare systems, efficient task scheduling and load balancing are crucial for improving response time, energy efficiency, makespan, and the overall performance of the system. This manuscript introduces a novel hybrid load balancing framework that amalgamates KMeans clustering with the Firefly Algorithm (FA) to tackle the dynamic task scheduling complexities inherent in fog-based healthcare environments. The K-Means methodology is employed to categorize tasks according to their computational necessities and resource requirements, thereby establishing clusters that facilitate optimized resource distribution. The Firefly Algorithm, recognized for its proficiency in addressing optimization challenges, is harnessed to ascertain the optimal scheduling arrangement within each task cluster by adeptly modifying task allocation among fog nodes in response to light intensity, which is directly proportional to the overall performance of the system. Empirical findings indicate that the proposed mechanism markedly surpasses conventional algorithms regarding the optimization of response time, makespan, cost, and energy consumption, thereby presenting a viable solution for the management of postoperative care within healthcare systems employing fog computing infrastructure. The results substantiate that our proposed model enhances response time by 18%, makespan by 17%, energy consumption by 15%, cost by 17%, and level of load balance by 18%.

关键词： Processor scheduling Heuristic algorithms Clustering algorithms Medical services Optimal scheduling Dynamic scheduling Load management Time factors Resource management Edge computing

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WiMate: Location-independent Material Identification Based on Commercial WiFi Devices

WiMate: Location-independent Material Identification Based o...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Yu Gu Yanan Zhu Jie Li Yusheng Ji School of Computer and Information Hefei University of Technology Hefei China Shanghai Jiao Tong University Shanghai China Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781728181059

Material identification is playing an increasingly important role in our daily lives such as public security checks. X-ray-based technologies are highly radioactive because they rely on specialized devices to transmit high-frequency signals. Ultrasound-based technologies are cumbersome due to their large size. RF-based approaches necessitate the use of RFID which is usually expensive to be used in home and office environments. To this end, WiFi-based material identification approach has emerged recently as a low-cost yet effective alternative. In this paper, we propose WiMate, a noncontact material identification system leveraging only off-the-shelf WiFi devices. The key enabler of WiMate is a novel theoretical model we build to characterize how the electromagnetic wave decays when penetrating different materials. Our model identifies a unique feature for each material that only depends on the material itself. Consequently, we can leverage this feature coupling with the machine learning techniques for robust and accurate material identification. We prototype WiMate using low-cost commodity WiFi devices and evaluate its performance in real-world. The empirical study shows that WiMate can identify six different materials, i.e., board, paperboard, nickel, wood chip, iron and titanium, with an average accuracy of 96.20%.

关键词： Performance evaluation Ultrasonic imaging Prototypes Titanium Physical layer Nickel Object recognition

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An Ising Machine Formulation for Design Updates in Topology Optimization of Flow Channels

arXiv

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arXiv 2024年

作者： Suzuki, Yudai Aoki, Shiori Key, Fabian Endo, Katsuhiro Matsuda, Yoshiki Tanaka, Shu Behr, Marek Muramatsu, Mayu Quantum Computing Center Keio University Yokohama Kanagawa Japan Graduate School of Science and Technology Keio University Yokohama Kanagawa Japan Institute of Lightweight Design and Structural Biomechanics TU Wien Austria Tsukuba Ibaraki Japan Fixstars Corporation Tokyo Japan Green Computing System Research Organization Waseda University Tokyo Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Kanagawa Japan Keio University Tokyo Japan Keio University Tokyo Japan Computational Analysis of Technical Systems RWTH Aachen University Germany Department of Mechanical Engineering Keio University Yokohama Kanagawa Japan

Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, including digital or quantum annealers, have been used as efficient solvers for combinatorial optimization problems. Beyond combinatorial optimization, recent works have demonstrated applicability to other engineering tasks by tailoring corresponding problem formulations. In this study, we present a novel Ising machine formulation for computing design updates during topology optimization with the goal of minimizing dissipation energy in flow channels. We explore the potential of this approach to improve the efficiency and performance of the optimization process. To this end, we conduct experiments to study the impact of various factors within the novel formulation. Additionally, we compare it to a classical method using the number of optimization steps and the final values of the objective function as indicators of the time intensity of the optimization and the performance of the resulting designs, respectively. Our findings show that the proposed update strategy can accelerate the topology optimization process while producing comparable designs. However, it tends to be less exploratory, which may lead to lower performance of the designs. These results highlight the potential of incorporating Ising formulations for optimization tasks but also show their limitations when used to compute design updates in an iterative optimization process. In conclusion, this work provides an efficient alternative for design updates in topology optimization and enhances the understanding of integrating Ising machine formulations in engineering optimization. © 2024, CC BY-NC-ND.

关键词： Shape optimization

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Screenplay summarization using latent narrative structure

arXiv

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arXiv 2020年

作者： Papalampidi, Pinelopi Keller, Frank Frermann, Lea Lapata, Mirella Institute for Language Cognition and Computation School of Informatics University of Edinburgh School of Computing and Information Systems University of Melbourne

Most general-purpose extractive summariza-tion models are trained on news articles, which are short and present all important information upfront. As a result, such models are biased by position and often perform a smart selection of sentences from the beginning of the doc-ument. When summarizing long narratives, which have complex structure and present information piecemeal, simple position heuris-tics are not sufficient. In this paper, we propose to explicitly incorporate the underlying structure of narratives into general unsuper-vised and supervised extractive summarization models. We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays (i.e., extract an optimal sequence of scenes). Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with im-portant aspects of a CSI episode and improve summarization performance over general ex-tractive algorithms, leading to more complete and diverse summaries. Copyright © 2020, The Authors. All rights reserved.

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Real-time Vital Signs Monitoring Based on COTS WiFi Devices

Real-time Vital Signs Monitoring Based on COTS WiFi Devices

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yu Gu Xiang Zhang Huan Yan Zhi Liu Yusheng Ji School of Computer and Information Hefei University of Technology Hefei China The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781665429825

Real-time vital signs (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Current solutions are mostly based on wearable sensors or cameras, the former affects the quality of sleep, while the latter is not conducive to privacy protection, and the cost of these methods is usually expensive. In this paper, we propose Wital, a real-time vital signs monitoring system based on the low-cost and widespread COTS WiFi device. Most of the existing WiFi-based vital signs monitoring solutions utilize the line of sight (LOS) WiFi signals to achieve powerful performance. However, in our daily environments, NLOS sensing is more common. In this article, we first model the relationship between the energy ratio of LOS/NLOS signals and the ability to monitor vital signs based on the Ricean-K theory and theoretically prove that blocking LOS signals in NLOS sensing is more beneficial. We have also established a real-time vital signs monitoring system to verify our method, and the experimental results prove the effectiveness of our method.

关键词： Performance evaluation Privacy Heart beat Tracking Prototypes Real-time systems Biomedical monitoring

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Adaptive Optimization and Resource Allocation (AORA) Model for IoT-Edge computing Using Hybrid Newton-Raphson and Dolphin Echolocation Algorithm (HNR-DEA) Technique

Adaptive Optimization and Resource Allocation (AORA) Model f...

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Advanced computing Technologies (ICoACT), International Conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan A. Shiny K. Dharani Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The swift expansion of Internet of Things (IoT) applications demands the implementation of real-time data processing and low-latency communication, thereby posing challenges to conventional cloud computing paradigms. This study confronts these issues by amalgamating edge computing with hybrid optimization methodologies, and introduces the AORA (Adaptive Optimization and Resource Allocation) framework. The AORA framework capitalizes on intelligent task scheduling, dynamic resource allocation, and energy-efficient approaches to augment the performance of edge *** research methodology encompasses the collection and preprocessing of data derived from IoT devices, feature extraction, prediction of resource demand, and the optimization of task offloading. It employs the Hybrid Newton Raphson and Dolphin Echolocation Algorithm (HNR-DEA) methodology, which synergizes Newton Raphson optimization technique for faster convergence rate around local optima with bio-inspired Dolphin Echolocation Algorithm (DEA) for comprehensive global *** Experimental outcomes reveal considerable advancements, including a 45% reduction in average latency, a 38% decrease in overall energy consumption, and a 23% enhancement in resource utilization. Furthermore, the model attained a 97% task completion rate alongside consistently elevated network throughput. Simulations were executed in CloudSim with 100 IoT devices generating dynamic workloads and edge servers equipped with multi-core processors. The experimental framework utilized Google Cluster Data to replicate workload variations, thereby ensuring scalability and robustness. These results underscore the AORA model's potential as a resilient and scalable solution for real-time IoT applications.

关键词： Adaptation models Computational modeling Scalability Prediction algorithms Real-time systems Internet of Things Resource management Newton method Optimization Dolphins

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