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Smart Grid and Green Energy (ICSGGE), International Conference on

作者： Ghulam Hafeez Hisham Alghamdi Baheej Alghamdi Ahmed S. Alsafran Safeer Ullah Sadia Murawwat Dept. of Electrical Eng University of Engineering and Technology Mardan Pakistan Dept. of Electrical Eng Najran University Najran Saudi Arabia Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Dept. of Electrical & Compt. Eng. King Abdulaziz University Jeddah Saudi Arabia Dept. of Electrical Eng King Faisal University Alahssa Saudi Arabia Quaid-e-Azam College of Eng. & Tech Sahiwal Pakistan Dept. of Electrical Eng Lahore College for Women University Lahore Pakistan

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

Rising electricity demand and the integration of renewable energy generation (REG) have led to increased variability in the daily load profile, posing significant energy management challenges. Smart grids (SGs), with their advanced demand response (DR) mechanisms and REG integration, offer promising solutions to these issues. This study presents a multi-objective optimization approach to schedule energy consumption for energy management in REG integrated SGs, targeting reducing operating expenses, decreasing emissions, and optimizing user comfort. A probability density function (PDF) is employed to model REG with greater accuracy. The proposed framework leverages the multi-objective fishswarm optimization algorithm (MOFSOA) to identify globally optimal solutions effectively. The findings indicate that MOFSOA with DR excels in simultaneously optimizing cost, emissions, and user comfort, outperforming solutions without DR and offering a robust strategy for sustainable and efficient energy management in SGs.

关键词： Green energy Schedules Costs Accuracy Electricity Probability density function Demand response Smart grids Energy management Optimization

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Three-dimensional quantitative phase imaging evaluating glioblastoma targeting CAR T-cell efficacy 11

Three-dimensional quantitative phase imaging evaluating glio...

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Quantitative Phase Imaging XI 2025

作者： Serafini, Caroline E. Davarzani, Amin Cappabianca, Dan Collazos, Daniela Baez Li, Zhenmin Mamaghani, Deniz Tommasi, Anna Sarko, Lauren La Vonne Denne, Nina Mao, Leidong Saha, Krishanu Karumbaiah, Lohitash Robles, Francisco E. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA United States Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA United States Regenerative Bioscience Center University of Georgia AthensGA United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Wisconsin Institute for Discovery University of Wisconsin-Madison MadisonWI United States School of Electrical and Computer Engineering Georgia Institute of Technology 266 Ferst Drive AtlantaGA United States Department of Cellular Biology University of Georgia AthensGA United States Division of Neuroscience Biomedical and Translational Sciences Institute University of Georgia AthensGA United States Edgar L. Rhodes Center for Animal and Dairy Science College of Agriculture and Environmental Science University of Georgia AthensGA United States

ISBN: (纸本)9781510684065

Glioblastoma (GBM) is a highly aggressive brain tumor with limited treatment options and poor patient outcomes. While patient-derived spheroids offer a platform for studying GBM treatment, existing imaging modalities are often destructive or limited to endpoint analyses. Here, we present quantitative Oblique Back- illumination Microscopy (qOBM) as a novel, label-free imaging technique for real-time, non-invasive monitoring of GBM spheroids treated with tumor targeting chimeric antigen receptor T-cells (CAR T-cells). qOBM overcomes existing imaging limitations by providing high-resolution, 3D quantitative phase imaging through epi-illumination, enabling longitudinal visualization of cellular and subcellular processes within thick spheroids. Here, we apply qOBM to monitor the responses of GBM spheroids derived from three unique patient cell lines to targeted CAR T-cell immunotherapy and non-targeted controls. With this, we capture changes in the spheroids over 72 hours of treatment, allowing for visualization of processes such as CAR T-cell infiltration, mitosis, and apoptosis. Further, using images from GBM/CAR T cell cocultures, we develop a machine learning model for predicting spheroid cytotoxicity rates. Our findings establish qOBM as a powerful, non-destructive tool for longitudinal GBM spheroid toxicity monitoring and demonstrate its potential to advance personalized cancer therapy by providing real-time insight into treatment efficacy. © 2025 SPIE.

关键词： T-cells

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Comparison of Whole Blood Coagulation Profiles in COVID-19 and Sepsis Patients Using a Handheld Dielectric Coagulometer

Comparison of Whole Blood Coagulation Profiles in COVID-19 a...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Sina Pourang Michael A. Suster Asha Thomas Stephanie D. Lapping Lalitha V. Nayak Pedram Mohseni Dept. of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland OH USA Division of Hematology/Oncology University Hospitals Cleveland Medical Center Cleveland OH USA

Rapid point-of-care (POC) assessment of thrombosis is clinically important in patients who develop significant blood coagulation abnormalities such as noted with sepsis or COVID-19. In this work, we compare the coagulation profiles of whole blood in sepsis and COVID-19 patients using a handheld dielectric coagulometer termed ClotChip ® . ClotChip ® is a three-dimensional, parallel-plate, capacitive sensor integrated into a single-use microfluidic channel with a total volume of <20 µL for sample analysis. The readout curve is defined as the temporal variation in the real part of dielectric permittivity of whole blood at 1 MHz. ClotChip ® is sensitized towards detecting altered coagulation states by adding recombinant thrombomodulin (rTM) or activated protein C (APC). Using a handheld ClotChip ® device, we measure the coagulation status in whole blood samples from hospitalized patients with sepsis and COVID-19 (both regular floor and intensive care unit) and compare it to samples from healthy donors. The coagulation profiles show a difference between COVID-19 and sepsis patients when samples are treated with rTM, as well as a difference between moderate and severe COVID-19 infections when samples are treated with APC. This study demonstrates that ClotChip ® measures a coagulation profile in COVID-19 that is different from that in sepsis, highlighting its future potential as a POC diagnostic/prognostic tool in COVID-19-associated coagulopathy.

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Visual Field Prediction for Fundus Image with Generative AI

Visual Field Prediction for Fundus Image with Generative AI

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Honggu Kang Seonghyeon Ko Juchan Kim Duc-Tai Le Junghyun Bum Jongchul Han Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Sungkyun AI Research Institute Sungkyunkwan University Suwon South Korea Dept. of Ophthalmology Samsung Medical Center Seoul South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time.

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Zero-Shot Noise2Noise: Efficient Image Denoising without any Data

arXiv

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arXiv 2023年

作者： Mansour, Youssef Heckel, Reinhard School of Computation Information and Technology Technical University of Munich Germany Munich Center for Machine Learning Germany Dept. of Electrical and Computer Engineering Rice University United States

Recently, self-supervised neural networks have shown excellent image denoising performance. However, current dataset free methods are either computationally expensive, require a noise model, or have inadequate image quality. In this work we show that a simple 2-layer network, without any training data or knowledge of the noise distribution, can enable high-quality image denoising at low computational cost. Our approach is motivated by Noise2Noise and Neighbor2Neighbor and works well for denoising pixel-wise independent noise. Our experiments on artificial, real-world camera, and microscope noise show that our method termed ZS-N2N (Zero Shot Noise2Noise) often outperforms existing dataset-free methods at a reduced cost, making it suitable for use cases with scarce data availability and limited compute resources. A demo of our implementation including our code and hyperparameters can be found in the following colab notebook. © 2023, CC BY.

关键词： Image denoising

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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Wavelet Frequency Transformation for Specific Weeds Recognition 1

Wavelet Frequency Transformation for Specific Weeds Recognit...

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1st International Conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Habib, Shaban Khan, Ishrat Islam, Muhammad Albattah, Waleed Mohammed Alyahya, Saleh Khan, Sheroz Kamrul Hassan, Mohammad Qassim University College of Computer Dept. of Information Technology Buraydah Saudi Arabia Prince Sultan University College of Computer and Inofrmation Science Dept. of Information Technology Computer Science Department Riyadh11586 Saudi Arabia College of Engineering and Information Technology Onaizah Colleges Dept. of Electrical Engineering Saudi Arabia Universiti Kebangsaan Malaysia Center for Cyber Security Bangi Malaysia

ISBN: (纸本)9780738131771

Saudi Arabia is experiencing depleting water level which ultimately leading to having reduced level of weed and crops farms. The ongoing practice of watering for all kind of weeds at farms is manual which is laborious and slow besides waste of unregulated use of water. Therefore, there arises the need of automated water control, making the automated watering system as the viable option for precision weed control system. This paper has presented the development of real-time automated water sprinkle system for the target weeding area. The technique of wavelet frequency is developed as a software interface using MATLAB program detecting need of water sprinkling from the pictures of the leaves obtained. Software-based results are applied to hardware for real-time grass detection and classification based on shape and density due to reason that the leaves may be wide open, shrunk and leaves those with curved in features. The real-time system is capable of thus deciding the proportionate amount of water needed to be sprinkled over the weeds using a purposely developed hardware system. The system can detect areas where more, more or less water is needed, through a high-accuracy connected camera. © 2021 IEEE.

关键词： Real time systems

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Real-time gesture control UAV with a low resource framework

Real-time gesture control UAV with a low resource framework

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2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation, IRIA 2021

作者： Bhat, Manohar Mahto, Gopikishan Kesaria, Smit Femandes, Vikrant Arya, Kavi BMS College of Engineering Dept. of Electrical and Electronics Bangalore560019 India UPU Government Polytechnic Dept. of Computer Science and Engg. Durg491001 India Indian Institute of Technology Dept. of Systems and Control Mumbai400076 India Indian Institute of Technology Dept. of Computer Science and Engg. Mumbai400076 India

ISBN: (纸本)9781665433235

This study showcases a low-resource framework that enables people with no technical know-how to interact with drones, it also explores the capabilities of 2D- computer vision and deep learning techniques for gesture based interface systems on a low-cost micro drone with an onboard RGB camera. This Human-Robot Interaction system processes the real-time human pose to allow a user to command the drone, i.e., by providing direction to move and execute actions. A linear PD controller and image processing techniques are implemented to track humans whilst maintaining a safe distance from the user by perceiving dept. information through pose estimation. We incorporated the gesture recognition results into a drone using the Robot Operating System (ROS) and evaluated system performance indoor and outdoor. This low computation framework can be applied further to control robotic arms or mobile robots. © 2021 IEEE.

关键词： Drones

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Combining Knee Acoustic Emissions, Patient-Reported Measures, and Machine Learning to Assess Osteoarthritis Severity

Combining Knee Acoustic Emissions, Patient-Reported Measures...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Christopher Nichols H. Trask Crane Dave Ewart Omer T. Inan Bioengineering Graduate Program Georgia Institute of Technology Atlanta GA USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Minneapolis Veterans Affairs Medical Center Minneapolis MN USA Coulter Dept. of Biomedical Engineering School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Current methods for quantifying osteoarthritis severity have limited resolution and accessibility. Patient-recorded outcome measures such as the Knee Injury and Osteoarthritis Outcome Score (KOOS) capture symptom severity, but are subjectively reported and have little correlation with quantifiable metrics of disease such as Kellgren-Lawrence x-ray grade or MRI findings. Knee acoustic emissions (KAEs) offer a convenient, noninvasive option for quantifying joint health. Here, we use machine learning and wearable design to create an interpretable two-stage algorithm for combining KAEs and KOOS scores into an objective, more accessible method of quantifying disease severity. Our algorithm successfully discriminated between early and late-stage osteoarthritis (balanced accuracy = 85%, ROC-AUC = 0.88). The addition of KAEs improved classification of osteoarthritis severity over the use of KAEs (balanced accuracy = 53%, ROC-AUC = 0.786) or KOOS scores alone (balanced accuracy = 63%, ROC-AUC = 0.593). The findings suggest that KAEs combined with patient-recorded metrics can be used to make a more objective and accessible metric for digitally monitoring knee joint health.

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Failed yet successful: Learning from discontinued civic tech initiatives

Failed yet successful: Learning from discontinued civic tech...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing systems, CHI EA 2023

作者： Hamm, Andrea Shibuya, Yuya Cerratto Pargman, Teresa Bendor, Roy Hansen, Nicolai Brodersen Raetzsch, Christoph Shoji, Masahiko Bieber, Christoph Hendawy, Mennatullah Klerks, Gwen Schouten, Ben Weizenbaum Institute for the Networked Society Germany The University of Tokyo Japan Dept. of Computer and Systems Sciences Stockholm University Sweden Industrial Design Engineering Delft University of Technology Netherlands Computer Science Aalborg University Denmark Aarhus University Denmark Musashi University Japan Digital Democratic Innovations Center for Advanced Internet Studies Germany Urban Planning and Design Ain Shams University Egypt Industrial Design Eindhoven University of Technology Netherlands Transdisciplinary Innovation University of Technology Sydney Australia

ISBN: (纸本)9781450394222

The design of civic tech is often confronted with impediments, barriers, and a lack of resources. These and other causes may lead to the discontinuation and even abandonment of initiatives. Since seemingly failed projects are much more difficult to publish as articles, this workshop will provide academics and practitioners with a rare opportunity to exchange experiences and insights on discontinued civic tech initiatives. The goal of the workshop is to develop a better understanding of why some civic tech initiatives fail and ask whether discontinued initiatives may still somehow contribute to social change and the growth of digital civics. A variety of sub-questions around discontinued civic tech will be addressed in the workshop, including matters of participation, citizen science, public management, power structures and biases, and communication. © 2023 Owner/Author.

关键词： E-learning

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