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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Sayeedurrahman Sabir, Bushra Khan, Rashid Ahmed Masood, Dawood Nat, Aslam Aligarh Muslim University Z.H. College of Engineering & Technology Department of Electrical Engineering Aligarh202002 India National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan Sapienza University of Rome Department of Computer Control Management Engineering 00185 Italy

ISBN: (纸本)9798350338003

Solar energy is a clean and simple method of electricity generation. This electricity is utilized for various purposes, ranging from modest electrical gadgets and cars to major enterprises, satellites, and space exploration. The title of this project, "Solar tracking system with light-dependent resistors and a stepper motor controlled by a microcontroller"alludes to the project's purpose, which is to harvest as much solar energy as possible from a solar panel throughout the day using a specific Sun tracking mechanism. To do this, a mechanism was developed that tracks the sun's position and rotates the solar panel correspondingly so that it receives maximum sunlight at all times of the day. This is accomplished by mounting a stepper motor and a couple of light-dependent resistors (LDRs) on the solar panel, which adjusts its orientation based on the location of the sun. This project has three primary components: the solar panel, the stepper motor, and the LDRs. Microcontroller, sensor, and stepper motor. For this aim, an Arduino UNO microcontroller has been utilized. It accepts an output signal from a sensor as input, processes it, and controls stepper motors based on the supplied program. A stepper motor is attached to the shaft on which a panel is positioned to raise and lower the panel. Since maximal solar rays always strike the surface of the solar panel module, it is possible to obtain maximum power output, increasing its total efficiency as compared to the static panel. © 2023 IEEE.

关键词： Microcontrollers

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Community Membership Hiding via Gradient-based Optimization

arXiv

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arXiv 2025年

作者： Silvestri, Matteo Gabrielli, Edoardo Silvestri, Fabrizio Tolomei, Gabriele Department of Computer Science Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

We tackle the problem of community membership hiding, which involves strategically altering a network’s structure to obscure a target node’s membership in a specific community identified by a detection algorithm. We reformulate the original discrete counterfactual graph objective as a differentiable constrained optimization task. To solve this, we propose ∇- CMH, a gradient-based method that modifies the network’s structure within the feasible bounds for an individual target node, effectively concealing its membership. Experimental results across multiple datasets and community detection algorithms show that our approach surpasses existing baselines, offering a better balance between accuracy and computational efficiency. © 2025, CC BY.

关键词： Optimization algorithms

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Consistent Submodular Maximization

arXiv

引用

arXiv 2024年

作者： Dütting, Paul Fusco, Federico Lattanzi, Silvio Norouzi-Fard, Ashkan Zadimoghaddam, Morteza Google Research. Department of Computer Control and Management Engineering Sapienza University of Rome Italy

Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper we study this problem in a dynamic environment with consistency constraints: elements arrive in a streaming fashion and the goal is maintaining a constant approximation to the optimal solution while having a stable solution (i.e., the number of changes between two consecutive solutions is bounded). We provide algorithms in this setting with different trade-offs between consistency and approximation quality. We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances. © 2024, CC BY.

关键词： Data mining

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Deep Deterministic Policy Gradient control of Type 1 Diabetes

Deep Deterministic Policy Gradient Control of Type 1 Diabete...

引用

European control Conference (ECC)

作者： Federico Baldisseri Danilo Menegatti Andrea Wrona Department of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Rome Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Type 1 diabetes is one of the major concerns in current medical studies. Traditional clinical practice involves non-autonomous manual injection of insulin in the blood, while current research in the field of autonomous regulation of blood glucose concentration mostly focuses on model-based control techniques. This paper introduces a novel Reinforcement Learning-based controller for autonomous glycemic regulation in the treatment of type 1 diabetes, building on the Deep Deterministic Policy Gradient algorithm. The proposed control method is validated through in-vitro simulations on the Bergman glucoregulatory model, proving that it successfully preserves healthy values of blood glucose concentration, while overcoming both standard clinical practice and classical model-based control techniques in terms of both control effort and computational efficiency for real-time applications.

关键词： Computational modeling Predictive models Regulation Real-time systems Diabetes Glucose Logic

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Deep Reinforcement Learning control of Type-1 Diabetes with Cross-Patient Generalization

Deep Reinforcement Learning Control of Type-1 Diabetes with ...

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Mediterranean Conference on control and Automation (MED)

作者： Mohab M. H. Atanasious Valentina Becchetti Federico Baldisseri Danilo Menegatti Andrea Wrona Department of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Rome Italy

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Type 1 diabetes is one of the major concerns in current medical studies, as the World Health Organisation plans to reduce mortality due to such disease by one third by 2030. Standard clinical practice involves self-administered injections of insulin, while current research in the field of automatic control of blood glucose concentration mostly focuses on model-based control techniques. This work presents an application of a Deep Reinforcement Learning-based controller for autonomous treatment of type 1 diabetes, building on the Deep Determin-istic Policy Gradient algorithm. Such control framework is applied for the first time on the Python implementation of the UVA/Padova simulator, named Simglucose. The proposed methodology is validated through in-vitro simulations on an inter-cluster cross-generalization group of virtual adult patients, showing that normoglycemia is successfully preserved while assuring cross-patient generalization and outperforming clinical practice, without the direct knowledge of the amount of ingested carbohydrates.

关键词： Training Pediatrics Robustness Stability analysis Diabetes Safety Glucose

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A Study on Impact-Aware Aerial Robots Colliding with the Environment at Non-Vanishing Speed

A Study on Impact-Aware Aerial Robots Colliding with the Env...

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2025 International Conference on Unmanned Aircraft Systems, ICUAS 2025

作者： Indukumar, Gayatri Saccon, Alessandro Franchi, Antonio Gabellieri, Chiara Eindhoven University of Technology Department of Mechanical Engineering De Zaale Eindhoven 5600 MB Netherlands University of Twente Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty Robotics and Mechatronics Department Enschede 7500 AE Netherlands Sapienza University of Rome Department of Computer Control and Management Engineering Rome 00185 Italy

ISBN: (纸本)9798331513283

Enabling aerial robots to handle dynamic contacts happening at non-vanishing speeds can enlarge the range of their applications. In this work, we propose an impactaware strategy to allow aerial multirotor robots to recover from impacts. The method leverages a reactive strategy not requiring low-level changes to the motion controller commonly implemented onboard quadrotors, which might be not viable or not desirable for most users. Extensive simulation tests show that the proposed strategy considerably increases the tolerated velocity at impact in tasks in which the robot either picks an object up or collides against an object to clear its way. Preliminary experimental results using Crazyflie UAVs are also presented. © 2025 IEEE.

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Can Robots "Taste" Grapes? Estimating SSC with Simple RGB Sensors

arXiv

引用

arXiv 2024年

作者： Ciarfuglia, Thomas Alessandro Motoi, Ionut Marian Saraceni, Leonardo Nardi, Daniele Sapienza University of Rome Department of Computer Control and Management Engineering RM Rome00185 Italy

In table grape cultivation, harvesting depends on accurately assessing fruit quality. While some characteristics, like color, are visible, others, such as Soluble Solid Content (SSC), or sugar content measured in degrees Brix (°Brix), require specific tools. SSC is a key quality factor that correlates with ripeness, but lacks a direct causal relationship with color. Hyperspectral cameras can estimate SSC with high accuracy under controlled laboratory conditions, but their practicality in field environments is limited. This study investigates the potential of simple RGB sensors under uncontrolled lighting to estimate SSC and color, enabling cost-effective, robot-assisted harvesting. Over the 2021 and 2022 summer seasons, we collected grape images with corresponding SSC and color labels to evaluate algorithmic solutions for SSC estimation on embedded devices commonly used in robotics and smartphones. Our results demonstrate that SSC can be estimated from visual appearance with human-like performance. We propose computationally efficient histogram-based methods for resource-constrained robots and deep learning approaches for more complex applications. © 2024, CC BY-NC-ND.

关键词： Fruits

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Synthetic Data Generation for Anomaly Detection on Table Grapes

arXiv

引用

arXiv 2024年

作者： Motoi, Ionut M. Belli, Valerio Carpineto, Alberto Nardi, Daniele Ciarfuglia, Thomas A. Sapienza University of Rome Department of Computer Control and Management Engineering RM Rome00185 Italy

Early detection of illnesses and pest infestations in fruit cultivation is critical for maintaining yield quality and plant health. computer vision and robotics are increasingly employed for the automatic detection of such issues, particularly using data-driven solutions. However, the rarity of these problems makes acquiring and processing the necessary data to train such algorithms a significant obstacle. One solution to this scarcity is the generation of synthetic high-quality anomalous samples. While numerous methods exist for this task, most require highly trained individuals for setup. This work addresses the challenge of generating synthetic anomalies in an automatic fashion that requires only an initial collection of normal and anomalous samples from the user-a task that is straightforward for farmers. We demonstrate the approach in the context of table grape cultivation. Specifically, based on the observation that normal berries present relatively smooth surfaces, while defects result in more complex textures, we introduce a Dual-Canny Edge Detection (DCED) filter. This filter emphasizes the additional texture indicative of diseases, pest infestations, or other defects. Using segmentation masks provided by the Segment Anything Model, we then select and seamlessly blend anomalous berries onto normal ones. We show that the proposed dataset augmentation technique improves the accuracy of an anomaly classifier for table grapes and that the approach can be generalized to other fruit types. © 2024, CC BY-NC-ND.

关键词： Fruits

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Adaptive particle swarm optimization model for resource leveling

引用

Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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