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Long-range-interacting topological photonic lattices breaking channel-bandwidth limit

Light(Science & Applications) 2024年第10期13卷 2367-2375页

作者： Gyunghun Kim Joseph Suh Dayeong Lee Namkyoo Park Sunkyu Yu Department of Electrical and Computer Engineering Intelligent Wave Systems LaboratorySeoul National UniversitySeoul 08826Korea Department of Electrical and Computer Engineering Photonic Systems LaboratorySeoul National UniversitySeoul 08826Korea

The presence of long-range interactions is crucial in distinguishing between abstract complex networks and wave *** photonics,because electromagnetic interactions between optical elements generally decay rapidly with spatial distance,most wave phenomena are modeled with neighboring interactions,which account for only a small part of conceptually possible ***,we explore the impact of substantial long-range interactions in topological *** demonstrate that a crystalline structure,characterized by long-range interactions in the absence of neighboring ones,can be interpreted as an overlapped *** overlap model facilitates the realization of higher values of topological invariants while maintaining bandgap width in photonic topological *** breaking of topology-bandgap tradeoff enables topologically protected multichannel signal processing with broad *** practically accessible system parameters,the result paves the way to the extension of topological physics to network science.

关键词： topological breaking neighboring

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Constrained temperature and relative humidity predictive control:Agricultural greenhouse case of study

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Information Processing in Agriculture 2024年第3期11卷 409-420页

作者： Hafsa Hamidane Samira EL Faiz Iliass Rkik Mohamed El Khayat Mohammed Guerbaoui Abdelali Ed-Dahhak Abdeslam Lachhab Computer Engineering and Intelligent Electrical Systems Laboratory ModelingMaterials and Control of Systems TeamHigher School of TechnologyMoulay Ismail University of MeknesMorocco

The importance of Model Predictive Control(MPC)has significant applications in the agricultural industry,more specifically for greenhouse’s control ***,the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the *** methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed *** this paper,we introduce an application of Constrained Model Predictive Control(CMPC)for a greenhouse temperature and relative *** this purpose,two Multi Input Single Output(MISO)systems,using Numerical Subspace State Space System Identification(N4SID)algorithm,are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation *** this sense,linear state space models were adopted in order to evaluate the robustness of the control *** the system is identified,the MPC technique is applied for the temperature and the humidity *** results show that the regulation of the temperature and the relative humidity under constraints was guaranteed,both parameters respect the ranges 15℃≤T_(int)≤30℃and 50%≤H_(int)≤70%*** the other hand,the control signals uf and uh applied to the fan and the heater,respect the hard constraints notion,the control signals for the fan and the heater did not exceed 0≤uf≤4.3 Volts and 0≤uh≤5 Volts,respectively,which proves the effectiveness of the MPC and the tracking ***,we show that with the proposed technique,using a new optimization toolbox,the computational complexity has been significantly *** greenhouse in question is devoted to Schefflera Arboricola cultivation.

关键词： Constrained model predictive control Greenhouse MISO systems Optimization Relative humidity Temperature

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Design of a Remote Controlled Puppet Robot Imitating a Manual Driven Puppet Using Deep Learning Pose Detection 12

Design of a Remote Controlled Puppet Robot Imitating a Manua...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Hamzeh, Hamed Shahroozi, Kiarash Nabipour, Ahmad Kazemi, Parham Malek-Zahedi, Mohammad Hallajian, Mehdi Moradia, Hadi School of Electrical and Computer Engineering College of Engineering University of Tehran Advanced Robotics and Intelligent Systems Laboratory Iran

ISBN: (纸本)9798331529734

Traditional puppet manipulation systems often require human operators to be physically present in tight and cramped locations. This leads to challenges in the positioning and effective operation of puppets, particularly in the presence of children in educational settings. In this paper, we present the design, development, and integration of a cost-effective robotic system for remotely manipulating puppets in real-time. Additionally, the system integrates a YOLOv8 Pose Detection model, trained on a custom dataset with an accuracy of 91.4%, enabling real-time gesture recognition and allowing the robot to be controlled by imitating the motions of a manually operated puppet. The system demonstrates the potential for many applications, including interactive entertainment, teleoperation, and education. © 2024 IEEE.

关键词： Gesture recognition

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Bifunctional nanoprobe for simultaneous detection of intracellular reactive oxygen species and temperature in single cells

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Microsystems & Nanoengineering 2024年第6期10卷 161-172页

作者： Yanmei Ma Weikang Hu Jian Hu Muyang Ruan Jie Hu Ming Yang Yi Zhang Hanhan Xie Chengzhi Hu Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518000China Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhen 518000China

Living cells can rapidly adjust their metabolic activities in response to external stimuli,leading to fluctuations in intracellular temperature and reactive oxygen species(ROS)*** these parameters is essential for understanding cellular metabolism,particularly during dynamic biological *** this study,we present a bifunctional nanoprobe capable of simultaneous measurement of ROS levels and temperature within single *** nanoprobe features two individually addressable nanoelectrodes,with platinum(Pt)and nickel(Ni)coatings on both *** the tip,these two metal layers form a nano-thermocouple,enabling precise intracellular temperature measurements,while the Pt layer facilitates selective ROS *** dual functionality allows for real-time monitoring of cellular responses during synergistic chemo-photothermal therapy of cancer cells and zebrafish embryos subjected to mitochondrial toxic *** results demonstrate that the nanoprobe effectively measures increases in temperature and ROS levels in HeLa cells undergoing chemo-photothermal therapy,as well as in chemically stimulated zebrafish *** providing detailed analysis of submicrometer-scale temperature and ROS variations within living cells,this nanoprobe offers valuable insights into cellular processes and holds promise for early disease detection and drug development.

关键词： metabolism simultaneous holds

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Towards Green AI by Reducing Training Effort of Recurrent Neural Networks Using Hyper-Parameter Optimization with Dynamic Stopping Criteria 22

Towards Green AI by Reducing Training Effort of Recurrent Ne...

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22nd IEEE International Symposium on intelligent systems and Informatics, SISY 2024

作者： Podgorelec, Vili Fister, Iztok Vrbancic, Grega University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9798350385601

Neural networks have become a leading model in modern machine learning, able to model even the most complex data. For them to be properly trained, however, a lot of computational resources are required. With the carbon footprint of ever-growing adoption of neural networks in mind, an approach to reduce the required training resources would be very welcome. We designed a new training effort reduction method based on the calculation of area under the normalized loss curve and assessed it on the electricity consumption forecasting problem with the recurrent neural networks. The results show that the proposed method was able to considerably reduce the amount of computational resources, while maintaining the predictive performance, and thus contributing towards the Green AI. © 2024 IEEE.

关键词： Green AI hyper-parameter optimization machine learning neural networks resource optimization

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Adaptive Feature-Based Plant Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 335-346页

作者： Shirzi, Moteaal Asadi Kermani, Mehrdad R. Western University Advanced Robotics and Mechatronic Systems Laboratory Electrical and Computer Engineering Department LondonONN6A 5B9 Canada Western University Advanced Robotics and Mechatronic Systems Laboratory The Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

In this article, we propose a new algorithm to improve plant recognition through the use of feature descriptors. The accurate results from this identification method are essential for enabling autonomous tasks, such as stem-stake coupling, in precision agriculture. The proposed method divides the input seedling color image into subimages within the International Commission on Illumination, for three color axes, L for lightness, A for the green-red component, and B for the blue-yellow component, color space and extracts seven key feature descriptors for each subimage. It then uses feature descriptors to create a matrix, which is employed to train an artificial neural network to determine optimized cutoff values. This network suggests cutoff values for a multilevel threshold segmentation for plant recognition. The method provides robust and real-time adaptive segmentation adaptable to various seedlings, backgrounds, and lighting conditions. By enabling accurate segmentation of the plant, morphological image processing can more effectively eliminate leaves to locate the seedling stem. This methodology automates image analysis in seedling propagation facilities and greenhouses and enables a wide range of precision agricultural tasks. © 2023 IEEE.

关键词： Machine vision

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Control Aspects in Programming by Demonstation for Robotic Deep- Micro- Hole Drilling 32

Control Aspects in Programming by Demonstation for Robotic D...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Maric, Bruno Petric, Frano Orsag, Matko University of Zagreb LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (纸本)9798350395440

This paper explores control aspects in a collab-orative robotic system designed to replicate human operators' drilling skills in deep- micro- hole tasks, focusing on the glass-container mould industry. To replicate human performance, we develop a single-DOF Impedance-based drill controller for a standard industrial position-controlled robot, enhancing safe and smooth interactions between the drill bit and the mould. To address high feed rates observed in worker demonstrations and prevent bit breaks, we focus on minimizing non-axial motion during drilling on an off-the-shelf available industrial manipulator KUKA KR 10. This involves analyzing the impact of joint dynamics on drill motion and designing a control system for the robot's low-level control to achieve axial drill motion. We show that the proposed joint control system that takes into account relationship between joint and tool dynamics outperforms available robot low-level control in minimizing non-axial motion, leading to the robot being able to outperform the human in deep- micro- hole drilling. © 2024 IEEE.

关键词： Molds

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Design and Development of a Small UGV for Object Retrieval in Domestic Environments

Design and Development of a Small UGV for Object Retrieval i...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Jogeshwar, Bhaavin Kishore Bohannon, Joshua Sevil, Hakki Erhan Department of Intelligent Systems and Robotics PensacolaFL32514 United States Department of Electrical and Computer Engineering PensacolaFL32514 United States

ISBN: (纸本)9798331504847

The objective of this project is to develop an unmanned ground vehicle (UGV) prototype capable of retrieving small objects in domestic environments. As the robotics field continues to advance, the demand for autonomous systems that enhance daily life is rapidly increasing. Autonomous systems have the potential to improve efficiency by performing routine tasks, thereby reducing human effort and promoting convenience in household settings. This study explores the development of a small UGV designed to identify, navigate toward, and retrieve small objects while autonomously avoiding obstacles such as walls and furniture in domestic environments. The system utilizes neural networks for real-time object detection. The UGV design consists of an object retrieval mechanism suitable for handling items typically left scattered in domestic environments. The proof-of-concept results show the potential of designed UGV domestic, and could potentially enable further advancements in autonomous household assistance technologies. © 2025 IEEE.

关键词： Object detection

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A Novel Technology for Semiautomatic and Automatic Stem–Stake Coupling of Seedlings and Plants

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 254-262页

作者： Shirzi, Moteaal Asadi Kermani, Mehrdad R. Western University Advanced Robotics and Mechatronic Systems Laboratory Electrical and Computer Engineering Department LondonN6A 5B9 Canada Western University Department of Electrical and Computer Engineering LondonN6A 3K7 Canada Advanced Robotics and Mechatronic Systems Laboratory LondonN6A 5B9 Canada

This article introduces a novel mechatronic system for coupling the stems of seedlings and plants to wooden stakes or ropes, a crucial process for supporting them during growth, transportation, and fruiting in plant propagation facilities and greenhouses. The stem–stake coupling device utilizes interconnected mechanisms and an impedance control method to adjust motor torque and speed, shaping metallic wire into clips of various shapes and dimensions, effectively securing plant stems to stakes or ropes. In a robotic system, a claw-shaped arm mechanism, a stereo camera, and real-time vision techniques are integrated into the stem–stake coupling device to identify the optimal coupling point and automate the coupling task. This innovation addresses the labor-intensive task of manual coupling, offering a scalable solution for growers through handheld devices or fully automated robotic systems. In the context of increasing labor shortages and rising costs, the technology offers a sustainable and efficient alternative with significant potential to enhance operational efficiency in greenhouses and propagation facilities. © 2023 IEEE.

关键词： Seed

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Small Object Detection Without Attention for Aerial Surveillance 13th

Small Object Detection Without Attention for Aerial Surveil...

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13th International Conference on Information Technology and its Applications, CITA 2024

作者： Choi, Jehwan Nguyen, Duy-Linh Vo, Xuan-Thuy Jo, Kang-Hyun Intelligent Systems Laboratory Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan44610 Korea Republic of

ISBN: (纸本)9783031741265

This paper introduces the development of an essential deep-learning model for surveillance systems utilizing high-mounted CCTV or drones. Objects seen from elevated angles often look smaller and may appear at different angles compared to ground-level observations. To improve the detection of small objects, we propose a network incorporating an element-wise multiplication module based on the vanilla Vision Transformer (ViT) architecture. However, traditional transformer models need significant computational resources, which may not be practical for edge devices like CCTV cameras or drones. Therefore, we apply the Attention-Free Transformer (AFT) to reduce computational requirements enabling real-time operation on low-capacity devices. We validate the performance by combining ViT and AFT with the YOLOv5 real-time object detection model. Practical applicability is confirmed by implementing it on the low-capacity device named ODROID H3+. Validation datasets include Autonomous Driving Drone, VisDrone, AerialMaritime, and PKLot, all containing numerous small-sized objects. Experimental results on the VisDrone dataset show that YOLOv5 nano + AFT reduces parameter count by 4.6% while increasing accuracy by 1%, making it an efficient network. The model size is suitable for edge device implementation at 3.7 MB. Similarly, Aerial Maritime and PKLot datasets indicate a decreased amount of parameters and increased accuracy. Hence, the proposed deep learning model is applicable for aerial surveillance systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drones

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