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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications"

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End-Effector Pose Estimation and Control for 3D Printing with Articulated Excavators

End-Effector Pose Estimation and Control for 3D Printing wit...

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International conference on advanced Robotics (ICAR)

作者： Grzegorz Malczyk Marco Hutter Robotic Systems Lab ETH Zurich Switzerland

We introduce a novel large-scale autonomous mobile manipulator system based on an instrumented and automated construction machine for precise on-site sensing and fabrication. The system is based on an automated hydraulic walking excavator equipped with IMUs and LiDAR units. In this work, we develop the technology to precisely map, localize, and move the printhead in the environment. By fusing GNSS localization with kinematic sensing of the mobile machine and end-effector, we get a globally consistent and locally accurate positioning for in situ robotic construction printing process. Moreover, we present a control approach that enables the excavator to move the end-effector precisely along predefined trajectories. We evaluate the performance of the proposed system in a variety of real-world tests in the field and analyze different sensor modalities and arrangements. Finally, we discuss the potential applications, including the fabrication of non-standard architectural forms and increased safety.

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A Novel Flexible Thermoelectric Generator For Harvesting Low Thermal Heat Waste For Self-Powered Sensing System

A Novel Flexible Thermoelectric Generator For Harvesting Low...

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IEEE International conference on Nano/Micro Engineered and Molecular systems (NEMS)

作者： Nguyen Van Toan Truong Thi Kim Tuoi Masaya Toda Nguyen Van Hieu Takahito Ono Department of Mechanical System Engineering Tohoku University Sendai Japan Advanced Institute fro Material Research Tohoku Univerisity Sendai Japan Vietnam National Univerisity HoChiMinh City Viet Nam

ISBN: (数字)9798350359831

ISBN: (纸本)9798350359848

This work reports an innovative flexible thermoelectric generator (TEG) and investigates its integration into an autonomous wireless IoT sensing system. Constructed on a polyimide (PI) substrate with electrodeposited thermoelectric elements, the TEG demonstrates remarkable output power and power density, surpassing 95mW and 600 µW/cm, respectively, under a harvested difference of 52°C. The successful demonstration of a self-powered wireless sensing system, utilizing the fabricated flexible TEG to convert energy from low thermal heat waste (a thermal electric pot), highlights its potential in converting such waste into usable electricity. This research opens new avenues for future applications of flexible TEGs in harvesting energy from low thermal heat waste, particularly for autonomous IoT sensing systems.

关键词： Wireless communication Wireless sensor networks Power system measurements Electric potential Heat engines Generators sensors

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Advancements in Pest Management Through Remote Sensing and Artificial Intelligence for Sustainable and Smart Agriculture

Advancements in Pest Management Through Remote Sensing and A...

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ACS/IEEE International conference on Computer systems and applications

作者： Leila Aissaoui Ferhi Fethi Choubani Ridha Bouallègue Innov'COM Laboratory Higher School of Communication of Tunis (SUPCOM) Virtual University of Tunis (UVT) Ariana Tunisia Innov'COM Laboratory Higher School of Communication of Tunis (SUPCOM) University of carthage (UCAR) Ariana Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Agricultural sectors worldwide grapple with substantial economic losses and food insecurity, largely driven by pest infestations. The limitations of traditional manual inspection methods-marked by inefficiency and a lack of scalability-are particularly pronounced across extensive agricultural landscapes. In response, this study explores the cutting-edge integration of remote sensing techniques and artificial intelligence (AI) in pest detection, positioning these technologies as transformative tools in modern agriculture. advanced remote sensing systems, including satellite imagery, aerial drones, and ground-based sensors, equipped with multispectral, hyperspectral, and thermal imaging capabilities, provide a nuanced understanding of crop health and pest vulnerability, enabling early and precise detection of infestations. Concurrently, AI's capacity for rapid and accurate analysis of vast datasets facilitates real-time monitoring and predictive analytics, offering a proactive approach to pest management. The synergistic application of these technologies promises to revolutionize pest control, significantly reducing reliance on chemical pesticides and mitigating environmental impacts. However, challenges persist, particularly in ensuring the robustness of data acquisition and the adaptability of AI models across diverse agro-ecological zones. This study underscores the strategic potential of integrating remote sensing technologies with AI to advance sustainable and resilient agricultural practices.

关键词： Smart agriculture Technological innovation Accuracy Crops Thermal sensors Autonomous aerial vehicles sensor systems Data models Satellite images Artificial intelligence

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Wireless Body Area Network (WBAN) Centric Healthcare System for Continuous Monitoring

Wireless Body Area Network (WBAN) Centric Healthcare System ...

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International conference on advanced Computing and Communication systems (ICACCS)

作者： M. Anand Kumar V. Valli Mayil T. Ratha Jeyalakshmi Anshul Ghanshala Anshika Gupta Dept of Computer Applications Graphic Era University Dehradun India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Data Science Jain University School of Sciences Banglore India Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Stroke is one the most dangerous disease all across the world which would lead to serious disability or death among all aged people due to changes in environmental factors. Recent reports indicate that a mere 75 million people will suffer from stroke-related disabilities by the year 2030. It was also identified that a stroke occurs every 40 seconds on average across the globe. One out of every five stroke-related patients would die in a year. A Recent health survey indicates that most developed countries face a high number of stroke patients in very recent years. There is also a rapid growth in the number of stroke cases in developing countries. To prevent the high-risk factors related to stroke diseases, there is much need for continuous observation of physiological signals in real-time and to analyze the data. One of the main strategies is wearable technology that employs wireless sensor networks to link numerous medical devices and associated systems. They have to ability to function independently across several platforms. Wearable devices are used in most medical applications because of their noteworthy qualities, including adaptability, versatility, inexpensiveness, and efficiency in various platforms. Integration of the Internet of Things (IoT) with wearable devices and Fog Computing provides a more flexible mechanism to deploy a continuous healthcare monitoring system to monitor vital symptoms and to detect the early phase of stroke factors instantly. To predict stroke-related disorders, this research proposes a Wireless Body Area Network (WBAN)-centric healthcare system for continuous monitoring and examination of physiological signals. Initially, the proposed model predicts the nature of the symptoms based on the parameters collected by the IoT sensors. Deep learning techniques are then applied to predict the risk factors that are critical to the stroke patient during the emergency condition. The key contribution of this research work is to provide an emerg

关键词： Wireless communication Wireless sensor networks Wearable computers Medical services Predictive models Stroke (medical condition) Body area networks

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Enhancing Battery Management systems with Machine Learning and 5G Connectivity beyond Maritime Transport Software Architectures

Enhancing Battery Management Systems with Machine Learning a...

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International conference on Communications (COMM)

作者： Constantin-Laurentiu Paraschiv Larisa Mihaela Tufeanu Marius-Constantin Vochin Department of Telecommunications Faculty of Electronics POLITEHNICA Bucharest Bucharest Romania

ISBN: (数字)9798350355956

ISBN: (纸本)9798350355963

The growing advancement in battery technology has revolutionized various domains and services, notably transportation and energy storage. This research focuses on improving battery management systems (BMS) for electric vehicles (EV) and hybrid vehicles (HEV) by leveraging advanced machine learning models and 5G connectivity. This study proposes an integrated software architecture designed for both industrial and road applications, aiming to optimize battery performance and safety by analyzing collected data and predicting the anomaly events by training Random Forest and Gradient Boosting machine learning (ML) models. Throughout the research, key battery performance parameters and their normal operating ranges, such as voltage, current, temperature, charge/discharge rates, internal Resistance and State of Charge (SoC) are analyzed, according to the official regulations and utilized in Python scripting. The Python script implemented simulates battery operations by updating sensor values and logging incidents with factors such as latency, jitter, and encryption time. The script trains and compares machine learning models to determine the most accurate one for anomaly detection. The final part of the script simulates battery performance under different connectivity modes (5G Non-Standalone (NSA) and 5G Standalone (SA)) to assess the impact on data transmission and real-time monitoring. Ultimately, obtained results demonstrate not only advanced, sustainable, mobility solutions by improving the safety and performance of Battery Management systems in electric and hybrid vehicles, but also the importance of leveraging cutting-edge technologies (such as ML) and connectivity (5G).

关键词： 5G mobile communication Software architecture Battery management systems Transportation Boosting Real-time systems Batteries Safety Anomaly detection Python

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Real-Time Occlusion-Aware Object Tracking

Real-Time Occlusion-Aware Object Tracking

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Applied Research in Electrical Engineering (AREE), National and International conference on

作者： Mahdi Seyfipoor Mohammad Javad Samii Zafarqandi Siamak Mohammadi School of Electrical and Computer Engineering University of Tehran Tehran Iran Computer Engineering University of Tehran Tehran Iran Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350377170

ISBN: (纸本)9798350377187

This paper presents a novel system for real-time object tracking in occlusion-prone environments, addressing critical challenges in achieving robustness, accuracy, and efficiency under dynamic and cluttered conditions. The proposed system integrates geometric analysis using Intersection over Union (IoU), probabilistic validation via a Hidden Markov Model (HMM), and an advanced object detection module to ensure consistent performance during both partial and complete occlusions. Evaluated on the KITTI dataset, the system achieves a Success Rate of $\mathbf{8 3 \%}$, Precision of $\mathbf{9 3 \%}$, Recall of $\mathbf{8 6 \%}$, and an F1 Score of 0.91, outperforming state-of-the-art methods such as Query-Guided Redetection Tracker (QRDT) and Siamese Occlusion-Aware Network (SiamON) in terms of accuracy and reaction time, with a low processing delay of 35 ms. While QRDT and SiamON offer competitive results, the proposed system demonstrates superior resilience to prolonged occlusions and enhanced suitability for time-critical applications like autonomous driving and surveillance. Limitations related to identity consistency during extended occlusions are acknowledged, with future directions focused on incorporating advanced trajectory prediction and multi-modal sensor fusion. This research establishes a robust foundation for occlusion-aware tracking and contributes significantly to the domains of autonomous systems, robotics, and computer vision

关键词： Accuracy Hidden Markov models Object detection Radar tracking Probabilistic logic Real-time systems Robustness Trajectory Object tracking Time factors

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AI-Driven IoT Framework for Vehicle Accident Avoidance and Detection with Cloud Integrated Energy Efficient Solutions

AI-Driven IoT Framework for Vehicle Accident Avoidance and D...

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Innovations in Communications, Electrical and Computer Engineering (ICICEC), International conference on

作者： G Roopini Nagraj Rao P P Dankan Gowda V Balaji Shesharao Ingole Shatrudhan Pandey S Hema Chandra Department of Electronics and Communication and Engineering Sri Venkatesa Perumal College of Engineering and Technology Andhra Pradesh India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India GA USA Marwadi University Research Center Faculty of Management Studies Marwadi University Rajkot Gujarat India Department of EEE School of Engineering Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

IoT based machine learning solution formulated in this paper aims to enhance the real time accident analysis and prevention for vehicles. The IoT sensors interface with advanced deep learning models and the cloud computing system to form a real-time analytics system that acknowledges the data gathered by the sensors, assesses existing risks of accidents, and provides actions that can be taken to prevent them. Energy is one of the main characteristics implemented, where sleep scheduling and adaptive data transmission is used to maximize the battery life of battery driven IoT devices. The use of cloud integration also helps increase the scalability of the given system by referenced computation needs and the favorite continuous learning from accumulated logs. Simulation outputs show better identification accuracy well over 95% on accidents and reduced power usage relative to other IoT applications. Due to the capacity to optimally discern and integrate performance, energetic, and real-time responsiveness, the system offers a feasible strategy for increasing vehicular safety at smart transportation systems.

关键词： Cloud computing Accuracy Scalability sensor phenomena and characterization Real-time systems sensor systems Energy efficiency Safety Internet of Things Accidents

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Electrical Impedance Tomography Image Reconstruction with apriori Knowledge for Gesture Recognition 19

Electrical Impedance Tomography Image Reconstruction with ap...

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19th IEEE International Multi-conference on systems, Signals and Devices, SSD 2022

作者： Alasasfeh, Hayat Hafsa, Mariem Bader, Oumaima Ibbini, Mohammed Hasan, Sameer Kanoun, Olfa Jordan University of Science and Technology Biomedical Engineering Department Irbid Jordan Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical impedance tomography (EIT) reconstructs the conductivity images of an object based on the voltages and the currents at the surface of the object. Among the clinical applications of EIT thorax imaging, brain function monitoring and breast cancer screening. However, EIT suffers from a poor signal-to-noise ratio and spatial resolution. To improve the quality of reconstructed images and provide clear information about the structure, the paper proposes to include prior knowledge about the structure from MRI. The proposed techniques could be very promising with more work. To illustrate this technique in this paper, eight electrodes with the adjacent current pattern were employed in this research. For each gesture, forty data points are recorded. The Gauss-Newton and Back-Projection algorithms are used for image reconstruction with prior knowledge of the human forearm from MRI images. The resultant contours of the Forearm, Ulna, and Radius have been used to build the mesh. In the Gauss-Newton algorithm, the results reveal that structural information improves image quality and conductivity distribution, particularly in the region of two bones. In addition, the reconstructed images using the Gauss-Newton algorithm performed better than images reconstructed using the back-projection algorithm. © 2022 IEEE.

关键词： Image reconstruction

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Integration of a UHF Fractal Antenna into a 1U CubeSat for Low-Earth Orbit Mission 1

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International conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Inojosa, Raynell A. Co, Celso B. Cho, Mengu Spacecraft Payload and Communications Systems Development Division Philippine Space Agency Quezon City Philippines Graduate School of Engineering Batangas State University Batangas City Philippines Laboratory of Lean Satellite Enterprises and In-Orbit Experiments Department of Electrical and Space Systems Engineering Kyushu Institute of Technology Kitakyushu Japan

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

While a number of novel patch antennas have been designed and integrated into a cube satellite (CubeSat), very few attempts have been made to exploit the self-similarity features of the so-called fractals in the antenna design to improve the satellite communications performance. This research work presents an initial attempt to utilize these fractal properties in the design of a patch antenna, adopting a geometry that allows it to be incorporated into a standard 10 cm × 10 cm × 10 cm (or 1U) CubeSat. Prior to fabrication, a preliminary design was analyzed and simulated using a commercial finite element method (FEM) solver for electromagnetic structures software, High Frequency Structure Simulator (HFSS)™. The proposed antenna was designed at a solution frequency, f= 920 MHz and resonates well from 800 MHz to 1 GHz. The antenna was fabricated on an FR4 substrate (Formula presented). Comparison of the simulated and measured results showed a good agreement in terms of reflection coefficient and voltage standing wave ratio (VSWR) of the proposed antenna at the frequency of interest. For comparison, a commercial off-the-shelf (COTS) antenna was also characterized and measured. The measured peak gain of the proposed fractal antenna is at 3.148 dBi as confirmed by the radiation pattern results. It also exhibits a reflection coefficient of −26.110 dB, a VSWR of 1.104, and a wide bandwidth (BW) across the ultra-high frequency (UHF) band. This type of antenna has the potential to provide a reliable link between a CubeSat and a ground sensor terminal (GST) as confirmed by the long-range communication tests. The proposed fractal antenna is expected to meet the satellite data rate requirement of a CubeSat-based Internet of Things (IoT) for low-earth orbit (LEO) mission. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Microstrip antennas

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Ultrasensitive Detection of Pb2+ Ions in Water Using WS2 Nanoflowers

Ultrasensitive Detection of Pb2+ Ions in Water Using WS2 Nan...

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IEEE conference on Nanotechnology

作者： Sumit Chaudhary Chandrabhan Patel Brahmadutta Mahapatra Kumari Jyoti Mayank Dubey Saurabh Yadav Shaibal Mukherjee Department of Electrical Engineering Hybrid Nanodevice Research Group (HNRG) Indian Institute of Technology Indore Indore India Center for Advanced Electronics (CAE) IIT Indore Madhya Pradesh India Centre for Advance Electronics (CAE) and Centre for Rural Development and Technology (CRDT) Indian Institute of Technology Indore Indore India School of Engineering RMIT University Melbourne Victoria Australia

ISBN: (数字)9798350386240

ISBN: (纸本)9798350386257

The swift and accurate detection of waterborne pollutants is crucial due to the significant risks they pose to both human health and the environment. Lead and its compounds are highly toxic and can cause various illnesses. However, current lead detection systems face several limitations, including slow response times, high costs, and limited mobility. In this investigation, we have successfully engineered a sensor capable of detecting trace levels of toxic Pb 2 + ions with excellent sensitivity, remarkable selectivity, and rapid transient performance. The developed sensor comprised of CTAB-WS2 as a sensing layer drop-casted on interdigitated electrodes (IDEs) device, sensing layer synthesized through a facile hydrothermal method. Surface morphological, crystal structure and elemental composition of the synthesized CTAB-WS2 is investigated via FESEM, XRD and EDX analysis. Moreover, sensing performance of the developed sensor display outstanding selectivity towards Pb 2 + ions in the presence of other heavy metal ions. With a sensitivity of 418 µA/ppb and a remarkable detection limit of 89.9 ppt, the sensor demonstrated exceptional performance. Moreover, its rapid response time of less than 5 seconds makes it suitable for real-time detection applications. These superior attributes position CTAB-WS2-based sensors as a promising candidate for deployment in real- time environmental quality monitoring and toxic metal detection endeavors.

关键词： Performance evaluation Sensitivity X-ray scattering Tungsten Lead Ions Real-time systems

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