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15th German Microwave conference (GeMiC)

作者： Iwamatsu, Shuya Headland, Daniel Mohammad, Israa Fujita, Masayuki Stoehr, Andreas Univ Duisburg Essen Dept Optoelect Duisburg Germany Univ Carlos III Madrid Optoelect & Laser Technol Grp Madrid Spain Osaka Univ Grad Sch Engn Sci Toyonaka Osaka Japan

ISBN: (纸本)9783982039732

Terahertz (THz) time- domain spectroscopy (TDS) technology has greatly advanced various THz applications ranging from fundamental science to industrial non-destructive imaging. However, traditional THz-TDS systems rely on complex setups with femtosecond pulse lasers and bulky free-space optical components, hindering their miniaturization. A more practical approach introduces THz frequency domain imaging using semiconductor laser diodes, which replace femtosecond laser and facilitate integration into photonic integrated circuit (PIC) chips. Despite its promise, addressing real-world imaging applications remains challenging due to the need for free-space optics, which also negatively impact resolution due to the diffraction limit. To overcome this limitation, this study explores the use of silicon (Si) microphotonics, focusing on an integrated Y-junction that operates as an interferometer. The Y-junction essentially replaces all free-space components, paving the way towards compact, monolithically integrated all-silicon structures that span just similar to 1 cm. We demonstrate near-field imaging at 325 GHz employing the Y-junction. The experiments achieve a 500-mu m spatial resolution and a 100-mu m depth resolution, showcasing potential for handheld THz near-field imaging sensors.

关键词： chip-scale terahertz imaging integrated photonics interferometer silicon Y-junction

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advanced IoT-Based Wireless sensors for Remote Geotechnical Monitoring and Structural Diagnostics 18

Advanced IoT-Based Wireless Sensors for Remote Geotechnical ...

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18th IFAC conference on Programmable Devices and Embedded systems (PDES)

作者： Pies, Martin Velicka, Jan Hajovsky, Radovan VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Cybernet & Biomed Engn 17 Listopadu 15 Ostrava Czech Republic

This paper presents an innovative design and implementation of a wireless sensor module using low-power Internet of Things (IoT) technologies for applications in geotechnical engineering and structural diagnostics, with a main focus on monitoring rock formations below the water surface in areas without access to electrical power. Designed for continuous monitoring of linear displacements and other critical parameters in structural engineering, geotechnical engineering and engineering diagnostics, this sensor module is particularly useful in remote locations. Utilizing wireless data transmission and advanced IoT technologies such as LoRa, IQRF and NB-IoT, this system allows for flexible coverage options and efficient remote management. The system allows users to remotely change measurement parameters, the period of messaging and set thresholds for alarm activation. The study highlights the importance of innovative IoT solutions to improve accuracy and efficiency in monitoring geotechnical and civil engineering structures, especially in challenging sub-surface environments, opening new opportunities for predictive maintenance and risk management in these specific applications. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Multi sensor systems Networks of sensors and actuators Measurement and instrumentation IoT sensor Networks Geotechnical Monitoring Wireless Data Transmission Underwater Rock Formations Remote Energy-independent systems

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BLDC Motor Trapezoidal Regulation Using Hall Effect sensor

BLDC Motor Trapezoidal Regulation Using Hall Effect Sensor

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2024 IEEE International conference on Intelligent systems and advanced applications, ICISAA 2024

作者： Bhatiya, Hansa Patil, Dushyant Makune, Shriram Kadam, Swaraj Mahajan, Vishal Vaidya, Harshal Dr. D. Y. Patil Institute of Technology Department of Electrical Engineering Pimpri Pune India R. C. Patel College of Polytechnic Department of Electrical Engineering India Marathwada Mitramandal's IoT Lohgaon Department of Engineering Science Pune India

ISBN: (纸本)9798331539948

BLDC motors provide excellent performance, durability, and efficiency, making them ideal for a variety of applications. The motor was operated by means of the six-step commutation process of the trapezoidal control approach, which was applied to the exact rotor position data obtained via Hall effect sensors. This approach perfectly times the commutation sequence, reducing torque ripple and improving motor performance. The design, modeling, and experimental findings validated the effectiveness of the proposed control technique, suggesting that it can be used to maximize BLDC motor performance in commercial and industrial applications. © 2024 IEEE.

关键词： Hall effect transducers

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A ROS-Integrated Skeleton Detection Framework Enabling Human-Centered Robotic applications

A ROS-Integrated Skeleton Detection Framework Enabling Human...

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International conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Mora, Alicia Ozeki, Tomoe Mouri, Tetsuya Barber, Ramon Univ Carlos III Madrid RoboticsLab Leganes Spain Aichi Univ Technol Fac Engn Gamagori Japan Gifu Univ Fac Engn Gifu Japan

ISBN: (纸本)9798350352351;9798350352344

The escalating demand for assistive robots, fueled by an aging population and the quest for technological solutions to enhance quality of life, requires an effective comprehension of data coming from users interacting with robots. This paper proposes a holistic solution to the challenge of human skeleton detection and analysis by integrating a 3D skeleton detector and RGBD camera spatial data within the Robot Operating System (ROS) middleware. This integration facilitates the publication of user pose and location, forming a versatile foundation for diverse assistive applications. Additionally, a whole-body joint analysis method based on analytical geometry is introduced, expanding the robot's understanding of the user's body and enabling a broader range of applications. The paper showcases the framework's versatility through two applications: humanoid robot pose retargeting and pose classification for user behavior study, proving that the proposed solution works in real time and serves as a modular, adaptable solution for diverse assistive robotic applications.

关键词： 3D Human Skeleton ROS Joint Analysis Pose Retargeting Pose Classification

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Multi-Modal AI for Enhanced Forest Fire Early Detection: Scalar and Image Fusion

Multi-Modal AI for Enhanced Forest Fire Early Detection: Sca...

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International conference on advanced systems and Emergent Technologies (ICASET)

作者： Azzabi, Tarek Jeridi, Mohamed Hechmi Mejri, Ikbel Ezzedine, Tahar Tunisian Naval Acad Lab LR DN02 19 Menzel Bourguiba 7050 Tunisia Tunisian Naval Acad Menzel Bourguiba 7050 Tunisia Natl Engn Sch Tunis ENIT SYSCOM LR ES21 99 Tunis Tunisia

ISBN: (纸本)9798350384901;9798350384895

Forest fires pose significant threats to both the environment and human safety, underscoring the critical importance of early detection methods. This article introduces a novel approach grounded in artificial intelligence to enhance the early detection of forest fires. The fusion of sensor technology and imagery holds substantial potential for augmenting prediction accuracy. sensors furnish crucial environmental data encompassing factors such as temperature, humidity, and wind speed, all pivotal in the ignition and propagation of forest fires. In contrast, imagery supplies visual evidence of fire or smoke presence, a particularly invaluable asset in densely forested regions where vegetation cover can obscure sensor-derived information. Employing machine learning algorithms proficient in the analysis and interpretation of this composite data, we achieve a heightened level of accuracy in forest fire detection and probability prediction. This methodology undergoes rigorous evaluation using historical forest fire datasets, demonstrating impressive effectiveness in detecting fires. The potential applications extend to the enhancement of early warning systems for forest fires, ultimately mitigating the loss of life and property attributable to these catastrophic events.

关键词： Forest Fires Scalar sensors Artificial intelligence Machine Learning Deep Learning rTPNN CNN Multi-sensor fusion Concentrations Smoke density

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advanced Autonomous Navigation Technologies for UAVs: Challenges and Opportunities 4

Advanced Autonomous Navigation Technologies for UAVs: Challe...

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4th International conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Senhaji-Mouhaddib, Issam Bouzakri, Samir Lagrat, Ismail Ibn Tofail University Ensa Lab Of Advanced Systems Engineering Kenitra Morocco

ISBN: (纸本)9798350309508

The utilization of Unmanned Aerial Vehicles (UAVs) has revolutionized numerous industries, with navigation being a crucial factor in ensuring their operational success. This literature review examines the subject of UAV navigation, discussing fundamental concepts, GPS-denied challenges, and cutting-edge technologies. The review commences with an explanation of the basic principles of UAV navigation, including the employment of IMUs, GPS, LiDAR, and Stereo Vision systems. A dedicated section then delves into the challenges posed by GPS-denied environments, emphasizing the importance of GPS in navigation and exploring alternative approaches. The subsequent sections investigate advanced navigation technologies, such as INS/IMU, Multi-sensor Fusion, and AI applications. These innovative research areas cover enhancements to sensors, the development of hybrid navigation systems, deep learning for sensor fusion, and other related topics. The conclusion emphasizes the transformative potential of emerging technologies, providing a roadmap for researchers and practitioners. The identified research directions aim to address current limitations and develop next-generation UAV navigation systems capable of precise navigation in complex and challenging environments. © 2024 IEEE.

关键词： Global positioning system

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Leveraging AI-Driven Chatbots for Legal Literacy: advanced Computing and Communication Technologies for Legal Awareness

Leveraging AI-Driven Chatbots for Legal Literacy: Advanced C...

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2024 International conference on Artificial Intelligence and Quantum Computation-Based sensor applications, ICAIQSA 2024

作者： Trivedi, Chetan Kumar, Sunil Mohd, Imran Bhalla, Rajiv Ahmad Lone, Naseer Dogra, Disha University Institute of Legal Studies Chandigarh University Punjab India

ISBN: (纸本)9798331517953

In a democratic society, public legal awareness is crucial for empowering citizens with knowledge of their rights and obligations. However, the vast complexity and volume of legal information can make it difficult for non-experts to access and understand their legal standing. This paper explores the development and deployment of AI-driven chatbot systems aimed at enhancing public legal literacy. These systems leverage advanced computing techniques, including natural language processing (NLP) and machine learning (ML) algorithms, to process legal queries and deliver personalized legal information to users in real time. The architecture, scalability, and implementation challenges of deploying these chatbots across diverse communication platforms (e.g., web, mobile) are examined. Additionally, the paper addresses the potential of these systems to bridge the gap in legal knowledge for underserved populations and discusses the importance of real-time communication technologies for ensuring effective system performance. Finally, the paper explores future advancements to further integrate AI and communication technologies for widespread legal literacy. © 2024 IEEE.

关键词： Natural language processing systems

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Assuring Drug Quality Monitoring beyond Hospital using multi-sensor IoT-based node 31

Assuring Drug Quality Monitoring beyond Hospital using multi...

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31st conference on Electronics Circuits and systems

作者： Frontera-Bergas, Maria Oliver-Riera, Bartomeu Vinaixa-Fernandez, Miguel Ramis-Bibiloni, Jaume Isern, Eugeni Alorda-Ladaria, Bartomeu Univ Balearic Isl Dept Ind Engn & Construct Palma De Mallorca Spain Univ Balearic Isl Dept Math & Comp Sci Hlth Res Inst Balearic Isl IdISBa Palma De Mallorca Spain Univ Balearic Isl Dept Ind Engn & Construct Hlth Res Inst Balearic Isl IdISBa Palma De Mallorca Spain

ISBN: (纸本)9798350377217;9798350377200

Today, there are several monitoring tools and systems that can be used to keep the quality of drugs stored in medical environments under control. These tools are mandatory in professional environments because medicines are substances that are sensitive to changes in temperature, humidity, UV radiation, or mechanical stress. But when the patient is discharged from the hospital because the treatment can be continued at home, the drugs are kept at the patient's home until administration with the available resources. As previous exploratory work has shown, these medications are not stored diligently, largely because the refrigerators available in our homes do not have adequate specifications. In this work, we present a proposed sensor node capable of monitoring the conservation status of the drug by attending to multiple variables. The proposal is designed to be integrated into an IoT platform that would provide drug quality data to the hospital pharmacy department. The results show an efficient use of the memory resources and battery consumption, making it possible to design very versatile applications to respond to multiple remote monitoring scenarios.

关键词： Drug Quality Chain multi-sensor node BLE remote storage monitoring

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Robust Multimodal and Multi-Object Tracking for Autonomous Driving applications 21

Robust Multimodal and Multi-Object Tracking for Autonomous D...

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21st International conference on advanced Robotics (ICAR)

作者： Perez, Marc Agudo, Antonio CSIC UPC Inst Robot & Informat Ind Barcelona Spain Applus IDIADA Tarragona Spain

ISBN: (纸本)9798350342291

In this work, we present a method for MultiObject Tracking (MOT) that uses unsynchronized multimodal detections from a configurable set of sensors such as cameras, radars and lidars. All the information is processed from the sensors with modality-specific detectors and then combined in the MOT module that incorporates a Kalman filter and tracklet management logic. To be able to deploy our system in real-world driving applications, we handle localization errors, misclassifications and partial bounding-box detections from the object detector in the MOT module. We show promising results and compare them with respect to competing approaches in two challenging real-world scenarios, including a traffic jam chauffeur as well as a traffic monitoring application on highways.

关键词： Multiple Object Tracking sensor Fusion.

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A proposed plant classification framework for smart agricultural applications using UAV images and artificial intelligence techniques

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ALEXANDRIA ENGINEERING JOURNAL 2024年 109卷 466-481页

作者： Eladl, Shymaa G. Haikal, Amira Y. Saafan, Mahmoud M. Eldin, Hanaa Y. Zain Mansoura Univ Fac Engn Comp & Control Syst Engn Mansoura Egypt Nile Higher Inst Sci & Comp Technol Cairo Egypt

Utilizing Wireless sensor Networks (WSNs), Internet of Things (IoTs) sensors, and Unmanned Aerial Vehicles (UAVs), in conjunction with optimization techniques and machine learning algorithms, can present a novel approach to Precision Agricultural (PA) crops. Agriculture has transitioned from traditional legacy systems to incorporate advanced smart technologies. Several complex farming problems are utilized to promote a variety of UAV sensing and Artificial Intelligence (AI) algorithms in PA applications of smart agriculture, especially in developing countries. This paper proposes the design of a conceptual UAV sensing system for crop management using a novel classification framework. UAV-based image datasets can be implemented and expanded to various types of crops. In addition, it can classify various types of rice species and detect weed farms as it consists of a multistage process for identifying and monitoring different crops. The proposed classification framework is evaluated using three different real UAV image datasets compared with Naive Bayes, Decision Tree (DT), Bagging, and Random Forest (RF) techniques. The classification performance metrics obtained are as follows: i) the WeedNet dataset with 100% F1-score, 100% recall, 100% precision, and 100% accuracy;ii) the Rice Seedling dataset with 99.5% F1-score, 99.5% recall, 99.5% precision, and 99.5% accuracy;and iii) the Rice Varieties dataset with 97.99% F1-score, 97.99% recall, 98.14% precision, and 97.99% accuracy. The success rates achieved by the proposed classification framework outperform those of other recent state-of-the-art techniques, demonstrating its efficacy in crop management applications.

关键词： Precision agriculture Crop management Plant classification Federated learning Deep learning Remote sensing Weed monitoring Rice seeding Artificial intelligence

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