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

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Enhancing Mobility and Safety for Vulnerable Populations: An IoT-Enabled Smart Walking Frame with Mobile Application Integration

Enhancing Mobility and Safety for Vulnerable Populations: An...

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International conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Ying Mei Leong Yau Kit Chung Lip Khi Lim Faculty of Computing and Engineering Quest International University Ipoh Malaysia School of Computing Newcastle University Newcastle United Kingdom

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

The Smart Walking Frame revolutionizes mobility assistance for amputees and elderly individuals by seamlessly integrating Internet of Things (IoT) technology with a conventional walking frame. This innovative device addresses critical challenges in safety, independence, and emergency response for mobility-impaired users. The Smart Walking Frame incorporates an Arduino Uno microcontroller and a dedicated mobile application, offering advanced functionalities through embedded buttons. These include an alarm system for immediate attention, an SOS feature for emergencies, and sophisticated obstacle and water detection capabilities. By tackling issues such as limited situational awareness, difficulty in summoning immediate help, and lack of real-time location tracking, the device significantly enhances user mobility and security. Key innovations include the integration of multiple sensors for comprehensive environmental awareness, a Bluetooth-enabled mobile application for seamless control and monitoring, and a real-time GPS tracking system with SOS alerts. The accompanying mobile application extends the device's utility by enabling location tracking and allowing users to configure multiple emergency contacts. By combining these advanced features with the familiar form factor of a walking frame, the Smart Walking Frame offers a comprehensive solution to improve the quality of life and safety of individuals with limited mobility or self-defense capabilities, promising greater independence and peace of mind for users and their caregivers.

关键词： Legged locomotion Technological innovation Navigation sensor systems and applications Real-time systems Safety Mobile applications Internet of Things Older adults Monitoring

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Enhanced Clustering Framework for Unveiling Hidden Patterns in IoT-Based Structured and Unstructured Data

Enhanced Clustering Framework for Unveiling Hidden Patterns ...

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International Symposium on advanced Computing and Communication (ISACC)

作者： Prabhat Das Karthik Kovuri Sajal Saha Department of IT The Assam Kaziranga University Department of CSE Adamas University Department of CSE The Assam Kaziranga University

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Clustering algorithms play a pivotal role in uncovering hidden patterns and relationships within diverse datasets. This study discusses about an enhanced real-time clustering approach based on the Birch methodology, optimized for analyzing both structured and unstructured data. Structured data, consisting of IoT sensor readings, and unstructured image data, captured using Raspberry Pi devices deployed in various locations, were analyzed to identify meaningful patterns. For structured data, the algorithm effectively clustered environmental parameters, revealing correlations and anomalies. For unstructured data, high-dimensional image features extracted using a pre-trained VGG16 model were clustered, uncovering patterns in texture, color, and structure. Experimental results demonstrate the algorithm’s scalability and efficiency, achieving high-quality clusters with average Silhouette Scores of 0.87 and 0.81 for structured and unstructured datasets, respectively. These findings highlight the algorithm’s adaptability and its potential for real-time analytics in diverse applications such as environmental monitoring and image-based decision-making systems.

关键词： Visualization Temperature distribution Correlation Image color analysis Scalability Clustering algorithms Feature extraction Real-time systems Environmental monitoring Object recognition

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Transforming Obesity Care Through Artificial Intelligence: Real-Case Implementations and Personalized Solutions

Transforming Obesity Care Through Artificial Intelligence: R...

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Computational Intelligence Approaches and applications (ICCIAA), International conference on

作者： Mayyas Al-Remawi Rami A. Abdel-Rahem Faisal Al-Akayleh Faisal Aburub Ahmed S.A. Ali Agha Department of Pharmaceutics and Pharmaceutical Technology University of Petra Amman Jordan Department of Chemistry Faculty of Arts and Sciences University of Petra Amman Jordan Faculty of Administrative & Financial Sciences University of Petra Amman Jordan Department of Pharmaceutical Sciences The University of Jordan Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Obesity is a multifactorial condition that significantly increases the risk of metabolic syndrome, cardiovascular disease, and various other comorbidities. Conventional obesity management strategies-encompassing body mass index (BMI) measurements, dietary counseling, and caloric tracking-often fail to account for the heterogeneity of individual genetic, behavioral, and metabolic factors. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a transformative approach, offering data-driven, personalized solutions that integrate electronic health records (EHRs), wearable sensor data, and multi-omics information. This article examines contemporary AI applications for obesity care, highlighting real-case implementations, technical and ethical considerations, and the future potential for optimizing patient outcomes. By leveraging advanced analytics, healthcare providers can enhance diagnostic precision, tailor interventions to individualized needs, and support sustained weight control, thus redefining the paradigm of obesity management.

关键词： Deep learning Obesity Ethics Weight control Force measurement Imaging Medical services Genetics Real-time systems Wearable sensors

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Detecting End Effector Command Pose Error Using Perfect Bit Field Imaging

Detecting End Effector Command Pose Error Using Perfect Bit ...

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International Carpathian Control conference (ICCC)

作者： Tomáš Buchta Jaroslav Hlava Tomáš Martinec Jaroslav Buchta Yegor Boyarchikov Department of Mechatronic Systems and Robotics Technical University of Liberec Liberec Czechia Institute of Mechatronics and Computer Engineering Technical University of Liberec Liberec Czechia

ISBN: (数字)9798331501273

ISBN: (纸本)9798331501280

This paper presents a novel approach to improving the precision of robotic arm operations in complex environments by using Perfect Bit Field Imaging (PBFI) for end effector command pose error detection. Accurate pose determination is essential for tasks such as manipulation, assembly, and autonomous navigation, particularly in space or hazardous settings where precision is critical. PBFI leverages high-resolution industrial cameras to capture detailed pixel-level information that can be used to correct the end effector’s position. The system’s key advantage is its low-cost nature, making it an attractive alternative to expensive sensor-based approaches. advanced image processing techniques are used to interpret bit fields, allowing for quick and reliable pose corrections that reduce the need for recalibration and minimize the impact of external disturbances. Additionally, the approach is adaptable to various robotic arm configurations and operational conditions, making it suitable for a wide range of industrial and space-based applications. This research opens new avenues for enhancing robotic precision in challenging environments, contributing to the advancement of cost-effective autonomous systems.

关键词： Accuracy Service robots Image processing Robot vision systems Refining End effectors Real-time systems Error correction Robots Optimization

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Wireless Sensing for Simultaneous Human Vocal Sound and Heart Sound Recognition

Wireless Sensing for Simultaneous Human Vocal Sound and Hear...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu Rong Kumar Vijay Mishra Daniel W. Bliss Center for Wireless Information Systems and Computational Architectures (WISCA) Arizona State University Tempe AZ USA United States Army Research Laboratory Adelphi MD USA

Remote vibrometry using wireless signals is a recently introduced novel technique with a wide range of applications such as remote microphones and structural health monitoring. These use cases require high sensitivity and coherence in the sensing system. In this context, radar is a suitable all-weather sensor compared to conventional acoustic acquisition. We investigate human vocal sound and heart sound detection and separation using a single millimeter-wave radar sensor and advanced array processing techniques to achieve superior motion sensitivity.

关键词： Heart Wireless communication Wireless sensor networks Sensitivity Speech recognition Signal processing Acoustics

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Implicit Neural Fusion of RGB and Far-Infrared 3D Imagery for Invisible Scenes

Implicit Neural Fusion of RGB and Far-Infrared 3D Imagery fo...

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IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Xiangjie Li Shuxiang Xie Ken Sakurada Ryusuke Sagawa Takeshi Oishi Institute of Industrial Science The University of Tokyo Tokyo Japan National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical sensors, such as the Far Infrared (FIR) sensor, have demonstrated advantages over traditional imaging. For example, 3D reconstruction in the FIR field captures the heat distribution of a scene that is invisible to RGB, aiding various applications like gas leak detection. However, less texture information and challenges in acquiring FIR frames hinder the reconstruction process. Given that implicit neural representations (INRs) can integrate geometric information across different sensors, we propose Implicit Neural Fusion (INF) of RGB and FIR for 3D reconstruction of invisible scenes in the FIR field. Our method first obtains a neural density field of objects from RGB frames. Then, with the trained object density field, a separate neural density field of gases is optimized using limited view inputs of FIR frames. Our method not only demonstrates outstanding reconstruction quality in the FIR field through extensive experiments but also can isolate the geometric information of the invisible, offering a new dimension of scene understanding.

关键词： Training Gases Three-dimensional displays Finite impulse response filters Robot vision systems sensor fusion Rendering (computer graphics) Cameras Image reconstruction Synthetic data

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Hybrid Image-Based Motion Tracking and Adaptive Polynomial Kalman Filter for a Bio-Inspired Dual-Sheath Needle System

Hybrid Image-Based Motion Tracking and Adaptive Polynomial K...

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IEEE International conference on Robotics and Biomimetics

作者： Dileep Sivaraman Cholatip Wiratkapun Branesh M. Pillai Jackrit Suthakorn Boriphat Methachan Sakol Nakdhamabhorn Songpol Ongwattanakul Department of Biomedical Engineering Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand Department of Radiology Breast Diagnostic Center Division of Diagnostic Radiology Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok Thailand Functional Materials for Novel Engineering Applications National Science and Technology Development Agency Thailand

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This preliminary study investigated sensor-fusion methods for accurate motion estimation in a bio-inspired dual-sheath needle system designed for biopsy applications. The needle system draws inspiration from the segmented dual-sheath mechanism of ovipositors found in Hymenoptera species, which allows controlled and precise movement. The aim is to develop a reliable tracking system for both the needle tip and base, which is essential for accurate needle placement during biopsy procedures. A hybrid tracking approach was used to achieve this, combining image-based tracking of the needle tip with an Inertial Measurement Unit (IMU) sensor for needle-base tracking. An Adaptive Polynomial Kalman Filter (APKF) was applied to improve the motion estimation accuracy. Performance tests on a linear-rail system demonstrated the ability of the developed system to provide accurate and consistent motion estimates. The results suggest that this bio-inspired needle system using sensor fusion and advanced filtering holds promise for use in medical robotics, potentially improving the precision of biopsy procedures.

关键词： Accuracy Tracking Motion segmentation Motion estimation Biopsy Needles Robot sensing systems Polynomials Kalman filters Biomedical imaging

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advanced Real-Time Volumetric Modeling of Stockpiles in Industrial applications Using Depth Camera

Advanced Real-Time Volumetric Modeling of Stockpiles in Indu...

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International Research conference on Smart Computing and systems Engineering (SCSE)

作者： K. D. Gayantha M. H. P. P. Thilakarathna W. G. C. W. Kumara P. M. K. Alahakoon P. R. D. S. Rudrigu Dept. of Electrical & Telecom. Eng. Faculty of Engineering South Eastern Univ. of Sri Lanka Oluvil Sri Lanka Dept. of Computer Science & Eng. Faculty of Engineering South Eastern Univ. of Sri Lanka Oluvil Sri Lanka Siam City Cement (Lanka) Ltd Puttalam Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

Accurately estimating the volume of dynamically changing stockpiled materials is one of the most critical challenges in effective material management for Siam City Cement (Lanka) Limited. Traditional approaches, such as manual surveying or simple volumetric approximations, are not only laborious and time-consuming but also prone to errors, which may result in less-than-optimal outcomes with regard to resource utilization and operating costs. This paper presents a novel method based on 3D point cloud data acquired by a Kinect depth camera prototype for the estimation of stockpile volumes with increased efficiency and accuracy. The system allows accurate volumetric modeling in real-time using advanced computational algorithms, which turns out to be a striking improvement over traditional methods. It involves registration of several point cloud captures, reorientation of data by using Principal Component Analysis (PCA), noise filtering using clustering techniques, and creation of the accurate surface representation by using Delaunay triangulation. The above approach ensures the limitation brought by the Kinect’s field of view and its depth resolution are effectively compensated. However, to enhance the system’s robustness and scalability, the proposed design will incorporate a Light Detection and Ranging (LiDAR) sensor in its future version. The LiDAR has greater depth accuracy, the ability to acquire data faster, and consistent performance in varying environmental conditions, thus overcoming the intrinsic limitations of the Kinect sensor and enabling easy adaptation to industrial-scale applications. Such research can provide the development of a more reliable and automated volumetric estimation system that will transform material management: reduce manual effort, increase precision, and enable real-time monitoring of stockpile dynamics. This new solution aligns with modern industrial requirements by offering a practical and scalable tool for material inventory optimiz

关键词： Solid modeling Accuracy Laser radar Three-dimensional displays Shape Volume measurement Estimation Cameras Real-time systems sensors

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Machine Learning-Driven Hybrid Adaptive Beamforming for Efficient Interference Mitigation in Dense Wireless sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

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Visual Analytics and Data Visualization (ICVADV), International conference on

作者： P. Loganathan A.T. Priyeshkumar K. Kodeeswari K Jose Reena I. Poonguzhali M. Balaji Department of Electronics and Communication Engineering Excel Engineering College(Autonomous) Namakkal Department of Electronics and Communication Engineering Mahendra College of Engineering Salem Department of Electronics and Communication Engineering Kongu Engineering College(Autonomous) Erode Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Vivekananda College of Technology for Women Tiruchenhode

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The Adaptive Beamforming Algorithm (ABA) represents a promising approach to suppress interference in dense wireless sensor networks (WSNs), enhancing signal fidelity and network throughput. In dense WSNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array Signal Processing (AASP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (SINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant SINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WSNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(SINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency.

关键词： Wireless sensor networks Adaptive systems Array signal processing Scalability Prevention and mitigation Interference Throughput Real-time systems Energy efficiency Signal to noise ratio

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Volcano-structured Double-gated Field Emitter Arrays;Emission Characteristics and applications

Volcano-structured Double-gated Field Emitter Arrays;Emissio...

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International conference on Vacuum Electronics

作者： Hidenori Mimura Yoichiro Neo Masayoshi Nagao Kunio Tsutaki Yoshihiro Kajikawa Takatsugu Munehiro Research Institute of Electronics Shizuoka Uiversity Hamamatsu Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan Microwave Tubes Division NEC Network and Sensor Systems Ltd. Sagamihara Japan

Volcano-structured double-gated field emitter arrays (VDG-FEAs) have realized both highly focusing electron beam and high emission current. The prototype image sensor using Spindt-type VDG-FEA has obtained a reproduced image with uniform image intensity without magnetic focusing. we describe a compact image sensor using the VDG-FEA. We are developing a 300 GHz band traveling wave tube (TWT) using a thermal cathode. We believe that the VDG-FEA is a promising cathode for the 300 GHz TWT in the future.

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