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LONG-TERM GEO-POSITIONED RE-IDENTIFICATION DATASET OF URBAN ELEMENTS 31

LONG-TERM GEO-POSITIONED RE-IDENTIFICATION DATASET OF URBAN ...

引用

2024 international conference on Image processing

作者： Moral, Paula Garcia-Martin, Alvaro Martinez, Jose M. Univ Autonoma Madrid Video Proc & Understanding Lab Madrid Spain

ISBN: (纸本)9798350349405;9798350349399

This paper introduces UrbAM-ReID, a new long-term geo-positioned urban ReID dataset. It is composed by four sub-datasets recording the same trajectory at the UAM Campus, each one recorded in different seasons and including an inverse direction recording. While most of the current datasets in the state-of-the-art focus on person re-identification, with vehicles as the second most explored object, our work specifically addresses urban objects re-identification, currently, waste containers, rubbish bins, and crosswalks. The dataset provides different attributes of the annotated objects, like their classes, their foreground or background status and the geo-position. Several evaluation configurations can be defined to simulate realistic scenarios that may arise in actual situations within the management of urban elements, considering the utilization of just visual data, or incorporating additional attributes, providing different complexity levels. Finally, the dataset is used for defining a benchmark where two state-of-the-art systems are evaluated. The dataset and supplementary material is available in https://***/vpulab/UrbAM-ReID

关键词： Re-Identification Deep Learning Computer vision Urban Environment

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Accurate Measurement and Real-Time Control of Digital signal processing in Fiber Optic Ranging Systems 3

Accurate Measurement and Real-Time Control of Digital Signal...

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3rd international conference on Artificial Intelligence and Autonomous robot Systems, AIARS 2024

作者： Zhou, Dinghe Huang, Junbo Harbin Engineering University Harbin Heilongjiang150000 China Hebei University of Technology Tianjin300130 China

ISBN: (纸本)9798350376173

Digital signal processing (DSP) technology is a technology that completes data statistics and processing through digital computing, and is widely used in communication, measurement, statistics and other technical fields. Fiber optic has been widely used in communication, electronics, and power due to its advantages such as bandwidth, low loss, and strong anti-interference ability. Fiber optic sensing has been widely used in modern society due to its long sensing distance, multiple measurable parameters, and high sensitivity. With the rapid growth of fiber optic technology, fiber optic ranging technology has been widely applied in various fields. Fiber optic ranging technology has the characteristics of large range and high accuracy, and is one of the most important technologies in high-precision distance measurement. To address the shortcomings of complex calibration process and low calibration accuracy in fiber optic measurement error calibration, this paper proposes a fiber optic ranging system based on DSP technology. By filtering, amplifying, and denoising the collected signal, the signal-to-noise ratio of the signal can be improved, and distance can be measured more accurately. At the same time, the system also has real-time control and adjustment functions to ensure the accuracy of distance measurement. The experimental verification results show that the system has excellent performance and can be used in various fields. © 2024 IEEE.

关键词： Calibration

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An Experimental Study on 5G Mobility Management: A Drive Test Approach 6

An Experimental Study on 5G Mobility Management: A Drive Tes...

引用

6th international conference on Communications signal processing and their Applications

作者： El-Saleh, Ayman A. Alhammadi, Abdulraqeb Al Jahdhami, Majan Abdullah Kabbani, Adnan Shayea, Ibraheem Thiagarajah, Siva Priya ASharqiyah Univ ASU Coll Engn Ibra 400 Oman UTM Malaysia Japan Int Inst Technol Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur 54100 Malaysia Istanbul Tech Univ ITU Dept Elect & Commun Engn TR-34467 Istanbul Turkiye Multimedia Univ Fac Engn Cyberjaya 63100 Selangor Malaysia

ISBN: (纸本)9798350384826;9798350384819

Drive testing plays a crucial role in assessing and optimizing the performance of mobile networks for operators. With the transition from fourth-generation (4G) to fifth-generation (5G) networks, it becomes essential to evaluate and compare the performance of these technologies in real-world scenarios. Drive test real measurement data assumes a central role in optimizing mobile networks. Operators gain invaluable insights into critical factors such as signal quality, data throughput, and handover events by conducting physical drive tests. This empirical data empowers them to identify areas with inadequate coverage, optimize antenna settings, refine handover parameters, and strategically deploy additional base stations. This paper provides symmetrical performance analyses based on an experimental study of operating 5G cellular networks in Muscat, Oman. The data measurement campaign was collected using mobile devices from three mobile network providers (Omantel, Ooredoo, and Vodafone). The network's performance was evaluated using various metrics, including channel quality indicators, data rates and handover events. Assessment results show that the leading networks in the measured area are 5G networks for all mobile network providers. While 5G has showcased incremental improvements over 4G in some instances, it's still in its nascent phase. Recommendations include continuous network monitoring by operators and augmented 5G base station deployment to heighten the quality of service by ensuring higher data rates and minimal latency.

关键词： Quality of experience handover quality of service QoS Mobile Broadband 5G data rate

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Advanced robotics: Exploring AI and IoT in Multi-Control Systems 3

Advanced Robotics: Exploring AI and IoT in Multi-Control Sys...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Nazeer, Rabiya Himanshu Lovely Professional University Department of Computer Science and Engineering Punjab 144411 India

ISBN: (纸本)9798350354348

The combination of electromyography (EMG) and electroencephalography (EEG) sensors with Internet of Things (IoT) enabled robots has marked a turning point in human-robot interaction. This chapter examines the groundbreaking potential of this integration and shows how contemporary technology and physiological signals are convergent. Results indicate improved sensory integration, allowing robots to interpret environmental cues and human intention with unprecedented precision. Although real-time feedback techniques increase efficiency and safety, complex signal processing and ethical challenges persist. Still, research toward a future where robotics, IoT, EMG, and EEG all work together to revolutionize human-machine collaboration in various circumstances is stimulated by the promise of personalized prostheses, easily navigable assistive devices, and extensible systems. Interaction between humans and machines is being transformed by robots because EMG and EEG sensors are integrated into IoT-connected robotic systems, and they can comprehend physical orders through EMG signals and read cognitive intent through EEG signals. Scholars are working to enhance signal processing algorithms to lower noise and increase the accuracy and reliability of brain signal interpretation as they become more aware of the possible uses of IoT-integrated EMG and EEG sensors in robotics. © 2024 IEEE.

关键词： Human robot interaction

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Robust vision Transformer Model Against Adversarial Attacks in Medical Image Classification 47

Robust Vision Transformer Model Against Adversarial Attacks ...

引用

47th international conference on Telecommunications and signal processing, TSP 2024

作者： Kanca, Elif Gulsoy, Tolgahan Avas, Selen Kablan, Elif Baykal Karadeniz Technical University Department of Software Engineering Trabzon Turkey Karadeniz Technical University Department of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798350365597

Recent advancements in deep learning have signif-icantly enhanced the rapid and precise classification of medical images. vision transformers, an advanced model, have started replacing CNN s in several medical image tasks. However, recent research indicates that these sophisticated models are susceptible to adversarial attacks generated by various methods. This paper proposes a ViT-based model to bolster the robustness of vision Transformers against such attacks. Extensive experiments across diverse datasets demonstrate that the ViT model attains higher accuracy on clean images compared to the proposed model. However, the proposed classification model exhibits greater re-silience against adversarial attacks. Across all datasets and attack methods, the proposed model achieves the lowest accuracy of 66.67%, contrasting with 0% for the ViT model. These findings underscore the paramount importance of constructing robust models for medical image classification. © 2024 IEEE.

关键词： Deep learning

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Leveraging Machine Learning for Gait Phase Classification with Varied Training Methods 7

Leveraging Machine Learning for Gait Phase Classification wi...

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IEEE 7th international conference on Advanced Technologies, signal and Image processing (ATSIP)

作者： Mekni, Amal Narayan, Jyotindra Gritli, Hassene Univ Carthage Higher Inst Informat & Commun Technol RISC Lab LRI6ES07 Tunis 1164 Tunisia Imperial Coll London Dept Comp London SW7 2AZ England Univ Bayreuth Chair Digital Hlth D-95445 Bayreuth Germany Univ Tunis El Manar Lab Robot Informat & Complex Syst RISC Lab LRI6ES07 Natl Engn Sch Tunis Tunis 1002 Tunisia

ISBN: (纸本)9798350351491;9798350351484

Accurate gait phase recognition is crucial for real-time analysis and intervention in rehabilitation, biomechanics, and prosthetics. However, achieving this is challenging due to the diverse machine learning (ML) training methods. This study employs ML algorithms to classify gait phases, focusing on stance and swing phases, utilizing open-source data from 100 participants (41.91 +/- 5.3 yrs). The classification algorithms considered are k-Nearest Neighbor (k-NN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayesian (NB) algorithms. The study evaluates these algorithm performances using two training methods with five and ten lower body movements: one randomly selects 80% of stance and swing phase data for training, while the other divides data by participants, allocating 80% for training and 20% for testing. When assessing accuracy with five movements, RF achieved 99.8% for both training methods. With ten movements, RF achieved a high accuracy of 99.9% using the second method. Notably, the performance of all ML algorithms exhibited improvement when considering data from ten movements versus five. Additionally, it was observed that the second training method proved more effective with five-movement data compared to data involving ten movements. This comprehensive evaluation highlights the potential of machine learning algorithms for accurate gait phase recognition in diverse applications with varied training methods.

关键词： Gait recognition Machine Learning Classification algorithms Training methods Accuracy

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2024 9th international conference on signal and Image processing, ICSIP 2024

2024 9th International Conference on Signal and Image Proces...

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9th international conference on signal and Image processing, ICSIP 2024

ISBN: (纸本)9798350350920

The proceedings contain 145 papers. The topics discussed include: design and implementation of mimic defense gateway;attenuation correction of CT-guided PET images based on deep convolutional neural networks;low-bit hybrid RIS enhanced beamforming design with imperfect CSI;channel estimation algorithm based on multi-modal neural network;an active visual perception methodology for the safety of human-robot collaborations;radar forward-looking super-resolution imaging based on joint projection and non-convex two-step regularization;invisible and multi-triggers backdoor attack approach on deep neural networks through frequency domain;establishment and research of liver medical image online database based on deep learning;and aerial target classification using micro-doppler spectrum based on PCT and ResNet34.

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Demo Abstract: Accessible WiFi sensing leveraging robot Operating System 22

Demo Abstract: Accessible WiFi sensing leveraging Robot Oper...

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22nd ACM/IEEE international conference on Information processing in Sensor Networks, IPSN 2023

作者： Arun, Aditya Hunter, William Bharadia, Dinesh University of California San Diego United States

ISBN: (纸本)9798400701184

RF signals can be leveraged for many sensing and monitoring tasks in industrial, home, or robot applications. Despite the advantages of leveraging WiFi sensing modality, no versatile WiFi sensors are available. We develop WiROS to address this immediate need. We leverage the robot operating system (ROS) framework to expose real-time WiFi sensing information to an end-user. Specifically, we demonstrate a plug-and-play toolbox providing access to coarse-grained WiFi signal strength (RSSI), fine-grained WiFi channel state information (CSI), and other MAC-layer information (device address, packet id's or frequency-channel information). Additionally, we opensource state-of-art algorithms to calibrate and process WiFi measurements to intuitively visualize/debug measurements and measure signal path parameters like the signal's angles of arrival or departure. The open-sourced repository is https://***/ucsdwcsng/WiROS. © 2023 Owner/Author.

关键词： robot Operating System

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AURA: Advancing Human-robot Interaction Through Unified Voice and Gesture Control 8

AURA: Advancing Human-Robot Interaction Through Unified Voic...

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8th international conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Kumar, Lakshya Anand, Pooja Ganpatye, Ameya Amity University Greater Dept. of Electronics & Communication Engineering Noida India MIT World Peace University Dept. of Electrical & Electronics Engineering Pune India

ISBN: (纸本)9798331520762

The development of artificial intelligence (AI) assistants has revolutionized human-machine interaction, but traditional systems often rely on singular input modalities such as voice commands. This project, AURA: AI robot with Unified Voice and Gesture Control, integrates voice and air gesture recognition to create a seamless and intuitive user interface. Utilizing a Raspberry Pi, camera, speaker, and microphone, the system combines computer vision and natural language processing to interpret user commands dynamically. The AI assistant is designed to respond accurately to both vocal instructions and gestures, enabling hands-free and voice-free operation in various scenarios. AURA's innovative approach enhances accessibility, convenience, and user experience, with potential applications in smart homes, healthcare, and personal robotics. This unified control mechanism demonstrates the future of multimodal interaction, emphasizing efficiency and adaptability in AI-driven systems. © 2025 IEEE.

关键词： Intelligent robots

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Research on accuracy range of shaft-hole assembly with robot based on image recognition

Research on accuracy range of shaft-hole assembly with robot...

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2024 international conference on Intelligent robotics and Automatic Control

作者： Jiao, Qing Lei, Guoqing Lu, Lei Sun, Lining Soochow Univ Sch Mech & Elect Engn Suzhou Peoples R China

ISBN: (纸本)9798350389814;9798350389807

In this paper, the automatic assembly of shaft-hole with robot is researched, and the accuracy range of robot shaft hole assembly based on image recognition algorithm is explored. According to the accuracy experiment requirements, the robot system and shaft-hole assembly platform are built, the eye-in-hand system calibration scheme is designed. A recognition algorithm combining image preprocessing technology, edge detection and Hough transform circle detection is proposed to locate the mounting hole and realize the shaft-hole assembly with the robot. In order to improve the image recognition process, image preprocessing technologies such as gray level processing, Gaussian filter are used to reduce the influence of the surrounding environment and noise points. Edge detection with Sobel operator is adopted to enhance the target features of the image and improve the accuracy of circle center recognition. Hole center coordinates is obtained by Hough transform circle detection. Finally, the hand-eye relationship obtained by calibration is used to drive the robot to realize the shaft-hole assembly. In order to obtain the accuracy range of shaft-hole assembly, several experiments were carried out, and the experimental results showed that the error of shaft-hole assembly is limited to +/- 1 mm.

关键词： Shaft-hole assembly Camera calibration Edge detection robot vision

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