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Multi-object Multi-sensor Tracking Simulation Using Poisson Multi-Bernoulli Mixture Filter 23

Multi-object Multi-sensor Tracking Simulation Using Poisson ...

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23rd International conference on Digital Signal Processing and its applications, DSPA 2021

作者： Thanh, Le Ba Alexandrovich, Pogibelsky Dmitry Ruben, Paliyan Moscow Institute of Physics and Technology Department of Microprocessor Technologies in Intelligent Control Systems Dolgoprudniy Russia

ISBN: (纸本)9781728195513

Multi-object tracking and multi-sensor fusion algorithms are used in many areas of science and technology. For example, in an advanced field such as autonomous vehicles, multi-object tracking and multi-sensor fusion are indispensable technologies. The purpose of these technologies is to processing noisy measurements from different sensors to determine the number and state of objects with high accuracy. Some of the algorithms frequently used in multi-object tracking applications are Global Nearest Neighbor Filter (GNN), Joint Probabilistic Data Association Filter (JPDA), Multiple Hypothesis Tracking (MHT), Probability Hypothesis Density Filter (PHD), and other algorithms. These algorithms are very effective for estimating the state of objects and can combine measurements from multiple sensors to improve estimation accuracy [1,2]. But an even more powerful tool for multi-object tracking is the Poisson multi-Bernoulli mixture filter (PMBM Filter) [1]. In this paper, we will introduce the PMBM filter and simulate an algorithm using a PMBM filter that combines measurements from multiple automotive sensors to estimates object's states. At the same time, we also compare the performance of the aforementioned algorithms in some specific simulation scenarios. © 2021 IEEE.

关键词： Mixtures

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sensor Architecture Model for Unmanned Aerial Vehicles Dedicated to Electrical Tower Inspections 2nd

Sensor Architecture Model for Unmanned Aerial Vehicles Dedic...

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2nd International conference on Optimization, Learning algorithms and applications (OL2A)

作者： Berger, Guido S. Braun, Joao Junior, Alexandre O. Lima, Jose Pinto, Milena F. Pereira, Ana I. Valente, Antonio Soares, Salviano F. P. Rech, Lucas C. Cantieri, Alvaro R. Wehrmeister, Marco A. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Regioes Montanha Su Braganca Portugal Univ Tras Os Montes & Alto Douro Vila Real Portugal INESC Technol & Sci Porto Portugal Fed Univ Technol Curitiba Parana Brazil Fed Inst Parana Curitiba Parana Brazil Ctr Fed Tecnol Celso Suckow Fonseca Rio De Janeiro Brazil Univ Munster Munster Germany Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783031232350;9783031232367

This research proposes positioning obstacle detection sensors by multirotor unmanned aerial vehicles (UAVs) dedicated to detailed inspections in high voltage towers. Different obstacle detection sensors are analyzed to compose a multisensory architecture in a multirotor UAV. The representation of the beam pattern of the sensors is modeled in the CoppeliaSim simulator to analyze the sensors' coverage and detection performance in simulation. A multirotor UAV is designed to carry the same sensor architecture modeled in the simulation. The aircraft is used to perform flights over a deactivated electrical tower, aiming to evaluate the detection performance of the sensory architecture embedded in the aircraft. The results obtained in the simulation were compared with those obtained in a real scenario of electrical inspections. The proposed method achieved its goals as a mechanism to early evaluate the detection capability of different previously characterized sensor architectures used in multirotor UAV for electrical inspections.

关键词： Unmanned aerial vehicles sensor modelling Robotic simulator Electrical inspections

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Whose Data are Reliable: sensor Declared Data Reliability

Whose Data are Reliable: Sensor Declared Data Reliability

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IEEE International conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Sakib Shahriar Shafin Gour Karmakar Iven Mareels Venki Balasubramanian Ramachandra Rao Kolluri Centre for Smart Analytics (CSA) Institute of Innovation Science and Sustainability Federation University Australia Ballarat VIC Australia IBM Consulting IBM Australia Pty Ltd

sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor’s historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.

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CNN-Based Obstacle Avoidance Using RGB-Depth Image fusion 6th

CNN-Based Obstacle Avoidance Using RGB-Depth Image Fusion

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6th International conference on Wireless Technologies, Embedded and Intelligent Systems, WITS 2020

作者： Mechal, Chaymae El El Idrissi, Najiba El Amrani Mesbah, Mostefa Faculty of Sciences and Technology of Fez University Sidi Mohamed Ben Abdellah B.P. 2202 Road of Imouzzer Fez Morocco Sultan Qaboos University Al Khoudh Muscat 123 Muscat Oman

ISBN: (纸本)9789813368927

In the last few years, deep learning has attracted wide interest and achieved great success in many computer vision related applications, such as image classification, object detection, object tracking, pose estimation and action recognition. One specific application that can greatly benefit from the recent advance of deep learning is robot vision-based obstacle avoidance. Vision-based obstacle avoidance systems are mostly based on classification algorithms. Most of these algorithms use either color images or depth images as the main source of information. In this paper, the aim is to investigate whether using information extracted from both types of images simultaneously would give better performance than using each one separately. To do this, we chose the convolutional neural network (CNN) as the classifier and HSV-based method to achieve the fusion. We tested this approach using two widely used pre-trained CNN architectures, namely Resnet-50 and GoogLeNet using a dataset locally collected. The results indicate that the image fusion-based classification algorithm achieve a higher accuracy (91.3%) than the one based on depth images (80.4%) but lower than the one based on color images (93.7%). These results can be partly explained by the fact that the used classifiers were pre-trained using color image datasets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Image fusion

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Time Series in sensor Data Using State-of-the-Art Deep Learning Approaches: A Systematic Literature Review 7th

Time Series in Sensor Data Using State-of-the-Art Deep Learn...

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7th International conference on Science, Technology and Innovation for Society, CITIS 2021

作者： Jácome-Galarza, Luis-Roberto Realpe-Robalino, Miguel-Andrés Paillacho-Corredores, Jonathan Benavides-Maldonado, José-Leonardo Facultad de Ingeniería en Electricidad y Computación Escuela Superior Politécnica del Litoral ESPOL CIDIS. Gustavo Galindo Km 30.5 Vía Perimetral Guayaquil Ecuador Universidad Nacional de Loja La Argelia Loja Ecuador

ISBN: (纸本)9789811641251

Internet of things (IoT) and artificial intelligence (AI) are becoming support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time-series data, we have: long short-term memory (LSTM) for its high precision in prediction and the ability to automatically process input sequences;convolutional neural networks (CNN) mainly in human activity recognition;hybrid architectures in which there is a convolutional layer for data pre-processing and recurrent neural networks (RNN) for data fusion from different sensors and their subsequent classification, and stacked LSTM autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing. Finally, well-known technologies in natural language processing are also used in time-series data prediction, such as the attention mechanism and embeddings obtaining promising results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Visual Simultaneous Localisation and Mapping (VSLAM) for Unstructured Environment

Visual Simultaneous Localisation and Mapping (VSLAM) for Uns...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Sindhu A Suresh A Bindu B Priyanka P Department of CSE(DS) Dayananda Sagar University

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Visual Simultaneous Localization and Mapping (vSLAM) is an important area of computer vision and robotics that enables autonomous systems to use visual input to navigate and construct maps of unknown environments while simultaneously constructing detailed maps. This paper explores key principles, challenges, and advances in visual SLAM algorithms, including Extended Kalman Filter (EKF), FastSLAM, and GraphSLAM. Using the data collected from visual sensors such as cameras along with the measured distance values, robots achieve unparalleled perception and motion execution for autonomous navigation. The fusion of vision and mapping propels robots into uncharted territory, revolutionizing the future research in the domain of robotics. This article explains the concept of SLAM and its importance in modern robotics and augmented reality applications. We review the theoretical underpinnings of Visual SLAM, including camera geometry, feature extraction, and corresponding matching algorithms, which are essential for accurate pose estimation and map construction

关键词： Visualization Computer vision Simultaneous localization and mapping Robot vision systems sensor fusion Cameras Acoustics Telecommunication computing Robots Augmented reality

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Multi-channel Image fusion in Ultraviolet Dynamic Detection on Electric Railways 13

Multi-channel Image Fusion in Ultraviolet Dynamic Detection ...

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13th International conference on Graphics and Image Processing (ICGIP)

作者： Wang, Yan-guo Fang, Jun Qi, Liusheng Xie, Dapeng Li, Liandong Gu, Zichen Liu, Guoyue Tao, Kai Sheng, Liang China Acad Railway Sci Corp Ltd Inst Infrastruct Inspect Beijing Peoples R China China Railway Wuhan Grp Corp Ltd Beijing Peoples R China INMAI Railway Technol Corp Ltd Beijing Peoples R China

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

Catenary is an important infrastructure along electric railways, which should be periodically checked to guarantee the normal working status of power supply for the trains. Technologies of ultraviolet detection has been introduced into catenary inspection, and it can be used to detect the electrical state for insulators that are the isolation components between high voltage and low voltage in catenary. However, due to the dynamic detection condition with the running of catenary inspection car, the performance of multi-channel image fusion becomes a key issue in the application on electric railways. In this paper, we proposed a multi-channel image fusion method for the ultraviolet and visible image in dynamic detection on electric railways. The ultraviolet detection module is designed with dual optical coupling alignment, which guarantees the consistency of multi-channel in spatial domain. The post detection is adopted and used as an external trigger signal for both the ultraviolet and visible cameras, which are working in triggered acquisition mode with good time synchronization. Finally, insulator localization algorithms based on deep learning are utilized in the automatic detection of insulator area in visible image, and the ultraviolet signals are successfully extracted from the image fusion results. The performance of multi-channel image fusion method proposed in the paper has been validated, in both lab test and dynamic test on electric railways.

关键词： Visual inspection ultraviolet detection sensor applications and deployments image and video acquisition

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Enhancing Fraud Detection via On-Chain Ethereum and Off-Chain X Data fusion

Enhancing Fraud Detection via On-Chain Ethereum and Off-Chai...

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2025 2nd International conference on algorithms, Software Engineering and Network Security (ASENS)

作者： Yunpeng Niu Guang Li Yangtian Mi Jieying Zhou Weigang Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Although Ethereum stands as the dominant blockchain for smart contracts and decentralized applications, faces persistent security challenges from fraudulent activities. Such activities often correlate with off-chain platform, such as blog platform and social media. Existing methods analyze fraud activities primarily rely on on-chain transaction data, neglecting interdependencies between on-chain and off-chain activities. In this paper, we observe that there are associations between airdrop campaigns in X platform, a famous social platform and Ethereum fraudulent activities. Further, we crawl Ethereum addresses and posts of these users in airdrop campaigns, and construct a cross-platform datasets from X to Ethereum, including matching pairs of Ethereum addresses to X users, Ethereum transactions and X post data. Due to inherent heterogeneity between Ethereum transactions (structured graphs) and X data (unstructured text/images), we design a multimodal fusion framework leveraging transformer architectures to fuse on-chain transaction features with off-chain content features (text and image representations). Finally, the fused features are leveraged to construct downstream fraud transactions classifiers. Experimental results demonstrate that classifiers using fused features outperform classifiers using transaction features, achieving a 12% improvement in Recall. Our findings highlight the critical role of off-chain data in enhancing fraud detection accuracy.

关键词： Accuracy Social networking (online) Soft sensors Software algorithms Smart contracts Feature extraction Transformers Blockchains Fraud Software engineering

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GNSS-Independent Navigation of UAV through Utilization of sensor fusion and Intelligence System

GNSS-Independent Navigation of UAV through Utilization of Se...

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2022 International conference on Information and Communication Technology for Development for Africa, ICT4DA 2022

作者： Meheretu, Simegnew Eshetu Hailesilassie, Serkalem Yikeber Nigussie, Ethiopia Bahir Dar University Department of Computer Science Bahir Dar Ethiopia University of Turku Department of Computing Turku Finland

ISBN: (数字)9781665455879

ISBN: (纸本)9781665455879

Unmanned Aerial Vehicles (UAVs) have gained a lot of attention due to their applications in various sectors. Most of fully autonomous UAVs navigation system is dependent on GNSS for localization. But the security threats that emanates from GPS system;spoofing and jamming makes the reliance on GNSS problematic. In this work, we proposed a fully autonomous UAV navigation system without GNSS and wireless communication. The application area of interest in this work is military battlefield. The proposed navigation method combines information gathered by Intelligence Preparation for Battlefield (IPB) and Intelligence Surveillance Reconnaissance(ISR) group and onboard sensors. Then it applies AI-enabled multi sensor fusion, motion planning and decision making to accomplish the navigation successfully. The integrated way of navigation used to assess the conceptual possibility of autonomous navigation without the use of external navigation system and reliable operation of UAV at GPS and wireless signal denied environment. In addition, to ensure reliable operation of the navigation system, devices health monitoring system through the use of distributed monitoring agent and AI algorithms are proposed. The conceptual architecture is designed as six layer architecture with cross-layer interaction feature. © 2022 IEEE.

关键词： Motion planning

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Byzantine Fault-Tolerant Distributed Passive Localization with Dual-Layer Data fusion

Byzantine Fault-Tolerant Distributed Passive Localization wi...

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Control Theory and applications (ICoCTA), International conference on

作者： Zijian Song Jing Zhu College of Automation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331506155

ISBN: (纸本)9798331506162

This paper proposes a distributed passive localization system that utilizes dual-layer data fusion through smart contracts to enhance the accuracy and security of positioning in the presence of Byzantine nodes. Firstly, the system groups sensors based on distance and utilizes passive sensors for Angle of Arrival (AOA) measurements, ensuring both precise localization and operational concealment. Communication between nodes is based on blockchain technology, with encryption algorithms and hash signatures safeguarding data security. Subsequently, the proposed method sequentially carries out intra-group and inter-group data fusion, effectively detecting and mitigating the influence of Byzantine nodes, leading to highly accurate position estimations. Finally, the Practical Byzantine Fault Tolerance (PBFT) algorithm ensures consensus on the positioning results across all nodes. Experimental validation and analysis demonstrate that the proposed approach significantly enhances the accuracy, robustness, and security of distributed localization in adversarial environments, offering a reliable solution for applications requiring high levels of trust and precision.

关键词： Location awareness Fault tolerance Accuracy Smart contracts Fault tolerant systems Data integration Estimation sensor systems Blockchains Intelligent sensors

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