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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： v. Mariselvam S.R. vishal T. Surya S. vishnuram Electronics and Communication Engineering M.Kumarasamy College of Engineering Karur India

Sifting is an important activity in a wide range of applications, including image, video, and sensor fusion, ATM and network shifting, statistical modeling, forecasting, intelligent systems and robotics, and data gathering and databases. The proposed work’s main objective is to develop a new data comparison technique that offers a limited technique for ordering or ranking networks based on speed, energy, and region. In this research, we offer a new area-efficient and comparative estimator for median filter application. This proposed system was written in verilog HDL, simulated with Modelsim 6.4 c, then synthesized with the Xilinx tool. The suggested system is built on an FPGA Spartan 3 XC3S 200 TQ-144. The design of a low-power, high-speed multiway merge sorting network for median filter applications is presented in this study. To achieve a balance between power efficiency and sorting performance, the suggested network employs a hybrid sorting strategy that incorporates the benefits of both parallel and serial sorting algorithms. The network is built utilizing a hybrid of digital and analogue circuit approaches, resulting in substantial power savings while retaining good sorting performance. The suggested median filter is assessed using simulation and experimental data, which show that it is more successful than existing techniques at lowering power consumption and improving sorting speed.

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Simultaneous Localization and Mapping Algorithm based on 3D Laser for Unmanned Aerial vehicle 4th

Simultaneous Localization and Mapping Algorithm based on 3D ...

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4th International Conference on Electrical Engineering and Control applications, ICEECA 2019

作者： Demim, Fethi Nemra, Abdelkrim Mouali, Oumima Hedir, Mehdia Rouigueb, Abdenebi Hamerlain, Mustapha Bendoumi, Mohamed Amine Bazoula, Abdelouahab Laboratoire des Véhicules Autonomes Intelligents Ecole Militaire Polytechnique EMP Bordj El Bahri Algiers Algeria Faculté des Sciences Mathématiques Physiques et Naturelles Université del Manar Tunis Tunisia Image Processing and Radiation Laboratory Faculty of Electronic and Informatics USTHB Bab Ezzouar Algiers Algeria Data Fusion and Analysis Laboratory Ecole Militaire Polytechnique EMP Bordj El Bahri Algiers Algeria Centre de Développement des Technologie Avancées CDTA Baba Hassen Algeria

ISBN: (纸本)9789811564024

This paper focuses on developing a robust solution for the Simultaneous Localization and Mapping (SLAM) problem to increase the autonomy of Unmanned Aerial vehicles (UAv). The investigated topics are related to data fusion, localization, features extraction and matching, map building, 3D pose estimation and SLAM. One important aspect of the autonomous navigation which should be investigated as well as the fusion of data from the different sensor. The data fusion algorithms are very important and their performances are closely dependent on both performances of the constructed map and the accuracy of the UAv position within this map. Optimal and robust filter using centred Gaussian noises is implemented. The original contribution of this work is the proposition and the adaptation of the Extended Kalman Filter (EKF) to solve the Inertial Navigation Systems INS/3D laser UAv navigation problem. Simulation results for 3D flight scenario are presented to demonstrate the advantages of the hybrid localization based on EKF compared with results of the INS relative localization based technique. Good results were obtained with the EKF using noise process and/or measurement noise characteristics, particularly in the case of centred Gaussian noises. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAv)

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verifying RADAR Data Using Two-Dimensional QIM-based Data Hiding

Verifying RADAR Data Using Two-Dimensional QIM-based Data Hi...

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作者： Fedoruk, Brandonlib. University of Michigan

Modern vehicles have evolved into supporting advanced internal networks and connecting System Based Chips (SBC), System in a Package (SiP) solutions or traditional micro controllers to foster an electronic ecosystem for high speed data transfers, precision and real-time control. The use of Controller Area Networks (CAN) is widely adopted as the backbone of internal vehicle communication infrastructure. Automotive applications such as ADAS, autonomous driving, battery management systems, power train systems, telematics and infotainment, all utilize CAN transmissions directly or through gateway management. The network transmissions lack robust integrity verification mechanisms to validate authentic data payloads, making it vulnerable to packet replay, spoofing, insertion, deletion and denial of service attacks. Additional methods exist to secure network data such as traditional cryptography. Utilizing this method will increase the computational complexity, processing latency and increase overall system cost. This thesis proposes a robust, light and adaptive solution to validate the authenticity of automotive sensor data using CAN network protocol. We propose using a two-dimensional Quantization Index Modulation (QIM) data hiding technique, to create a means of verification. Analysis of the proposed framework will be conducted in a sensor transmission scenario for RADAR sensors in an autonomous vehicle setting. The detection and effects of distortion on the application are tested through the implementation of sensor fusion algorithms and the results are observed and analyzed. The proposed framework offers a needed capability to maintain transmission integrity without the compromise of data quality and low design complexity. This framework could also be applied to different network architectures, as well as its operational scope could be modified to operate with more abstract types of data.

关键词： sensor networks RADAR Data integrity Data security ADAS Digital watermarking Connected vehicle Thesis

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On the Adaptivity of Unscented Particle Filter for GNSS/INS Tightly-Integrated Navigation Unit in Urban Environment

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IEEE ACCESS 2021年 9卷 144157-144170页

作者： vouch, Oliviero Minetto, Alex Falco, Gianluca Dovis, Fabio Politecn Torino Dept Elect & Telecommun DET I-10129 Turin Italy LINKS Fdn Space & Nav Technol Area I-10138 Turin Italy

Tight integration algorithms fusing Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have become popular in many high-accuracy positioning and navigation applications. Despite their reliability, common integration architectures can still run into accuracy drops under challenging navigation settings. The growing computational power of low-cost, embedded systems has allowed for the exploitation of several advanced Bayesian state estimation algorithms, such as the Particle Filter (PF) and its hybrid variants, e.g. Unscented Particle Filter (UPF). Although sophisticated, these architectures are not immune from multipath scattering and Non-Line-of-Sight (NLOS) signal receptions, which frequently corrupt satellite measurements and jeopardise GNSS/INS solutions. Hence, a certain level of modelling adaptivity should be granted to avoid severe drifts in the estimated states. Given these premises, the paper presents a novel Adaptive Unscented Particle Filter (AUPF) architecture leveraging two cascading stages to cope with disruptive, biased GNSS input observables in harsh conditions. A INS-based signal processing block is implemented upstream of a Redundant Measurement Noise Covariance Estimation (RMNCE) stage to strengthen the adaptation of observables' statistics and improve the state estimation. An experimental assessment is provided for the proposed robust AUPF that demonstrates a 10 % average reduction of the horizontal position error above the 75-th percentile. In addition, a comparative analysis both with previous adaptive architectures and a plain UPF is carried out to highlight the improved performance of the proposed methodology.

关键词： Global navigation satellite system Navigation Measurement uncertainty Noise measurement Adaptation models Satellites Position measurement Bayes methods sensor fusion inertial navigation satellite navigation systems particle filters adaptive estimation position accuracy

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Efficient fusion network with label generation and branch transformations for visible and infrared images fusion

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Infrared Physics & Technology 2025年 150卷

作者： Dongdong Sun Chuanyun Wang Tian Wang Qian Gao Zhaokui Li College of Artificial Intelligence Shenyang Aerospace University Shenyang 110136 China Institute of Artificial Intelligence Beihang University Beijing 100191 China School of Computer Science Shenyang Aerospace University Shenyang 110136 China

The information obtained from a single sensor is often insufficient for accurate environmental perception. In contrast, multi-sensor fusion significantly enhances perceptual accuracy, with the integration of infrared and visible light sensors being one of the most common approaches in multi-sensor fusion. Infrared and visible image fusion aims to combine the texture details of visible images with the thermal radiation information of infrared images to produce fused images with enhanced visual quality, which find broad applications in night vision, surveillance, and target detection. Existing fusion methods primarily focus on improving the quality of fused images, often at the expense of computational efficiency due to the reliance on complex, multi-layered architectures. This limitation makes them unsuitable for deployment on low-power terminal devices. To address these challenges, this paper proposes a novel image fusion framework, termed TSfusion, which leverages teacher-student learning to achieve both high-quality fusion and high computational efficiency. A teacher network, equipped with sequence-model-based feature extraction blocks and progressive integration modules, generates high-quality fusion labels by overcoming the limited receptive field of conventional convolutional networks. The lightweight student network adopts a reparameterisation-based Fast Inception Module, ensuring robust fitting capabilities while maintaining high-speed operation. Additionally, a novel multi-scale loss function is introduced to guide the student network in learning from the teacher at both semantic and pixel levels. Experimental results on three benchmark datasets demonstrate that the proposed TSfusion framework achieves superior visual and quantitative performance compared to state-of-the-art methods. Furthermore, TSfusion exhibits exceptional inference speed, outperforming all existing fusion algorithms, making it highly suitable for real-time applications. The implementatio

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中厚板多层多道焊视觉测量与工艺规划

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天津工业大学学报 2024年第3期43卷 75-81页

作者：王天琪张树浩龙斌王克宽天津工业大学天津市现代机电装备技术重点试验室天津300387 中国石油集团工程技术研究有限公司天津300451

针对中厚板多层多道焊的焊道测量问题,提出利用焊缝检测和焊道尺寸视觉测量的信息融合自适应微调焊枪位置的方法。首先基于结构光视觉传感器系统采集焊缝图像,在典型图像处理算法的基础上,结合FROSAC提取算法提取焊缝特征信息;将提取到... 详细信息

针对中厚板多层多道焊的焊道测量问题,提出利用焊缝检测和焊道尺寸视觉测量的信息融合自适应微调焊枪位置的方法。首先基于结构光视觉传感器系统采集焊缝图像,在典型图像处理算法的基础上,结合FROSAC提取算法提取焊缝特征信息;将提取到的特征点进行坐标转换,采用视觉测量获得焊道轮廓和尺寸信息,来修正机器人的运动路径;根据焊缝特征信息分析工艺参数对焊道成型的影响,确定焊道层数、各焊道的工艺参数以及焊枪的偏移量,完成多层多道焊接工艺规划;最后基于搭建的机器人焊接视觉系统在12 mm母材上进行v形坡口多层多道焊接试验。结果表明:该方法下坡口填充良好,焊道尺寸平均测量误差小于0.2 mm,满足多层多道焊接工业应用需求。

关键词：多层多道规划结构光视觉传感器三维检测 FROSAC算法焊接工艺参数

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Precise Indoor Positioning System for Mobile Robots via Smoothed UWB/IMU sensor fusion

Precise Indoor Positioning System for Mobile Robots via Smoo...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Mahmoud Elsanhoury Jyri Nieminen Petri välisuo Akpojoto Siemuri Janne Koljonen Mohammed Elmusrati Heidi Kuusniemi Digital Economy Platform University of Vaasa Vaasa Finland VEBIC Platform Digital Economy Platform University of Vaasa Vaasa Finland School of Technology and Innovations Digital Economy Platform University of Vaasa Vaasa Finland School of Technology and Innovations University of Vaasa Vaasa Finland Digital Economy Platform School of Technology and Innovations University of Vaasa Vaasa Finland

Indoor positioning systems (IPSs) are the foundations for all indoor location-based services and applications. In this article, we present a precise and robust IPS using ultra wide-band (UWB) technology fused with an inertial measurement unit (IMU). Both technologies are integrated to account for the non-line-of-sight (NLOS) problems arising in a dense challenging environment found within an industrial laboratory in Finland. Besides the conventional estimation techniques e.g. extended Kalman filter (EKF), we employ the Rauch-Tung-Striebel (RTS) smoothing algorithm in addition to a multivariate regression-based offset compensation method to improve the overall positioning accuracy of the system. The recommended number of distributed UWB anchors versus the coverage area is also discussed and tested in this article. The experiments were held by a patrolling mobile robot with millimeter accuracy, which acted as a ground truth reference to all used algorithms. The positioning estimation results showed a superior performance by the proposed method (UWB/IMU EKF-RTS-LR) with mean accuracy of 4.7 cm, and 9.6 cm for more than 95% of the time.

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fusion of Data from Radar and Depth sensors Applied for Healthcare-Oriented Characterization of Persons' Gait 23

Fusion of Data from Radar and Depth Sensors Applied for Heal...

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23rd IEEE Conference on Signal Processing algorithms, architectures, Arrangements, and applications (SPA)

作者： Mazurek, Pawel Wagner, Jakub Morawski, Roman Z. Warsaw Univ Technol Fac Elect & Informat Technol Inst Radioelect & Multimedia Technol Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9788362065363

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： measurement data fusion impulse-radar sensor depth sensor healthcare

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An improved energy efficient cluster head selection protocol using the double cluster heads and data fusion methods for IoT applications

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COGNITIvE SYSTEMS RESEARCH 2019年 57卷 101-106页

作者： Jesudurai, S. Anthony Senthilkumar, A. Dr Mahalingam Coll Engn & Technol Dept EIE Coimbatore Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept EEE Coimbatore Tamil Nadu India

The more energy consumption is the major issue for wireless sensor network. In wireless sensor network (WSN), sensor nodes are fixed in various routing algorithms. In the same networks, mobile sensor and fixed sensor nodes are combine and its used in few applications. The function is corruption while mobility was achieved, since these nodes have minimum battery power, lower range of communication and a lessor amount of memory. To overcome this issue, Improved Energy Efficient Cluster Head Selection protocol (IEECHS-WSN) is proposed, in these technique is used to transfer the received information by using energy efficient routing protocol. In the CH election method, two cluster heads are selected in a separated cluster and its work in various functions, this can be prolong the network lifetime and decrease the energy consumption of IoT applications. Proposed technique is described on clustering of dual CHs in the method of data fusion for data entropy. This information entropy is used for fusion and classification, the result of fusion and classification are accurate and efficient for data transmission. Our proposed IEECHS protocol has better throughput, lifetime of network and energy consumption compared than the existing technique. (C) 2018 Elsevier B.v. All rights reserved.

关键词： Cluster head Wireless sensor networks Analog to digital converter

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Focussing Learned Image Compression to Semantic Classes for v2X applications 31

Focussing Learned Image Compression to Semantic Classes for ...

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31st IEEE Intelligent vehicles Symposium (Iv)

作者： Loehdefink, Jonas Baer, Andreas Schmidt, Nico M. Hueger, Fabian Schlicht, Peter Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Inst Commun Technol Schleinitzstr 22 D-38106 Braunschweig Germany Volkswagen Grp Innovat Berliner Ring 2 D-38440 Wolfsburg Germany

ISBN: (纸本)9781728166735

Cooperative perception with many sensors involved greatly improves the performance of perceptual systems in autonomous vehicles. However, the increasing amount of sensor data leads to a bottleneck due to limited capacity of vehicle-to-X (v2X) communication channels. We leverage lossy learned image compression by means of an autoencoder with adversarial loss function to reduce the overall bitrate. Our key contribution is to focus image compression on regions of interest (ROIs) governed by a binary mask. A transmitter-sided semantic segmentation network extracts semantically important classes being the basis for the generation of a ROI. A second key contribution is that the mask is not transmitted as side information, only the quantized bottleneck data is transmitted. To train the network, we use a loss function operating only on the pixels in the ROI. We report peak-signal-to-noise ratio (PSNR) both in the entire image and only in the ROI, evaluating various fusion architectures and fusion operations involving input image and mask. Showing the high generalizability of our approach, we achieve consistent improvements in the ROI in all experiments on the Cityscapes dataset.

关键词： Image compression

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