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arXiv 2024年

作者： Gallagher, James E. Oughton, Edward J. Department of Geography Geoinformation Science Department George Mason University FairfaxVA22030 United States

Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles, to agriculture, infrastructure monitoring and environmental assessment. The combination of these technologies has led to significant advancements in object detection, classification, and segmentation tasks in the non-visible light spectrum. This paper considers 400 total papers, reviewing 200 in detail to provide an authoritative meta-review of multispectral imaging technologies, deep learning models, and their applications, considering the evolution and adaptation of You Only Look Once (YOLO) methods. Ground-based collection is the most prevalent approach, totaling 63% of the papers reviewed, although uncrewed aerial systems (UAS) for YOLO-multispectral applications have doubled since 2020. The most prevalent sensor fusion is Red-Green-Blue (RGB) with Long-Wave Infrared (LWIR), comprising 39% of the literature. YOLOv5 remains the most used variant for adaption to multispectral applications, consisting of 33% of all modified YOLO models reviewed. 58% of multispectral-YOLO research is being conducted in China, with broadly similar research quality to other countries (with a mean journal impact factor of 4.45 versus 4.36 for papers not originating from Chinese institutions). Future research needs to focus on (i) developing adaptive YOLO architectures capable of handling diverse spectral inputs that do not require extensive architectural modifications, (ii) exploring methods to generate large synthetic multispectral datasets, (iii) advancing multispectral YOLO transfer learning techniques to address dataset scarcity, and (iv) innovating fusion research with other sensor types beyond RGB and LWIR. © 2024, CC BY.

关键词： Convolutional neural networks

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量测不一致性条件下多传感器融合算法研究

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现代制造工程 2018年第8期 123-127,47页

作者：徐建亮顾怡红魏小华衢州职业技术学院机电工程学院衢州324000 浙江大学机械工程学院杭州310027

由于传感器提供的抽象数据总是受到测量过程中不确定性因素的影响,多传感器利用多源数据融合算法,消除传感器间可能存在的虚假或错误数据,以降低其不确定性。提出一种基于粒子滤波算法(PFA)的改进贝叶斯方法(IBA),其中基于粒子滤波有3... 详细信息

由于传感器提供的抽象数据总是受到测量过程中不确定性因素的影响,多传感器利用多源数据融合算法,消除传感器间可能存在的虚假或错误数据,以降低其不确定性。提出一种基于粒子滤波算法(PFA)的改进贝叶斯方法(IBA),其中基于粒子滤波有3种算法结构,即:前置粒子滤波算法(P-IBA)、后置粒子滤波算法(IBA-P)和前后置粒子滤波算法(P-IBA-P)。通过三维点云数据分析实验表明,P-IBA具有最小的CPU运行时间t,P-IBA-P比IBA-P拥有最小的评价函数值EF,可见,在P-IBA-P和IBA-P两种算法中,由于粒子滤波器的存在且起到决定性的作用,使得基于粒子滤波的改进贝叶斯算法(P-IBA-P)误差最小,精度最高,融合效果最好。改进的贝叶斯方法与粒子滤波相结合的技术,有助于处理集中式和分散式融合架构的数据不一致问题,在对测量时间要求不高的情况下,使用P-IBA-P算法的测量效果最好。

关键词：多传感器融合改进贝叶斯方法粒子滤波算法

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Stress Detection using Context-Aware sensor fusion from Wearable Devices

arXiv

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arXiv 2023年

作者： Rashid, Nafiul Mortlock, Trier Al Faruque, Mohammad Abdullah The Department of Electrical Engineering and Computer Science The Department of Mechanical and Aerospace Engineering Electrical Engineering and Computer Science and Mechanical and Aerospace Engineering The University of California IrvineCA92697 United States

Wearable medical technology has become increasingly popular in recent years. One function of wearable health devices is stress detection, which relies on sensor inputs to determine a patient’s mental state. This continuous, real-time monitoring can provide healthcare professionals with vital physiological data and enhance the quality of patient care. Current methods of stress detection lack: (i) robustness—wearable health sensors contain high levels of measurement noise that degrades performance, and (ii) adaptation—static architectures fail to adapt to changing contexts in sensing conditions. We propose to address these deficiencies with SELF-CARE, a generalized selective sensor fusion method of stress detection that employs novel techniques of context identification and ensemble machine learning. SELF-CARE uses a learning-based classifier to process sensor features and model the environmental variations in sensing conditions known as the noise context. SELF-CARE uses noise context to selectively fuse different sensor combinations across an ensemble of models to perform robust stress classification. Our findings suggest that for wrist-worn devices, sensors that measure motion are most suitable to understand noise context, while for chest-worn devices, the most suitable sensors are those that detect muscle contraction. We demonstrate SELF-CARE’s state-of-the-art performance on the WESAD dataset. Using wrist-based sensors, SELF-CARE achieves 86.34% and 94.12% accuracy for the 3-class and 2-class stress classification problems, respectively. For chest-based wearable sensors, SELF-CARE achieves 86.19% (3-class) and 93.68% (2-class) classification accuracy. This work demonstrates the benefits of utilizing selective, context-aware sensor fusion in mobile health sensing that can be applied broadly to Internet of Things applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

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'Distributed' signal processing: new opportunities and challenges

'Distributed' signal processing: new opportunities and chall...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： K. Ramchandran University of California Berkeley USA

Summary form only given. We are at a novel crossroads in technology where we are witnessing the confluence of computing, communicating and networking. A number of exciting applications are both driving and being driven by this confluence, including low-power sensor networks, large-scale ad hoc wireless networks, and wireless multimedia transmission. Many of these applications demand a move away from classical centralized architectures and algorithms towards more decentralized and distributed ones. Signal processing plays a key role in this revolution- not in isolation but rather as a pivotal interdisciplinary systems component, intimately integrated with communications, information theory, coding theory, and networking protocols. sensor networks represent a particularly rich applications base. We would provide a snapshot of the sensor network related activities in a number of research groups at Berkeley. Motivated by the communications and computational constraints imposed by large-scale low-power sensor networks, we would describe some of our signal processing centric research including: (i) distributed sampling; (ii) distributed source coding; (iii) distributed estimation; and (iv) robust transmission. We would highlight the key foundational role played by multi-user information theory, particularly the so-called area of side-information coding for both source coding (compression) and channel coding (transmission). A deeper look reveals a beautiful functional duality between source and channel coding with side-information. This unexpectedly unifies a host of seemingly unrelated problem areas like distributed compression, digital watermarking, multimedia transmission over packet-error networks, and seamless digital upgrade of analog TV. Finally, as a microcosm of the expressive power of interdisciplinary thinking, we would describe a novel video compression paradigm dubbed PRISM (power-efficient, robust, hlh-compression, syndrome-based multimedia coding). PRISM's a

关键词： Signal processing Wireless sensor networks Computer networks Large-scale systems Computer architecture Signal processing algorithms Information theory Source coding Robustness Channel coding

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Research on the System Design and Application of Mobile Manipulator Hybrid Robot for Transporting in Intelligent Factory

Research on the System Design and Application of Mobile Mani...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Xiaohong Qin Lianhao Zhang Bin Zhao Shenyang Institute of Science and Technology Shenyang China Northeastern University Shenyang China SIASUN Robot & Automation Co. Shenyang China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Based on the development and application status of mobile manipulator hybrid robot, this paper takes the handling process requirements of intelligent factories in China as the research objective to design and develop a composite robot platform system. The hardware system mainly includes mobile platforms, manipulators, and sensors. The software system mainly consists of control systems, software architectures, algorithm libraries and corresponding communication protocols. This platform system has the key technologies of scene information perception, mobile path planning and self-collision detection of the manipulator during the grasping process, which are necessary in engineering project applications. The corresponding innovative algorithms are embedded in it, and scene tests and effect analysis are carried out in a memory module production factory. Test results show that, compared with mainstream products and application technologies in the field, this mobile manipulator hybrid robot can use multiple sensors to obtain scene information and conduct high-precision data fusion, simultaneously meet multiple measurement indicators of mobile path planning, and quickly achieve pre-collision warning without the application of sensors, verifying the effectiveness and advancement of the independently developed platform system.

关键词： Software architecture Service robots Software algorithms Memory modules sensor fusion Manipulators Robot sensing systems Production facilities Path planning Intelligent sensors

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A multi-sensor integration method of signals in a metal cutting operation via application of multilayer perceptron neural networks

A multi-sensor integration method of signals in a metal cutt...

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Fifth International Conference on Artificial Neural Networks (Conf. Publ. No. 440)

作者： D.E. Dimia P.M. Lister N.J. Leighton Eng. Res. Group Univ. of Wolverhampton UK Engineering Research Group SEBE University of Wolverhampton UK

The potential application of neural networks in manufacturing scenarios is increasingly becoming feasible. Typical of such manufacturing scenario is the integration of metal cutting sensor signals in pursuance of reliable tool condition monitoring (TCM) system. Successful application of this method of sensor integration could save downtime and costs, that would otherwise not have been realised through traditional tool changing philosophies. Unfortunately, the network algorithms used have complicated, requiring detailed sensor signal pre-processing. Partly as a consequence, developed systems have found very limited applications to-date. In this paper, the authors present a simple sensor fusion method via the neural networks approach to the TCM problem. Turning tests were conducted from which the static cutting force, dynamic cutting force and the vibration signature were recorded. The obtained data was used to investigate the classification capability of simple multilayer perceptron (MLP) neural network architectures to the detection of tool wear. Obtained results showed classification accuracy of well over 90%.

关键词： Neural networks

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Electromagnetic Diversity and EMI Implications for Multiple Co-sited Radars and Targeting applications

Electromagnetic Diversity and EMI Implications for Multiple ...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Andrew Drozd Irina Kasperovich Ruixin Niu Pramod K. Varshney ANDRO Computational Solutions Limited Liability Company Rome NY USA Department of Electrical Engineering & Computer Science Syracuse University Syracuse NY USA

Real-time fusion of data collected from a variety of co-located radars that acquire information in a cooperative manner from multiple perspectives and/or different frequencies, is being shown to provide a more accurate and effective way of tracking complex targets in a multi-target scenario. This is more advantageous than employing a single radar or a group of radars operating independently. This paper describes a cooperative multi-sensor approach in which multiple radars operate together in a non-interference limited manner. A three-fold approach is presented: (i) applying multiobjective joint optimization algorithms to set limits on the operational parameters of the radars to preclude electromagnetic interference; (ii) measuring and processing radar returns in a shared manner for target feature extraction based on electromagnetic diversity principles in conjunction with target scattering cross sections; and (iii) employing feature-aided track/fusion algorithms to detect, discriminate, and follow real targets from clutter noise. The results of computer simulations are provided that demonstrate the advantages of this approach.

关键词： Electromagnetic interference Radar applications Radar tracking Radar scattering Target tracking Radar clutter Radar cross section Radar measurements Electromagnetic scattering Frequency

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Plenary talk IV: Robust 3-D vision techniques: algorithms and applications

Plenary talk IV: Robust 3-D vision techniques: Algorithms an...

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SICE Annual Conference

作者： In So Kweon KAIST South Korea

Summary form only given: For the 3D reconstruction of static and dynamic scenes, we have developed several robust computer vision methods ranging from the image enhancement to the design of novel camera systems. In this talk, we first present a sparse-model based approach to recover the camera response function, which is crucial for many color image processing problems such as high dynamic range imaging and photo-consistent mosaicking. We also present a sensor-fusion based approach to obtain super-resolution (SR) depth images from low-resolution depth images and high-resolution color images. A learning-based optimization framework successfully recovers the SR depth images. The second part of the talk introduces new camera systems to capture the 3D information accurately and efficiently. The camera systems include (i) “camera + depth” fusion camera systems, (ii) a fast bundle adjustment based approach for large-scale datasets, (iii) a novel coded-light photometric stereo for modeling 3-D dynamic scenes. Specifically, we present a hand-held fusion sensor system, consisting of four cameras and two 2D laser scanners, to capture 3D information of large-scale scenes. This new approach allows boosting the advantages of two sensor systems and complements the weakness of the two. As an important application of 3D vision techniques, we demonstrate the robustness of the methods by automatically reconstructing a large-scale environment, such as KAIST campus.

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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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Sampling Jitter mitigation in latency-critical state-estimation applications using particle filters

Sampling Jitter mitigation in latency-critical state-estimat...

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SICE International Symposium on Control Systems (ISCS)

作者： Viktorio S. El Hakim Marco J.G. Bekooij Department of Embedded Software and Signal Processing NXP Semiconductors Eindhoven The Netherlands Department of Computer Architectures for Embedded Systems University of Twente Enschede The Netherlands

ISBN: (纸本)9781509056149

State estimation algorithms, such as the Kalman filter, are applied for conditioning and sensor fusion in digital control loops. It is desirable that these algorithms can be executed on embedded multiprocessor systems. However this results in large worst-case execution times with a consequence that a large sampling period must selected, which degrades the estimation and control performance. In this paper, we propose a free-running state-estimation approach in which the next sample is taken as soon as the current iteration completes. The approach utilizes the particle filter algorithm to mitigate the effects of sampling jitter, introduced by the variation in the execution times of tasks. As a result of the reduced interval between subsequent sampling moments the estimation accuracy is improved. The delay introduced by the estimator in a control loop is reduced by enabling execution of the prediction step in parallel with other control tasks. We compare simulation results obtained for our approach with a Kalman filter based approach, by estimating the state of a DC motor. These results show that our approach minimizes the estimation error, as a result of sampling jitter, by up to a factor of 10. Additionally we show that the approach does not require precise knowledge of the distribution of the execution times of the tasks.

关键词： Jitter Schedules Delays State estimation Signal processing algorithms

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