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Conference on Big Data vi - Learning, Analytics, and applications

作者： Sandrakumar, Shageenth Khan, Simon Air Force Res Lab Rome NY 13441 USA

ISBN: (纸本)9781510673915;9781510673908

The Department of Defense (DoD) including the US Air force have been seeing increased physical security risks, such as gate runners, active shooter situations, and other use cases (such as Drone ISR, Rapid Deployment, Force Protection, and Security Force), requiring increased coordination and data cooperation. Additionally, technology and operational environments are getting more complex, interconnected and diverse. Recently a team of developers developed a plug-in for the Android Team Awareness Kit (ATAK). This plug-in analyzes sensor data from tactical edge device's (e.g., cell phone) onboard accelerometer and gyroscope to determine the movement of a person when they walk. The plug-in also uses machine learning (ML) algorithms to create a model of that person's gait, and then sends pertinent data through the associated human gait model to authenticate a user. The novelty of our effort lies into enhancing this human gait authentication by using different features extracted from spectral information of the accelerometer and gyroscope signals from the smartphone using a public human activity recognition dataset (WISDM) as a proof of concept, marking a previously unexplored approach. By leveraging spectral data, we seek to enhance the accuracy and robustness of authentication systems in military contexts. After the feature extraction is performed, Kernal Discriminant Analysis (KDA) is utilized to reduce the dimensions of the spectral features to 50;after including the non-spectral features, our total number of final features becomes 65. After adding the total features, we perform feature-level fusion utilizing ML algorithms and the performance shows promising for authentication utilizing 51 users. The SVM-rbf classifiers achieved a mean Equal Error Rate (EER) and mean Accuracy (ACC) of 2% and 97.3%, while the GBM classifiers achieved a mean EER and ACC of 0.4% and 99.1%, and the CNN classifiers achieved a mean EER and ACC of 10% and 90.4% respectively.

关键词： Machine Learning Spectral Feature Extraction Spectogram Gait Welch's Method Envelope Power Spectrum Kernel Discriminant Analysis Feature-level fusion

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Non-Invasive Human-Machine Interface (HMI) Systems With Hybrid On-Body sensors for Controlling Upper-Limb Prosthesis: A Review

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IEEE sensorS JOURNAL 2022年第11期22卷 10292-10307页

作者： Zhou, Hao Alici, Gursel Univ Wollongong ARC Ctr Excellence Electromat Sci Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW 2522 Australia

In this work, we present a systematic review on non-invasive HMIs employing hybrid wearable sensor modalities for recognition of upper limb intentions. Different combinations of the sensors are investigated. As sEMG is dominant in the applications of externally powered prosthetic hands, it is involved in most hybrid sensor combinations. The combined use of sEMG and IMU is most studied in the literature as IMU is easy to be integrated. Though limited, the investigation on other hybrid modalities has been drawing more and more research attention and efforts, especially those with FMG and NIRS. For all the reported hybrid sensors, it is verified that this strategy can enrich the information of user intention and help the pattern recognition and/or intensity regulation of robotic hand/arm prosthesis. Though it is the trend, the development of these hybrid-sensor-based HMIs are still at the preliminary stage. More dedicated sensor fusion models and system architectures as well as new hybrid features and algorithms need to be developed to make the best use of each sensing modality's strength to achieve robust and stable user intention recognition, which is essential for the progress and user acceptance of upper limb prosthesis.

关键词： sensors Muscles Robot sensing systems Robustness Electroencephalography Biosensors sensor systems Human-machine-interface HMI upper limb prosthesis hybrid modalities multi-modal sensor fusion sEMG plus MMG sEMG plus FMG sEMG plus SMG sEMG plus NIRS robustness adaptive learning

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Data fusion in Wireless sensor Network: An Overview

Data Fusion in Wireless Sensor Network: An Overview

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2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon 2023

作者： Bernal, Daniel Fernando Quintero Kaschel, Hector Kern, John Universidad de Santiago de Chile Departamento de Ingeniería Eléctrica Santiago Chile

ISBN: (纸本)9798350369533

Due to the massive increase in data sets in contemporary applications and the difficulty of their processing in various approaches, data fusion remains a predominant way to obtain outstanding results, in terms of reliability, efficiency, and precision. The following document presents an overview of data fusion in wireless sensor networks;in which, various concepts to applications are considered to generate a base document for future research. This article gives a comprehensive and current view of models or architectures, techniques, methodologies or algorithms, and a current review of applications in different areas. © 2023 IEEE.

关键词： Wireless sensor networks

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LIPSFUS: A neuromorphic dataset for audio-visual sensory fusion of lip reading 56

LIPSFUS: A neuromorphic dataset for audio-visual sensory fus...

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56th IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Rios-Navarro, A. Pinero-Fuentes, E. Canas-Moreno, S. Javed, A. Harkin, J. Linares-Barranco, A. Univ Seville SCORE I3US Robot & Technol Comp Lab Seville Spain Sch Comp Engn & Intel Syst Magee Campus Derry Londonderry North Ireland

ISBN: (纸本)9781665451093

This paper presents a sensory fusion neuromorphic dataset collected with precise temporal synchronization using a set of Address-Event-Representation sensors and tools. The target application is the lip reading of several keywords for different machine learning applications, such as digits, robotic commands, and auxiliary rich phonetic short words. The dataset is enlarged with a spiking version of an audio-visual lip reading dataset collected with frame-based cameras. LIPSFUS is publicly available and it has been validated with a deep learning architecture for audio and visual classification. It is intended for sensory fusion architectures based on both artificial and spiking neural network algorithms.

关键词： Neuromorphic dataset sensory fusion dynamic vision sensor neuromorphic auditory sensor

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Advancements in Object Detection for Unmanned Aerial Vehicles: applications, Challenges, and Future Perspectives 12

Advancements in Object Detection for Unmanned Aerial Vehicle...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Sezgin, Anil Boyaci, Aytug Siemens Corporate Technology Istanbul Turkey National Defence University Air Force Academy Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350330366

Unmanned Aerial Vehicles (UAVs) are used in different fields ranging from recreational vehicles to agriculture, environmental monitoring, infrastructure inspection, disaster management, security, surveillance and logistics. This paper provides an overview of UAV applications and highlights the importance of object detection in improving drone autonomy. Object detection facilitates tasks such as precision agriculture, disaster response, environmental protection, infrastructure inspection and autonomous navigation. The paper reviews recent advances in UAV-specific object detection algorithms and methodologies and highlights challenges such as varying altitudes, motion blur, real-time processing constraints and limited computational resources. Solutions and adaptations are discussed to overcome these challenges, including lightweight neural network architectures, transfer learning, data augmentation, edge computing, multi-sensor fusion, attention mechanisms and adaptive algorithms. The study highlights the importance of integrating object detection systems into UAVs to improve their use in various sectors and contribute to improvements in surveillance missions. © 2024 IEEE.

关键词： Object detection

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Whose Data are Reliable: sensor Declared Data Reliability 19

Whose Data are Reliable: Sensor Declared Data Reliability

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19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

作者： Shafin, Sakib Shahriar Karmakar, Gour Mareels, Iven Balasubramanian, Venki Kolluri, Ramachandra Rao Federat Univ Australia Ctr Smart Analyt CSA Ballarat Vic 3350 Australia Federat Univ Australia Inst Innovat Sci & Sustainabil Ballarat Vic 3350 Australia IBM Australia Pty Ltd IBM Consulting St Leonards Australia

ISBN: (纸本)9798350336672

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.

关键词： Reliability

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DSP architectures of Covariance Intersection fusion Algorithm for Automotive sensor fusion

DSP Architectures of Covariance Intersection Fusion Algorith...

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International Conference on Computing and Networking Technology (ICCNT)

作者： D.S.L. Praharshita Achala G Pathipati Srihari U. Shripathi Acharya Bethi Pardhasaradhi Engineering Development Group (EDG) India ECE Dept. National Institute of Technology Karnataka India ADAS Dept. Continental Autonomous Mobility India Pvt Ltd India

The data fusion from sensors within the automotive vehicle is vital for improved accuracy and safety. The centralized and information matrix fusion (IMF) algorithms are famous for providing an optimal fusion estimate. However, the IMF is not viable in automotive sensor fusion applications due to the limited bandwidth and low hardware resources. Hence, distributed fusion technology is widely adopted in the automotive sensor applications to achieve high-speed and low-area realizations. This paper proposes three digital signal processing (DSP) architectures for covariance intersection (CI) fusion algorithm: Pipelined-traditional CI, adder-ladder CI, and pipelined adder-ladder CI. The proposed DSP architectures are evaluated with hardware resource consumption (multipliers, adders, and delays), maximum achievable frequency, and latency of the architecture. In addition, proposed CI algorithms for Digital Signal Processing (DSP) architectures are compared with IMF DSP architectures. The hardware resources and optimal pipeline stages required for CI with respect to N number of sensors are provided. The traditional pipeline algorithm requires N number of stages where as the proposed pipelined version of adder-ladder CI requires a N−1 pipeline stage with additional 7N−1 and 7N−3 delay elements for even and odd number of sensors to achieve the overall system operating frequency to an operation of multiplier. The proposed DSP architectures are suitable for automotive sensor fusion due to their high operating frequency and low hardware resources.

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Data fusion for Intelligent Crowd Monitoring and Management Systems: A Survey

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IEEE ACCESS 2021年 9卷 47069-47083页

作者： Li, Xianzhi Yu, Qiao Alzahrani, Bander Barnawi, Ahmed Alhindi, Ahmed Alghazzawi, Daniyal Miao, Yiming Chinese Univ Hong Kong CUHK Dept Comp Sci & Engn Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia Umm Al Qura Univ Dept Comp Sci Mecca Saudi Arabia

Intelligent Crowd Monitoring and Management Systems (ICMMSs) have become effective resources for strengthening safety and security along with enhancing early-warning capabilities to manage emergencies in crowded situations of smart cities and massive gatherings events. The main advantage of such systems is their ability to detect multiple features associated with the crowd gathering, as they enable multi-source sensors, multi-modal data, and powerful intelligent and analytical methods. Unlike traditional crowd monitoring systems, which make use of simplex forms of different data types, data and information associated with crowded scenarios can be collected, fused, processed and analyzed in large quantities for accurate global assessment and enhanced decision making processes in an ICMMS. Therefore, data fusion is introduced as an enabler to decrease data quantity, reduce data dimensions, and improve data quality. In this paper, we first survey the literature on data fusion application in crowd monitoring systems as we are developing a state-of-the-art ICMMS with data fusion as a major platform enabler. Next, we discuss some popular data fusion architectures and classifications from different perspectives. Based on this, we propose a multi-sensor, multi-modal, and dimensional ICMMS architecture based on data fusion. Then, we identify the data fusion processes in the ICMMS and classify them into sensor fusion, feature-based data fusion, and decision fusion. Relevant algorithms, applications and examples of three classes are elaborated. Finally, future data fusion research directions are discussed.

关键词： Data integration Monitoring sensor fusion sensors Cameras Intelligent sensors Temperature sensors Autonomous system crowd monitoring data fusion decision fusion sensor fusion

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Parallel Computing architectures for Robotic applications: A Comprehensive Review

arXiv

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arXiv 2024年

作者： Islam, Md Rafid Electrical and Computer Engineering North South University Dhaka Bangladesh

With the growing complexity and capability of contemporary robotic systems, the necessity of sophisticated computing solutions to efficiently handle tasks such as real-time processing, sensor integration, decision-making, and control algorithms is also increasing. Conventional serial computing frequently fails to meet these requirements, underscoring the necessity for high-performance computing alternatives. Parallel computing, the utilization of several processing elements simultaneously to solve computational problems, offers a possible answer. Various parallel computing designs, such as multi-core CPUs, GPUs, FPGAs, and distributed systems, provide substantial enhancements in processing capacity and efficiency. By utilizing these architectures, robotic systems can attain improved performance in functionalities such as real-time image processing, sensor fusion, and path planning. The transformative potential of parallel computing architectures in advancing robotic technology has been underscored, real-life case studies of these architectures in the robotics field have been discussed, and comparisons are presented. Challenges pertaining to these architectures have been explored, and possible solutions have been mentioned for further research and enhancement of the robotic applications. © 2024, CC BY.

关键词： Decision making

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Multi-sensor information fusion technology: principle, applications and key technologies explorations

Multi-sensor information fusion technology: principle, appli...

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2nd International Conference on Mechatronic Automation and Electrical Engineering (ICMAEE 2024)

作者： Wenchen Wu Ruizhe Xu Guangdong Country Garden School Foshan 528312 People's Republic of China The Affiliated Middle School of Fujian Normal University Fuzhou 350007 People's Republic of China

ISBN: (数字)9781837242672

Multi-sensor information fusion (MSIF) has emerged as a critical technology in various fields due to the growing complexity of systems and the demand for precise information. This paper explores the fundamental principles, key technologies, and potential applications of MSIF. The paper delves into the design and implementation of MSIF systems, discussing various system architectures such as centralized, distributed, and hybrid models. Key technologies crucial to MSIF, including sensor calibration, data association, filtering methods, and fusion algorithms like Bayesian Criterion, Dempster-Shafer Evidence Theory, Fuzzy Theory, and Neural Networks, are thoroughly examined. The study highlights the importance of data preprocessing and feature extraction techniques in achieving efficient data fusion. Furthermore, the potential of MSIF in diagnosing complex systems is demonstrated through applications in industrial automation, environmental monitoring, and healthcare. MSIF also faces challenges related to data synchronization, algorithm complexity, and the integration of diverse data sources. With ongoing advancements in sensor technology and data processing, MSIF is poised to play an increasingly significant role in intelligent systems, including autonomous vehicles and smart cities.

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