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Knowledge Transfer for Body sensor Networks: Characterization and Resistance of Negative Transfer in Overconstrained Environments

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 31959-31973页

作者： Shi, Han Liu, Yang Luo, Hao Zhao, Hai Liaoning Univ Coll Informat Shenyang 110036 Peoples R China Liaoning Univ Sch Phys Shenyang 110036 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China

Transfer learning (TL) has shown potential for body sensor network (BSN) applications. However, TL performance is influenced by various factors, such as the BSN environment, user-specific physiological signs, or sensor modalities, leading to negative transfer (NT) effects. Limited research has defined NT from perspectives, such as data quality, domain transferability, and transfer components, but the cognition of NT in multimodal BSNs remains insufficient. In this article, we explore the correlation between transfer modes and NT, proposing a knowledge transfer framework composed of common domain adaptation constraints to reveal the NT effects in overconstrained environments. This effect can explain why strongly constrained transfer algorithms are effective but not necessarily optimal in terms of performance. In the three typical BSN scenarios of emotion recognition, fall detection, and daily activity recognition, we demonstrate the NT in overconstrained environments and its potential influencing factors using different constraint combinations and based on different multimodal body sensor fusion architectures. Extensive experiments focus on discussing the impact of category-condition constraints, domain similarity, and fusion architectures on the NT effect in BSNs. This work can provide a new perspective for the design of multimodal BSN knowledge transfer scheme.

关键词： Transfer learning Task analysis Data models Internet of Things Knowledge transfer Accuracy Training Body sensor network (BSN) information fusion negative transfer (NT) transfer learning (TL)

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RGB-LiDAR fusion for accurate 2D and 3D object detection

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MACHINE viSION AND applications 2023年第5期34卷 1-11页

作者： Mousa-Pasandi, Morteza Liu, Tianran Massoud, Yahya Laganiere, Robert Univeristy Ottawa Ottawa ON Canada

Effective detection of road objects in diverse environmental conditions is a critical requirement for autonomous driving systems. Multi-modal sensor fusion is a promising approach for improving perception, as it enables the combination of information from multiple sensor streams in order to optimize the integration of their respective data. fusion operators are employed within fully convolutional architectures to combine features derived from different modalities. In this research, we present a framework that utilizes early fusion mechanisms to train and evaluate 2D object detection algorithms. Our evaluation shows that sensor fusion outperforms RGB-only detection methods, yielding a boost of +15.07% for car detection, +10.81% for pedestrian detection, and +19.86% for cyclist detection. In our comparative study, we evaluated three arithmetic-based fusion operators and two learnable fusion operators. Furthermore, we conducted a performance comparison between early- and mid-level fusion techniques and investigated the effects of early fusion on state-of-the-art 3D object detectors. Lastly, we provide a comprehensive analysis of the computational complexity of our proposed framework, along with an ablation study.

关键词： Multimodal deep learning sensor fusion fusion operators Object detection Autonomous driving Convolutional neural networks

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DSP architectures of Covariance Intersection fusion Algorithm for Automotive sensor fusion 14

DSP Architectures of Covariance Intersection Fusion Algorith...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Praharshita, D.S.L. Achala, G. Srihari, Pathipati Acharya, U. Shripathi Pardhasaradhi, Bethi India National Institute of Technology Karnataka ECE Dept. India Continental Autonomous Mobility India Pvt Ltd Adas Dept. India

ISBN: (纸本)9798350335095

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. © 2023 IEEE.

关键词： Digital signal processing

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MULTI-sensor INFORMATION fusion TECHNOLOGY: PRINCIPLE, applications AND KEY TECHNOLOGIES EXPLORATIONS 2

MULTI-SENSOR INFORMATION FUSION TECHNOLOGY: PRINCIPLE, APPLI...

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

作者： Wu, Wenchen Xu, Ruizhe Guangdong Country Garden School Foshan528312 China The Affiliated Middle School of Fujian Normal University Fuzhou350007 China

ISBN: (纸本)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. © The Institution of Engineering & Technology 2024.

关键词： Information fusion

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Optimizing Long-Term Robot Tracking with Multi-Platform sensor fusion

Optimizing Long-Term Robot Tracking with Multi-Platform Sens...

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IEEE/CVF Winter Conference on applications of Computer vision (WACV)

作者： Albanese, Giuliano Mitra, Arka Zaech, Jan-Nico Zhao, Yupeng Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium INSAIT Sofia Bulgaria

ISBN: (纸本)9798350318920;9798350318937

Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking in these games requires handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make effective use of the information coming from the robot sensors, we propose a robust tracking and identification pipeline. It fuses external non-calibrated camera data with the robots' internal states using quadratic optimization for tracklet matching. The approach is validated using game recordings from previous RoboCup World Cup tournaments.

关键词： algorithms algorithms and algorithms applications formulations Machine learning architectures Robotics video recognition and understanding

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Comparative Analysis and Implementation of Energy-Quality Scalable Multiply-Accumulate architectures for TinyML applications 31

Comparative Analysis and Implementation of Energy-Quality Sc...

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31st Conference on Electronics Circuits and Systems

作者： Pelayo, Kenn Danielle C. Alvarez, Anastacia B. Chua, Adelson N. Galapon, Fredrick Angelo R. Tan, Allen Jason A. Quizon, Lawrence Roman A. Univ Philippines Diliman Elect & Elect Engn Inst Quezon City Philippines

ISBN: (纸本)9798350377217;9798350377200

Recent trends in machine learning algorithms have shifted from computer vision applications to near-sensor intelligent data analytics, such as wireless sensor nodes, which are considered as ultra-low power devices. This shift presents new challenges in energy consumption, throughput, and area budgets. This work implements and optimizes Bit fusion, a 2D-scalable divide-and-conquer (D&C) MAC architecture with precision-scaling from 8 to 2 bits, through bitwidth optimizations and by trading-off complexity overheads that are not aligned with the target MobileNet-v2 model. The designs are synthesized using 22-nm FD-SOI CMOS technology. It was determined that the throughput trade-offs of 1-level architectures in 2bx2b mode for 17.37% less energy per operation and 26.84% more throughput per area would be preferred. Architecture variations were also explored wherein sum-together (ST) architectures outperformed sum-apart (SA) architectures with 21.03% reduction in energy per operation and 33.32% increase in throughput per area.

关键词： ASIC deep neural networks digital accelerators multiply-accumulate MAC precision-scalable circuits energy-quality scalable circuits

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Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches

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INFORMATION fusion 2023年第1期95卷 62-90页

作者： Zhuang, Yuan Sun, Xiao Li, You Huai, Jianzhu Hua, Luchi Yang, Xiansheng Cao, Xiaoxiang Zhang, Peng Cao, Yue Qi, Longning Yang, Jun El-Bendary, Nashwa El-Sheimy, Naser Thompson, John Chen, Ruizhi Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen Peoples R China Southeast Univ Natl ASIC Ctr Nanjing Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China Arab Acad Sci Technol & Maritime Transport AASTMT Aswan Egypt Univ Calgary Dept Geomat Engn Calgary AB Canada Univ Edinburgh Sch Engn Edinburgh Scotland

Navigation/positioning systems have become critical to many applications, such as autonomous driving, Internet of Things (IoT), Unmanned Aerial Vehicle (UAV), and smart cities. However, it is difficult to provide a robust, accurate, and seamless solution with single navigation/positioning technology. For example, the Global Navigation Satellite System (GNSS) cannot perform satisfactorily indoors;consequently, multi-sensor integrated systems provide the solution, as they compensate for the limitations of single technology by using the complementary characteristics of different sensors. This article describes a thorough investigation into multi-sensor data fusion, which over the last ten years has been used for integrated positioning/navigation systems. In this article, different navigation/positioning systems are classified and elaborated upon from three aspects: (1) sources, (2) algorithms and architectures, and (3) scenarios, which we further divide into two categories: (i) analytics-based fusion and (ii) learning-based fusion. For analytics-based fusion, we discuss the Kalman filter and its variants, graph optimization methods, and integrated schemes. For learning-based fusion, several supervised, unsupervised, reinforcement learning, and deep learning techniques are illustrated in multi -sensor integrated positioning/navigation systems. Design consideration of these integrated systems is discussed in detail from several aspects and their application scenarios are categorized. Finally, future directions for their research and implementation are discussed.

关键词： Machine learning Data fusion Estimation Integrated navigation system Multi-sensor Positioning

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Data stream fusion for accurate quantile tracking and analysis

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INFORMATION fusion 2023年 89卷 155-165页

作者： Cafaro, Massimo Melle, Catiuscia Epicoco, Italo Pulimeno, Marco Univ Salento Lecce Italy

UDDSketch is a recent algorithm for accurate tracking of quantiles in data streams, derived from the DDSketch algorithm. UDDSketch provides accuracy guarantees covering the full range of quantiles independently of the input distribution and greatly improves the accuracy with regard to DDSketch. In this paper we show how to compress and fuse two or more data streams (or datasets) by leveraging the mergeability of the UDDSketch data summaries. In general, two summaries on two data streams are said to be mergeable if there exists an algorithm that allows combining the two summaries into a single one related to the union of the two datasets, simultaneously preserving the error and size guarantees. The property of mergeability of a sketch enables the parallel and distributed processing of big volume data streams that can be compressed and fused by means of such mergeable data structures. Among the applications strictly related to accurate tracking of quantiles, requiring parallel and/or distributed processing we recall here estimating the latency of a web site, database query optimizers and the need of succinctly summarizing the distribution of values occurring over a sensor network. We prove that UDDSketch is fully mergeable and introduce PUDDSketch, a parallel version of UDDSketch suitable for message-passing based architectures. We formally prove its correctness and compare it to a parallel version of DDSketch, showing through extensive experimental results that our parallel algorithm almost always outperforms the parallel DDSketch algorithm with regard to the overall accuracy in determining the quantiles. Moreover, we also design and implement parallel versions of both the state of the art KLL and REQ sequential algorithms in order to compare and contrast PUDDSketch versus the corresponding parallel algorithms. Our experiments clearly show that PUDDSketch is faster or on par with regard to parallel running time, whilst providing simultaneously greater accuracy.

关键词： Quantiles Sketches Message-passing

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A Simple Self-Supervised IMU Denoising Method for Inertial Aided Navigation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第2期8卷 944-950页

作者： Yuan, Kaiwen Wang, Z. Jane Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

Inertial Measurement Unit (IMU) plays an important role in inertial aided navigation on robots. However, raw IMU data could be noisy, especially for low-cost IMUs, and thus requires efficient pre-processing or denoising before applying further navigation algorithms. Conventional IMU denoising approaches are mostly hand-crafted and may face concerns such as sensor modelling errors and generalization issues. Several recent works leverage deep neural networks (DNNs) to tackle this problem and achieve promising results. However, currently reported deep learning methods are based on supervised learning, requiring sufficient and accurate annotations. While in real-world applications, such annotations can be expensive or unavailable, making these methods not practical. To address the above research gap, we propose incorporating self-supervised learning and future-aware inference for IMU denoising. The end-to-end navigation evaluation results on EuRoC and TUM-vi datasets are promising. The code will be publicly available at https://***/KleinYuan/IMUDB.

关键词： Noise reduction Task analysis Supervised learning Self-supervised learning Bit error rate Deep learning Robot sensing systems Deep learning methods AI-Enabled robotics sensor fusion self-supervised learning IMU denoising

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Human-Based Gait Authentication Using Multi-modal sensors at the Tactical Edge Device: Smartphone 6

Human-Based Gait Authentication Using Multi-modal Sensors at...

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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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