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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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Reconstruction of the lost Saraswati river course and its associated palaeochannels using multi-resolution SAR and MSS images in the northwestern desertic plains of Rajasthan, India

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REMOTE SENSING applications-SOCIETY AND ENvIRONMENT 2025年 38卷

作者： Biswas, Raja Rathore, v. S. Krishna, A. P. Das, Anup Kumar Bhattacharya, Avik Porwal, Alok Birla Inst Technol Dept Remote Sensing Ranchi India Space Applicat Ctr Dept Space Ahmadabad India Indian Inst Technol Ctr Studies Resources Engn Mumbai India

The lost Saraswati River offers a significant challenge for geomorphologists and archaeologists. It is believed that the river used to flow through the present-day Thar Desert during the vedic age. It desiccated between similar to 7000 and similar to 1200 BC due to tectonic and climatic changes, leaving behind palaeochannels and playas in northwestern Rajasthan. This study aims to delineate the ancient Saraswati River and its associated palaeochannels using multi-sensor satellite data, including SAR (Sentinel-1A, ALOS PALSAR), multispectral (Sentinel-2A), and DEM. Multiple fusion algorithms (IHS, GS, PCA, Wavelet, and Ehlers) were used to fuse SAR and optical data, enhancing the visibility of the river course and palaeochannels. various image indices assessing surface moisture and vegetation patterns further helped in palaeochannel detection. Among the fused images, the IHS, GS, and PCA techniques, combining Sentinel-2 and ALOS PALSAR data, were found to be the most effective in highlighting palaeochannels. Further, image indices such as NDvI, NDWI and NDMI led to confirm palaeochannels and the old river course by showing linearly oriented vegetation and soil moisture. The study successfully traced two major palaeo-courses of the Saraswati River, originating from the Ghaggar River near Anupgarh and flowing through Beriyawali, Bahla, Tanot, and Jaisalmer before emptying into the Great Rann of Kutch. Additionally, three major palaeo-drainage systems of the Saraswati River could be delineated. Moreover, the association of the Harappan archaeological sites distribution along with the delineated Saraswati River course and its paleochannels, evidence from the historical maps, and bore-well drilling data (groundwater levels and lithologs) also confirm the results of this study.

关键词： Saraswati Palaeochannels Image-fusion SAR Remote Sensing

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

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

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Kenn Danielle C. Pelayo Anastacia B. Alvarez Adelson N. Chua Fredrick Angelo R. Galapon Allen Jason A. Tan Lawrence Roman A. Quizon Electrical and Electronics Engineering Institute University of the Philippines - Diliman Quezon City Philippines

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

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 precisionscaling 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 $\mathbf{2 b} \times 2 \ \text{b}$ mode for $\mathbf{1 7. 3 7 \%}$ less energy per operation and $\mathbf{2 6. 8 4 \%}$ more throughput per area would be preferred. Architecture variations were also explored wherein sum-together (ST) architectures outperformed sum-apart (SA) architectures with $\mathbf{2 1. 0 3 \%}$ reduction in energy per operation and $\mathbf{3 3. 3 2 \%}$ increase in throughput per area.

关键词： Wireless communication Semiconductor device modeling Wireless sensor networks Quantization (signal) Tiny machine learning Computer architecture Throughput CMOS technology Complexity theory Reliability

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A low-cost, open-source, and distributed ocean profiling sensor node 17

A low-cost, open-source, and distributed ocean profiling sen...

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17th IEEE sensors applications Symposium (SAS)

作者： Rico, Andres Prasad, Suparnamaaya Larson, Kent MIT Media Lab Cambridge MA 02139 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665409810

ISBN: (纸本)9781665409810

Ocean exploration is emerging as a critical field of research for understanding and combating climate change. While coastal waters house vital marine ecosystems, such as coral reefs, they are still vastly unexplored. sensor nodes in coastal waters can give local communities and researchers relevant data for understanding the relationship between climate change, blue-economy activities, and ecosystem degradation. We present and validate a sensor node that can be opportunistically used by non-industrial fishing boats in order to collectively source ocean profiles containing temperature, light, and acidity levels at multiple depths. We describe the complete system architecture and validate our design with deployments in three different types of water bodies. First, we validate the device's temperature, light, and depth sensors in a submersion within a controlled pool environment. We then evaluate the software-based power cycling for intermittent data acquisition algorithms through a river deployment. Finally, we demonstrate the complete system's functionality and possible ways to incorporate acidity sensing with an open ocean deployment. In addition to field testing, we describe and test possible architectures for adding networking capabilities through a WiFi-based system. We conclude that the device's electrical and mechanical characteristics significantly contribute towards enabling low-cost, community-sourced data acquisition for ocean coastal water profiling.

关键词： Ocean IoT Opportunistic Sensing sensor fusion

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An Overview of Factors Influencing the Mass Adoption of Self-Driving vehicles 9

An Overview of Factors Influencing the Mass Adoption of Self...

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IEEE 9th International Conference on Power Electronics Systems and applications (PESA)

作者： Li, Zilin Cheng, Ka Wai Eric Chan, Kevin K. W. Hong Kong Polytech Univ Dept Elect Engn Power Elect Res Ctr Hong Kong Peoples R China

ISBN: (纸本)9781665486682

A self-driving vehicle used to be found in science fiction but would promisingly and gradually become a reality in our daily life in the foreseeable future. Self-driving or autonomous vehicles can bring about disruptive changes to the current transportation system, commuting ways, the concept of vehicle ownership, risk management, legal liability, regulations, urban planning, etc. A highly/fully self-driving vehicle is one of the most complex man-made systems and involves various aspects of modern society. Nevertheless, the public seems to lack a clear enough understanding of self-driving vehicles. To further the development of self-driving technologies, it is necessary to develop a comprehensive understanding of multifaceted factors that could hinder the mass adoption of self-driving vehicles. Therefore, this paper intends to present an overview of some technical factors that could influence the advancement of self-driving vehicles. First, this paper reviews state-of-the-art architectures of a self-driving vehicle. various sensor technologies are followed with a discussion of their limitations. As two essential software subsystems for realizing a self-driving vehicle, the perception and decision-making systems are presented with an overview of their major tasks and challenges. This paper is concluded with a summary of the technical challenges, being a helpful resource for engineers, the public, policy-makers, and social researchers.

关键词： autonomous vehicles architectures camera Lidar overview Radar self-driving car sensor technologies sensor fusion algorithms

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Advances in multi-sensor fusion for body sensor networks: algorithms, architectures, and applications

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INFORMATION fusion 2019年 45卷 150-152页

作者： Fortino, Giancarlo Gravina, Raffaele Ghasemzadeh, Hassan Liu, Peter X. Poon, Carmen C. Y. Wang, Zhelong Univ Calabria Commenda Di Rende Italy Washington State Univ Pullman WA 99164 USA Carleton Univ Ottawa ON Canada Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Dalian Univ Technol Dalian Peoples R China

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GesturesSpeak: An IoT-Based Gesture to Speech Translator 4

GesturesSpeak: An IoT-Based Gesture to Speech Translator

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakravarthi, Sangapu Sreenivasa Manogna, S.v.S. Reddy, Shenagapally Aashish Sountharrajan, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Cse Chennai India

ISBN: (纸本)9798350387063

Hand gestures serve as a natural and intuitive means for humans to communicate ideas and intents. For people with speaking or hearing disabilities, sign languages based on visual hand gestures are indispensable for their expression. With recent advances in machine perception and proliferation of Internet-of- Things (IoT) devices, automating gesture recognition to generate textual or speech output has transformative potential to augment accessibility. This paper surveys key innovations in gesture- to-text conversion spanning machine learning, deep learning and IoT-driven approaches. The research first motivate the assistive technology applications of an IoT framework leveraging specialized sensors and edge devices for gesture tracking, coupled with intelligent algorithms to interpret gestures and convert them into language. Our study covers convolutional and recurrent neural networks for spatial and temporal modelling, as well as multimodal fusion techniques. Challenges related to occlusion, sensor constraints and model generalization are discussed. The paper highlights the tremendous value of gesture-driven systems in enabling more immersive human-computer interaction and accessible interfaces for diverse communities. © 2024 IEEE.

关键词： Gesture recognition

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