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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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Analysis of Sorting algorithms Using a WSN and Environmental Pollution Data based on FPGA 27

Analysis of Sorting Algorithms Using a WSN and Environmental...

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27th International Conference on Applied Electronics (AE)

作者： Montesdeoca, Guillermo Asanza, victor Chica, Kevin Peluffo-Ordonez, Diego H. ESPOL Escuela Super Politecn Litoral Fac Ingn Elect & Comp Campus Gustavo Galindo Km 30-5 Via Perimetral Guayaquil Ecuador SDAS Res Grp Smart Data Anal Syst Grp Ben Guerir 47963 Morocco Mohammed VI Polytech Univ Modeling Simulat & Data Anal MSDA Res Program Ben Guerir 47963 Morocco

ISBN: (数字)9781665494823

ISBN: (纸本)9781665494823

Wireless Snesor Network (WSN) based systems with a focus on Internet of Things (IoT) applications generate a large amount of data. Many applications that need to process data in real time make use of microcontroller-based architectures with sequential programming. Systems based on sequential programming can emulate parallelism up to a certain number of instructions, which is not the case with Field Programmable Gate Array (FPGA). The main objective of this work is to monitor a network of 40 CO2 sensors and to perform real-time sorting of all data. In addition, the run time analysis of two sorting algorithms is performed: bubble sort and insertion sort. For this purpose, an FPGA-based architecture is implemented, controlled by a finite state machine(FSM), which executes each of the sorting algorithms. The results show that the insertion sort algorithm is faster than the burbble sort, but consumes more hardware resources in the FPGA.

关键词： FPGA Sorting Algortims WSN sensor Network

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Mitigation of Motion Artifacts in Pulse Oximetry through Redundant sensors 25

Mitigation of Motion Artifacts in Pulse Oximetry through Red...

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25th IEEE Conference on Signal Processing - algorithms, architectures, Arrangements, and applications (IEEE SPA)

作者： Alkhoury, Ludvik Choi, Jiwon Wang, Chizhong Mahoney, Sean Shender, Barry S. Hrebien, Leonid Kam, Moshe New Jersey Inst Technol Dept Elect & Comp Engn Newark Coll Engn Newark NJ 07102 USA Athena GTX Regulatory Affairs Dept Athena IA 50113 USA Naval Air Warfare Ctr Aircraft Div Human Syst Dept Patuxent River MD 20670 USA Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

Photoplethysmography (PPG) signals are used in pulse oximetry to calculate peripheral capillary oxygen saturation (SpO(2)) levels. It is well known that the accuracy of the calculated SpO(2) is susceptible to motion noise. Using field data collected from human subjects during rest and while exhibiting medium to high-intensity physical exercises, we show how sensor redundancy can mitigate motion effects by employing two channels of red and infrared PPG signals. We demonstrate the advantage of redundancy using two SpO(2) calculation methods. These methods are widely used "Red over Infrared" (RoI) method and the Discrete Saturation Transform (DST) algorithm both, assisted by pre-filtering by a heart-rate tuned comb filter. The data were collected from individuals at rest and while exercising. SpO(2) levels are calculated for two channels, each using a red and an infrared PPG signal. We integrated the SpO(2) computed from the two channels with a Kalman filter (KF) and computed the SpO(2) mean absolute error (MAE) from each channel individually and after integrating the two channels. The SpO(2) MAEs of each one of the two channels separately were greater (and statistically significant) than the SpO(2) MAE obtained from the two channels integrated with a KF. We observed that when two channels of PPG (red and infrared) signals were available, then combining SpO(2) levels obtained from these two channels exhibited a smoother and more reliable estimate of the blood oxygen saturation level as compared the SpO(2) levels computed from each channel separately. The reason is that if the data for the two channels are collected at sufficient physical distance from one another, the motion artifacts affecting the two channels are uncorrelated. The KF is then able to use past measurements and modeling of the dynamics to attenuate the effect of the motion artifacts.

关键词： pulse oximetry peripheral capillary oxygen saturation (SpO(2)) electrocardiography (ECG) motion artifact motion resistant oximetry data fusion comb filter red over infrared Discrete Saturation Transform (DST) Kalman Filter wearable unit multiple photoplethysmography (PPG) channels

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Data fusion in Wireless sensor Network: An Overview

Data Fusion in Wireless Sensor Network: An Overview

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CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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

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.

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Flow-Based Programming for Real-Time Multi-sensor Data fusion 35

Flow-Based Programming for Real-Time Multi-Sensor Data Fusio...

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35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022

作者： Topp, Thomas Hobiger, Thomas Institute of Navigation University of Stuttgart Germany

ISBN: (纸本)9781713871361

Flow-based programming (FBP) splits software functionality into modules which are triggered by data elements flowing from one module to the next. Thus, modules, which are not directly dependent on data from each other, can run independently and applications can be parallelized on a basic level what is a major improvement for the performance of multi-sensor data fusion algorithms. This paper discusses the flow-based software INSTINCT, which is a solid framework for implementing PNT algorithms and helps to reduce development times by making algorithms reusable. It provides an intuitive graphical user interface (GUI) that makes it usable in research and teaching. Inside the software, a variety of file formats and sensors for IMU und GNSS data processing are implemented. The realization of an INS/GNSS loosely-coupled Kalman filter flow is discussed and the results are compared to a flow representing a single point positioning (SPP) solution. Finally, a performance study of the algorithm on a 4th generation Intel CPU is presented, demonstrating that real-time capabilities can be ensured even on older systems. © 2022 by Institute of Navigation. All rights reserved.

关键词： Graphical user interfaces

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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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Multiple IMU fusion Algorithm Comparison for Sounding Rocket Attitude applications 35

Multiple IMU Fusion Algorithm Comparison for Sounding Rocket...

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35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022

作者： Gómez, M.A. García, Laura Train Rico, Alberto Solera Gómez-Pérez, Ignacio Sánchez, Eusebio Valero Universidad Politécnica de Madrid Spain Universidad Carlos III de Madrid Spain Instituto Nacional de Técnica Aeroespacial Spain

ISBN: (纸本)9781713871361

Micro-electro-mechanical (MEMS) technology is known to offer a wide variety of advantages for navigation and guidance purposes in terms of cost, size and weight. However, sensor accuracy limits its applications. The use of a Redundant Inertial Measurement Unit (RIMU) configuration allows combining inertial observations not only to obtain a better performances for attitude estimation, but also to be able to detect and isolate a sensor not properly working. The output signals of uncorrelated IMU sensors can be integrated using a data fusion algorithm (e.g Extended Kalman Filter, EKF). The aim of this study is to present the implementation of several filters for an array of consumer grade IMUs placed on a "skew-redundant" configuration in a sounding rocket vehicle. The filtering method allows designing an attitude estimator capable of improving the kinematic predictions from gyroscope measurement integration by applying a correction method from accelerometer and magnetometer data. Moreover, simulations are performed to analyze the resilience and fault detection, while laboratory tests check the performance of the two proposed architectures. With the aim of testing accuracy of the algorithms for the RIMU a software simulation tool and laboratory tests are presented. © 2022 35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022. All rights reserved.

关键词： Extended Kalman filters

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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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