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Asilomar conference on Signals, Systems & Computers

作者： Lili Zhu Petros Spachos Konstantinos N. Plataniotis School of Engineering University of Guelph Guelph ON Canada Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada

The fast tempo of modern society has brought people a series of emotional changes and mental pressures. Therefore, research have emerged to help people pay attention to and regulate their mental health. Physiological signals are used in studies from different fields to monitor and detect emotional change and stress. Electrodermal activity (EDA) is such a physiological signal that can reflect changes in skin conductivity when people's emotions change. The nature of neuromodulation makes such changes not easily controlled by people subjectively so EDA is an ideal emotion and stress monitoring indicator. Especially with the popularity of wearable devices in the market, wearable devices with built-in EDA sensors will be more competitive for the functions that help people regulate their mental health. However, since the EDA sensor usually acquires signals through fingers, palms, or wrists, artifacts will inevitably be generated when users move their hands or arms, and the artifacts will affect the accuracy of emotional change detection. As a result, removing artifacts in EDA signals is a challenging and important topic. In this work, multiple signal processing methods are applied to realize the objective of removing artifacts in the EDA signals from the AMIGOS dataset. The results show that the proposed method is promising and has the potential to be utilized in real-time EDA signal processing and emotional change detection applications.

关键词： Wrist Wearable computers Signal processing algorithms Mental health Signal processing Skin Biomedical monitoring

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Hand Angle Estimation Based on sEMG and Inertial sensor fusion 1st

Hand Angle Estimation Based on sEMG and Inertial Sensor Fusi...

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1st International conference on Smart Technologies, Systems and applications, SmartTech-IC 2019

作者： Lobaina Delgado, Alfredo Quinayás, César Ruiz, Andrés F. da Rocha, Adson López Delis, Alberto Department of Biomedical Universidad de Oriente Santiago de Cuba Cuba Antonio Nariño University Bogotá Colombia Department Electrical Engineering University of Brasilia Brasilia Brazil Center of Medical Biophysics Universidad de Oriente Santiago de Cuba Cuba

ISBN: (纸本)9783030467845

Continuous kinematics estimation based systems from surface electromyographic (sEMG) signals can predict movement intention to drive upper-limb prostheses in a more natural and intuitive way. Recently studies about inertial sensors combined with this kind of biosignal have improved the regression algorithms performance significantly. The goal of this work is to propose and evaluate strategies to control prostheses that can be applied in a virtual hand training platform during the training phase of amputee patients. This work presented strategies based on MLP neural networks to estimate 18 DoFs across hand joint angles using sEMG and Inertial sensor fusion (ISF). The dataset used was obtained of the seventh Ninapro database. The regression algorithms applied are based on time-domain features, such as auto-regressive model using recursive least squares, slope-sign changes and waveform length. The sEMG and ISF based strategy showed equivalent performance compared to strategy based exclusively on sEMG signals. The results suggest the usability of inertial sensors as a source signal in multi-modal control of upper-limb prostheses. © Springer Nature Switzerland AG 2020.

关键词： Artificial limbs

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Dynamic probabilistic model checking for sensor validation in industry 4.0 applications 4

Dynamic probabilistic model checking for sensor validation i...

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4th IEEE International conference on Smart Internet of Things, SmartIoT 2020

作者： Xin, Xin Keoh, Sye Loong Sevegnani, Michele Saerbeck, Martin University of Glasgow School of Computing Science Glasgow United Kingdom Tüv Süd Asia Pacific Digital Service Centre of Excellence Singapore Singapore

ISBN: (纸本)9781728165141

Industry 4.0 adopts Internet of Things (IoT) and service-oriented architectures to integrate Cyber-Physical Systems and Enterprise Planning into manufacturing operations. This kind of integration consists of a combination of connected sensors and run-Time control algorithms. Consequential control decisions are driven by sensor-generated data. Hence, the trustworthiness of the sensor network readings is increasingly crucial to guarantee the performance and the quality of a manufacturing task. However, existing methodologies to test such systems often do not scale to the complexity and dynamic nature of today's sensor networks. This paper proposes a novel run-Time verification framework combining sensor-level fault detection and system-level probabilistic model checking. This framework can rigorously quantify the trustworthiness of sensor readings, hence enabling formal reasoning for system failure prediction. We evaluated our approach on an industrial turn-mill machine equipped with a sensor network to monitor its main components continuously. The results indicate that the proposed verification framework involving the quantified sensor's trustworthiness enhances the accuracy of the system failure prediction. © 2020 IEEE.

关键词： Industry 4.0

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Special Section on sensors applications Symposium

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2019年第7期68卷 2333-2334页

作者： Sen Gupta, Gourab Bilas, Vedran Massey Univ Sch Engn & Adv Technol Palmerston North New Zealand Massey Univ Sch Engn & Adv Technol Food & Engn Palmerston North New Zealand Univ Zagreb Fac Elect Engn & Comp Elect Engn Zagreb Croatia Univ Zagreb Fac Elect Engn & Comp Lab Intelligent Sensor Syst Zagreb Croatia

The IEEE sensors applications Symposium (SAS) 2018 was the 13th edition of this conference. SAS is a fertile ground for emerging and top-class researchers to mingle together to discuss latest advancements and bright ideas in the areas of sensor development, sensing methodologies, algorithms, and novel sensor applications. The conference has always maintained a strong focus on providing students and young researchers a conducive venue to interact with their peers and experienced researchers. A diverse range of subject areas were covered in SAS 2018 such as energy harvesting, energy conservation in sensor networks, smart homes and cities, smart agriculture, remote sensing, sensors for automotive and aerospace, flexible and wearable sensors, and sensor data fusion. In addition, the highly important topics such as assisted living for the aging population, the Internet of Things (IoT), and data visualization were also addressed.

关键词： Special issues and sections Meetings sensor systems and applications

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An interpretable assessment of sensor’s orientation in the Quaternion domain

An interpretable assessment of sensor’s orientation in the ...

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International conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Debora Gil Resina Manuel Navarrete Esmitt Ramírez Carles Sanchez Ramos Carlos García Calvo David Castells-Rufas Jordi Carrabina Computer Vision Center Universitat Autònoma de Barcelona Bellaterra Spain Universitat Autònoma de Barcelona Bellaterra Spain Institut Anna Gironella de Mundet Barcelona Spain

Magnetic and inertial measurement unit (MIMU) systems have been universally adopted in numerous navigation applications. These include domains such as aircraft, pedestrians, home automation, robots, etc. due to their advantages referring to price, size and accuracy. The fusion of the values recorded from magnetic and inertial sensors (magnetometer, accelerometer, gyroscope) can provide orientation with respect to the navigation path. Orientation can be given as either Euler angles or quaternions representing the rotation matrix associated with the orientation. The first is the commonest way since Euler angles can be easily interpreted in terms of yaw, pitch, and roll. However, their computation is ill-conditioned for some angulations due to a bad propagation of errors. Such intrinsic computational errors limit their use for free indoor, but equally affect the comparison and assessment of sensor fusion algorithms. In this paper, we present an assessment of orientation based on quaternion distances easy to interpret in terms of rotation axis and angle. We compare our approach to the standard assessment of orientation based on Euler angles in rotational trajectories around the three axes made using a Stäubli robotic arm. Results show the more superior reliability of the quaternion distance and the intrinsic artifacts of Euler angles for representing the whole space of rotations.

关键词： Measurement units Magnetometers Quaternions Magnetic domains sensor fusion Robot sensing systems Aircraft navigation

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System fusion with deep ensembles 20

System fusion with deep ensembles

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10th ACM International conference on Multimedia Retrieval, ICMR 2020

作者： Ştefan, Liviu-Daniel Constantin, Mihai Gabriel Ionescu, Bogdan University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781450370875

Deep neural networks (DNNs) are universal estimators that have achieved state-of-the-art performance in a broad spectrum of classification tasks, opening new perspectives for many applications. One of them is addressing ensemble learning. In this paper, we introduce a set of deep learning techniques for ensemble learning with dense, attention, and convolutional neural network layers. Our approach automatically discovers patterns and correlations between the decisions of individual classifiers, therefore, alleviating the difficulty of building such architectures. To assess its robustness, we evaluate our approach on two complex data sets that target different perspectives of predicting the user perception of multimedia data, i.e., interestingness and violence. The proposed approach outperforms the existing state-of-the-art algorithms by a large margin. © 2020 ACM.

关键词： Deep neural networks

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Comparison studies of hierarchical cluster-based routing protocols in wireless sensor networks 35

Comparison studies of hierarchical cluster-based routing pro...

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35th International conference on Computers and Their applications, CATA 2020

作者： Nam, Deok Hee Engineering and Computer Science Wilberforce University WilberforceOH United States

The usage of the appropriate routing protocol algorithm in wireless sensor network (WSN) research is an important issue. Depending upon the deployed network topology, routing protocols can be classified in many ways including hierarchical cluster-based routing protocol. The hierarchical cluster-based routing protocol is pursuing an energyefficient way to reduce the overall energy consumption within the monitoring cluster area by performing data aggregation along with data fusion. The objective of this study is to present a state-of-the-art survey on selected hierarchical cluster-based routing protocols in WSNs. In this paper, hierarchical cluster-based routing protocol algorithms are reviewed and compared with their advantages and disadvantages along with their main contributions. Additionally, each hierarchical cluster-based routing protocol algorithm is analyzed by comparing the measurement parameters of their performance. © 2020, EasyChair. All rights reserved.

关键词： Routing protocols

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ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems

ORSA: Outlier Robust Stacked Aggregation for Best- and Worst...

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International conference on Machine Learning and applications (ICMLA)

作者： Peter Domanski Dirk Pflüger Raphaël Latty Jochen Rivoir Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Advantest Europe GmbH Applied Research and Venture Team

ISBN: (纸本)9781665443388

In recent years, the usage of ensemble learning in applications has grown significantly due to increasing computational power allowing the training of large ensembles in reasonable time frames. Many applications, e.g., malware detection, face recognition, or financial decision-making, use a finite set of learning algorithms and do aggregate them in a way that a better predictive performance is obtained than any other of the individual learning algorithms. In the field of Post-Silicon Validation for semiconductor devices (PSV), data sets are typically provided that consist of various devices like, e.g., chips of different manufacturing lines. In PSV, the task is to approximate the underlying function of the data with multiple learning algorithms, each trained on a device-specific subset, instead of improving the performance of arbitrary classifiers on the entire data set. Furthermore, the expectation is that an unknown number of subsets describe functions showing very different characteristics. Corresponding ensemble members, which are called outliers, can heavily influence the approximation. Our method aims to find a suitable approximation that is robust to outliers and represents the best or worst case in a way that will apply to as many types as possible. A ‘softmax’ or ‘soft-min’ function is used in place of a maximum or minimum operator. A Neural Network (NN) is trained to learn this ‘soft-function’ in a two-stage process. First, we select a subset of ensemble members that is representative of the best or worst case. Second, we combine these members and define a weighting that uses the properties of the Local Outlier Factor (LOF) to increase the influence of non-outliers and to decrease outliers. The weighting ensures robustness to outliers and makes sure that approximations are suitable for most types.

关键词： Performance evaluation Training Semiconductor devices Weather forecasting sensor fusion Prediction algorithms Approximation algorithms

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Development of Contactless Employee Management System with Mask Detection and Body Temperature Measurement using TensorFlow

Development of Contactless Employee Management System with M...

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International conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Pranesh Ulleri Mukund S. Sudarshan K. Kiran Zenith Sai Shibu N.B. Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9781665440868;9781665431309

CoVID-19 pandemic signified the importance of social distancing and non-contact routines. Even after the pandemic, everyone should abide by the same protocols for a hygienic life. Institutions must put in place pre-emptive measures before people return. These include marking the presence of employees and monitoring their health status. This paper focuses on developing a contactless employee management system with sensor fusion and facial recognition technology. Image processing capability in the system track employees entering the institution. Body temperature sensor collect the health status of each employee at the entrance. With the onboard cameras and deep learning algorithms, the system also makes sure if the employee is wearing a mask or not. The aim is to authenticate an employee and check whether they abide by the protocols of the institution. Ease of use, maintenance, and low-cost installation is the motivation behind the system design. The best beneficiaries are educational institutions and corporate offices. The system records employee health data and uses it for the contact tracing. This paper discusses the system design and algorithms of the non-invasive employee management system.

关键词： Temperature measurement Wireless communication Temperature sensors Pandemics Face recognition Signal processing algorithms Signal processing

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4th International conference on New Trends in Information and Communications Technology applications, NTICT 2020

4th International Conference on New Trends in Information an...

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4th International conference on New Trends in Information and Communications Technology applications, NTICT 2020

ISBN: (纸本)9783030553395

The proceedings contain 18 papers. The special focus in this conference is on New Trends in Information and Communications Technology applications. The topics include: Parallel genetic algorithm for optimizing compiler sequences ordering;preface;latency evaluation of an sdn controlled by flowvisor and two heterogeneous controllers;using machine learning to predict the sequences of optimization passes;a proposed method for feature extraction to enhance classification algorithms performance;heart disease prediction system using optimization techniques;optimizing energy in cooperative sensing cognitive radios;real-time sickle cell anemia diagnosis based hardware accelerator;energy-efficient particle swarm optimization for lifetime coverage prolongation in wireless sensor networks;iris recognition using localized zernike features with partial iris pattern;analysis of hollywood’s film production using background subtraction visual activity index based on computer vision algorithm;breast cancer recognition by computer aided based on improved fuzzy c-mean and ann;brain computer interface enhancement based on stones blind source separation and naive bayes classifier;policing based traffic engineering fast reroute in sd-wan architectures: Approach development and investigation;performance comparison between traditional network and broadcast cognitive radio networks using idle probability;a developed cloud security cryptosystem using minhash technique.

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