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International Conference on computer Supported Cooperative Work in Design

作者： Ana Clara Correa Da Silva Gabriel S. Luna Alberto Cecí lio Xavier Viviane Cunha Farias Da Costa Luiz Oliveira Jano De Souza Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Niter&#x00F3 i Campus Federal Institute of Rio de Janeiro Niter&#x00F3 i Brazil

The Internet of Everything (IoE) is the paradigm of intelligent services that supports a ubiquitous and always-connected world of smart sensors and actuators, human and non-human in a cyber-physical lifestyle. Recent years have witnessed the fast proliferation of smart devices. In addition, these functional smart sensors possess powerful computation and communication capabilities and contribute to intelligent services outcomes of IoE-based applications. Then by adopting a knowledge-based approach on sensors smartness, we make a comparative assessment and classify smart sensors (image sensors, temperature sensors, proximity sensors, speed sensors, and sound sensors) regarding intelligence levels. We extracted valuable conclusions and inferences from this classification, providing insights and future directions towards promised benefits offered by distilled intelligence in smart sensors applications. (Abstract)

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Data Warehouse Design for Soil Nutrients with IoT Based Data Sources

Data Warehouse Design for Soil Nutrients with IoT Based Data...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Abdul Rahman Ermatita Dedik Budianta Faculty of Engineering Sriwijaya University doctoral program student(lecture at AMIK MDP) Inderalaya Indonesia Faculty of Computer Science Sriwijaya University Inderalaya Indonesia Faculty of Agriculture Sriwijaya University Inderalaya Indonesia

ISBN: (纸本)9781728129310

Plant fertility is very dependent on the levels of nutrients present in the soil. Measurement of soil nutrients can be done through laboratory tests and can also be done using IoT technology. IoT equipment is supported by sensors that can be used to measure soil nutrients and the location of the soil is measured. Data from land measurements made through IoT technology can be stored and displayed in real-time through the cloud. This paper designs data sources derived from IoT -based nutrient measurement data to build data warehouse locations and soil nutrients. A data warehouse that is formed later can be done for analysis of the types of plants and fertilizers needed at a soil location.

关键词： agriculture cloud computing data handling data warehouses Internet of Things soil Data warehousing soil nutrients Internet of things data processing Cloud Computing Data sources agriculture Soil plant fertility

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An augmented reality training environment for post-stroke finger extension rehabilitation

An augmented reality training environment for post-stroke fi...

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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Xun Luo R.V. Kenyon T. Kline H.C. Waldinger D.G. Kamper Sensory Motor Performance Program Rehabilitation Institute of Chicago and Northwestern University USA Computer Science Department University of Illinois Chicago USA Neuromechanics Laboratory Department of Biomedical Engineering Marquette University USA

Finger extension is an important hand function and is crucial for object exploration and manipulation. Unfortunately, the impairment of this motor function is common among stroke survivors. A training environment incorporating augmented reality (AR) in conjunction with assistive devices has been developed for the rehabilitation of finger extension. The environment consists of three components: the stroke survivor user element consisting of AR equipment/software and body-powered orthosis; the therapist element comprised of monitoring/control interface with visual, audio and force feedback; and the networking module which interconnects these two. In this paper we present the structure of this environment along with the results from a pilot case study with a stroke survivor.

关键词： Augmented reality Fingers Rehabilitation robotics USA Councils

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Gramian Angular Field and Recurrence Plots as Feature engineering Techniques on Residential Appliances Labeling: A Comparative Analysis

Gramian Angular Field and Recurrence Plots as Feature Engine...

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IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America)

作者： J. S. Corrêa D. L. Cavalca R. A. S. Fernandes Department of Electrical Engineering Federal University of São Carlos São Carlos Brazil Graduate Program in Computer Science Federal University of São Carlos São Carlos Brazil

The demand for electricity has increased rapidly and, for this reason, there is a need to efficiently use it. In this way, the identification of residential appliances enables such use for consumers and is crucial for demand response programs. Due to the variety of appliances in homes and their dynamic behavior, the search for patterns that explain and allow the correct labeling of temporal windows becomes a challenging task, since a window may contain more than one appliance. In this sense, the present paper proposes the transformation of time-series into images, using Gramian angular field and recurrence plots. The dataset composed of images was submitted to the labeling process, considering the use of convolutional neural networks. A comparative analysis was performed using the UK-DALE dataset. The results demonstrated the effectiveness of the proposed feature engineering stage, since the labeling task reached F1-scores until 94 %.

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Multi-index Sequential Monte Carlo ratio estimators for Bayesian Inverse problems

arXiv

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arXiv 2022年

作者： Law, Kody J.H. Walton, Neil Yang, Shangda Jasra, Ajay School of Mathematics University of Manchester ManchesterM13 9PL United Kingdom Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. This setting is ubiquitous across science and engineering applications, for example in the context of Bayesian inference where a physics-based model governed by an intractable partial differential equation (PDE) appears in the likelihood. A multi-index Sequential Monte Carlo (MISMC) method is used to construct ratio estimators which provably enjoy the complexity improvements of multi-index Monte Carlo (MIMC) as well as the efficiency of Sequential Monte Carlo (SMC) for inference. In particular, the proposed method provably achieves the canonical complexity of MSE−1, while single level methods require MSE−ξ for ξ > 1. This is illustrated on examples of Bayesian inverse problems with an elliptic PDE forward model in 1 and 2 spatial dimensions, where ξ = 5/4 and ξ = 3/2, respectively. It is also illustrated on more challenging log Gaussian process models, where single level complexity is approximately ξ = 9/4 and multilevel Monte Carlo (or MIMC with an inappropriate index set) gives ξ = 5/4 + ω, for any ω > 0, whereas our method is again canonical. We also provide novel theoretical verification of the product-form convergence results which MIMC requires for Gaussian processes built in spaces of mixed regularity defined in the spectral domain, which facilitates acceleration with fast Fourier transform methods via a cumulant embedding strategy, and may be of independent interest in the context of spatial statistics and machine learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Elementary science classroom learning with wireless response devices implementing active and experiential learning

Elementary science classroom learning with wireless response...

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IEEE International Workshop on Wireless, Mobile and Ubiquitous Technology in Education (WMUTE)

作者： Yu-Fen Chen Chen-Chung Liu Ming-Hung Yu Sung-Bin Chang Yun-Chen Lu Tak-Wai Chan Department of Computer Science Information Engineering National Central University Taiwan Graduate Institute of Network Learning Technology National Central University Taiwan Degree Program of E-Learning College of Science National Chiao Tung University Taiwan Chu-Wei Elementary School Taiwan

Active learning enables learners to actively engage in learning. Learning not only transfers material to students for learning, but also encourages greater mental engagement and more extensive student-student and student-instructor interaction than does a typical lecture class. Peer instruction (PI) engages students in active learning by achieving continuous instructor-student interaction in a physics lecture. However, the methodologies and the effectiveness of implementing PI for elementary school students have seldom been clarified. This study explores the possibility of adopting PI in an elementary science classroom. The research considerations of the study are as follows: (1) how wireless technology can enhance PI in elementary science classroom; (2) how a teacher can engage students in pre-class reading, and (3) whether elementary school students have sufficient social skills to perform a PI discussion? These questions are examined by observing how the PI pedagogical model worked with a wireless response system in elementary science classroom. Based on the observation, this study also proposes a way of improving the PI learning experience of elementary school students by adding experiments and observations during peer discussion to explain concepts and phenomena in physics.

关键词： Educational institutions Physics Chemical elements computer science Materials science and technology Electronic learning Probes Astrochemistry Physiology Astronomy

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Biological network epitomes via topological compression

Biological network epitomes via topological compression

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Gil Alterovitz Marco F. Ramon Health Science and Technology/Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge USA Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology / Harvard Medical School- Partners Center for Genetics and Genomics Boston 02115 USA.

High-throughput generation of new types of relational biological datasets is creating a demand for network-based signal processing and pattern recognition to provide new insights. Such networks are often too large to interpret visually and too complicated to be explained solely based on local topological properties. Just as signal processing and statistical techniques have been used in traditional, sequential-based biological datasets, so too are methodologies needed to automatically discern patterns in the huge, emerging networks. One way to do this is by transforming these very large networks into discernable epitomes, or abstracts, of the original networks. This work presents an approach for doing this via topological compression. Through capturing nodes' global topologies and subsequent compression, a new network epitome can be derived. Here, this is done with an E. Coli gene regulation network, resulting in biological findings that could not be derived from the local topology of the original network.

关键词： Biological information theory Network topology Genomics Bioinformatics Biomedical signal processing Compression algorithms Visualization Biology Gas insulated transmission lines computer science

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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Conference on Lasers and Electro-Optics (CLEO)

作者： Hanyu Zheng Quan Liu You Zhou Ivan I. Kravchenko Yuankai Huo Jason Valentine Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA Department of Computer Science Vanderbilt University Nashville TN USA Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN USA Department of Mechanical Engineering Vanderbilt University Nashville TN USA

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac... 详细信息

ISBN: (纸本)9781957171258

关键词： Image analysis Image resolution Machine vision Neural networks Optical systems Signal processing

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Low-Crosstalk in 2D Waveguide Crossings by Metaheuristic Optimization Methods

Low-Crosstalk in 2D Waveguide Crossings by Metaheuristic Opt...

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Optics and Photonics Conference (SBFoton IOPC), SBFoton International

作者： Luana da França Vieira C. E. Rubio-Mercedes Vitaly Felix Rodriguez Esquerre Department of Computer Science Federal University of Western Bahia Barreiras Brazil Physics Engineering Program State University of Mato Grosso do Sul Dourados Brazil Graduate School of Electrical Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798350388176

ISBN: (纸本)9798350388183

This work describes the investigation of planar waveguide crossings using metaheuristic methods associated with the two-dimensional finite element method (2D-FEM). An optimization and measurement process are performed on the power transmission and crosstalk of the waveguide crossings and then the values calculated are compared with other studies in the literature. The best performing adjusted crossing is optimally designed by applying the following metaheuristics: Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and a hybrid algorithm combining the Greedy Randomized Adaptive Search Procedure and Simulated Annealing (GRASP-SA). Transmission efficiency results higher than 97% and crosstalk below the -50 dB over a wide wavelength range, with a footprint of $\mathbf{4.9}\times \mathbf{4.9}\ \boldsymbol{\mu} \mathbf{m}^{\mathbf{2}}$ .

关键词： Performance evaluation Power measurement Metaheuristics Crosstalk Power transmission Simulated annealing Silicon Planar waveguides Optical waveguides Photonics

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Detection of P, QRS, and T Components of ECG using wavelet transformation

Detection of P, QRS, and T Components of ECG using wavelet t...

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IEEE/ICME International Conference on Complex Medical engineering, CME

作者： Abed Al Raoof Bsoul Soo-Yeon Ji Kevin Ward Kayvan Najarian Department of Computer Science at Virginia Commonwealth University Richmond VA USA Department of Emergency Medicine at Virginia Commonwealth University and Associate Director of Virginia Commonwealth University Reanimation Engineering Shock Richmond VA USA Computer Science at the Virginia Commonwealth University and director of the Biomedical Signal Analysis program at Virginia Commonwealth University Reanimation Engineering Shock (VCURES) Richmond VA USA

Electrocardiogram (ECG) signals are composed of five important waves: P, Q, R, S, and T. Sometimes, a sixth wave (U) may follow T. Q, R, and S are grouped together to form the QRS-complex. Detection of these waves is a vital step in ECG signal analysis to extract hidden patterns. Many prior studies have focused only on detection of the QRS-complex, because P and T waves are sparse and harder to isolate from the signal. In this paper, we develop an algorithm to detect all five waves - P, Q, R, S, and T in ECG signals using wavelet transformation. The accuracy for P wave detection is 99.5%, 99.8% for QRS complex, and 99.2% for T waves.

关键词： Electrocardiography Electric shock computer science Signal analysis Heart rate detection Biomedical engineering Helium Cardiac disease Fibrillation Heart rate

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