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IEEE International Conference on Image Processing

作者： Øyvind Meinich-Bache Kjersti Engan Trygve Eftestøl Ivar Austvoll Dep. of Electrical Engineering and Computer Science University of Stavanger Norway

Quality cardiopulmonary resuscitation (CPR) increases the chances of survival from out-of-hospital cardiac arrest. CPR measurement devices with real-time feedback could assist in the provision of this. Others have proposed accelerometer-based feedback systems by using specialized cards, smartwatches or hand-held smartphones. Our group have previous proposed a system that measure chest compression (CC) rate and hands-off-time utilizing a smartphone camera with a phone-on-the-floor solution. In this paper we have investigated the possibilities of also measuring the important CPR metric CC dep.h. Solutions using smartwatches or smartphones estimate CC parameters based on the bystanders movement. However, there are no reported work on analyzing different bystanders movement during CCs. In this work, a CC modelling experiment using Microsoft Kinect is performed to measure the degree of variations in CC techniques, providing knowledge on limitations when considering digital strategies for CC dep.h measurements. Although variations between the CC techniques were discovered, the results indicate that smartphone dep.h-cameras and accelerometer-sensors could in most cases be used for CC dep.h measurement with acceptable accuracy.

关键词： Cameras Wrist Elbow Motion measurement Three-dimensional displays Accelerometers Real-time systems

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Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks

arXiv

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arXiv 2020年

作者： Dev, Kapal Khowaja, Sunder Ali Jaiswal, Aman Bist, Ankur Singh Saini, Vaibhav Bhatia, Surbhi CONNECT Centre Trinity College Dublin Ireland Faculty of Engineering and Technology University of Sindh Jamshoro Pakistan Department KIET Group of Institutions Ghaziabad India Signy Advanced Technologies. Gurugram Haryana India Dep Indian Institute of Technology Dehli India Department of Information Systems College of Computer Science and Information Technology King Faisal University Saudi Arabia

The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for potential alternatives to reverse transcriptionpolymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of largescale annotated data makes the clinical implementation of machine learning-based COVID detection methods difficult. Another important issue is the usage of ImageNet pretrained networks which does not guarantee to extract reliable feature representations. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from wellknown pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison to the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity. © 2020, CC-BY.

关键词： Convolution

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A Gaussian Process Regression for Natural Gas Consumption Prediction Based on Time Series Data

A Gaussian Process Regression for Natural Gas Consumption Pr...

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International Conference on Information Fusion

作者： Oussama Laib Mohamed Tarek Khadir Lyudmila Mihaylova Dep. of Computer Science University of Badji Mokhtar Annaba (UBMA) Annaba Algeria Dep. of Automatic Control and Systems Engineering (ACSE) University of Sheffield Sheffield UK

For several economical, financial and operational reasons, forecasting energy demand becomes a key instrument in energy system management. This paper develops a natural gas forecasting approach, which consists of two major phases: 1) it classifies the natural gas consumption daily pattern sequences into different groups with similar attributes. 2) the design and training of multiple autoregressive Gaussian Process models phase is carried out using the Algerian natural gas market data together with exogenous inputs consisting in weather (temperature) and calendar (day of the week, hour indicator) factors. The main novelty in this work consists of the investigation of multiple different clustering techniques for better analysis and clustering of natural gas consumption data. The impact of the obtained clusters, by each technique, is then summarized and evaluated with respect to the prediction accuracy.

关键词： Natural gas Forecasting Gaussian processes Load modeling Predictive models Clustering methods Kernel

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A Compact 9 GHz Microwave Imaging System

A Compact 9 GHz Microwave Imaging System

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2018 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2018

作者： Peng, Kang-Chun Lin, Chiu-Chin Li, Cyuan-Fong Hung, Cheng-Yuan Hsieh, Yu-Sung Dep. of Computer and Communication Engineering National Kaohsiung Univ. of Science and Technology Kaohsiung Taiwan Opto-electronics Equipment Department Metal Industries Research Development Center Kaohsiung Taiwan

ISBN: (纸本)9781538648346

This work develops a compact 9 GHz microwave imaging system. The core of the system is a vector network analyzer (VNA) which utilizes range-correlation architecture to suppress the phase noise of the received signal. To overcome the bottleneck that conventional mixer-based phase detectors have limited phase detection range, a novel quadrature phase detection algorithm is proposed. The experimental results show that the microwave imaging system can well detect the bone image of a chicken wing. © 2018 IEEE.

关键词： Signal detection

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Are My Arguments Trustworthy? Abstract Argumentation with Subjective Logic

Are My Arguments Trustworthy? Abstract Argumentation with Su...

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International Conference on Information Fusion

作者： Francesco Santini Audun Josang Maria Silvia Pini Dep. of Maths and Computer Science University of Perugia Italy Dep. of Informatics University of Oslo Norway Dep. of Information Engineering University of Padova Italy

An Abstract Argumentation Framework (AAF) is an abstract structure consisting of a set arguments, whose origin, nature, and possible internal organisation is not specified, and by a binary relation of attack on the set of arguments, whose meaning is not specified either. Subjective logic provides a standard set of logical operators, intended for use in domains containing uncertainty. In this paper, we define an extension of AAFs in which each argument and attacks is evaluated with an opinion, by revisiting the constellations approach developed for probabilistic AAFs. In this way, different agents can merge their opinions on how much arguments and attacks are "trustworthy", e.g., they do not represent fallacies or enthymemes. Finally, subjective logic operators can be used to fuse the belief of different possible worlds (i.e., a constellation of sub-graphs in the original AAF) containing different arguments and attacks.

关键词： Uncertainty Semantics Visualization Probabilistic logic Probability density function Cognition computer science

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Controlling symmetries and clustered dynamics of complex networks

arXiv

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arXiv 2020年

作者： Gambuzza, Lucia Valentina Frasca, M. Sorrentino, F. Pecora, L.M. Boccaletti, S. Dipartimento di Ingegneria Elettrica Elettronica e Informatica University of Catania Catania95029 Italy Dipartimento di Ingegneria Elettrica Elettronica e Informatica University of Catania Catania95029 Italy CNR-IASI Institute for Systems Analysis and Computer Science A. Ruberti Rome00185 Italy Department of Mechanical Engineering Univ. of New Mexico AlbuquerqueNM87131 United States Dep. of Electrical and Computer Engineering Univ. of New Mexico AlbuquerqueNM87131 United States U.S. Naval Research Laboratory WashingtonDC20375 United States CNR Institute of Complex Systems Via Madonna del Piano 10 Florence50019 Italy Unmanned Systems Research Institute Northwestern Polytechnical University Xi’an710072 China Moscow Institute of Physics and Technology Dolgoprudny Moscow Region141701 Russia

Symmetries are an essential feature of complex networks as they regulate how the graph collective dynamics organizes into clustered states. We here show how to control network symmetries, and how to enforce patterned states of synchronization with nodes clustered in a desired way. Our approach consists of perturbing the original network connectivity, either by adding new edges or by adding/removing links together with modifying their weights. By solving suitable optimization problems, we furthermore guarantee that changes made on the existing topology are minimal. The conditions for the stability of the enforced pattern are derived for the general case, and the performance of the method is illustrated with paradigmatic examples. Our results are relevant to all the practical situations in which coordination of the networked systems into diverse groups may be desirable, such as for teams of robots, unmanned autonomous vehicles, power grids and central pattern generators. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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Optimal explanations of linear models

arXiv

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arXiv 2019年

作者： Bersimas, Dimitris Jaillet, Patrick Delarue, Arthur Martin, Sebastien Sloan School of Management Massachusetts Institute of Technology CambridgeMA02139 Dep. of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 Operations Research Center Massachusetts Institute of Technology CambridgeMA02139

When predictive models are used to support complex and important decisions, the ability to explain a model's reasoning can increase trust, expose hidden biases, and reduce vulnerability to adversarial attacks. However, attempts at interpreting models are often ad hoc and application-specific, and the concept of interpretability itself is not well-defined. We propose a general optimization framework to create explanations for linear models. Our methodology decomposes a linear model into a sequence of models of increasing complexity using coordinate updates on the coefficients. Computing this decomposition optimally is a difficult optimization problem for which we propose exact algorithms and scalable heuristics. By solving this problem, we can derive a parametrized family of interpretability metrics for linear models that generalizes typical proxies, and study the tradeoff between interpretability and predictive accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

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Ultra-low-power wireless anchor load monitoring system based on internet of things technology

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IOP Conference Series: Materials science and engineering 2019年第1期615卷

作者： J Tong T H Lee Y Lin Dep. of Computer Science and Information Engineering Hungkuang University Taichung Taiwan Dep. of Computer Science National Taichung University Taichung Taiwan Dep. of Civil Engineering National Chung Hsing University Taichung Taiwan

Ground anchoring is a popular method to stabilize the slope. It is essential for stability evaluation to measure the tension value of anchor from installed load cell. In this paper, a wireless anchor load measuring system is proposed for continuously on-site slope stability monitoring. Each anchor load cell is connected to a MCU-based acquisition unit and become a wireless IoT end-device. This system is scalable so the sensor dep.oyment will be much more flexible than that in conventional data acquisition system. The excitation circuit, signal amplification and analog-to-digital conversion are completed by a small-size device next to the anchor so that very low-noise result can be obtained. Each measurement consumes less than 10mAs so this device can be drive only by a 55mm*70mm*40mm solar power module. Verified by almost 3-year's on-site test, it is confirmed the proposed monitoring system can continuously capture and send one measurement per minute to the cloud server without any other electric power. The experimental and on-site test results show the practicability of proposed system and its reliability of long-term measurement. It has very high application potential on real-time slope stability monitoring by collecting and analyzing the load values of all installed anchors.

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A Load Balancing Algorithm for Resource Allocation in IEEE 802.15.4e Networks

A Load Balancing Algorithm for Resource Allocation in IEEE 8...

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2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018

作者： Kralevska, Katina Dimitrios Vergados, J. Jiang, Yuming Michalas, Angelos Dep. of Information Security and Communication Technology NTNU Norwegian University of Science and Technology Norway School of Electrical and Computer Engineering National Technical University of Athens Greece Department of Informatics Engineering Technological Education Institute of Western Macedonia Kastoria Greece

ISBN: (纸本)9781538632277

The recently created IETF 6TiSCH working group combines the high reliability and low-energy consumption of IEEE 802. 15.4e Time Slotted Channel Hopping with IPv6 for industrial Internet of Things. We propose a distributed link scheduling algorithm, called Local Voting, for 6TiSCH networks that adapts the schedule to the network conditions. The algorithm tries to equalize the link load (defined as the ratio of the queue length over the number of allocated cells) through cell reallocation. Local Voting calculates the number of cells to be added or released by the 6TiSCH Operation Sublayer 6top. Compared to a representative algorithm from the literature, Local Voting provides simultaneously high reliability and low end-to-end latency while consuming significantly less energy. Its performance has been examined and compared to On-the- fly algorithm in 6TiSCH simulator by modeling an industrial environment with 50 sensors. © 2018 IEEE.

关键词： Resource allocation

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Temporal Estimates of Crop Growth in Sorghum and Maize Breeding Enabled by Unmanned Aerial systems

Plant Phenome Journal

引用

Plant Phenome Journal 2018年第1期1卷 1-10页

作者： Pugh, N. Ace Horne, David W. Murray, Seth C. Carvalho, Geraldo Malambo, Lonesome Jung, Jinha Chang, Anjin Maeda, Murilo Popescu, Sorin Chu, Tianxing Starek, Michael J. Brewer, Michael J. Richardson, Grant Rooney, William L. Dep. of Soil and Crop Sciences Texas A&M Univ. College Station 77843 TX United States Dep. of Ecosystem Science and Management Texas A&M Univ. College Station 77843 TX United States Dep. of Engineering and Computer Science Texas A&M Univ. Corpus Christi 78412 TX United States Texas A&M AgriLife Research Texas A&M Univ. System Corpus Christi 78406 TX United States Conrad Blucher Institute for Surveying and Science School of Engineering and Computing Sciences Texas A&M Univ. Corpus Christi 78412 TX United States

Core Ideas: We comprehensively validated the use of UAS in sorghum and maize breeding programs. Temporal estimates of plant growth will allow researchers to elucidate new phenotypes. The stage of the breeding pipeline dictates the applicability of UAS platforms. The implementation of UAS is demonstrated in different crop species. Monetary and time costs should be considered before implementation of UAS. To meet future world food and fiber demands, plant breeders must increase the rate of genetic improvement of important agricultural crops. One of the biggest obstacles now facing crop scientists is a phenotyping bottleneck. To ease this burden, the emerging technology of unmanned aerial systems (UAS) presents an exciting opportunity. To assess the utility of UAS, it is important to investigate their application across multiple crop species. Terminal plant height is of great importance to maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] breeders and has been hypothesized to be useful but has been logistically impractical to measure in the field. In this study, we statistically analyzed in dep.h the ability of UAS to estimate height in sorghum (advanced and early generation material) and maize (optimal and late material) and the application of these estimates in breeding programs. We found that UAS explain genotypic variation similarly to ground-truth methods and that the repeatability of the methodology is high (R = 0.61–0.99), indicating effective differentiation of genotypes. Additionally, correlations between ground truth and UAS measurements were moderate to high for all materials (r = 0.4–0.9). Finally, we present a novel application for the technology in the form of high-resolution temporal growth curves. Using these UAS-generated growth curves, new physiological insights can be obtained and new avenues of scientific investigation are possible. Copyright © American Society of Agronomy and Crop science Society of America

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