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ieee World Forum on Internet of Things (WF-IoT)

作者： Melchizedek Alipio Gianne Marcus Obusan Julianne Abbe Ong Samuel Perez Anton Luis Garces Miroslav Bures Department of Computer Science System Testing Intelligent Laboratory FEE Czech Technical University in Prague Prague 2 Czech Republic Department of Electronics and Computer Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Goat farming shows potential as one of the so-lutions for providing quality livestock products in developing countries. Goat health is essential in determining the quality of goods obtained from the goat. Smart livestock farming has been a reliable solution to achieve high-yield and high-quality goat products. However, current smart livestock monitoring systems do not consider the feeding pattern and behavior of the animal as a factor in its overall health. Hence, this study aims to develop a system that effectively monitors the feeding behavior of the goat using wearable devices, machine learning, and a visualization interface. The wearable device consists of an accelerometer, gyrometer, and temperature sensors to monitor the goat's feeding behavior and movement. The data collected by the wearable device are wirelessly transmitted to a base station to build a sensor-based dataset. This dataset is used to generate a k-means clustering model to classify the feeding behavior of the goats. Results show that the system was able to classify the feeding behavior of the goat with an accuracy of 98.02 % and a reliability of 100% in comparison with the actual goat behavior on the farm. Lastly, a graphical user interface is developed to provide monitoring and other relevant information to help the farmers in grazing the goats.

关键词： Temperature sensors Temperature measurement Temperature distribution Animals Agriculture Reliability Wearable devices

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CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding

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ieee TRANSACTIONS ON GEOscience AND REMOTE SENSING 2023年 61卷 1页

作者： Muhtar, Dilxat Zhang, Xueliang Xiao, Pengfeng Li, Zhenshi Gu, Feng Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing 210023 Peoples R China

Self-supervised learning (SSL) has gained wide-spread attention in the remote sensing (RS) and Earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited to learning either global semantic separable or local spatial perceptible representations. We argue that this learning strategy is suboptimal in the realm of RS since the required representations for different RS downstream tasks are often varied and complex. In this study, we proposed a unified SSL framework that is better suited for RS image representation learning. The proposed SSL framework, contrastive mask image distillation (CMID), is capable of learning representations with both global semantic separability and local spatial perceptibility by combining contrastive learning (CL) with masked image modeling (MIM) in a self-distillation way. Furthermore, our CMID learning framework is architecture-agnostic, which is compatible with both convolutional neural networks (CNNs) and vision transformers (ViTs), allowing CMID to be easily adapted to a variety of deep learning (DL) applications for RS understanding. Comprehensive experiments have been carried out on four downstream tasks (i.e., scene classification, semantic segmentation, object detection, and change detection) and the results show that models pretrained using CMID achieve a better performance than other state-of-the-art SSL methods on multiple downstream tasks. The code and pretrained models will be made available at https://***/NJU-LHRS/official-CMIDhttps://***/NJU-LHRS/official-CMID to facilitate SSL research and speed up the development of RS images DL applications.

关键词： Semantics Task analysis data models visualization Remote sensing Image reconstruction Convolutional neural networks Contrastive learning (CL) deep learning (DL) masked image modeling (MIM) remote sensing (RS) pretraining self-supervised learning (SSL)

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STS-DGNN: Vehicle Trajectory Prediction via Dynamic Graph Neural Network With Spatial-Temporal Synchronization

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Li, Feng-Jie Zhang, Chun-Yang Chen, C. L. Philip Fuzhou Univ Sch Comp & Data Sci Fuzhou 350025 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

Accurate prediction of vehicle trajectories is crucial to the safety and comfort of autonomous vehicles. Although several graph-based models have exhibited substantial progress in acquiring spatiotemporal dependencies among vehicles in the driving environment, the potential for additional exploration in this domain persists. The main reason is that they concentrated on independently capturing the spatial relations and temporal dependencies, neglecting to incorporate the temporal feature into the spatial feature for co-training, which limits their ability to yield satisfactory predictive accuracy. Typically, spatial and temporal correlations are coupled and should be modeled jointly. Inspired by this, a novel dynamic graph neural network with spatial-temporal synchronization (STS-DGNN) for vehicle trajectory prediction is proposed, which constructs the driving scene as dynamic graphs and can jointly extract spatial-temporal features. Specifically, low-order and high-order dynamics of vehicle trajectories are considered collaboratively in a one-stage framework rather than independently modeling the spatial relationship and temporal correlations of vehicles in two-stage models. The proposed model also considers the dynamic nature of graph sequence by utilizing gate recurrent unit (GRU) to update the graph neural network (GNN) parameters dynamically. The spatial-temporal features are subsequently conveyed to convolutional neural networks (CNNs) and processed by a multilayer perceptron (MLP) to generate the ultimate trajectories. Finally, to illustrate the effectiveness of the STSDGNN model, the model is assessed on three well-known datasets, namely highD, EWAP, and UCY. The results confirm that our model performs better at making predictions than cuttingedge models. The visualization results intuitively explain that our method can extract sophisticated and subtle multivehicle interactions, resulting in accurate predictions.

关键词： ~Autonomous driving dynamic graph graph neural network (GNN) spatial-temporal dependencies vehicle trajectory prediction

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A Novel Method Combining Global Visual Features and Local Structural Features for SAR ATR

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ieee GEOscience AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Li, Chen Du, Lan Li, Yi Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The mainstream synthetic aperture radar (SAR) automatic target recognition (ATR) methods commonly use convolutional neural network (CNN) to extract the visual information of SAR targets, while the physical structural information is seldom considered. Scattering center features can describe the targets' physical structure information and are robust to the local variations of targets, which can be exploited to reflect the local structural characteristic of SAR targets. Therefore, we propose a novel method that effectively combines global visual features and local structural features for SAR ATR. The local structural features here contain not only the local physical structure information but also the local visual information. Our proposed method consists of three parts: global-based module, local-based module, and feature fusion module. Global-based module utilizes CNN to extract global visual features from SAR images. Local-based module first extracts attributed scattering centers (ASCs) from the complex SAR image and models each ASC as a node to construct graph data, from which we further use a multiscale graph convolutional network (GCN) to extract local structural features. The node features for GCN learning are constructed by multiplying the corresponding local visual features in shallow CNN feature maps with the ASC reconstruction maps to better reflect the local structure characteristic. Then, the learned local structural features from GCN are further fused with global visual features to achieve SAR ATR. As far as authors know, this is the first work combining CNN and GCN to effectively extract global visual features and local structural features simultaneously in SAR ATR. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset show that our proposed method outperforms SOTA methods in terms of classification accuracy.

关键词： Feature extraction visualization Synthetic aperture radar Convolutional neural networks data mining Radar polarimetry Image reconstruction Attributed scattering center (ASC) convolutional neural network (CNN) graph convolutional network (GCN) synthetic aperture radar (SAR) target recognition

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Multi-Representation Convolutional Neural Network to Recognize Human Emotion

Multi-Representation Convolutional Neural Network to Recogni...

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Information Technology and Digital Applications (ICITDA), ieee International Conference on

作者： Dwi Wahyu Prabowo Noor Akhmad Setiawan Johan Debayle Hanung Adi Nugroho Department of Electrical and Information Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia Department of Information System Faculty of Computer Science Universitas Darwan Ali Sampit Indonesia MINES Saint-Etienne CNRS UMR 5307 LGF Centre SPIN Saint-Etienne France

Emotion recognition using brain-computer interfaces (BCIs) is an emerging field with the ongoing challenge of developing robust and efficient classification methods. This study introduces an original approach, the multi-representation convolutional neural network (MrCNN), designed to exploit the complementary features of recurrence plots (RP) and spectrograms. The efficacy of this new method was empirically evaluated and compared with the standalone RP and spectrogram methods in emotion classification tasks using electroencephalogram (EEG) signals. Two widely used datasets, DEAP and SEED, were used as the experimental platforms for this study. Employing a multifaceted analytical approach involving descriptive statistics, data visualization, and hypothesis tests, this study revealed that MrCNN significantly outperforms the standalone RP and spectrogram methods in terms of key performance metrics. Specifically, MrCNN achieved an accuracy of 0.9663, sensitivity of 0.9848, specificity of 0.9825, kappa score of 0.9511, and F1 score of 0.9656 for the DEAP dataset. For the SEED dataset, the accuracy was 0.9695, with sensitivity, specificity, kappa scores, and F1 score of 0.9844, 0.9842, 0.9543, and 0.9695, respectively. These results were statistically validated, and a comparative analysis with existing literature substantiated the robustness and superiority of the MrCNN method. This study sets new performance benchmarks and offers a reliable and effective approach for emotion classification using EEG signals.

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Dengue Dashboard for Forecasting the Future Trend of Dengue Cases in Pahang 7

Dengue Dashboard for Forecasting the Future Trend of Dengue ...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Syafiq Mustafa, Muhammad Khairul Sulaiman, Junaida Pekan Pahang Malaysia

ISBN: (纸本)9781665414074

The study is concerned about the rising number of dengue cases in Pahang. As a preventive solution, an analytical dashboard is proposed to forecast the trend of dengue cases in Pahang for the next two years using Auto Regressive Integrated Moving Average (ARIMA) model. Due to its efficiency to solve linear problems, ARIMA model is a popular model used by the researchers and economists to forecast a time-series data. The dashboard contains various information of dengue cases for instances the total number of dengue cases in Pahang, total number of dengue cases by district, a map that shows the green zone and the red zone which indicates the risk of the dengue infection in that area and a graph that displays the forecasted trend of dengue cases in Pahang from 2021 to 2023 using the best- fitted ARIMA model. The dashboard and the forecasted data trend of dengue is generated to assist JKN Pahang to plan a preventive measure regarding the rising number of dengue cases in thefuture. © 2021 ieee.

关键词： data visualization

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The HoloLens in medicine: A systematic review and taxonomy

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MEDICAL IMAGE ANALYSIS 2023年 85卷 102757页

作者： Gsaxner, Christina Li, Jianning Pepe, Antonio Jin, Yuan Kleesiek, Jens Schmalstieg, Dieter Egger, Jan Graz Univ Technol Inst Comp Grap & Vis A-8010 Graz Austria Univ Med Essen Inst Med D-45131 Essen Germany Res Ctr Connected Healthcare Big Data Zhejiang Lab Hangzhou 311121 Zhejiang Peoples R China BioTechMed A-8010 Graz Austria Univ Med Essen Canc Res Ctr Cologne Essen D-45147 Essen Germany

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until the year of 2021. We identified 217 relevant publications through a systematic search of the PubMed, Scopus, ieee Xplore and SpringerLink databases. We propose a new taxonomy including use case, technical methodology for registration and tracking, data sources, visualization as well as validation and evaluation, and analyze the retrieved publications accordingly. We find that the bulk of research focuses on supporting physicians during interventions, where the HoloLens is promising for procedures usually performed without image guidance. However, the consensus is that accuracy and reliability are still too low to replace conventional guidance systems. Medical students are the second most common target group, where AR-enhanced medical simulators emerge as a promising technology. While concerns about human- computer interactions, usability and perception are frequently mentioned, hardly any concepts to overcome these issues have been proposed. Instead, registration and tracking lie at the core of most reviewed publications, nevertheless only few of them propose innovative concepts in this direction. Finally, we find that the validation of HoloLens applications suffers from a lack of standardized and rigorous evaluation protocols. We hope that this review can advance medical AR research by identifying gaps in the current literature, to pave the way for novel, innovative directions and translation into the medical routine.

关键词： HoloLens Medicine Review Taxonomy Augmented reality Healthcare Surgery

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EMD-SCS: A Dynamic Behavioral Approach for Early Malware Detection with Sonification of System Call Sequences

EMD-SCS: A Dynamic Behavioral Approach for Early Malware Det...

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ieee International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Raghav Bhardwaj Morteza Noferesti Madeline Janecek Naser Ezzati-Jivan Department of Computer Science Brock University St. Catharines Canada

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The privacy and security of users are increasingly threatened due to the rising frequency of malware assaults. Both Host-based Intrusion Detection Systems (HIDSes) and Antivirus software rely on signature-based or anomaly-based techniques for malware detection. However, the escalating diversity and sophistication of malware pose significant obstacles. In this research, we introduce EMD-SCS, an early malware detection methodology, employing sonification and system call sequence analysis. In our methodology, we interpret an executing program/process as a sequence of system calls, leveraging a Long Short-Term Memory network (LSTM) to hold a record of preceding system calls within this chain, consequently facilitating the prediction of future calls. After this prediction phase, the BLEU and hamming distance scores are utilized to classify the system call sequence. Importantly, these results are attained by analyzing just a small segment of the data for early prediction, which is crucial for a sonification-based approach as it enables us to notify administrators in advance of potential threats. This early warning system would allow admins to protect the host before a potential compromise. EMD-SCS uses sonification to convey the prediction outcomes using natural and animal sounds, offering a broader monitoring scope than visual observation. Evaluation results from the ADFA-LD dataset suggest that EMD-SCS surpasses prior techniques in early malware detection with an accuracy of 91.2%, a detection rate of 87.7%, and a false-positive rate of 15.3%, achieved by only processing 40% of the input system call sequences before they infiltrate the host.

关键词： Privacy visualization Sequences Animals Intrusion detection Sonification Malware

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Parameter Estimation and Application of Spatial Extreme Value Modeling with Peaks over Threshold - Kumaraswamy Generalized Pareto (POT-KumGP) Method (Case Study: Extreme Rainfall in Maluku province)

Parameter Estimation and Application of Spatial Extreme Valu...

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ieee Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)

作者： Felix Usmany Sutikno Vita Ratnasari Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

Extreme weather events are one of the phenomena that cause unavoidable losses in various sectors and have a serious impact on aspects of life. These impacts can be minimized by studying the patterns and characteristics of extreme events. Extreme Value Theory (EVT) is one of the statistical techniques to detect extreme events. In order to detect extreme events in many locations (multivariate), this technique was then developed into spatial extreme value (SEV). This research applies the Peaks Over-Threshold method to excessive rainfall situations using an extreme value model based on the Kumaraswamy Generalized-Pareto Distribution. This model is performed by combining the generalized Pareto and Kumaraswamy distributions. In this study, the POT-KumGP model will be applied to monthly rainfall data from 1990–2023 in Maluku Province. Extreme values are determined based on threshold selection with visualization of the mean residual life plot. A distribution suitability test using Kolmogorov-Smirnov test statistics and a probability plot is performed to validate whether the model fits the Kum-GP distribution. If the model is suitable, parameter estimation will be carried out using maximum likelihood estimation (MLE) and continued with BFGS numerical iteration. Then a trend surface model will be formed to connect longitude and latitude variables with parameter estimates so as to further increase the robustness of the analysis. The best model is selected based on the smallest AIC value and used to calculated the return level for the next few periods. This research aims to build a POT-KumGP spatial extreme value model and get the return level estimation results for the next few periods in Maluku Province in order to inform future disaster management policies or strategies.

关键词： Maximum likelihood estimation Analytical models Adaptation models Rain Parameter estimation Computational modeling Tail Market research Robustness Numerical models

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A Real-time, visualization and Cloud-Linked Health Monitoring System for Disabled Elderly: Elderly Health Monitoring System 22

A Real-time, Visualization and Cloud-Linked Health Monitorin...

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Proceedings of the 2022 5th International Conference on Digital Medicine and Image Processing

作者： Liqing Yang Lin Chen Lu Liu Haibo Chen Key Laboratory of Biorheological Science and Technology of Ministry of Education Chongqing University China

ISBN: (纸本)9781450397643

With the aging process, the prevalence of chronic diseases and the incidence of disability among the elderly has also increased rapidly. The disabled elderly who are in bed for a long time need long-term monitoring of physiological parameters because of the serious decline of physical function. At the same time, it is also necessary to monitor the surface contact pressure between patients and nursing beds to prevent pressure ulcers. In traditional systems, physiological and stress monitoring are kept separate and are expensive and difficult to operate. Therefore, this paper proposes a real-time, visualization, and cloud-linked disabled elderly monitoring system based on WiFi technology. It can not only monitor physiological parameters in real-time but also monitor surface contact pressure. This system consists of four levels: (1) The sensor terminal nodes collect electrocardiogram (ECG), heart rate (HR), oxygen saturation (SpO2), and collect interface pressure in real-time. (2) The network transport layer adopts WIFI (ieee 802.11b) protocol. (3) The application layer designs a visual sever written by Qt software. (4) Cloud layer is deployed in the Ali-Cloud IoT server to connect to other cloud medical services. Six subjects took participated in the test and each subject was tested five times. The experimental results demonstrate that the system has a good performance to acquire data, visualize real-time, to connect clouds with a small delay.

关键词： Monitoring Real-time The elderly

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