The objective of this work was to investigate the Amplitude Modulation - Frequency Modulation (AM-FM) texture feature variability in carotid ultrasound video during the cardiac cycle at systole and diastole. The goal ...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
The objective of this work was to investigate the Amplitude Modulation - Frequency Modulation (AM-FM) texture feature variability in carotid ultrasound video during the cardiac cycle at systole and diastole. The goal here was to identify AM-FM features that are associated with increased risk of stroke. We computed the inst.ntaneous amplitude, inst.ntaneous phase and the magnitude of inst.ntaneous frequency to extract plaque histogram features. A small dataset of 5 asymptomatic and 3 symptomatic videos were analyzed. Selected AM-FM plaque histogram texture features extracted during the cardiac cycle at the systolic and diastolic states were statistically significantly different between asymptomatic and symptomatic videos. However, further evaluation with more subjects needs to be carried out to exploit the usefulness of the proposed analysis in the clinical context.
The disruptions experienced by the processes in the last mile delivery during the SARS-CoV-2 pandemic raised the dilemma of up-to-date last mile approaches for Urban Logistics (UL) issues. Self-Collection Delivery Sys...
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Businesses across different areas of interest are increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, inter-organizational data sharing is proposed, e.g. i...
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ISBN:
(数字)9798350380262
ISBN:
(纸本)9798350380279
Businesses across different areas of interest are increasingly depending on data, particularly for machine learning (ML) applications. To ensure data provisioning, inter-organizational data sharing is proposed, e.g. in the form of data ecosystems. The aim of this study was to perform an exploratory investigation into the data sharing practices that exist in business-to-business (B2B) and business-to-customers (B2C) relations, in order to shape a knowledge foundation for future research. We launched a qualitative survey, using interviews as data collection method. We conducted and analyzed eleven interviews with representatives from seven different companies across several industries with the aim of finding key practices, differences and similarities between approaches, so we could formulate the future research goals and questions. We grouped the core findings of this study into three categories: organizational aspects of data sharing, where we noticed the importance of data sharing and data ownership as business driver; technical aspects of data sharing, related to data types, formats, maintenance and infrastructures; and challenges, with privacy being the highest concern along with the data volumes and cost of data.
The use of 3-Dimensional Light Detection and Ranging (3D LiDAR) point cloud as the alternative data to reduce privacy exposure in monitoring systems has been carried out in several studies. Unfortunately, various chal...
The use of 3-Dimensional Light Detection and Ranging (3D LiDAR) point cloud as the alternative data to reduce privacy exposure in monitoring systems has been carried out in several studies. Unfortunately, various challenges in using point clouds intersect with the amount of data and computational costs. Several studies attempted to optimize the point cloud processing approach by segmenting the ground plane to get the object clusters separated. However, many unnecessary points can still burden the computation process. Since the ground plane mainly represents the horizontal planar plane on the x, y axis, this study tried to reduce the points on the vertical planar plane on the x, z and y, z axes with the x, y horizontal planar plane as well based on the surface normal vector direction of each point. The proposed approach has successfully reduced the raw point cloud by 79.29% removing the point cloud that indicates the planar surface of the three axes while maintaining the essential object of the monitoring system on the KITTI raw dataset. Therefore, the object cluster can be minimized, supporting the computational costs for further research in human activity monitoring systems.
We experimentally report on a real-time self-guided method to search for maximal CHSH violations between two observers sharing polarization entangled photon pairs using uncalibrated piezoelectric fiber squeezers as po...
Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive antidepressant neuromodulation therapy for treatment-resistant depression (TRD). However, the remission rate of patients remains unsatisfactory pos...
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ISBN:
(纸本)9781665465007
Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive antidepressant neuromodulation therapy for treatment-resistant depression (TRD). However, the remission rate of patients remains unsatisfactory possibly due to the suboptimal configuration of conventional rTMS protocol. This work aims to design a close-loop TMS system and validate the practicability of brain-state-dependent stimulation based on real-time monitoring of electroencephalogram (EEG). We propose a novel method of phase estimation to enhance the precision of EEG phase-triggered firing of TMS. Our implementation supports subsequent studies on personalized brain-state-dependent neuromodulation for clinical applications.
作者：
Iwap Saputra BatanEko Mulyanto YuniarnoMauridhi Hery PurnomoAhmad RamadhaniDept. of Electrical Engineering
Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering
Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering
Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia
During the pandemic, we must maintain our body’s immunity at best. Outdoor activities have been closed; therefore, there are limitations to activities that can be done. A solution to staying healthy during the pandem...
During the pandemic, we must maintain our body’s immunity at best. Outdoor activities have been closed; therefore, there are limitations to activities that can be done. A solution to staying healthy during the pandemic is indoor exercise. One type of indoor exercise is regularly riding stationary bikes. We maintain the body’s fitness and burn calories by riding stationary bikes. However, some stationary bikes could not show accurate calories burned; they calculated it only based on speed, resistance, and duration, so people with different weights seem to burn the same amount of calories. Therefore, this paper proposed an additional parameter essential in calorie burning: body weight. We used the human body pose estimator to detect the activity of riding the stationary bike and calculated it by adding the person’s weight. The result shows a difference between the estimated calorie burned and the data directly from the stationary bike. The difference is between 0.0225 to 2.8775. The heavier the person, the more differences in the calories burned. The proposed method helped prove that a person’s weight affects the calories burned while doing an activity, especially riding a stationary bike.
In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a dr...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions.
Multicast transmission is a recurrent problem in wireless networking as the system has to cater to multiple numbers of users at the same time. Furthermore, video payload adds more complexity to the problem, i.e., vide...
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The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefor...
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ISBN:
(数字)9798331517601
ISBN:
(纸本)9798331517618
The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefore, this classification was used to perform pattern recognition of woven fabric in the system. The process allowed the system’s ability to be tested by applying the pattern recognition algorithm to woven images. In this context, the feature extraction method used was gray level co-occurrence matrix (GLCM), while the method used for classification was artificial neural network (ANN). The results showed an accuracy of $87.5 \%$ in the system. Finally, GUI system was developed, enabling tests to be performed on woven images for the identification of Timor Weaving image patterns.
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