作者：
Vu, Thai-HocDa Costa, Daniel BenevidesKim, SunghwanPham, Quoc-VietUniversity of Ulsan
Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of
Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Kyonggi University
School of Electronic Engineering Kyonggi Korea Republic of University of Dublin
School of Computer Science and Statistics Trinity College Dublin Dublin 2 D02PN40 Ireland
This paper comprehensively investigates the performance of downlink multi-user rate-splitting multiple access (RSMA) networks under Nakagami-m fading channels. We first develop the mathematical outage probability (OP)...
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Work from home (WFH) has become a global phenomenon that continues to grow, especially since the COVID-19 pandemic hit the world. Many organizations and companies are forced to adopt a remote working model to keep the...
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Work from home (WFH) has become a global phenomenon that continues to grow, especially since the COVID-19 pandemic hit the world. Many organizations and companies are forced to adopt a remote working model to keep their employees safe. The usefulness of WFH is still up for dispute, though. Text mining analysis can be utilized to determine how beneficial working from home is. Sentiment analysis uses text analysis to gather ten thousand data from social media Tweets. Joy emotion is predicted to dominate with 83.98 percent according to the mining performed by the vocabulary valence aware dictionary and sentiment reasoner (VADER).
Accurate and continuous monitoring of blood glucose levels is crucial for effective diabetes management. Photoplethysmography (PPG) has emerged as a promising modality for non-invasive blood glucose monitoring. In thi...
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ISBN:
(数字)9798331529710
ISBN:
(纸本)9798331529727
Accurate and continuous monitoring of blood glucose levels is crucial for effective diabetes management. Photoplethysmography (PPG) has emerged as a promising modality for non-invasive blood glucose monitoring. In this study, we investigate how blood glucose levels can be measured using PPG signals. To do this, we analyze a largescale dataset from VitalDB, which includes information from 6,388 subjects. By including diverse patient profiles in our dataset, we ensure that our models can be applied to a wide range of individuals. To capture the complex patterns and relationships in PPG signals, we employ a complicated deep learning approach based on convolutional neural networks (CNNs). Our models have achieved a mean absolute error (MAE) of 23.99, demonstrating their accuracy and reliability. Furthermore, we evaluate the clinical accuracy of the predictions using the Clarke Error Grid analysis, which reveals a high agreement within clinically acceptable ranges. By utilizing the comprehensive VitalDB dataset, our study contributes to a deeper understanding of PPG-based blood glucose prediction and validates the effectiveness of our models on a large-scale cohort. Additionally, we perform a comparison with recent papers, demonstrating why our model excels in various aspects such as model generality with a large dataset, high clinical accuracy, and the requirement of only 10 seconds of PPG data for effective prediction. The results highlight the potential of PPG signals as a valuable tool for non-invasive blood glucose monitoring.
Robotics has been hailed as a solution for repetitive, tedious, and dangerous physical activities. One such activity which has a need for robotics is Vehicle Refuelling. Automatic Vehicle Refuelling systems have been ...
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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 high-speed classifiers that are demonstrated for hand...
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This paper proposes an algorithm to optimize the walking of humanoid robots based on the inverse kinematic model combined with a Genetic Algorithm. The objectives are to improve the sagittal displacement of the robot ...
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ISBN:
(数字)9798350373974
ISBN:
(纸本)9798350373981
This paper proposes an algorithm to optimize the walking of humanoid robots based on the inverse kinematic model combined with a Genetic Algorithm. The objectives are to improve the sagittal displacement of the robot and reduce possible lateral deviations during a predetermined path. The foot of the humanoid performs a tapered motion, an approximate ellipse. Horizontal and vertical speeds and the angulation of the humanoid trunk are the input parameters of the algorithm. The algorithm utilizes the input information to calculate the inverse kinematics, and then it submits the obtained result to an evaluation function. We develop a virtual simulator and a robotic platform with 14 degrees of freedom to validate the proposed algorithm. We then test a prototype using the best result obtained in the simulations.
We report the experimental discovery of the topological quadratic-node semimetal in a photonic microring lattice with a robust second-order nodal point at the Brillouin zone center and two Dirac points at the Brilloui...
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ISBN:
(纸本)9781957171258
We report the experimental discovery of the topological quadratic-node semimetal in a photonic microring lattice with a robust second-order nodal point at the Brillouin zone center and two Dirac points at the Brillouin zone boundary.
In 2015, the Ministry of Energy created a wholesale electricity market in Mexico. As a result, the country shifted from a vertically integrated regulation to a competitive market framework. In this context, the day-ah...
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The rapid development of cognitive cities, driven by IoT and AI technologies, introduces unique cybersecurity challenges aggravated by human-centric vulnerabilities. The objective of this paper is to examine the role ...
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The rapid development of cognitive cities, driven by IoT and AI technologies, introduces unique cybersecurity challenges aggravated by human-centric vulnerabilities. The objective of this paper is to examine the role of human factors in the cybersecurity landscape of both smart and cognitive cities. Through a literature review, we investigate how human interactions with digital infrastructures impact security and privacy. We highlight the need to address human errors, social engineering attacks, and insider threats while distinguishing how differences in infrastructure, intelligence, and citizen roles between smart and cognitive cities shape the management of human-centric vulnerabilities. Our review of existing research on integrating human behavioral data into AI systems suggests significant threat detection and response improvements. The findings suggest that human-centric AI detection tools not only mitigate risks of human errors and latent failures but also significantly improve protection for vulnerable populations, thereby promoting stronger human-AI collaboration. Ultimately, this paper advocates for advancing these tools to bolster urban cybersecurity frameworks.
Hand Gesture Recognition (HGR) is a form of perceptual computing with applications in human-machine interaction, virtual/augmented reality, and human behavior analysis. Within the HGR domain, several frameworks have b...
Hand Gesture Recognition (HGR) is a form of perceptual computing with applications in human-machine interaction, virtual/augmented reality, and human behavior analysis. Within the HGR domain, several frameworks have been developed with different combinations of input modalities and network architectures with varying levels of efficacy. Such frameworks maximized performance at the expense of increased hardware and computational requirements. These drawbacks can be tackled by transforming the relatively complex dynamic hand gesture recognition task into a simpler image classification task. This paper presents a skeleton-based HGR framework that implements data-level fusion for encoding spatiotemporal information from dynamic gestures into static representational images. Said static images are subsequently processed by a custom, end-to-end trainable multi-stream CNN architecture for gesture classification. Our framework reduces the hardware and computational requirements of the HGR task while remaining competitive with the state-of-the-art on the CNR, FPHA, LMDHG, SHREC2017, and DHG142S benchmark datasets. We demonstrated the practical utility of our framework by creating a lightweight real-time application that makes use of skeleton data extracted from RGB video streams captured by a standard inbuilt PC webcam. The application operates successfully with minimal CPU and RAM footprint while achieving 93.46% classification accuracy with approximately 2s latency at 15 frames per second.
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