The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of...
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In this paper, a novel on–off linear quadratic regulator (LQR) control for satellite rendezvous as an example of linear systems with on–off inputs has been proposed for the first time. It simultaneously benefits fro...
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A novel e-Gear selector single-electrode-based triboelectric nanogenerator (TENG) was successfully designed, fabricated, and tested on flexible substrates. The proposed device consists of four TENG sensors representin...
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Federated Learning (FL), an emerging distributed Artificial Intelligence (AI) technique, is susceptible to jamming attacks during the wireless transmission of trained models. In this letter, we introduce a jamming att...
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The global health crisis caused by the COVID-19 pandemic has brought new challenges to speaker identification systems, particularly due to the acoustic alterations caused by the widespread use of face masks. Aiming to...
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Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcia...
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Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcial intelligence models predicting short-term and long-term health outcomes forpatients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solutionfor generating realistic and useful synthetic data that can be used to train such models. Methods: Despitetheir promise, GANs have not been widely adopted in the congenital heart disease research communitydue, in some part, to a lack of knowledge on how to employ them. In this research study, a GAN was usedto generate synthetic data from the Pediatric Heart Network Fontan I dataset. A subset of data consistingof the echocardiographic and BNP measures collected from Fontan patients was used to train the *** sets of synthetic data were created to understand the effect of data missingness on synthetic datageneration. Synthetic data was created from real data in which the missing values were imputed usingMultiple Imputation by Chained Equations (MICE) (referred to as synthetic from imputed real samples). Inaddition, synthetic data was created from real data in which the missing values were dropped (referred to assynthetic from dropped real samples). Both synthetic datasets were evaluated for ffdelity by using visualmethods which involved comparing histograms and principal component analysis (PCA) plots. Fidelitywas measured quantitatively by (1) comparing synthetic and real data using the Kolmogorov-Smirnovtest to evaluate the similarity between two distributions and (2) training a neural network to distinguishbetween real and synthetic samples. Both synthetic datasets were evaluated for utility by training aneural network with synthetic data and testing the neural network on its ability to classify patients thathave ventricular dysfunction using echocardiograph measures an
This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease ...
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This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen
In this paper, an advanced algorithm is presented that utilizes artificial neural networks (ANN) for estimating the inertia of synchronous generators (SGs). The algorithm is enhanced by integrating a modified equal ar...
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This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnos...
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This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.
The ultra-dense network (UDN) concept with multi-connectivity (MC) has emerged as a promising scenario for millimeter-wave (mmWave) communications due to its synergistic effect. However, mmWave UDNs with MC face chall...
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The ultra-dense network (UDN) concept with multi-connectivity (MC) has emerged as a promising scenario for millimeter-wave (mmWave) communications due to its synergistic effect. However, mmWave UDNs with MC face challenges such as escalated energy consumption and computational complexity for traffic splitting decisions. To save energy and reduce the need for frequent traffic splitting decisions in the considered scenario, we formulate the problem as a mixed integer programming (MIP). Our objective is to determine which base stations (BSs) to enable for energy saving and how to split user traffic to maintain robust communication in MC enabled UDNs. To this end, this paper proposes energy-efficient and robust communication (), an ensemble of heuristic methods and convex optimization, to achieve our goal without directly solving the computationally expensive MIP. In addition, we consider greedy algorithm that potentially offers enhanced optimality. We analyze the complexity of each scheme and demonstrate that is the most viable option considering both performance and complexity. Through extensive simulations, we demonstrate that achieves over 98% optimality for small-sized networks. More importantly, we evaluate in realistic scenarios using a 3-D ray-tracing simulator and confirm that performs very well even in urban terrain environments with buildings. IEEE
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