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A modular microfluidic system based on a multilayered configuration to generate large-scale perfusable microvascular networks

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Microsystems & Nanoengineering 2021年第1期7卷 63-75页

作者： Tao Yue Da Zhao Duc T.T.Phan Xiaolin Wang Joshua Jonghyun Park Zayn Biviji Christopher C.W.Hughes Abraham P.Lee Department of Biomedical Engineering University of CaliforniaIrvineCAUSA School of Mechatronic Engineering and Automation Shanghai UniversityShanghaiChina Department of Molecular Biology and Biochemistry University of CaliforniaIrvineCAUSA Department of Micro/Nano Electronics Shanghai Jiao Tong UniversityShanghaiChina National Key Laboratory of Science and Technology for Micro/Nano Fabrication Shanghai Jiao Tong UniversityShanghaiChina Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Shanghai Jiao Tong UniversityShanghaiChina Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCAUSA. Department of Applied Mathematics-Biology Brown UniversityProvidenceRIUSA. Department of Mechanical and Aerospace Engineering University of CaliforniaIrvineCAUSA

The vascular network of the circulatory system plays a vital role in maintaining homeostasis in the human *** this paper,a novel modular microfluidic system with a vertical two-layered configuration is developed to generate largescale perfused microvascular networks in *** two-layer polydimethylsiloxane(PDMS)configuration allows the tissue chambers and medium channels not only to be designed and fabricated independently but also to be aligned and bonded *** method can produce a modular microfluidic system that has high flexibility and scalability to design an integrated platform with multiple perfused vascularized tissues with high *** medium channel was designed with a rhombic shape and fabricated to be semiclosed to form a capillary burst valve in the vertical direction,serving as the interface between the medium channels and tissue *** and anastomosis at the vertical interface were successfully achieved by using different combinations of tissue chambers and medium *** large-scale microvascular networks were generated and quantified in terms of vessel length and *** leakage of the perfused 70-kDa FITC-dextran confirmed the lumenization of the microvascular networks and the formation of tight vertical interconnections between the microvascular networks and medium channels in different structural *** platform enables the culturing of interconnected,large-scale perfused vascularized tissue networks with high density and scalability for a wide range of multiorgan-on-a-chip applications,including basic biological studies and drug screening.

关键词： interconnection networks configuration

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Impact of Techno-Economic Parameters on Optimal Sizing of EV Fast Charging Stations

Impact of Techno-Economic Parameters on Optimal Sizing of EV...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

As the adoption for electric vehicles (EV) increases, the need for proper charging infrastructure also increases. As such, it is necessary to ensure proper charging infrastructure to meet the charging demand. Hence, studies concerning the planning of EV charging stations which include the sizing of distributed energy resources (DERs) in the station to reduce the peak demand on the grid. In this paper, EV charging stations were designed keeping in mind factors such as the EV charging load profile (on demand charging versus deferred charging), uncertainties in the load profile as well as the tariff rates. A sensitivity analysis was also carried out in order to examine the impact of several techno-economic parameters on the overall cost of the system.

关键词： Vehicle-to-grid Costs Uncertainty Sensitivity analysis Tariffs Vehicular ad hoc networks Transportation

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GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale 3D Scene Reconstruction

arXiv

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arXiv 2024年

作者： Zhang, Hanyue Yang, Zhiliu Zuo, Xinhe Tong, Yuxin Long, Ying Liu, Chen School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Yunnan Kunming650500 China Department of Electrical and Computer Engineering Clarkson University PotsdamNY13699 United States

This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced by existing methods. For tackling the scalability issue, we split the large scene into multiple cells, and the candidate point-cloud and camera views of each cell are correlated through a visibility-based camera selection and a progressive point-cloud extension. To reinforce the rendering quality, three highlighted improvements are made in comparison with vanilla 3DGS, which are a strategy of the ray-Gaussian intersection and the novel Gaussians density control for learning efficiency, an appearance decoupling module based on ConvKAN network to solve uneven lighting conditions in large-scale scenes, and a refined final loss with the color loss, the depth distortion loss, and the normal consistency loss. Finally, the seamless stitching procedure is executed to merge the individual Gaussian radiance field for novel view synthesis across different cells. Evaluation of Mill19, Urban3D, and MatrixCity datasets shows that our method consistently generates more high-fidelity rendering results than state-of-the-art methods of large-scale scene reconstruction. We further validate the generalizability of the proposed approach by rendering on self-collected video clips recorded by a commercial drone. © 2024, CC BY.

关键词： 3D reconstruction

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Development of a Near-Field Sensor for Diagnosis of Liver Diseases Using 3D-EM Simulation of Mouse Model

Development of a Near-Field Sensor for Diagnosis of Liver Di...

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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Poulami Samaddar Sunil Gaddam Md Mirazur Rahman Shuvashis Dey Sayan Roy Dipankar Mitra Shivaram P. Arunachalam Department of Medicine Mayo Clinic Division of Gastroenterology and Hepatology Microwave Engineering & Imaging Laboratory Rochester MN USA Department of Electrical and Computer Engineering North Dakota State University Fargo ND USA Division of Energy Matter and Systems School of Science and Engineering University of Missouri-Kansas City Kansas City MO USA Department of Computer Science & Computer Engineering University of Wisconsin-La Crosse La Crosse WI USA

In this work, we studied the response of an antipodal Vivaldi antenna in the presence of different types of mice liver models. The scope of the study was limited to two types of liver diseases, i.e., nonalcoholic steatohepatitis (NASH) and fibrosis. The complex dielectric properties of each liver type were measured using the open-ended coaxial probe dielectric spectroscopy method. A Vivaldi antenna for near-field sensing was designed and then simulated in CST Studio to determine the antenna characteristics. The antenna performance from the simulation matched the reflection coefficients (S11) measurements collected with the fabricated antenna in an anechoic chamber. This verified antenna was used for further measurements of different liver models placed near the antenna. The antenna response, S11 measurements, varied from one tissue type to another, demonstrating the potential of using the Vivaldi antenna as a near-field sensor to differentiate liver disease models from the healthy liver model.

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LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising

arXiv

引用

arXiv 2023年

作者： Wang, Dayang Xu, Yongshun Han, Shuo Wu, Zhan Zhou, Li Morovati, Bahareh Yu, Hengyong The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The Laboratory of Image Science and Technology Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In the fields of computer vision and natural language processing, masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers, due to their exceptional feature representation ability. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE’s robustness and generability across a variety of noise levels. Experiments results show that the proposed LoMAE can enhance the transformer’s denoising performance and greatly relieve the dependence on the ground truth clean data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels. © 2023, CC BY.

关键词： Image quality

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Fiber-optic based wearables for the continuous monitoring of respiratory rate 2

Fiber-optic based wearables for the continuous monitoring of...

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Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables II 2021

作者： Issatayeva, Aizhan Beisenova, Aidana Tosi, Daniele Molardi, Carlo Nazarbayev University Department of Computer and Electrical Engineering Kabanbay batyr 53 Nur-Sultan010000 Kazakhstan Laboratory of Biosensors and Bioinstruments National Laboratory of Astana Kabanbay batyr 53 Nur-Sultan010000 Kazakhstan

ISBN: (纸本)9781510641112

Fiber optic sensor technology is considered as a promising candidate for the monitoring of biophysical parameters due to its sensitivity to strain and temperature, immunity to electromagnetic interference, lightweight and opportunity to be mounted on the textiles. Therefore, this paper suggests using one of the fiber optic technologies, namely Fiber Bragg Grating (FBG) sensors, for the respiratory rate monitoring of the hospital patients or sportsmen. Two FBG arrays consisting of 5 sensors each have been sewed to two elastic belts. The belts have been put on volunteer‘s chest and abdomen because the expansion of those regions causes the elastic belts to elongate and, as a result, FBG arrays experience strain changes. The use of ten FBGs located in different regions is required to use the diversity technique, which allows increasing the accuracy of the result by combining output from multiple sensing points. Therefore, a special algorithm has been used to analyze the strain pattern detected by all ten FBGs and to reconstruct the respiratory rate based on them. The obtained breathing rate of the volunteer has been compared with the reference rate measured by a mobile application. Several sets of experiments have been performed in order to identify the influence of the volunteer‘s position and speed of breathing on the accuracy of the measurements. © 2021 SPIE.

关键词： Fiber optic sensors

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Cost-Sensitive Neural Network for Prediction of Hypertension Using Class Imbalance Dataset 1

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Davagdorj, Khishigsuren Lee, Jong Seol Park, Kwang Ho Ryu, Keun Ho Database Bioinformatics Laboratory College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (数字)9789813367579

ISBN: (纸本)9789813367562

Hypertension is a serious medical condition that significantly increases the risk of chronic diseases. Early detection of individuals at risk for hypertension allows to prevent and delay the incidence of related diseases and strokes. In recent years, numerous researches have been focused on the decision support system for predicting hypertension. However, the class imbalance has commonly occurred problem in real-world applications. In this paper, we present the end-to-end cost-sensitive neural network (COST-NN) framework incorporated with a weighted random forest-based feature selection technique to predict hypertension among Korean adults. First, it identifies the best representative features using a weighted random forest-based feature selection technique. Then, we apply the COST-NN for predicting target among hypertensive and non-hypertensive individuals. In order to identify the accurate predictive model, we compare the various baseline models. Experimental results showed that COST-NN outperforms the regular state-of-the-art baseline models. In addition, the presented framework is expected to apply not only hypertension but also can support to prevent the patients from the risk of various diseases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Decision trees

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Iterative Gaussian-Laplacian Pyramid Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-22页

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming Information and Technology College Dalian116026 China University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan University of Maryland School of Medicine R Adams Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Gaussian pyramid (GP) is a commonly used image coding technique that encodes an image as a pyramid that is stacked by a set of images with Gaussian window-reduced sizes and multiple spatial resolutions. Associated with GP, a Laplacian pyramid (LP) can be also constructed to represent differential images between images in two consecutive layers of GP. Such resulting Gaussian-Laplacian pyramid (GLP) performs data compression in a lossless and lossy manner. A convolutional neural network (CNN) consists of a series of layers concatenated in a feedforward manner where each layer has a convolutional sublayer (CL) and a pooling sublayer (PL). Interestingly, each layer implemented by CL and PL in a CNN can be realized by a single layer in GP in the sense that CL and PL can be carried out by a low-pass Gaussian filter operated as a Gaussian kernel in a single layer of GP. This article develops a new approach to hyperspectral image classification (HSIC), called the GLP network (GLPN), which uses not only GP to realize CNN but also LP to capture differential information between two consecutive layers that CNN cannot. Furthermore, by incorporating an iterative process into GLPN, we can derive an iterative GLPN (IGLPN) that can be considered a companion of a recently developed iterative random training sampling CNN (IRTS-CNN) by replacing CNN with GLPN. Since GLPN can realize CNN in a better way, it is expected that IGLPN will perform better than IRTS-CNN and also significantly reduce computational efficiency compared to IRTS-CNN. © 1980-2012 IEEE.

关键词： Iterative methods

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

arXiv

引用

arXiv 2024年

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore CountyMD United States

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as non-negative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. Copyright © 2024, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

arXiv

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arXiv 2024年

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Theoretical Division Los Alamos National Laboratory Los Alamos United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore CountyMD United States

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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