检索结果-内蒙古大学图书馆

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Miranda Baldivieso, Thiago João da Silva Batista, Taise Grazielle Chagas, Fabio Suim Rodrigues Velho, Luiz Carlos Pacheco Ferreira Rosa, Paulo Fernando Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea RJ Rio de Janeiro Brazil Department of Science and Industry systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781665464543

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software-both commercial and open-source-and concluding with a qualitative evaluation of the results. © 2024 IEEE.

关键词： Aerial photography

来源：评论

学校读者我要写书评

暂无评论

NeuPh:scalable and generalizable neural phase retrieval with local conditional neural fields

引用

Advanced Photonics Nexus 2024年第5期3卷 67-76页

作者： Hao Wang Jiabei Zhu Yunzhe Li Qianwan Yang Lei Tian Boston University Department of Electrical and Computer EngineeringBostonMassachusettsUnited States Boston University Department of Biomedical EngineeringBostonMassachusettsUnited States

Deep learning has transformed computational imaging,but traditional pixel-based representations limit their ability to capture continuous multiscale object *** this gap,we introduce a local conditional neural field(LCNF)framework,which leverages a continuous neural representation to provide flexible object ***’s unique capabilities are demonstrated in solving the highly ill-posed phase retrieval problem of multiplexed Fourier ptychographic *** network,termed neural phase retrieval(NeuPh),enables continuous-domain resolution-enhanced phase reconstruction,offering scalability,robustness,accuracy,and generalizability that outperform existing *** integrates a local conditional neural representation and a coordinate-based training *** show that NeuPh can accurately reconstruct high-resolution phase images from low-resolution intensity ***,NeuPh consistently applies continuous object priors and effectively eliminates various phase artifacts,demonstrating robustness even when trained on imperfect ***,NeuPh improves accuracy and generalization compared with existing deep learning *** further investigate a hybrid training strategy combining both experimental and simulated datasets,elucidating the impact of domain shift between experiment and *** work underscores the potential of the LCNF framework in solving complex large-scale inverse problems,opening up new possibilities for deep-learning-based imaging techniques.

关键词： neural representation phase retrieval computational imaging deep learning computational microscopy

来源：评论

学校读者我要写书评

暂无评论

Visual Scene Understanding for Enhanced EMG Gesture Recognition 46

Visual Scene Understanding for Enhanced EMG Gesture Recognit...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chamberland, Felix Labbé, Thomas Tam, Simon Scheme, Erik Gosselin, Benoit Université Laval Department of Electrical and Computer Engineering QuébecQCG1V 0A6 Canada University of New Brunswick Institute of Biomedical Engineering FrederictonNBE3B 5A3 Canada

ISBN: (纸本)9798350371499

This work presents a multimodal approach combining electromyography (EMG) and computer vision (CV) for robust real-time gesture recognition in a real-world setting. A context-aware framework is proposed for myoelectric prosthesis control, wherein EMG hand gesture recognition is augmented by the visual detection of objects of interest in a scene, effectively mitigating risks of false movements. By supporting EMG gesture predictions produced by a Siamese deep convolution neural network (SDCNN) with context derived from object detection using a tailored YOLO computer vision model, the system prevents false detection during gesture onset and during static gesture maintenance. In a pilot experiment, this multimodal sensor fusion is shown to effectively augment human volitional control by enhancing both the robustness of the gesture control interface and the user's ability to maintain full command over it. © 2024 IEEE.

关键词： Electromyography

来源：评论

学校读者我要写书评

暂无评论

Capturing Physiological Correlates of Stress-Induced Blood Pressure Elevation Using a Multimodal Wearable Sensing Patch in Patients with a History of Myocardial Infarction 46

Capturing Physiological Correlates of Stress-Induced Blood P...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Nawar, Afra Sanchez-Perez, Jesus Antonio Rahman, Farhan N. Gazi, Asim H. Chan, Michael Ziegler, Carrie Daaboul, Obada Haddad, George Al-Abboud, Omar A. Ahmed, Hashir Murrah, Nancy Vaccarino, Viola Shah, Amit J. Inan, Omer T. Emory University Rollins School of Public Health Department of Epidemiology AtlantaGA United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States Georgia Institute of Technology School of Biomedical Engineering AtlantaGA United States

ISBN: (纸本)9798350371499

Persons who have experienced a prior myocardial infarction (MI) are at a greater risk of experiencing a secondary event. External factors, such as stress, can cause transient hypertension, further increasing cardiovascular disease risk. However, challenges to monitoring stress-induced hypertension include obtrusiveness of monitoring devices, unknown validity in high-risk populations, and unsubstantiated sensitivity in stressful environments. In this pilot study, we investigated data from a validated, multimodal, wearable patch to examine physiological correlates of laboratory-based hypertensive stress responses. The device collected electrocardiogram, seismocardiogram, and photoplethysmogram signals from 35 participants (26 post-MI and 9 healthy participants) during a protocol involving a public speaking stressor. We calculated the change from rest to stress in 10 features extracted from these signals and assessed their correlation to stress-induced changes in mean arterial pressure (MAP) derived from a validated brachial pressure cuff. MAP changes correlated significantly with changes in heart rate (p © 2024 IEEE.

关键词： Blood pressure

来源：评论

学校读者我要写书评

暂无评论

Difference Autocorrelation: A Novel Approach to Estimate Shear Wave Speed in the Presence of Compression Waves

arXiv

引用

arXiv 2024年

作者： Asemani, Hamidreza Rolland, Jannick P. Parker, Kevin J. The Institute of Optics The Department of Electrical and Computer Engineering University of Rochester RochesterNY United States The Institute of Optics The Department of Biomedical Engineering University of Rochester RochesterNY United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Rochester RochesterNY United States

In share wave elastography (SWE), the aim is to measure the velocity of shear waves, however unwanted compression waves and bulk tissue motion pose challenges in evaluating tissue stiffness. Conventional approaches often struggle to discriminate between shear and compression waves, leading to inaccurate shear wave speed (SWS) estimation. In this study, we propose a novel approach known as the difference autocorrelation estimator to accurately estimate reverberant SWS in the presence of compression waves and noise. Methods: The difference autocorrelation estimator, unlike conventional techniques, computes the subtraction of velocity between neighboring particles, effectively minimizing the impact of long wavelength compression waves and other wide-area movements such as those caused by respiration. We evaluated the effectiveness of the integrated difference autocorrelation (IDA) by: (1) using k-Wave simulations of a branching cylinder in a soft background, (2) using ultrasound elastography on a breast phantom, (3) using ultrasound elastography in the human liver-kidney region, and (4) using magnetic resonance elastography (MRE) on a brain phantom. Results: By applying IDA on the unfiltered contaminated wave fields of simulation and elastography experiments, the estimated SWSs are in good agreement with the ground truth values (i.e., less than 2% error for the simulation, 9% error for ultrasound elastography of breast phantom and 19% error for MRE). Conclusion: Our results demonstrate that IDA accurately estimates SWS, revealing the existence of a lesion, even in the presence of strong compression waves. Significance: IDA exhibits consistency in SWS estimation across different modalities and excitation scenarios, highlighting its robustness and potential clinical utility. © 2024, CC BY.

关键词： Shear waves

来源：评论

学校读者我要写书评

暂无评论

Noise Reduction in Industry Based on Virtual Instrumentation

引用

computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

来源：评论

学校读者我要写书评

暂无评论

MiSO: Optimizing brain stimulation to create neural population activity states 38

MiSO: Optimizing brain stimulation to create neural populati...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Minai, Yuki Soldado-Magraner, Joana Smith, Matthew A. Yu, Byron M. Neuroscience Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Biomedical Engineering Carnegie Mellon University United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Center for the Neural Basis of Cognition United States

Brain stimulation has the potential to create desired neural population activity states. However, it is challenging to search the large space of stimulation parameters, for example, selecting which subset of electrodes to be used for stimulation. In this scenario, creating a model that maps the configuration of stimulation parameters to the brain's response can be beneficial. Training such an expansive model usually requires more stimulation-response samples than can be collected in a given experimental session. Furthermore, changes in the properties of the recorded activity over time can make it challenging to merge stimulation-response samples across sessions. To address these challenges, we propose MiSO (MicroStimulation Optimization), a closed-loop stimulation framework to drive neural population activity toward specified states by optimizing over a large stimulation parameter space. MiSO consists of three key components: 1) a neural activity alignment method to merge stimulation-response samples across sessions, 2) a statistical model trained on the merged samples to predict the brain's response to untested stimulation parameter configurations, and 3) an online optimization algorithm to adaptively update the stimulation parameter configuration based on the model's predictions. In this study, we implemented MiSO with a factor analysis (FA) based alignment method, a convolutional neural network (CNN), and an epsilon greedy optimization algorithm. We tested MiSO in closed-loop experiments using electrical microstimulation in the prefrontal cortex of a non-human primate. Guided by the CNN predictions, MiSO successfully searched amongst thousands of stimulation parameter configurations to drive the neural population activity toward specified states. More broadly, MiSO increases the clinical viability of neuromodulation technologies by enabling the use of many-fold larger stimulation parameter spaces. © 2024 Neural information processing systems foundation. All right

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

引用

computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

来源：评论

学校读者我要写书评

暂无评论

Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication

Digital Twin-Based User-Centric Edge Continual Learning in I...

引用

IEEE International Conference on Communications (ICC)

作者： Shisheng Hu Jie Gao Xinyu Huang Mushu Li Kaige Qu Conghao Zhou Xuemin Shen Department of Electrical and Computer Engineering University of Waterloo Canada School of Information Technology Carleton University Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by jointly optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.

关键词： Continuing education Performance evaluation Costs Multi-access edge computing Training data Artificial neural networks Integrated sensing and communication

来源：评论

学校读者我要写书评

暂无评论

Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation

引用

IEEE Access 2024年 12卷 36267-36279页

作者： Ngo, Ba Hung Lam, Ba Thinh Nguyen, Thanh Huy Dinh, Quang Vinh Choi, Tae Jong Chonnam National University Graduate School of Data Science Gwangju61186 Korea Republic of University of Science Faculty of Information Technology Ho Chi Minh City700000 Viet Nam National Cheng Kung University Department of Biomedical Engineering Tainan70101 Taiwan Vietnamese-German University School of Electrical Engineering and Computer Science Ho Chi Minh City72000 Viet Nam

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend global information, particularly when trained on extensive datasets. Conversely, the Convolutional Neural Network (CNN) model is beneficial to training with small datasets for retaining essential local information. Inspired by these properties of ViT and CNN models, we introduce a hybrid framework that smoothly increases the cross-domain generalization in Semi-supervised Domain Adaptation (SSDA). To achieve this goal, we first train the ViT model on abundant labeled source data, while the CNN model is trained on a few labeled target samples. Then, these models dynamically exchange their knowledge for potential generalization to unlabeled target data via the proposed method, named Dual Dynamic Consistency Regularization (D2CR). Specifically, the ViT model provides its pseudo labels to update the global perspective for the CNN model. Similarly, the CNN model offers pseudo labels to complement the local perspective for the ViT model. The previous methods use a fixed threshold algorithm for the pseudo-labeling process. However, we utilize the dynamic threshold strategy to create pseudo labels for the bi-directional consistency regularization learning between the ViT and CNN models. We verify our approach across several SSDA benchmark datasets. The outstanding experimental results provide strong evidence of the effectiveness and superiority of our approach over previous state-of-the-art SSDA methods. © 2013 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：