检索结果-内蒙古大学图书馆

2024 IEEE Nanotechnology Materials and Devices Conference, NMDC 2024

作者： Uchayash, Sajid Que, Sunney Rubby, Md Fazlay Que, Long Iowa State University Department of Electrical and Computer Engineering AmesIA50011 United States Ames High School and Worked Partly on This Project NSF Outreach Program at ISU United States

ISBN: (纸本)9798331541439

This paper reports on the development of nanostructure-electrode-based electrochemical sensors for detecting lactate dehydrogenase (LDH). Using an ultrathin nanopore template, gold nanostructured electrodes were fabricated. An on-chip specific chemical surface functionalization process was developed for the sensors for detecting LDH for the first time. Experiments showed the sensitivity has been improved, compared to the electrochemical sensors with plain electrodes. It has been found that at least 5U/mL (61 nM) of LDH can be readily detected. © 2024 IEEE.

关键词： Nanopores

来源：评论

学校读者我要写书评

暂无评论

Camera-based Motion Correction for PET/MR Brain Imaging

Camera-based Motion Correction for PET/MR Brain Imaging

引用

2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Fisher, Jonathan Groll, Andrew Kim, Chris C. Innes, Derek Levin, Craig S. Stanford University Department of Electrical Engineering CA United States Molecular Imaging Program Stanford University Department of Radiology CA United States Stanford University Department of Computer Science CA United States Stanford University Physics Electrical Engineering and Bioengineering Department of Radiology CA United States

ISBN: (纸本)9781665488723

In this work, we evaluated the performance of a camera-based rigid body motion correction solution in PET studies. We compared the image quality obtained by reconstructing a static phantom scan to those obtained by reconstructing a moving phantom scan with and without applying motion correction. Our suggested solution uses a camera to track a 3D marker attached to the object to be imaged (in this work, a resolution phantom). This data was incorporated in the multiple acquisition frames (MAF) image reconstruction algorithm to compensate for motion. While we got multiple spatially shifted replicas of the resolution phantom in the motion study's reconstructed image when motion correction was not applied, the motion-corrected image was comparable to the static image, with some degradation in spatial resolution (3.2-4.2 vs. 2.8-3.2 mm) and lower peak to valley ratio (indicating lower contrast to noise ratio). The context of the results presented is in PET/MR imaging, but in principle, this motion compensation method could be used in any type of medical imaging modality. © 2022 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A CT-based Prediction System for Determining Respiratory Support Level in COVID-19 Patients

A CT-based Prediction System for Determining Respiratory Sup...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sharafeldeen, Ahmed Balaha, Hossam Magdy Farahat, Ibrahim Shawky Ghazal, Mohammed Connelly, James van Bogaert, Eric El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Department of Computer Science Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Radiology Faculty of Medicine University of Louisville LouisvilleKY United States

ISBN: (纸本)9798350368741

A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system's performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen's kappa, and accuracy, demonstrating impressive results. Specifically, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems. © 2025 IEEE.

关键词： computer-Aided Diagnosis (CAD) GLCM GLRLM Machine Learning (ML) Neural Network (NN) SFM

来源：评论

学校读者我要写书评

暂无评论

Automatic Cataract and Cholesterol Detection System Based on Recurrent Neural Network (RNN): A Comparison with Convolutional Neural Network (CNN) and Siamese Neural Network (SNN) 1

引用

19th International Conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Lie, Zener S. Widodo, Jatmiko A. F. Hanesi, Isabel K. P. Moningka, Matthew R. N. Mariana, Y. Astuti, Winda Akmeliawati, Rini Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Product Design program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Robotics and Automation Research Group School of Electrical and Mechanical Engineering The University of Adelaide North Terrace Campus Adelaide Australia

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

In Indonesia, the majority of blindness cases, approximately 81%, are attributed to cataract. Additionally, 35% of the population has higher cholesterol levels than the standard normal range. The lack of specialized medical equipment, such as the slit-lamp (Ruparelia in Dalhousie Med J 49(1), [1]) bio-microscope, and trained professionals, especially in rural areas, make it difficult to detect these conditions through eye examinations. To address this issue, a supervised machine learning method, the Recurrent Neural Network (RNN), has been utilized to create an iris scanner system that can identify cataract and high cholesterol in a patient. This system will classify eyes into three categories, i.e., cataract, high cholesterol, and normal iris. The computer simulations indicate that this technique is more accurate and requires less training time compared to the existing Convolutional Neural Network (ANN) and Siamese Neural Network (SNN)-based method, while achieving 100% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

To image,or not to image:class-specific diffractive cameras with all-optical erasure of undesired objects

引用

eLight 2022年第1期2卷 165-184页

作者： Bijie Bai Yi Luo Tianyi Gan Jingtian Hu Yuhang Li Yifan Zhao Deniz Mengu Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos Angeles 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCAUSA

Privacy protection is a growing concern in the digital era,with machine vision techniques widely used throughout public and private *** methods address this growing problem by,e.g.,encrypting camera images or obscuring/blurring the imaged information through digital ***,we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of *** diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input *** their fabrication,the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes,while instantaneously erasing objects of the other data classes at the output *** the same framework,we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras,where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption,while the other classes of objects are irreversibly erased from the output *** success of class-specific diffractive cameras was experimentally demonstrated using terahertz(THz)waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset,all-optically erasing the other handwritten *** diffractive camera design can be scaled to different parts of the electromagnetic spectrum,including,e.g.,the visible and infrared wavelengths,to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.

关键词： optical image specific

来源：评论

学校读者我要写书评

暂无评论

Design Concept of a Wearable Device for Sleep Related Brain Wave Detection and Stimulation 4

Design Concept of a Wearable Device for Sleep Related Brain ...

引用

4th IEEE International Conference on Data Science and computer Application, ICDSCA 2024

作者： Yin, Ariel R. Zhang, David Z. Mao, David D. Li, Sichuang Cheng, Haoliang Deng, Yangfan Guo, Yifan Mao, Helen X. Yin, Jijun Mao, Zhi-Hong Canyon Crest Academy San DiegoCA United States University High School IrvineCA United States North Allegheny Intermediate High School PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Computer Science University of Pittsburgh PittsburghPA United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Yale College Yale University New HavenCT United States Departments of Electrical and Computer Engineering and Bioengineering University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9798350368239

This paper presents a design concept of an innovative wearable device for brain wave detection and stimulation to aid sleep. Sleep’s importance for a healthy life emphasizes the value of studying the application of technology to improve sleep quality for individuals unable to receive a good night’s rest. Utilizing electroencephalography (EEG) for accurate sleep tracking, the device aims to be portable and efficient. It includes the EEG sensing and processing module, adaptive control and machine learning module, and electronic stimulation module, working together to guide the brain toward relaxation. Preliminary results demonstrate promising performances or necessities of the subsystems of the proposed design. ©2024 IEEE.

关键词： Sleep research

来源：评论

学校读者我要写书评

暂无评论

Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting 38

Introducing Spectral Attention for Long-Range Dependency in ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kang, Bong Gyun Lee, Dongjun Kim, HyunGi Chung, DoHyun Yoon, Sungroh Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Future Automotive Mobility Seoul National University Korea Republic of

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Human-Inspired Control of a Whip: Preparatory Movements Improve Hitting a Target

Human-Inspired Control of a Whip: Preparatory Movements Impr...

引用

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Mahdiar Edraki Rakshith Lokesh Aleksei Krotov Alireza Ramezani Dagmar Sternad Department of Mechanical and Industrial Engineering Department of Biology Department of Bioengineering Department of Electrical and Computer Engineering Department of Physics Northeastern University Boston MA USA

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Manipulating flexible and underactuated objects, such as a whip, remains a significant challenge in robotics. Remarkably, humans can skillfully manipulate such objects to achieve tasks, ranging from hitting distant targets to snapping a stick in someone's hand with the tip of a whip. This study explored this problem by constructing and modeling a 25-degree-of-freedom whip. Our goal was to investigate the strategies employed by humans when using a whip to strike a target. To that end, a human-inspired controller was devised that emulated two observed movement strategies: “striking only” and “preparing and striking”. While the latter strategy involved a more intricate and parameter-intensive trajectory definition, our findings revealed that the more complex “preparing and striking” approach enabled the whip to reach targets at greater distances. The outcomes of this study provided first insights into preparatory movements that humans employ when manipulating objects. By directly bridging between human and robot studies, we show how insights into human movements may inform effective robot control strategies for the manipulation of underactuated objects.

关键词： Robot control Biomechatronics Distance measurement Trajectory Robots

来源：评论

学校读者我要写书评

暂无评论

Role of Consumption Pattern in Optimal Allocation of Distributed Generators in Electric Power and Energy Systems

Role of Consumption Pattern in Optimal Allocation of Distrib...

引用

2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Mahdavi, Meisam Jurado, Francisco Schmitt, Konrad Chamana, Manohar Bayne, Stephen University of Jaén Linares Department of Electrical Engineering Jaén Spain Texas Tech University Electrical & Computer Engineering Department LubbockTX United States Texas Tech University Renewable Energy Program LubbockTX United States

ISBN: (纸本)9781665490719

The placement of distributed generation (DG) units in power systems is an efficient way for energy loss reduction, especially when the penetration of DG in modern systems is growing due to their impacts on environmental sustainability. On the other hand, load variations and methods of electricity consumption affect energy losses amount. Therefore, power demand variations have an essential role in the determination of energy loss amount and optimal generation of DG. However, considering the variability of load level in the DG allocation problem increases the burden and computational time, and neglecting it causes the energy losses to be calculated inaccurately. Therefore, this paper aims to evaluate the effect of load patterns on renewable DG allocation plans in order to find out the importance of considering load variations in energy loss minimization via DG placement. The analysis has been conducted on 7-, 12-, 16-, 28-, 30-, 33-, 59-, 69-, 70-, 84-, and 119-bus distribution systems by a classic optimization tool named AMPL. © 2023 IEEE.

关键词： Distributed power generation

来源：评论

学校读者我要写书评

暂无评论

Harnessing Vision Transformers for Precise and Explainable Breast Cancer Diagnosis 27th

Harnessing Vision Transformers for Precise and Explainable B...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Balaha, Hossam Magdy Ali, Khadiga M. Gondim, Dibson Ghazal, Mohammed El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Pathology Department Faculty of Medicine Mansoura University Mansoura Egypt Department of Pathology and Laboratory Medicine University of Louisville LouisvilleKY United States Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781940

Breast cancer (BC) remains a significant global health challenge, impacting millions of lives annually. Traditional histopathological analysis, while essential, can be subjective and time-consuming, potentially leading to diagnostic inaccuracies. This study proposes a novel computer-Aided Diagnosis (CAD) framework utilizing Vision Transformers (ViTs) for BC diagnosis from histopathology slides. ViTs excel in capturing global dependencies within images, offering enhanced diagnostic accuracy compared to conventional methods. The framework integrates ViTs with advanced decision-making techniques like 2-tier majority fusion and SHapley Additive exPlanations (SHAP) for improved interpretability. Experimental results on a dataset of post-neoadjuvant therapy breast cancer samples demonstrate the efficacy of the proposed approach, achieving high performance metrics and providing insights into model predictions. The proposed approach achieves state-of-the-art performance with an accuracy exceeding 97% surpassing existing methods both on the utilized dataset and an external benchmark, specifically the Breast Cancer Histopathological Database (BreakHis). Time complexity analysis suggests that the proposed framework offers computational efficiency, with the dominant factors influencing overall complexity being the number of patches, sequence length, and number of layers in the ViT model. This study contributes a robust methodology towards enhancing BC diagnostic precision and efficiency through cutting-edge AI technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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：