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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tingting Fu Ashirbad Pradhan Jiayuan He Chaoming He Ning Jiang School of Mechanical Engineering Southwest Jiaotong University Chengdu China Department of Systems Design Engineering Engineering Bionics Lab Faculty of Engineering University of Waterloo Waterloo Canada National Clinical Research Center for Geriatrics West China Hospital Sichuan University China Med-X Center for Manufacturing Sichuan University Chengdu China

Recently, electromyography (EMG) has been established as a promising new biometric trait that provides a unique dual mode security: biometrics and knowledge. For authentication that is used daily and long-term by general consumers, the wrist is a suitable location, which could be easily integrated into the existing form of smartwatches and fitness trackers. However, current EMG-based biometrics still follow the historical path of powered prosthetics research, where EMG signals were usually recorded from forearm positions. Moreover, the robustness of EMG processing algorithms across multiple days is still an open problem that needs to be addressed before for long-term reliable use. This study intends to investigate the difference in authentication performance between wrist and forearm EMG signals, in a within-day and two cross-day analyses. Our open dataset (GRABMyo dataset) was used to examine this difference, which contains forearm and wrist EMG data collected from 43 participants over three different days with long separation (Days 1, 8, and 29). The results showed wrist EMG signals led to at least comparable with forearm EMG signals in within-day Equal-error rate (EER). In cross-day analysis, the EER of the wrist EMG signals was higher than that of forearm signals. In general, the low median EER (<0.1) of wrist EMG in cumulative cross-day analysis demonstrates the promise of using wrist EMG signals for authentication in long-term applications.

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Natural pigment sensor for solar ultraviolet radiation measurement 14

Natural pigment sensor for solar ultraviolet radiation measu...

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Organic and Hybrid Sensors and Bioelectronics XIV 2021

作者： Castillo, D. Sánchez, A. González, J. Chamba, C. Lakshminarayanan, Vasudevan Chemistry Department Universidad Tecnica Particular de Loja Ap. 11-01-608 Loja Ecuador Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo ON Canada Departments of Physics Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

ISBN: (数字)9781510644595

ISBN: (纸本)9781510644588

The ultraviolet (UV) index is an international standard measure of the strength of solar ultraviolet radiation on the earth's surface at a specific place and time. Solar radiation with a high UV index can produce damage to the skin and eye (photoaging and photokeratitis). The levels of UV radiation are commonly detected using silicon-based optoelectronic sensors, which can be expensive. Here we propose a way to measure the UV index using natural organic pigments which fluoresce when exposed to UV radiation. In combination with an optical fiber, we have built a prototype sensor based on the pigment of turmeric or "Curcuma Longa". Curcuma longa fluoresces in the range of 500 to 680 nm when exposed to UV radiation. The system uses a filter to isolate the sunlight UV component. The sensor measures the variation in fluorescence intensity using a light dependent resistor to determine radiation levels and correlate it with the UV index. The sensor has been tested in Loja, Ecuador which is located at the equator (UV levels can reach up to 20.0 at the equator). When compared to a standard commercially available sensor (ML8511/LAPIS Semiconductor) this prototype has an error of ± 2.8%. We will describe the optical design and present measurements made with this novel inexpensive sensor. © 2021 SPIE.

关键词： Fluorescence

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Models to Identify Small Brain White Matter Hyperintensities Lesions

SSRN

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SSRN 2024年

作者： Castillo, Darwin Rodríguez-Álvarez, María José Samaniego, R. Lakshminarayanan, Vasudevan Departamento de Química Universidad Técnica Particular de Loja LojaAp. 11-01-608 Ecuador Valencia Spain Departmento de Radiología Hospital UTPL Loja Ecuador Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo ON Canada Department of Physics Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke, Alzheimer's Disease, and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain lesions constitute a critical area of research in medical imaging processing, significantly impacting both clinical practice and scientific advancement. This project aims to develop and implement deep learning models for detecting and classifying small brain White Matter hyperintensities (WMH) lesions in magnetic resonance images (MRI), specifically lesions concerning ischemic and demyelination diseases. The methods applied to segmentation were the UNet, Segmenting Anything model (SAM), YOLOV8, and Detectron2 (based on MaskRCNN). Experimental results show a Dice coefficient (DSC) of 0.94, 0.50,0.241,0.88 for segmentation of WMH lesion using the UNet, SAM, YOLOv8, and Detectron2, respectively. The Detectron2 model demonstrated an accuracy of 0.98 in detecting and 0.93 in classifying lesions, including small lesions where other models often fail. In conclusion, the methods developed give an outline to detect, segment, and classify small brain lesions, and that could significantly aid clinical diagnostics, providing reliable support for physicians and improving patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Reason for use: An opportunity to improve patient safety

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Studies in Health Technology and Informatics 2019年 257卷 47-52页

作者： Casares Li, Reicelis Hussein, Thana Bancsi, Ashley Grindrod, Kelly Burns, Catherine Advanced Interface Design Lab Systems Design Engineering EC4 University of Waterloo 200 University Ave. ONN2L 3G1 Canada School of Pharmacy University of Waterloo United States

ISBN: (纸本)9781614999508

The objective of this study is to understand how the reason for use (RFU) or the indication for medications are used, its effects on the decision-making process, the implications, and the willingness among prescribers, pharmacists, and patients to share RFU information. Methods, semi-structured interviews were conducted to retrieve the information needed from a total of 60 participants. Results, pharmacists, prescribers, and patients generally have positive opinions about including RFU information in their communications. Conclusion, there is a general agreement among participants that sharing RFU information will improve patient safety. © 2019 American Psychological Association Inc. All rights reserved.

关键词： Decision making

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Implementation of Grover's algorithm on the IBMQ: Constraints and error assessment

Implementation of Grover's algorithm on the IBMQ: Constraint...

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Photonics for Quantum 2021

作者： Eldridi, Asma Gueddana, Amor Lakshminarayanan, Vasudevan University of Carthage Department of Electrical Engineering National Engineering School of Carthage ENICarthage Tunis2035 Tunisia University of Carthage Higher School of Communication of Tunis Sup'Com Green and Smart Communication Systems Lab Gres'Com Ariana2083 Tunisia University of Waterloo School of Optometry and Vision Science Theoretical and Experimental Epistemology Lab TEEL 200 University Avenue West WaterlooONN2l 3G1 Canada University of Waterloo Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering 200 University Avenue West WaterlooONN2l 3G1 Canada

ISBN: (纸本)9781510645264

We use the IBMQ machine to implement a quantum database circuit. The database is made of a single multi-column table with multiple superposed records using Toffoli, CNOT and single quantum qubit gates. We present an optimized quantum circuit which allows us to select an item from the table, according to a specific oracle. Our circuit is based only on a single auxiliary qubit to perform the Selective Inversion (SI) and the Inversion Around the Average (IAA), which constitute the Grover operator. We analyze the difference between the generated transpired circuits while executing the original circuit on different IBMQ machines, namely the Melbourne and Athens quantum machines. We discuss the physical constraints causing the difference between the simulation on the composer and the executions of the circuits on the real IBMQ devices. © 2021 Copyright SPIE.

关键词： Timing circuits

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DenseNet for Breast Tumor Classification in Mammographic Images 1st

DenseNet for Breast Tumor Classification in Mammographic Ima...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Jiménez Gaona, Yuliana Rodriguez-Alvarez, María José Espino-Morato, Hector Castillo Malla, Darwin Lakshminarayanan, Vasudevan Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja San Cayetano Alto s/n CP1101608 Loja Ecuador Valencia46022 Spain Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science Waterloo Canada Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada Valencia46022 Spain

ISBN: (纸本)9783030881627

Breast cancer screening is an efficient method to detect breast lesions early. The common screening techniques are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload for pathologists, and hence is prone to diagnostic errors. Thus, the aim of this study was to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to automate RoI segmentation. Then feature extraction, selection and classification were carried out by the DenseNet architecture. Finally, the precision and accuracy of the model was evaluated by the AUC, accuracy and precision metrics. To summarize, the findings of this study show that the methodology may improve the diagnosis and efficiency in automatic tumor localization through medical image classification. © 2021, Springer Nature Switzerland AG.

关键词： Mammography

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Application of an enhanced deep super-resolution network in retinal image analysis 30

Application of an enhanced deep super-resolution network in ...

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30th Conference on Ophthalmic Technologies

作者： Gulati, Tanmay Sengupta, Sourya Lakshminarayanan, Vasudevan Department of Computer Science and Engineering Manipal Institute of Technology Manipal India Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo ON Canada Department of Systems Design Engineering University of Waterloo ON Canada

ISBN: (纸本)9781510631991

Fundus imaging is widely used for the diagnosis of retinal diseases. Major ophthalmic diseases like glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD) are diagnosed by examining retinal fundus images. Therefore, the efficient and reliable diagnosis largely depends upon the resolution of the images. In different diseased conditions, different pathologies and landmarks (haemorrhages, microaneurysms, exudates, blood vessels, optic disc and optic cup, fovea) of the retina get affected. In clinical situations it is often not possible to obtain good high-resolution images. Here, the techniques of super-resolution can be applied. The objective of super-resolution is to obtain a high-resolution image from a low-resolution input image. In this paper, we present results of the application of enhanced deep residual networks for single image super-resolution (EDSR) on retinal fundus images. This network is based on the SRResNet architecture involving skip connections. Using the public RIGA dataset, which consists of glaucoma and normal fundus images, we have trained the model using 2x, 4x and 8x scaling with three different optimizers each (namely ADAM, Stochastic Gradient Descent and RMSprop) to determine which optimizer is best for the different scales. We have also provided results obtained by varying the residual blocks in the network. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Diagnosis

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Unsupervised detection of lung nodules in chest radiography using generative adversarial networks

arXiv

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arXiv 2021年

作者： Bhatt, Nitish Prados, David Ramón Hodzic, Nedim Karanassios, Christos Tizhoosh, H.R. The Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada KIMIA Lab University of Waterloo WaterlooONN2L 3G1 Canada

Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN) by utilizing a progressive GAN and a convolutional encoder-decoder-encoder pipeline. Model training uses only unlabelled healthy lung patches extracted from the Indiana university Chest X-Ray Collection. External validation and testing are performed using healthy and unhealthy patches extracted from the ChestXray14 and Japanese Society for Radiological Technology datasets, respectively. Our model robustly identifies patches containing lung nodules in external validation and test data with ROC-AUC of 91.17% and 87.89%, respectively. These results show unsupervised methods may be useful in challenging tasks such as lung nodule detection in radiographs. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Exploring the Effects of Social VR Coupling Modes on Engagement and Task Performance for Older Adults 25

Exploring the Effects of Social VR Coupling Modes on Engagem...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Thuan T Vo Satabdi Das Shamim Noroozi Lakshay Dang Jaisie Sin Jennifer N Boger Jennifer Jakobi Khalad Hasan University of British Columbia Kelowna British Columbia Canada Computer Science The University of British Columbia Kelowna British Columbia Canada School of Engineering University of British Columbia-Okanagan Kelowna British Columbia Canada Okanagan Visualization and Interaction Lab The University of British Columbia Okanagan Kelowna British Columbia Canada Carleton University Ottawa Ontario Canada Systems Design Engineering University of Waterloo Waterloo Ontario Canada and The University of British Columbia British Columbia Canada Aging in Place Research Cluster The University of British Columbia Okanagan British Columbia Canada The University of British Columbia Okanagan Kelowna British Columbia Canada

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INFORMATION TECHNOLOGY systems AT THE SHARP END OF MEDICATION THERAPY MANAGEMENT 63rd

INFORMATION TECHNOLOGY SYSTEMS AT THE SHARP END OF MEDICATIO...

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63rd International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2019

作者： Li, Reicelis Casares Pereira, Rodrigo Arcuri Marques Jatobá, Alessandro Vidal, Mario Cesar Rodríguez de Carvalho, Paulo Victor Rodrigues Grindrod, Kelly Burns, Catherine Advanced Interface Design Lab Systems Design Engineering University of Waterloo ON Canada School of Pharmacy University of Waterloo ON Canada Rio de Janeiro Brazil Federal University of Rio de Janeiro Brazil Rio de Janeiro Brazil

ISBN: (纸本)9781713810407

This study explored how to inform the design of information systems for medication therapy management, in the context of the southwestern Ontario health system. The data collection comprised document analysis, interviews, and process mapping, and the analysis of previous interview records. The Functional Resonance Analysis Method (FRAM) was used for data analysis to identify the effects of functional variability in system outcomes, and to propose ways to redesign future systems. The results from the FRAM showed that shortcomings in the information systems require users to adapt in many ways. While these adjustments are essential to delivering care in everyday practice, they may lose their effectiveness in the face of specific situations, creating brittleness and risk of adverse outcomes as users are led to make challenging decisions. © 2019 by Human Factors and Ergonomics Society.

关键词： Information systems

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