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Generalization of CNOT-based Discrete Circular Quantum Walk: Simulation and Effect of Gate Errors

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arXiv 2020年

作者： Slimen, Iyed Ben Gueddana, Amor Lakshminarayanan, Vasudevan SysCom Lab National Engineering School of Tunis ENIT University of EL Manar 1002 Le Belvédère Tunis Tunisia Green & Smart Communication Systems Lab Gres’Com Engineering School of Communication of Tunis Sup’Com University of Carthage Ghazela Technopark Ariana2083 Tunisia Theoretical & Experimental Epistemology Lab TEEL School of Optometry and Vision Science University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada

We investigate the counterparts of random walk in universal quantum computing and their implementation using standard quantum circuits. Quantum walk have been recently well investigated for traversing graphs with certain oracles. We focus our study on traversing a 1-D graph, namely a circle, and show how to implement discrete circular quantum walk in quantum circuits built with universal CNOT and single quit gates. We review elementary quantum gates and circuit decomposition and propose a a generalized version of the all CNOT based quantum discrete circular walk. We simulated these circuits on an IBM quantum supercomputer London IBM-Q with 5 qubits. This quantum computer has non perfect gates based on superconducting qubits, therefore we analyze the impact of errors on the fidelity of the Walker circuit. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

Text Detection and Recognition in the Wild: A Review

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arXiv 2020年

作者： Raisi, Zobeir Naiel, Mohamed A. Fieguth, Paul Wardell, Steven Zelek, John Vision and Image Processing Lab and Department of Systems Design Engineering University of Waterloo ONN2L 3G1 Canada ATS Automation Tooling Systems Inc. CambridgeONN3H 4R7 Canada

Detection and recognition of text in natural im-ages are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and affected by several environmental conditions. The current state-of-the-art scene text detection and/or recognition methods have exploited the witnessed advancement in deep learning architectures and reported a superior accuracy on benchmark datasets when tackling multi-resolution and multi-oriented text. However, there are still several remaining challenges affecting text in the wild images that cause existing methods to underperform due to there models are not able to generalize to unseen data and the insufficient labeled data. Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assessespre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques. Second, identifying several existing challenges for detecting or recognizing text in the wild images, namely, in-plane-rotation, multi-oriented and multi-resolution text, perspective distortion, illumination reflection, partial occlusion, complex fonts, and special characters. Finally, the paper also presents insight into the potential research directions in this field to address some of the mentioned challenges that are still encountering scene text detection and recognition techniques. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

Models to Identify Small Brain White Matter Hyperintensities Lesions

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SSRN

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

MRI images, brain lesions and deep learning

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arXiv 2021年

作者： Castillo, Darwin Lakshminarayanan, Vasudevan Rodríguez-Álvarez, Maria J. Departamento de Química y Ciencias Exactas Sección Fisicoquímica y Matemáticas Universidad Técnica Particular de Loja Loja11-01-608 Ecuador Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo Ontario Canada Departments of Physics Electrical and Computer Engineering and Systems Design Engineering University of Waterloo Ontario Canada Valencia Spain

Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment. © 2021, CC BY.

关键词： Magnetic resonance imaging

Applying persuasive design techniques to influence data-entry behaviors in primary care: Repeated measures evaluation using statistical process control

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JMIR Human Factors 2018年第4期5卷 e28页

作者： St-Maurice, Justin Burns, Catherine Wolting, Justin Systems Design Engineering University of Waterloo 200 University Avenue West Waterloo N2L 3G1 ON Canada Applied Health Information Science Program School of Health and Life Sciences and Community Services Conestoga College Institute of Technology and Advanced Learning Kitchener ON Canada Guelph Family Health Team Guelph ON Canada

Background: Persuasive design is an approach that seeks to change the behaviors of users. In primary care, clinician behaviors and attitudes are important precursors to structured data entry, and there is an impact on overall data quality. We hypothesized that persuasive design changes data-entry behaviors in clinicians and thus improves data quality. Objective: The objective of this study was to use persuasive design principles to change clinician data-entry behaviors in a primary care environment and to increase data quality of data held in a family health team’s reporting system. Methods: We used the persuasive systems design framework to describe the persuasion context. Afterward, we designed and implemented new features into a summary screen that leveraged several persuasive design principles. We tested the influence of the new features by measuring its impact on 3 data quality measures (same-day entry, record completeness, and data validity). We also measured the impacts of the new features with a paired pre-post t test and generated XmR charts to contextualize the results. Survey responses were also collected from users. Results: A total of 53 users used the updated system that incorporated the new features over the course of 8 weeks. Based on a pre-post analysis, the new summary screen successfully encouraged users to enter more of their data on the same day as their encounter. On average, the percentage of same-day entries rose by 10.3% for each user (P<.001). During the first month of the postimplementation period, users compensated by sacrificing aspects of data completeness before returning to normal in the second month. Improvements to record validity were marginal over the study period (P=.05). Statistical process control techniques allowed us to study the XmR charts to contextualize our results and understand trends throughout the study period. Conclusions: By conducting a detailed systems analysis and introducing new persuasive design elements into a

关键词： Data accuracy Data collection Data entry Health care Persuasive design Persuasive systems design framework User interface

An inverse problem approach to computational active depth from defocus

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Journal of Physics: Conference Series 2018年第1期1047卷

作者： A Ma A Wong Vision and Image Processing Lab Systems Design Engineering University of Waterloo 200 University Ave W Waterloo ON N2L 3G1

A novel methodology for inferring depth measurements using active quasi-random colored point projection patterns is presented by recourse to computational inverse modelling within the context of depth from defocus. The forward problem is considered that when capturing objects using a camera, light reflected off of objects at different depths will result in blurry observations in the acquired image, whereas the inverse problem seeks to estimate the depth from the knowledge of the blurry observations. In the proposed approach, a colored quasi-random point projection pattern is projected onto the scene of interest, and each projection point in the image captured by a cellphone camera is analyzed using an inverse model to estimate the depth at that point. The proposed method has a relatively simple setup, consisting of a camera and a projector, and enables depth inference from a single capture. We conclude with qualitative and quantitative results that highlight the strong potential for enabling active depth sensing in a simple, efficient manner.

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

Densenet for breast tumor classification in mammographic images

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arXiv 2021年

作者： Gaona, Yuliana Jiménez Rodriguez-Alvarez, María José Morató, Hector Espinó Malla, Darwin Castillo Lakshminarayanan, Vasudevan Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja San Cayetano Alto s/n LojaCP1101608 Ecuador Instituto de Instrumentación para la Imagen Molecular I3M Universitat Politécnica de Valencia ValenciaE-46022 Spain Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada

Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. The common approaches of screening for women are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload by pathologists, who are prone to diagnostic errors. Thus, the aim of this study is 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 features selection and extraction;and the classification was carried out by DenseNet architecture. Finally, the precision and accuracy of the model is evaluated by cross validation matrix and AUC curve. To summarize, the findings of this study may provide a helpful to improve the diagnosis and efficiency in the automatic tumor localization through the medical image classification. © 2021, CC BY.

关键词： Convolutional neural networks

Test-retest evaluation of a notched-noise test using consumer-grade mobile audio equipment

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International Audiology 2024年第2期63卷

作者： Petteri Hyvärinen Michal Fereczkowski Ewen N. MacDonald a Acoustics Lab Department of Signal Processing and Acoustics Aalto University Espoo Finlandb Hearing Systems Section Department of Health Technology Technical University of Denmark Lyngby Denmark b Hearing Systems Section Department of Health Technology Technical University of Denmark Lyngby Denmarkc Institute of Clinical Research Faculty of Health Sciences University of Southern Denmark Odense Denmarkd Research Unit for ORL – Head & Neck Surgery and Audiology Odense University Hospital & University of Southern Denmark Odense Denmark b Hearing Systems Section Department of Health Technology Technical University of Denmark Lyngby Denmarke Department of Systems Design Engineering University of Waterloo Waterloo Canada

ObjectiveThe aim of this study was to investigate whether consumer-grade mobile audio equipment can be reliably used as a platform for the notched-noise test, including when the test is conducted outside the *** studies were conducted: Study 1 was a notched-noise masking experiment with three different setups: in a psychoacoustic test booth with a standard laboratory PC; in a psychoacoustic test booth with a mobile device; and in a quiet office room with a mobile device. Study 2 employed the same task as Study 1, but compared circumaural headphones to insert *** sampleNine and ten young, normal-hearing participants completed studies 1 and 2, *** test-retest accuracy of the notched-noise test on the mobile implementation did not differ from that for the laboratory setup. A possible effect of the earphone design was identified in Study 1, which was corroborated by Study 2, where test-retest variability was smallest when comparing results from experiments conducted using identical acoustic *** and test-retest repeatability comparable to standard laboratory settings for the notched-noise test can be obtained with mobile equipment outside the laboratory.

关键词： Notched-noise test telemedicine test-retest reliability