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Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

Journal of computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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Scheduling the Equipment Maintenance of an Electric Power Transmission Network Using Constraint Programming 28

Scheduling the Equipment Maintenance of an Electric Power Tr...

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28th International Conference on Principles and Practice of Constraint Programming, CP 2022

作者： Popovic, Louis Côté, Alain Gaha, Mohamed Nguewouo, Franklin Cappart, Quentin Computer Engineering and Software Engineering Department Polytechnique Montréal Canada IREQ Varennes Canada Hydro-Québec Canada

ISBN: (纸本)9783959772402

Modern electrical power utilities must maintain their electrical equipment and replace it when the end of its useful life arrives. The Transmission Maintenance Scheduling (TMS) problem consists in generating an annual maintenance plan for electric power transportation equipment while maintaining the stability of the network and ensuring a continuous power flow for customers. Each year, a list of equipment (power lines, capacitors, transistors, etc.) that needs to be maintained or replaced is available and the goal is to generate an optimal maintenance plan. This paper proposes a constraint-based scheduling approach for solving the TMS problem. The model considers two types of constraints: (1) constraints that can be naturally formalized inside a constraint programming model, and (2) complex constraints that do not have a proper formalization from the field specialists. The latter cannot be integrated inside the model due to their complexity. Their satisfaction is thus verified by a black box tool, which is a simulator that mimics the impact of a maintenance schedule on the real power network. The simulator is based on complex differential power-flow equations. Experiments are carried out at five strategic points of Hydro-Québec power grid infrastructure, and involve more than 200 electrical equipment and 300 withdrawal requests. Results show that the model is able to comply with most of the formalized and unformalized constraints. It also generates maintenance schedules within an execution time of only a few minutes. The generated schedules are similar to the ones proposed by a field specialist and can be used to simulate maintenance programs for the next 10 years. © Louis Popovic, Alain Côté, Mohamed Gaha, Franklin Nguewouo, and Quentin Cappart.

关键词： Electric power transmission networks

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Deep Learning for Data-Driven Districting-and-Routing

arXiv

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arXiv 2024年

作者： Ferraz, Arthur Cappart, Quentin Vidal, Thibaut Department of Computer Science Pontifical Catholic University of Rio de Janeiro Brazil Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Canada Department of Mathematics and Industrial Engineering Polytechnique Montréal Canada

Districting-and-routing is a strategic problem aiming to aggregate basic geographical units (e.g., zip codes) into delivery districts. Its goal is to minimize the expected long-term routing cost of performing deliveries in each district separately. Solving this stochastic problem poses critical challenges since repeatedly evaluating routing costs on a set of scenarios while searching for optimal districts takes considerable time. Consequently, solution approaches usually replace the true cost estimation with continuous cost approximation formulas extending Beardwood-Halton-Hammersley and Daganzo’s work. These formulas commit errors that can be magnified during the optimization step. To reconcile speed and solution quality, we introduce a supervised learning and optimization methodology leveraging a graph neural network for delivery-cost estimation. This network is trained to imitate known costs generated on a limited subset of training districts. It is used within an iterated local search procedure to produce high-quality districting plans. Our computational experiments, conducted on five metropolitan areas in the United Kingdom, demonstrate that the graph neural network predicts long-term district cost operations more accurately, and that optimizing over this oracle permits large economic gains (10.12% on average) over baseline methods that use continuous approximation formulas or shallow neural networks. Finally, we observe that having compact districts alone does not guarantee high-quality solutions and that other learnable geometrical features of the districts play an essential role. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Real-time, accurate, and open source upper-limb musculoskeletal analysis using a single RGBD camera — An exploratory hand-cycling study

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computers in Biology and Medicine 2025年 184卷 109434-109434页

作者： Ceglia, Amedeo Facon, Kael Begon, Mickaël Seoud, Lama Institute of Biomedical Engineering University of Montreal MontrealQC Canada École pour l'informatique et les techniques avancées Le Kremlin-Bicêtre France School of Kinesiology and Human Kinetics University of Montreal MontrealQC Canada Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada

Biomechanical biofeedback may enhance rehabilitation and provide clinicians with more objective task evaluation. These feedbacks often rely on expensive motion capture systems (∼$100 000), which restricts their widespread use, leading to the development of computer vision-based methods. These methods are subject to large joint angle errors, considering the upper limb, and exclude the scapula and clavicle motion in the analysis. Our open-source approach offers a user-friendly solution for high-fidelity upper-limb kinematics using a single consumer-grade depth-sensing camera (∼$500) and includes semi-automatic skin marker labeling. Real-time biomechanical analysis, ranging from kinematics to muscle force estimation, was conducted on eight participants performing a hand-cycling motion to demonstrate the applicability of our approach on the upper limb. Markers were recorded by the depth-sensing camera and an optoelectronic camera system, considered as a reference. Muscle activity and external load were recorded using eight electromyography sensors and instrumented hand pedals, respectively. Bland-Altman analysis revealed significant agreements in the 3D markers’ positions between the two motion capture methods, with errors averaging 3.3 ± 3.9 mm. The error propagation was low for the biomechanical analysis, with joint angle differences, for example, below 5° when comparing both systems. Biofeedback from the depth-sensing camera was provided at 68 Hz. Our study introduces a novel method for using a depth-sensing camera as a low-cost motion capture solution. Results from healthy participants suggest its potential for accurate kinematic reconstruction and comprehensive upper-limb biomechanical studies. Further investigation is needed to explore its clinical applications in pathological populations. © 2024 The Authors

关键词： Biofeedback

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PILAR: Studying and Mitigating the Influence of Configurations on Log Parsing

PILAR: Studying and Mitigating the Influence of Configuratio...

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International Conference on software engineering (ICSE)

作者： Hetong Dai Yiming Tang Heng Li Weiyi Shang Department of Computer Science and Software Engineering Concordia University Montreal Canada Polytechnique Montréal Montreal Canada

The significance of logs has been widely acknowledged with the adoption of various log analysis techniques that assist in software engineering tasks. Many log analysis techniques require structured logs as input while raw logs are typically unstructured. Automated log parsing is proposed to convert unstructured raw logs into structured log templates. Some log parsers achieve promising accuracy, yet they rely on significant efforts from the users to tune the parameters to achieve optimal results. In this paper, we first conduct an empirical study to understand the influence of the configurable parameters of six state-of-the-art log parsers on their parsing results on three aspects: 1) varying the parameters while using the same dataset, 2) keeping the same parameters while using different datasets, and 3) using different samples from the same dataset. Our results show that all these parsers are sensitive to the parameters, posing challenges to their adoption in practice. To mitigate such challenges, we propose PILAR (Parameter Insensitive Log Parser), an entropy-based log parsing approach. We compare PILAR with the existing log parsers on the same three aspects and find that PILAR is the most parameter-insensitive one. In addition, PILAR achieves the second highest parsing accuracy and efficiency among all the state-of-the-art log parsers. This paper paves the road for easing the adoption of log analysis in software engineer practices.

关键词：

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Identification of Out-of-Distribution Cases of CNN using Class-Based Surprise Adequacy

Identification of Out-of-Distribution Cases of CNN using Cla...

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AI engineering - software engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Mira Marhaba Ettore Merlo Foutse Khomh Giuliano Antoniol Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and calibration

ISBN: (纸本)9781665452069

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and calibration

关键词： Training Deep learning Particle separators Training data software systems Reliability engineering software reliability

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Studying the Characteristics of AIOps Projects on GitHub

arXiv

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arXiv 2022年

作者： Aghili, Roozbeh Li, Heng Khomh, Foutse Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada

Artificial Intelligence for IT Operations (AIOps) leverages AI approaches to handle the massive amount of data generated during the operations of software systems. Prior works have proposed various AIOps solutions to support different tasks in system operations and maintenance, such as anomaly detection. In this study, we conduct an in-depth analysis of open-source AIOps projects to understand the characteristics of AIOps in practice. We first carefully identify a set of AIOps projects from GitHub and analyze their repository metrics (e.g., the used programming languages). Then, we qualitatively examine the projects to understand their input data, analysis techniques, and goals. Finally, we assess the quality of these projects using different quality metrics, such as the number of bugs. To provide context, we also sample two sets of baseline projects from GitHub: a random sample of machine learning projects and a random sample of general-purposed projects. By comparing different metrics between our identified AIOps projects and these baselines, we derive meaningful insights. Our results reveal a recent and growing interest in AIOps solutions. However, the quality metrics indicate that AIOps projects suffer from more issues than our baseline projects. We also pinpoint the most common issues in AIOps approaches and discuss potential solutions to address these challenges. Our findings offer valuable guidance to researchers and practitioners, enabling them to comprehend the current state of AIOps practices and shed light on different ways of improving AIOps’ weaker aspects. To the best of our knowledge, this work marks the first attempt to characterize open-source AIOps projects. © 2022, CC BY.

关键词： Data mining

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Testing Refactoring Engine via Historical Bug Report driven LLM

arXiv

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arXiv 2025年

作者： Wang, Haibo Xu, Zhuolin Tan, Shin Hwei Department of Computer Science & Software Engineering Concordia University Montreal Canada

Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Similar to traditional software systems such as compilers, refactoring engines may also contain bugs that can lead to unexpected behaviors. In this paper, we propose a novel approach called RETESTER, a LLM-based framework for automated refactoring engine testing. Specifically, by using input program structure templates extracted from historical bug reports and input program characteristics that are error-prone, we design chain-of-thought (CoT) prompts to perform refactoring-preserving transformations. The generated variants are then tested on the latest version of refactoring engines using differential testing. We evaluate RETESTER on two most popular modern refactoring engines (i.e., ECLIPSE, and INTELLIJ IDEA). It successfully revealed 18 new bugs in the latest version of those refactoring engines. By the time we submit our paper, seven of them were confirmed by their developers, and three were fixed. © 2025, CC BY-NC-SA.

关键词： software testing

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Bug Characteristics in Quantum software Ecosystem

arXiv

引用

arXiv 2022年

作者： El Aoun, Mohamed Raed Li, Heng Khomh, Foutse Tidjon, Lionel Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada

With the advance in quantum computing (e.g., IBM’s quantum computers) in recent years, quantum software becomes vital for exploring the full potential of quantum computing systems. Quantum programming is different from classical programming in many different ways, for example, the state of a quantum program is probabilistic in nature, and a quantum computer is error-prone due to the instability of quantum mechanisms. Therefore, the characteristics of bugs in quantum software projects may be very different from that of classical software projects. This work aims to understand the characteristics of bugs occurring in quantum software projects, in order to provide insights that can help devise effective testing and debugging mechanisms for quantum software projects. To achieve this goal, we conduct an empirical study on the bug reports (in the forms of pull requests and issue reports) of 125 quantum software projects hosted on GitHub. We observe that quantum software projects are more buggy than comparable classical software projects and that quantum project bugs are more costly to fix (in terms of the code changed) than classical project bugs. We also identify the types of these bugs and the quantum programming components (e.g., state preparation) where they occurred. Our study shows that the bugs are spread across different components, but quantum-specific bugs particularly appear in the compiler, gate operation, and state preparation components. The three most occurring types of bugs are Program anomaly bugs, Configuration bugs, and Data type and structure bugs. Our study highlights some particularly challenging areas in quantum software development that are different from that of traditional software development, such as the lack of scientific quantum computation libraries that implement comprehensive mathematical functions for quantum computing algorithms and quantum gate operation definitions. Quantum developers also seek specialized data manipulation (e.g, array

关键词： software design

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Does Repeatedly Typing the Same Phrase Provide a Good Estimate of Expert Text Entry Performance?

Does Repeatedly Typing the Same Phrase Provide a Good Estima...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Mutasim, Aunnoy K. Batmaz, Anil Ufuk Hudhud Mughrabi, Moaaz Stuerzlinger, Wolfgang Simon Fraser University VancouverBC Canada Computer Science & Software Engineering Department Concordia University MontrealQC Canada Mechatronics Engineering Department Kadir Has University Istanbul Turkey

ISBN: (纸本)9781450394222

To identify if novel/unfamiliar keyboard layouts like OPTI can outperform QWERTY, lengthy training through longitudinal studies is typically required. To reduce this logistical bottleneck, a popular approach in the literature requires participants to type the same phrase repeatedly. However, it is still unknown whether this approach provides a good estimate of expert performance. To validate this method, we set up a study where participants were tasked with typing the same phrase 96 times for both OPTI and QWERTY. Results showed that this approach has the potential to estimate expert performance for novel/unfamiliar keyboards faster than the traditional approach with different phrases. Yet, we also found that accurate estimates still require training over several days and, therefore, do not eliminate the need for a longitudinal study. Our findings thus show the need for research on faster, easier, and more reliable empirical approaches to evaluate text entry systems. © 2023 Owner/Author.

关键词： Taps

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