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IEEE Instrumentation and Measurement Technology Conference

作者： Emma Farago Adrian D. C. Chan Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Electrode placement for surface electromyography (EMG) acquisition can be a difficult and time-consuming process. The use of electrode arrays and signal quality assessment for automatic channel selection has been proposed to facilitate EMG acquisition. Previous work examined signal quality assessment for high-density EMG (HD-EMG) arrays, but HD-EMG necessitates higher cost and complexity. This paper investigates automated signal quality assessment in low-density EMG (LD-EMG) arrays and single-channel EMG (SC-EMG). Machine learning regression models were trained to assess channel quality on a 3-point scale (Adequate, Good, Excellent), using signal quality ratings from two expert human raters as a ground truth. Performance was high for arrays with an inter-electrode distance ≤ 20 mm $(r_{s}$ ≥ 0.89, ICC (3, k) ≥ 0.93). Performance decreased for inter-electrode distance ≥ 30 mm, with random forest regression providing the best overall performance $(r_{s}$ ≥ 0.73, ICC (3, k) ≥ 0.61). Signal quality quantification of SC-EMG showed limited results $(r_{s}$ ≥ 0.51, ICC (3, k) ≥ 0.70); however, there was potential to distinguish between Adequate and Excellent quality channels, with a support vector classifier identifying Adequate channels with 84.7% precision and 72.1% recall.

关键词： Electrodes Accuracy Array signal processing Electromyography Vectors Quality assessment Instrumentation and measurement

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Comparison of Different Color Spaces for Abnormality Detection in Breast Thermograms

Comparison of Different Color Spaces for Abnormality Detecti...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Ankita Dey Sreeraman Rajan Systems and Computer Engineering Carleton University Ottawa Canada

Breast cancer is one of the most diagnosed cancers in women worldwide. Thermography serves as a potential screening modality for early breast abnormality detection. A breast thermogram is a pseudo-colored RGB image that is obtained using a thermal palette. Different types of thermal palettes can be used for pseudo-coloring breast thermograms and may influence abnormality detection in RGB breast thermograms. Universal color spaces/ color models that are derived from RGB color space can be used to mitigate the influence of the choice of different thermal palettes. In this work, RGB breast thermograms are mapped to five different color spaces (namely YIQ color space, YCbCr color space, XYZ color space, LAB color space, and HSV color space). The performance of an existing novel, non-learning, threshold-based breast abnormality detection methodology using these mapped thermograms is investigated. An exhaustive channel-wise analysis of different color spaces is also explored to study the influence of each color space channel on breast abnormality detection.

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Uncovering the DevOps Landscape: A Scoping Review and Conceptualization Framework

Uncovering the DevOps Landscape: A Scoping Review and Concep...

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Asia-Pacific Conference on Software engineering

作者： Xinrui Zhang Jason Jaskolka Systems and Computer Engineering Carleton University Ottawa ON Canada

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

The rapid proliferation of DevOps variants, collectively known as XOps, reflects the growing complexity and specialization within software development and operations. However, the diversity of these practices has led to inconsistencies and confusion, which complicates the development and standardization of the field. While there has been significant research on individual XOps, there is a lack of systematic, horizontal analysis across all XOps practices. This paper addresses this gap by conducting a scoping review of XOps literature, focusing on three fundamental research questions: the definition and categorization of XOps, the methodologies used to study their adoption, and the common challenges identified in their implementation. As the first study to systematically address these questions, we propose the XOps conceptualization framework, offering a structured approach to understanding and studying XOps. This framework serves as an initial step toward bringing clarity to the DevOps landscape, providing guidance in uncovering its complexities and laying the foundation for future research and the emergence of new XOps.

关键词： DevOps Systematics Reviews Standards organizations Focusing Standardization Organizations Complexity theory Software engineering

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DEVS Formal Modeling and Simulation in Manufacturing systems

DEVS Formal Modeling and Simulation in Manufacturing Systems

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Cristina Ruiz Martin Gabriel Wainer Department of Systems and Computer Engineering Carleton University Canada

ISBN: (数字)9781713899310

ISBN: (纸本)9798350350562

Bottlenecks are a major problem for manufacturing companies because they limit the throughput of the production line. Although analytical methods have been widely studied, these methods are impractical in many cases, and simulation-based approaches, where a model of the system is developed, are needed. In this work, we show how to use DEVS as a tool to model and simulate manufacturing systems. More specifically, we propose to use it to apply the Theory of Constraints, identifying bottlenecks in the manufacturing plants and large amounts of Work in Progress.

关键词： Employee welfare Analytical models Schedules Production Constraint theory Throughput Workstations Maintenance Time factors Manufacturing systems

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Understanding Defects in Generated Codes by Language Models

arXiv

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arXiv 2024年

作者： Esfahani, Ali Mohammadi Kahani, Nafiseh Ajila, Samuel A. Systems and Computer Engineering Carleton University Ottawa Canada

This study investigates the reliability of code generation by Large Language Models (LLMs), focusing on identifying and analyzing defects in the generated code. Despite the advanced capabilities of LLMs in automating code generation, ensuring the accuracy and functionality of the output remains a significant challenge. By using a structured defect classification method to understand their nature and origins this study categorizes and analyzes 367 identified defects from code snippets generated by LLMs, with a significant proportion being functionality and algorithm errors. These error categories indicate key areas where LLMs frequently fail, underscoring the need for targeted improvements. To enhance the accuracy of code generation, this paper implemented five prompt engineering techniques, including Scratchpad Prompting, Program of Thoughts Prompting, Chain-of-Thought Prompting, Chain of Code Prompting, and Structured Chain-of-Thought Prompting. These techniques were applied to refine the input prompts, aiming to reduce ambiguities and improve the models’ accuracy rate. The research findings suggest that precise and structured prompting significantly mitigates common defects, thereby increasing the reliability of LLM-generated code. Copyright © 2024, The Authors. All rights reserved.

关键词： Software testing

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Thermography-based Breast Abnormality Detection using Pre-Trained RadImageNet Models

Thermography-based Breast Abnormality Detection using Pre-Tr...

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International Symposium in Sensing and Instrumentation in IoT Era (ISSI)

作者： Huda Sheikh Ankita Dey Sreeraman Rajan Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350378689

ISBN: (纸本)9798350378696

With breast cancer accounting for a large number of cancer-related deaths worldwide, there exists a need to develop tools and imaging modalities that aid in the early detection of breast abnormalities. While mammography is considered the standard imaging modality for breast cancer diagnosis, it cannot be used on pregnant women and is not as effective with denser breasts. Thermography, which is a non-invasive and radiation-free technique, is an adjunct tool to help detect breast abnormalities. Various pre-trained models have been applied to breast thermograms in the literature to detect breast abnormalities. This is a preliminary study that explores the use of pre-trained models trained on a medically sourced database like RadImageNet, in the analysis of breast thermograms from the Database for Mastology Research (DMR). Models trained from medically-sourced databases like RadImageNet have been known to extract features and patterns more relevant for medical imaging analysis and classification. In this study, the performances of RadImageNet models were compared with models trained on non-medically sourced databases, like ImageNet. Preliminary findings suggest that ImageNet models outperform their equivalent RadImageNet models. This preliminary study emphasizes the importance of using relevant and targeted datasets for the development of models that aid in breast abnormality detection.

关键词： Analytical models Uncertainty Databases Infrared imaging Gray-scale Feature extraction Breast cancer Medical diagnostic imaging Standards Biomedical imaging

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Approximate Discrete-Event Method for Supervisory Control 23

Approximate Discrete-Event Method for Supervisory Control

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Proceedings of the Winter Simulation Conference

作者： Maaz Jamal Gabriel Wainer Systems and Computer Engineering Carleton University Ottawa ON CANADA

ISBN: (纸本)9798350369663

Supervisory systems are used to and act when certain events are detected. Studying supervisory using formal Discrete Event Modelling & Simulation allows analyzing an application and then using the model to build the controllers. Supervisors can lead to a state space explosion if the model size increases, thus, reducing the state space complexity can expand the practicality of the model. We present a method based on Discrete Event System Specifications using an approximate method that reduces the state space complexity. The plant models and synthesized controllers can then be deployed on embedded hardware providing model continuity. We discuss the method and present a case study of a supervisory system.

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Enhancing UAV Altitude Control: PID Tuning with Reinforcement Learning and Genetic Algorithm

Enhancing UAV Altitude Control: PID Tuning with Reinforcemen...

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Annual IEEE systems Conference

作者： Mahrokh Hosseinkhani Hezaveh Mohammad T. Ramezanlou Howard Schwartz Ioannis Lambadaris Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

This paper explores the optimization of Proportional-Integral-Derivative (PID) controller gains to enhance the altitude control of Unmanned Aerial Vehicle (UAV)s, focusing on achieving precise tracking of set points and trajectories. Traditional manual tuning methods often yield suboptimal performance, limiting the controller's ability to balance stability and responsiveness under varying conditions. To address this limitation, we investigate automated optimization techniques, employing Reinforcement Learning (RL) and Genetic algorithm (GA) to refine the PID gains systematically. These methods enable dynamic solution space exploration, identifying parameter combinations that maximize controller performance. Comparative analyses reveal that algorithmically optimized gains consistently outperform those manually tuned by human operators. The results show significant improvements in stability and responsiveness. Using gains tuned by the RL, the system achieves altitude control with minimum overshoot. Integrating advanced optimization approaches into PID tuning enhances controller efficiency and significantly reduces the time and effort required for calibration. Experimental validation demonstrates the practical potential of these techniques, underscoring their effectiveness in improving the reliability and precision of aerial vehicles. This study emphasizes the role of intelligent optimization in advancing UAV control systems, paving the way for broader applications in autonomous aerial navigation and control systems. The video of the experiment conducted at Ericsson's 5G Autonomous Vehicles Lab, located at carleton university, can be found here: https://***/oH3rQRhPMGs

关键词： Heuristic algorithms Manuals Reinforcement learning Autonomous aerial vehicles Stability analysis Space exploration Vehicle dynamics Tuning Trajectory optimization Genetic algorithms

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Requirements for Applying SCIA: A Structured Cyberattack Impact Analysis Approach for ICS

Requirements for Applying SCIA: A Structured Cyberattack Imp...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Alvi Jawad Zoe Arnott Jason Jaskolka Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

Modern industrial control systems (ICS) are increasingly integrating cyber-physical components to automate industrial processes. Such integration requires a rigorous exploration of how cyberattack impact propagates through an ICS. However, impact analysis approaches for ICS usually assume the use of specific modeling formalisms and tools, limiting their adoption by analysts familiar with potential alternatives. This work clarifies the rationale behind 20 requirements for applying SCIA: a Structured Cyberattack Impact Analysis approach with different modeling and simulation platforms. Based on a manufacturing ICS case study, we demonstrate two distinct applications of SCIA: (1) Application A, based on formal modeling and verification in UPPAAL-SMC, and (2) Application B, based on simulations in MATLAB/Simulink. We show how both applications are effective at visualizing the evolution of attacks and analyzing temporary and sustained impacts on ICS reliability and availability. In doing so, we detail the methodological differences, the extent of requirement satisfaction, and the associated trade-offs.

关键词： Analytical models Visualization Software quality Manufacturing Specification languages Software reliability Security

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Understanding Defects in Generated Codes by Language Models

Understanding Defects in Generated Codes by Language Models

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Collaborative Advances in Software and COmputiNg (CASCON), International Conference on

作者： Ali Mohammadi Esfahani Nafiseh Kahani Samuel A. Ajila Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798331504830

ISBN: (纸本)9798331515621

This study investigates the reliability of code generation by Large Language Models (LLMs), focusing on identifying and analyzing defects in the generated code. Despite the advanced capabilities of LLMs in automating code generation, ensuring the accuracy and functionality of the output remains a significant challenge. By using a structured defect classification method to understand their nature and origins this study categorizes and analyzes 367 identified defects from code snippets generated by LLMs, with a significant proportion being functionality and algorithm errors. These error categories indicate key areas where LLMs frequently fail, underscoring the need for targeted improvements. To enhance the accuracy of code generation, this paper implemented five prompt engineering techniques, including Scratchpad Prompting, Program of Thoughts Prompting, Chain-of-Thought Prompting, Chain of Code Prompting, and Structured Chain-of-Thought Prompting. These techniques were applied to refine the input prompts, aiming to reduce ambiguities and improve the models' accuracy rate. The research findings suggest that precise and structured prompting significantly miti-gates common defects, thereby increasing the reliability of LLM-aenerated code.

关键词： Codes Accuracy Large language models Focusing Collaboration Reliability engineering Software Encoding Software reliability Prompt engineering

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