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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Qian, Heng Li, Chuan Li, Chengli Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming China School of Physics and Electronic Information Yunnan Normal University Yunnan Key Laboratory of Opo-electronic Information Technology Kunming China

ISBN: (纸本)9798350312614

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad2/Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method. © 2023 IEEE.

关键词： distributed optical fiber sensing laser phase noise phase demodulation phase-sensitive optical time-domain reflectometry

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Enhancing Software Quality with Fault Detection and Prediction based on AI-Driven Model

Enhancing Software Quality with Fault Detection and Predicti...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Ramesh Bishukarma Sumeet Mathur Puneet Matapurkar Sandeep Gupta University of Waikato NZ – Joint Institute at Zhejiang University Hangzhou China Department of Mathematical Sciences & Computer Application Bundelkhand University Jhansi India Department of Computer Science Engineering SATI Vidisha India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Early defect detection and prediction are essential in software engineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on the CM1 dataset from NASA’s software metrics program. The data preprocessing steps include normalisation, feature selection, and the use of the ADASYN algorithm to enhance minority class detection. Faults are predicted using ML classifiers: Extra Trees (ET), Support Vector Machine (SVM), KNN, RF, MLP, and NB. F1 score, recall, accuracy, and precision are a metrics utilised to assess a models. When it comes to defect prediction, the Extra Trees Classifier (ETC) model outperforms all of the others with an accuracy91.51%, precision91.76%, and recall99%. Although the Naive Bayes model shows lower accuracy (77.61%) and efficiency, it offers a simpler approach for fault detection. Future research in this area should be guided by these findings, which highlight the promise of AI-driven approaches to enhance frameworks for software defect prediction and quality assurance.

关键词： Support vector machines Accuracy Fault detection Software algorithms Software quality Predictive models Nearest neighbor methods Software measurement Classification tree analysis Software engineering

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Quantitative and Qualitative Analysis of the human upper airway volumes 14

Quantitative and Qualitative Analysis of the human upper air...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Maken, Payal Gupta, Abhishek Rana, Shailendra Singh Kumar Gupta, Manoj Shri Mata Vaishno Devi University School of Computer Science & Engineering J&K Katra India CSIR-Central Scientific Instruments Organisation Biomedical Application Division Chandigarh160030 India All India Institute of Medical Sciences Bathinda India

ISBN: (纸本)9798350335095

A comprehensive review of quantitative and qualitative analysis of the human upper airway regions was performed. The human upper airway is divided into the airway region and paranasal sinuses. These structures are important for breathing and speaking activities. The quantitative and qualitative analysis of the human upper airway helps to understand the structures, sizes, and behaviors, and to develop a patient-specific treatment. This review provides various volumes and includes the factors which are affecting the sizes, shapes, or structure of the regions of the human upper airway with different imaging techniques. This comprehensive review study can be used by experts or researchers to study the quantitative and qualitative analysis of the regions of the human upper airway. © 2023 IEEE.

关键词： Medical imaging

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A Machine Learning Approach for offload Decision Making in Mobile Cloud Computing 7

A Machine Learning Approach for offload Decision Making in M...

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7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Mathur, Robin Prakash Sharma, Manmohan Lovely Professional University School of Computer Science Engineering Punjab India Lovely Professional University School of Computer Application Punjab India

ISBN: (纸本)9781665405201

Mobile cloud computing encompasses a computational offloading framework that helps deploy the compute-intensive task to the remote server to save energy and increase the mobile device's performance. A decision engine is a significant component in the offloading framework, which helps decide when to offload the task to the remote or cloud server. The decision engine's accuracy should be high for the flawless execution of the application during the offloading process. A technique has been proposed by performing a stack ensemble approach on machine learning techniques like the Gaussian approach, multi-layer perceptron, k-nearest neighbors, and linear regression. It considers the various dynamics of the environment like task size, bandwidth, device battery, and device mobility. The proposed model performs better than other decision-making algorithms in terms of execution time and CPU utilization and achieves higher accuracy in making decisions while offloading the compute-intensive task to the remote server. © 2021 IEEE.

关键词： Machine learning

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Enhanced Electrocardiogram Data Compression and Classification Using 2D Convolutional Neural Networks

Enhanced Electrocardiogram Data Compression and Classificati...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Amrendra Singh Yadav Preeti Mehta Deepti Mishra Rabindra Kumar Barik Department of Computer Science and Engineering AVM-Indian Institute of Information Technology and Management Gwalior India Department of Computer Science and Engineering National Institutes of Technology Delhi Delhi India School of Economics and Commerce KIIT Deemed to be University Bhubaneshwar India School of Computer Application KIIT Deemed to be University Bhubaneshwar India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The task of electrocardiogram (ECG) classification is a combination of data compression and classification of signals based on the patient’s health. This paper introduces a novel technique for compressing electrocardiogram (ECG) images and utilizing these compressed images for accurate classification. We transform ECG signals into images and compress them to reduce their size while preserving classification accuracy significantly. Our approach employs a 2-dimensional Convolutional Neural Network (2-D CNN) model and achieves an impressive accuracy of 97% even after compressing the images by up to 76%. The proposed technique was tested on the widely-used MIT-BIH Arrhythmia Dataset, demonstrating its robustness and efficacy. Maintaining high classification accuracy despite significant image compression is crucial for efficient storage and transmission in resource-constrained environments. This method holds substantial potential for real-time monitoring and diagnosis in telemedicine applications, emphasizing the importance of our contribution to the field.

关键词： Accuracy Image coding Biological system modeling Data compression Transforms Electrocardiography Robustness Convolutional neural networks Optimization Batch normalization

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A Hybrid Reinforcement Learning Framework for Optimizing Node Deployment in WSNs

A Hybrid Reinforcement Learning Framework for Optimizing Nod...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rahul Priyadarshi Jayant Kumar Rout Akash Kumar Gupta Rakesh Ranjan Amit Verma Panduranga Ravi Teja Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India Dept of ECE BIT Mesra Deoghar India School of Computer Science UPES Dehradun India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Efficient node deployment in Wireless Sensor Networks (WSNs) is critical for maximizing network coverage, enhancing energy efficiency, and prolonging network lifetime. However, achieving optimal node placement is challenging due to the limited energy resources and dynamic environmental conditions in WSNs. This paper presents a novel hybrid approach that combines Reinforcement Learning (RL) with the Differential Evolution (DE) algorithm to optimize node deployment in WSNs. In this method, RL dynamically adapts node positions based on local network states, while DE performs global optimization to refine these placements and improve coverage. Simulation results demonstrate that the proposed RL-DE hybrid approach significantly outperforms traditional deployment methods, standalone RL, and DE in terms of coverage, energy conservation, and network longevity. The results suggest that the RL-DE approach provides an adaptive and robust solution for node deployment in WSNs, with potential applications in real-world sensor network deployments.

关键词： Wireless sensor networks Adaptive systems Recurrent neural networks Smart cities Simulation Scalability Metaheuristics Reinforcement learning Energy efficiency Real-time systems

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A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment 30th

A Multi-Armed Bandits Learning-Based Approach to Service Ca...

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30th International Conference on Web Services, ICWS 2023

作者： Li, Jinpeng Zhao, Jiale Chen, Peng Xia, Yunni Li, Fan Li, Yin Zeng, Feng Liu, Hui School of Computer Chongqing University Chongqing400030 China School of Computer and Software Engineering Xihua University Chengdu610039 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Guangzhou Institute of Software Application Technology Guangzhou510990 China Limited Shenzhen518129 China School of Computer Science and Technology Beijing Institute of Technology Beijing100083 China

ISBN: (纸本)9783031448355

Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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An Enhanced Power Energy Environment and Intelligent Control System for Solar Energy Harvesting System

An Enhanced Power Energy Environment and Intelligent Control...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Birendra Kumar Saraswat Chandra Mani Akshita Chaudhary Devesh Tiwari Pramod Kumar Sagar Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Computer Application SRM Institute of Science and Technology Modinagar UP India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India

This paper affords a stronger energy power environment and wise management gadget (PEECS) for a solar strength harvesting device. The proposed gadget leverages statistics amassed from numerous assets inside the surroundings and utilizes intelligent algorithms to autonomously alter the photovoltaic (PV) panel and optimize the solar energy harvesting overall performance. Via deciding on the perfect actuators, the PEECS enables a solar strength harvesting gadget to function in a huge variety of PV panels with diverse specifications and environmental situations. The proposed machine video display units the strength generated on the PV panel degree detects faults inside the operation of the PV panel and actively adjusts its tilt and orientation to maximize the power harvested. The machine can autonomously hit upon the pleasant working configuration for the PV panel underneath converting environmental situations. This paper offers an in-intensity description of the hardware setup and associated algorithms used for operation. A laboratory-scale PV device with three one of a kind PV panel kinds changed into used to validate the system's overall performance and solidify the layout of the smart control algorithms. Outcomes show that the proposed system outperforms a static solar strength harvesting gadget and achieves 12.2% development in strength harvested.

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application of 3D Magnetic Resonance Imaging in the Characterization of Brain Tumors

Application of 3D Magnetic Resonance Imaging in the Characte...

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Disruptive Technologies (ICDT), International Conference on

作者： Samender Singh Lalit Kishore Arti Badhoutiya Pramod Kumar Sagar Laxmi Saraswat Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad UP India Department of Computer Application SRMIST Delhi NCR Modinagar Ghaziabad UP India Department of Electrical Engineering GLA University Mathura UP India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology SRMIST Ghaziabad UP India Department of Computer Science ABES Engineering College Ghaziabad UP India

MRI has revolutionized the analysis and remedy of disorder by using imparting precise and accurate images of soft tissue. This technology has been similarly superior by means of the improvement of 3-D MRI, which permits improved detection and characterization of tumors and improves accuracy of diagnosis and remedy. 3D MRI has been found to be in particular beneficial within the characterization of brain tumors as it offers a greater detailed assessment of tumor size, shape and composition, in comparison to traditional imaging techniques including X-ray or CT. This information can help in identifying the nature of the tumor that may impact its remedy and analysis. Furthermore, 3D MRI is useful for figuring out the chance of malignant invasion, which can provide important information for affected person control. Additionally, the advanced decision of 3-D MRI permits better delineation of areas of healthy and diseased tissue that can assist clinicians in making plans complex surgeries. (3-D) MRI has emerged as an increasingly more critical tool inside the characterization of brain tumors. The three-D imaging affords distinct anatomical visualization of the tumor and it's courting to surrounding systems, allowing for extra effective remedy making plans. Furthermore, the potential to visualize tumor tissue parameters together with edema, necrosis, and perfusion may be used to decide whether a tumor is malignant or no longer. 3D MRI has additionally enabled the evaluation of tumor vascularization, which serves as a vital prognostic indicator. The statistics received from 3-D imaging can then be used to be expecting the recurrence of a tumor following treatment, supporting to manual decisions on comply with-up remedies. The reduction in test time, decrease value, and better resolution of 3-D MRI in contrast to standard x-ray imaging also makes the technology more and more attractive for brain tumor management.

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Formation of precipitates and their effects on the mechanical properties in a Fe–Mn–Al–C austenitic low-density steel

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Journal of Materials Research and Technology 2025年 36卷 9422-9431页

作者： Yongxuan Shang Xue Cao Wenqing Jiang Lixin Sun Shuyong Jiang Zhongwu Zhang College of Materials Science and Chemical Engineering Harbin Engineering University Harbin 150001 China College of Computer Science and Technology Harbin Engineering University Harbin 150001 China State Key Laboratory of Metal Material for Marine Equipment and Application Iron & Steel Research Institute of Ansteel Group Corporation Anshan 114009 Liaoning China

κ-carbides serve as a strengthening phase in Fe–Mn–Al–C austenitic low-density steels, enhancing yield strength but reducing the work hardening rate due to slip plane softening effect. To address this issue, this study introduces strong carbide-forming elements Ti and V to regulate carbon distribution and then control κ-carbide precipitation. Without Ti and V addition, κ-carbides uniformly precipitate in the matrix after aging, with an average size of 10.8 ± 6.2 nm and a volume fraction of 7.4 ± 3.4 %. The addition of Ti and V leads to the formation of (Ti, V)C carbides at high temperatures, which act as carbon sinks, reducing the matrix carbon content and thereby suppressing the homogeneous precipitation of κ-carbides. As a result, the κ-carbides precipitate at the edge of the (Ti, V)C carbides, with an average radius of 9.7 ± 8.5 nm and a volume fraction of 3.2 ± 2.8 %. Compared to the uniform precipitation of κ-carbides in low-density steel without Ti/V additions, this modified precipitation behavior preserves the work-hardening rate and increases the tensile strength from 1004 MPa to 1096 MPa. TEM analysis reveals that both steels exhibit planar slip deformation, but the Ti/V-modified steel shows narrower slip band spacing and more intense plastic deformation. This study establishes a novel microalloying strategy for optimizing precipitate distribution and mechanical properties in low-density steels.

关键词： Low-density steels κ-carbides Precipitation Strengthening mechanisms Work-hardening

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