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Scalable Fault Handling Strategy

Scalable Fault Handling Strategy

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international conference for Convergence of Technology (I2CT)

作者： Jugal Kishore Sharma Puja Jaisal Meenu Gupta Centre for Development of Telematics New Delhi India

A range of Network Elements (NEs) along with Element Management Systems (EMSs) were installed for various organizations. It was observed at EMS that the status of some NEs and its components were not matching with their current and actual status. the same was reflected in alarms too. the investigation revealed a long queue of notifications due to transient faults generated by some NEs. A leaky bucket based strategy was tried to isolate and stop handling these notifications. It helped but the problem was not solved completely because notifications were handled sequentially. To preserve the sequence along with introduction of parallel processing, a separate UDP port and handling process were allocated to the notifications of a given set of NE types. this along with leaky bucket based strategy reduced the problem. But still a few faulty routers or aggregators caused delay and drop for the notifications of the non-faulty routers and aggregators. Dynamic load balancer algorithms can be an option but will result into out of sequence notification handling and wrong NE status and alarms. Here we introduce a comprehensive, scalable and dynamic fault handling strategy named Scalable Fault Handling Strategy (SFHS) which a) preserves sequence in notification handling, b) detects the overload and underload, c) differentiates between normal overload and overload due to faulty NEs, d) dynamically scales in and scales out the number of parallel notification handlers to keep queue size in check, e) isolates the faulty NEs and f) prevents the performance of notification handling of other NEs from getting effected by the faulty NE. In our simulation we compared SFHS with scalable dynamic load balancing and sequential fault handling. the simulation results show that in SFHS the throughput was sufficiently high, it prevented faulty NEs to affect the performance of other NEs and all the notifications were handled sequentially. the faulty NEs were isolated with 100% accuracy.

关键词： Simulation Heuristic algorithms Organizations parallel processing throughput Load management Delays

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Development of New Information processing Technologies Based on Lexical and Grammatical Models of Agglutinative Languages

Development of New Information Processing Technologies Based...

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international conference on Computer Science and Engineering (UBMK)

作者： Dzavdet Sh. Suleimanov Alexander Ya. Fridman Rinat A. Gilmullin Lenara Kubedinova Institute of Applied Semiotics Academy of Sciences of the Republic of Tatarstan Kazan Russia Institute for Informatics and Mathematical Modelling Kola Science Center RAS Apatity Russia

In this paper, we propose a new structure to model agglutinative natural languages (ANLs) on the basis of their lex-ical and grammatical features, namely recursion, morphological ellipsis, functional diversity and semantic multivalence of affixes (in particular, the affixes encoding underdefined information and fuzzy commands). this way we try to overcome the root cause of low efficiency of modern tools to accumulate and process knowledge in such languages. In our opinion, it lies in complexity of intellectualization of such tools created on the basis of primitive artificial programming languages that practically represent a subset of inflectional-analytical languages or artificial structures based on them. To reduce severity of the identified problem, it is proposed to build systems for modeling ANLs based on a decentralized system of technological means for ver-balization and meaning recognition, which consider the above-listed peculiarities of such languages. To evaluate and exemplify the proposed ideas, Tatar language will be used since authors have a fair experience in investigating this language.

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Automated rice counting using canny edge detection technique

Automated rice counting using canny edge detection technique

引用

8th IET Smart Cities Symposium (SCS 2024)

作者： Rahul Goraksha Durgesh Nandan Department of Robotics & Automation Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India School of Computer Science & Artificial Intelligence S R University Warangal Telangana 506371 India

ISBN: (数字)9781837243105

In many Asian countries, rice is a staple food of critical importance to local and global markets where rice quality matters. Rice quality has traditionally been appraised by human inspectors, but this approach is labor intensive, time-consuming and subjective. Such samples comprise whole kernels, broken kernels, damaged grains, paddy, stones and other impurities hence their sorting into grades according to the quality required. In this study a machine learning approach as well as image processing techniques for classification and segmentation of rice grain samples based on color texture features are proposed. Automated counting of rice is essential in farming processes such as estimation of yields, management of stores and transport planning. Manual counting is tasking, slow and likely to have errors. Machine vision technology provides an alternative that can be relied upon for automated rice counting systems. this paper gives details on methodology using machine vision for automating rice counting on the field. To identify and count rice grains in images Gaussian blur; thresholding; canny edge detection; contour extraction; are some examples of the image processing methods applied here. High accuracy rate was achieved when tested against a dataset containing several images of rice grains.

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Hybrid Compiler and Hardware Approach to Mitigate Side-Channel Vulnerabilities in parallel GPU Architectures

Hybrid Compiler and Hardware Approach to Mitigate Side-Chann...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Nelson Lungu Bibhuti Bhusan Dash Utpal De Chandra Bibek Bikram Dash Binod Kumar Pattanayak Sudhansu Shekhar Patra Electrical and Electronical Engineering University of Zambia Lusaka Zambia School of Computer Applications KIIT Deemed to be University Bhubaneswar India HealthWorksAI Bangalore India Department of Computer Science & Engineering ITER Siksha ‘O’ Anusandhan University Bhubaneswar Odisha India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Graphics processing units (GPUs) have become ubiquitous in computing systems, from personal devices to data centres. their massively parallel architecture delivers tremendous performance for graphics and general-purpose workloads. However, the open nature of GPUs also introduces security vulnerabilities that can be exploited through side-channel attacks. Defending against such threats often incurs performance overhead, creating tradeoffs between security and speed. this article examines recent research analysing and mitigating GPU side channel attacks while maintaining acceptable performance and presented a multi-faceted defence strategy combining compiler transformations, runtime monitoring, and microarchitectural techniques to balance security and performance. the methodology integrates formal threat models, empirical side-channel evaluations, and rigorous benchmarking. It implements and evaluates prototypes demonstrating security improvement with minimal impact on representative workloads. the results provide promising directions for practical GPU security defences.

关键词： Performance evaluation threat modeling Runtime Microarchitecture Prevention and mitigation Graphics processing units Prototypes Side-channel attacks Security Protection

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Arbitrary Array Distributed ISAR three-Dimensional Imaging of Ship Targets

Arbitrary Array Distributed ISAR Three-Dimensional Imaging o...

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international conference on Vision, Image and Signal processing (ICVISP)

作者： Junqiu Zhang Yong Wang Research Institute of Electronic Engineering Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

In the distributed radar systems, the high array alignment accuracy is often required to enable the coherent processing, however achieving this is challenging to achieve in practical applications. this study proposes a three-dimensional (3-D) radar imaging method for ships based on an arbitrary array configuration within a distributed interferometric inverse synthetic aperture radar (ISAR) system. And each base station consists of an interferometric with three orthogonal channels. Firstly, the separated base stations observe the ship target from arbitrary angles to acquire the echoes of the target from different observation angles. Each station then reconstructs a three-dimensional image of the target through the optimal time selection method and the interferometric technique. these 3-D images, presented as point clouds, contain complementary information due to different observation angles. Finally, a 3-D image fusion technique is applied to obtain the final three-dimensional image. the effectiveness and robustness of the proposed method have been validated through simulation experiments.

关键词： Point cloud compression Base stations three-dimensional displays Imaging Signal processing algorithms Radar imaging Robustness Marine vehicles Inverse synthetic aperture radar Image reconstruction

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Optimized Computation for Determinant of Multivariate Polynomial Matrices on GPGPU

Optimized Computation for Determinant of Multivariate Polyno...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Liangyu Chen Jianjun Wei Zhenbing Zeng Min Zhang Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China Department of Computer Science and Engineering Washington University in St. Louis St Louis USA Department of Mathematics Shanghai University Shanghai China

the determinant of the matrix is a fundamental concept in linear algebra and has various applications in research and engineering. Compared to the easy-calculating numeric matrix, a symbolic matrix with multivariate polynomial entries is hard to calculate because of immediate expression expansion, which often leads to drastic exhaustion of the physical memory. In this paper, we present a novel four-stage-approach to calculate the accurate determinant of multivariate polynomial matrices on GPGPU, through using the modular method, the Stockham Fast Fourier Transformation(FFT), the inverse FFT, and the Chinese Remainder theorem. In our approach, the computation tasks in each stage are deployed on GPGPU so that the calculation could be recovered without loss at any point in case of inevitable interruptions happened during the parallel processing. In addition, to control the parallel computation time, we propose a time prediction model for the computation of polynomial determinant according to the basic matrix attributes. the experiment results show that our approach owns substantial speedups compared to Maple, allowing memory and time usage in steady increments.

关键词： Computational modeling Memory management Linear algebra Predictive models parallel processing Task analysis

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Automated design of Convolutional Neural Network architecture using Gray Wolf Optimization for plant seedlings classification

Automated design of Convolutional Neural Network architectur...

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international conference on Image and Signal processing and their Applications (ISPA)

作者： Lamis Badis Wahiba Aliouat Kenza Bouchiba Department of Computer Sciences Faculty of Sciences and Applied Sciences University of Bouira Algeria Department of Computer Sciences LIM Laboratory Faculty of Sciences and Applied Sciences University of Bouira Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Convolutional Neural Networks (CNNs) have gained significant popularity in image classification tasks, yet achieving their optimal design remains a challenge due to the vast array of possible layer configurations and associated hyperparameters. Selecting the best CNN model for a given task often demands considerable time investment in training numerous models. To address this issue, we propose an automated method for CNN architecture design, utilizing pretrained models like the Backbone and employing Gray Wolf Optimization. this approach automatically generates and evaluates candidate CNN architectures for classifying plant seedlings. Our objective is to distinguish between weed and crop seedlings. Additionally, we introduce a gray wolf representation to encode CNN architectures and their hyperparameters. Our method combines the strengths of transfer learning from pre-trained models to extract meaningful image features with the optimization capabilities of the Gray Wolf Optimization (GWO) algorithm. By leveraging these techniques, our method achieves exceptional accuracy, surpassing state-of-the-art methods with a validation accuracy of up to 97.83%. this innovative approach offers a transformative tool for enhancing the accuracy of CNN models, tailored specifically to the dataset at hand.

关键词： Training Transfer learning Neural networks Signal processing algorithms Signal processing Feature extraction Service-oriented architecture

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A Class of Fast and Accurate Multi-layer Block Summation and Dot Product Algorithms 18th

A Class of Fast and Accurate Multi-layer Block Summation a...

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18th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2021

作者： He, Kang Barrio, Roberto Chen, Lin Jiang, Hao Liu, Jie Gu, Tongxiang Qi, Jin Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Department of Applied Mathematics University of Zaragoza ZaragozaE50009 Spain College of Computer National University of Defense Technology Changsha410073 China Institute of Applied Physics and Computational Mathematics Beijing100000 China

ISBN: (纸本)9783030935702

Basic recursive summation and common dot product algorithm have a backward error bound that grows linearly with the vector dimension. Blanchard [1] proposed a class of fast and accurate summation and dot product algorithms respectively called FABsum and FABdot, which trades off the calculation accuracy and speed by the block size. Castaldo [2] proposed a multi-layer block summation and dot product algorithm called SuperBlocksum and SuperBlockdot that can increase the accuracy while adding almost no additional calculations. We combine the idea of [1] with the multi-layer block structure to propose SuperFABsum (for "super fast and accurate block summation") and SuperFABdot (for "super fast and accurate block dot product"). Our algorithms have two variants, one is SuperFAB(within), the other is SuperFAB(outside). Our algorithms further improve accuracy and speed compared with FAB and SuperBlock. We conducted accuracy and speed tests on the high-performance FT2000+ processor. Experimental results show that SuperFABdot(within) algorithm is more accurate than FABdot and SuperBlockdot. Compared with FABdot, SuperFABdot(outside) algorithm can achieve up to 1.2 × performance speedup while ensuring similar accuracy. © 2022, IFIP international Federation for Information processing.

关键词： Artificial intelligence

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Epileptic Seizure Prediction through ML And DL Models: A Survey

Epileptic Seizure Prediction through ML And DL Models: A Sur...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： J. Viswanath S. Annamalai S. Ramesh Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore Karnataka

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Epileptic seizures are unpredictable and pose significant risks to individuals affected by epilepsy. Electroencephalogram (EEG) signals offer a promising avenue for early seizure prediction, enabling timely intervention and improved quality of life for patients. this survey paper provides a comprehensive review of Deep learning (DL) and Machine learning (ML) learning techniques applied to EEG-based epileptic seizure prediction. It covers traditional ML models, advanced DL architectures, and optimization algorithms, highlighting their effectiveness and limitations. Additionally, the paper discusses feature extraction methods, signal processing techniques, and evaluation metrics used in this domain. Real-world applications, current challenges, and future research directions are explored to offer a holistic view of the field. By examining state-of-the-art methods, this survey aims to provide valuable insights into the development of robust, real-time seizure prediction systems, fostering advancements in clinical applications and patient care.

关键词： Surveys Computational modeling Signal processing algorithms Epilepsy Predictive models Brain modeling Feature extraction Prediction algorithms Electroencephalography Optimization

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8th international conference on Cognitive Systems and Information processing, ICCSIP 2023

8th International Conference on Cognitive Systems and Inform...

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8th international conference on Cognitive Systems and Information processing, ICCSIP 2023

ISBN: (纸本)9789819980178

the proceedings contain 52 papers. the special focus in this conference is on Cognitive Systems and Information processing. the topics include: Exploring Model Depth Adaptation in Image Super-Resolution for Efficient Inference;RGB to L∗a∗b∗ Color Prediction Model Based on Color Cards;a Diverse Environment Coal Gangue Image Segmentation Model Combining Improved U-Net and Semi-supervised Automatic Annotation;RB-LIO: A SLAM Solution applied to Large-Scale Dynamic Scenes with Multiple Moving Objects;object Detection with Depth Information in Road Scenes;Mobilenetv2-SSD Target Detection Method Based on Multi-scale Feature Fusion;no-Reference Point Cloud Quality Assessment via Contextual Point-Wise Deep Learning Network;measurement of Solder Wetting Angle Based on Image processing and Polynomial Fitting;infrared Aircraft Anti-interference Recognition Based on Feature Enhancement CenterNet;Obstacle Avoidance Control Method for Robotic Assembly Process Based on Lagrange PPO;MHRN-ST: Go Board Recognition With Multi-stage Highlight Removal Network Based On Swin Transformer;construction of Minecraft Virtual Reality Scene Based on Voxel Point Cloud;zero-Shot Sim-To-Real Transfer of Robust and Generic Quadrotor Controller by Deep Reinforcement Learning;robot Manipulation Strategy for Explosives Disposal in Cluttered Scenes;Adaptive Neural Composite Learning Finite-Time Control for a QUAV with Guaranteed Tracking Performance;multimodal Wearable Device Signal Based Epilepsy Detection with Multi-scale Convolutional Neural Network;Improving EEG-Based Continuous Grip Force Decoding in Grasp-Lift Tasks by Considering Grip Force Levels;3D Path Planning and Tracking of Quadrotors Based on Improved Whale Optimization Algorithm;multimodal Pneumatic Control System for Soft Robot.

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