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TizenRT OS Based Data Compression algorithms for IoT devices 4

TizenRT OS Based Data Compression Algorithms for IoT Devices

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4th IEEE International conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Chamak, Tanmoy Roy Kaushik, Sachi Singhal, Shiven Dadheech, Aditya Anitha, Prof. A. Jenicka, Prof. S. Thapa, Vidisha School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Samsung Research Institute Karnataka Bangalore India

ISBN: (纸本)9798350306118

The paper investigates the integration and adaptation of the LZ4 compression algorithm within the context of TizenRT, in addition to existing algorithms Miniz and LZMA. TizenRT, a real-Time operating system tailored for Internet of Things (IoT) devices, presents unique challenges in balancing compression efficiency and resource constraints. The study evaluates the performance of LZ4 alongside Miniz and LZMA in terms of compression ratios, processing overhead, and memory utilization. Experimental results demonstrate the feasibility of LZ4's adoption in TizenRT, highlighting its potential to enhance data compression and decompression operations while adhering to the stringent requirements of IoT devices. The findings contribute to the optimization of data management strategies within the TizenRT ecosystem. © 2023 IEEE.

关键词： Internet of things

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Change Detection in optical Satellite Images Using Local Self-similarity information 11th

Change Detection in Optical Satellite Images Using Local Sel...

引用

11th International conference on Intelligent Computing and Applications, ICRTC 2023

作者： Shah, Bipin Gupta, Ayushi Paul, Sourabh Tamil Nadu Chennai India Tamil Nadu Chennai India

ISBN: (纸本)9789819717231

Change detection is considered as a challenging issue in the field of satellite image processing due to the presence of significant intensity differences between the input images. Although a variety of algorithms have been proposed in recent time to detect the changes in remote sensing optical images, most of them suffer from handling large intensity variations of the images. In this paper, an unsupervised change detection method is proposed which uses image local self-similarity information to take care of large intensity variations of the input images. Initially, feature vectors are constructed for each of the pixels of the input images using local self-similarity information of images. Then, binary feature vectors are created from these feature vectors using a thresholding criterion. Finally, these binary feature vectors of the corresponding locations of the images are utilized to identify the changed and unchanged pixels. Experiments on different sets of optical satellite images show the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Comparative Study of Image processing Neural Networks using Face Mask Dataset 1

Comparative Study of Image Processing Neural Networks using ...

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1st IEEE International conference on Advances in Electronics, Communication, Computing and Intelligent information systems, ICAECIS 2023

作者： Sunny Jindal, Rajni Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350348057

Over the years there has been huge improvements in the performance of Image processing algorithms due to increase in computation power of devices as well as use of Neural Networks. This paper focuses on comparison of size and performance of different Image Classification networks used widely. Each one of the machine learning models studied presents a new technique to process the input images that might be useful in different scenarios. Our main focus is to identify the framework with low latency, high accuracy as they can be used practically in surveillance equipment's that need to be fast and accurate. © 2023 IEEE.

关键词： Deep neural networks

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Infrared small target detection method based on full-direction dilation Gaussian filtering and local background estimation

Infrared small target detection method based on full-directi...

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2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Sun, Xiyu Pan, Shengda College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781510677685

Addressing the limitations of currently rare small target detection algorithms based on Human Visual systems (HVS) that struggle with achieving satisfactory performance in complex backgrounds and lack high real-time capabilities, this paper introduces an innovative small target detection algorithm. This approach is founded on the integration of the Full-Direction Dilation Difference of Gaussians (FDD-DOG) and background estimation based on local contrast (LCBE). First, the high-frequency signal in the target area is enhanced by using the FDD-DOG operation, based on the difference between the real target and the background. Then, the background noise is further suppressed by assigning appropriate weights to the enhanced image by using local contrast-based background estimation. After that, the target to be detected is extracted by the adaptive threshold segmentation method. Experiments comparing the algorithm proposed in this paper with other HVS-based detection algorithms show that the method in this paper exhibits better detection performance in different infrared image sequences, where the background suppression factor is improved by an average of about 25 times and the signal-to-clutter ratio gain is improved by an average of about 15 times. © 2024 SPIE.

关键词： Image enhancement

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Classification algorithms with a Complex Structure in Pattern Recognition 25

Classification Algorithms with a Complex Structure in Patter...

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25th IEEE International conference of Young Professionals in Electron devices and Materials, EDM 2024

作者： Nishanov, Akram Kh. Allamov, Oybek Rakhmanov, Askar T. Yusupova, Janar Ruzibaev, Ortik B. Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9798350389234

The article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed. © 2024 IEEE.

关键词： Consensus algorithm

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PACE: Pacing Operator Learning to Accurate optical Field Simulation for Complicated Photonic devices 38

PACE: Pacing Operator Learning to Accurate Optical Field Sim...

引用

38th conference on Neural information processing systems, NeurIPS 2024

作者： Zhu, Hanqing Cong, Wenyan Chen, Guojin Ning, Shupeng Chen, Ray T. Gu, Jiaqi Pan, David Z. Arizona State University United States The University of Texas Austin United States

Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation poses a significant bottleneck, hindering scalability and turnaround time in the photonic circuit design process. Neural operators offer a promising alternative, but existing SOTA approaches,NeurOLight, struggle with predicting high-fidelity fields for real-world complicated photonic devices, with the best reported 0.38 normalized mean absolute error in NeurOLight. The interplays of highly complex light-matter interaction, e.g., scattering and resonance, sensitivity to local structure details, non-uniform learning complexity for full-domain simulation, and rich frequency information, contribute to the failure of existing neural PDE solvers. In this work, we boost the prediction fidelity to an unprecedented level for simulating complex photonic devices with a novel operator design driven by the above challenges. We propose a novel cross-axis factorized PACE operator with a strong long-distance modeling capacity to connect the full-domain complex field pattern with local device structures. Inspired by human learning, we further divide and conquer the simulation task for extremely hard cases into two progressively easy tasks, with a first-stage model learning an initial solution refined by a second model. On various complicated photonic device benchmarks, we demonstrate one sole PACE model is capable of achieving 73% lower error with 50% fewer parameters compared with various recent ML for PDE solvers. The two-stage setup further advances high-fidelity simulation for even more intricate cases. In terms of runtime, PACE demonstrates 154-577× and 11.8-12× simulation speedup over numerical solver using scipy or highly-optimized pardiso solver, respectively. We open sourced the code and complicated optical device dataset at PACE-Light. © 2024 Neural information processing systems foundation. All

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Error Analysis of a Multi-Sensor Maritime Targeting System 20

Error Analysis of a Multi-Sensor Maritime Targeting System

引用

conference on Electro-optical and Infrared systems - Technology and Applications XX

作者： Hickman, Duncan L. Niebla, Maria Sang, Daekyu Kim, Boomin Baek, Jeonghyun Tektonex Ltd Long View Argyll Rd Kilcreggan Argyll & Bute Scotland Agcy Def Dev 160 Bugyuseong Dero 488beon Gil Daejeon South Korea

ISBN: (数字)9781510667044

ISBN: (纸本)9781510667037

Targeting systems are subject to multiple sources of error when operating in complex environments. To reduce the effect of these errors, modern targeting systems generally include both imaging and RF sensors. Data processing then provides target detection and classification information, and the detection streams are combined using a data fusion scheme to produce an optimal target location estimate with an associated latency. In this paper, the performance of a multi- sensor system in a maritime application is investigated using a mathematical simulator that has been developed to provide the system performance error analysis for different engagement scenarios and test conditions. This simulator is described together with the sources of targeting error such as image motion blur and radar glint. Additionally, the impact of flare and chaff countermeasures on the targeting performance is reviewed in terms of different types of target recognition and tracking algorithms.

关键词： Multisensor systems Error Analysis Image Blur Radar Glint Model Validation Countermeasure Model ATR processing Targeting systems

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Analysis on Different Optimization Methods, Applications, and Categories of optical Fiber Networks: A Review

Analysis on Different Optimization Methods, Applications, an...

引用

2023 International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Dubey, Arjun Singh, Harmeet Kaur, Simarpreet Chandigarh University Department of Ece Punjab Mohali India

ISBN: (纸本)9781665474511

OFs (optical Fibers) are broadly used for information broadcast systems due to their extensive information-carrying capability and dielectric environment. The OFs network architectures that use several WLs (wavelengths) per OFs connect thousands of users in central, urban, or large-scale applications with a wide range of transmission speeds and capabilities. This article defines a detailed introduction to optical fiber networks and general phases of communication of OF (optical Fiber) procedure. The taxonomy may be based on optical fibers such as;(i) Structure, (ii) number of modes, (iii) refractive index profile, (iv) dispersion, (v) signal processing capacity (vi) Polarization. After that, it explains the merits and demerits of optical fiber networks. They studied optical wireless communications applications such as SMs (shopping malls), UW (underwater), industries, etc. Several analyses with different optimization methods such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle-Swarm Optimization (PSO) algorithms. An effective resource-allocation method is required to reduce the issue of EE (energy-efficiency) and improve the presentation of quality-of-services of the ONs (optical Networks). © 2023 IEEE.

关键词： Genetic algorithms

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Predicting Stride Length from Acceleration Signals Using Lightweight Machine Learning algorithms

Predicting Stride Length from Acceleration Signals Using Lig...

引用

International conference on Internet of Everything and Quantum information processing, IEQIP 2023

作者： Dao, To-Hieu Tran, Duc-Nghia Tran, Van-An Vu, Hoang-Dieu Prakash, Kolla Bhanu Tran, Duc-Tan Phenikaa University Yen Nghia Hanoi Viet Nam PRATI A&A Green Phoenix Group JSC Cau Giay Hanoi Viet Nam Graduate University of Sciences and Technology VAST Hanoi Viet Nam Institute of Information Technology VAST Hanoi Viet Nam Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9783031619281

In the field of motion analysis and behavioral movement research, accurately measuring and predicting stride length from acceleration signals plays a critical role. Recent studies have undertaken the development and evaluation of numerous machine learning models to address this issue, focusing on the deployment of lightweight and efficient models on devices with limited computational capabilities, such as microcontrollers. Among the algorithms examined, random forest, k-nearest neighbors, support vector machines, decision trees, and extreme gradient boosting have been selected due to their balance between prediction accuracy and computational lightness. These algorithms not only ensure a high degree of accuracy in predicting stride length from collected acceleration data but also ensure that the models can be deployed directly on smart wearable devices and microcontrollers, extending the practical application of motion monitoring systems. The outcomes of these machine learning models promise to contribute to the development of personalized health monitoring solutions, as well as in applications related to sports and functional rehabilitation, where analyzing and improving gait is extremely important. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Optic Flow Approximated by a Homogeneous, Three-Dimensional Point Renewal Process 6th

Optic Flow Approximated by a Homogeneous, Three-Dimensional ...

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6th International conference on Software Testing, Machine Learning and Complex Process Analysis, TMPA 2021

作者： Dubinin, D.V. Kochegurov, A.I. Kochegurova, E.A. Geringer, V.E. Tomsk State University of Control Systems and Radioelectronics Prospect Lenina 40 Tomsk634050 Russia National Research Tomsk Polytechnic University Lenin Avenue 30 Tomsk634050 Russia Ravensburg Fallenbrunnen 2 Friedrichshafen88045 Germany

ISBN: (纸本)9783031504228

The paper describes a mechanism for modeling an optical flow as a random vector field – closed areas on the image plane with certain brightness and dynamics of changes in the vector field. The optical flow is formed by a homogeneous, three-dimensional point renewal process. The characteristics of the vector field on the image plane of the resulting optical flow are interconnected by the Palm’s formulas. The type of the constituent elements of the vector field (the alphabet, which determines the morphology of the field on the image plane) is chosen randomly. The proposed approach will produce various types of interconnected digital sequences of images with horizontal, vertical and diagonal elements;create the prerequisites for efficient and flexible motion analysis on digital video sequences;make it possible to use the probabilistic factor in researching image processing algorithms and comparing the algorithms based on a detailed factor analysis. © 2024, Springer Nature Switzerland AG.

关键词： optical flows

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