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检索条件"丛书名=Lecture Notes in Networks and Systems"

共 9894 条记录，以下是1861-1870 订阅

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Linear Motion Estimation of Fertilizer Granules: A Step Towards Parabolic Motion Analysis

Linear Motion Estimation of Fertilizer Granules: A Step Towa...

引用

Intelligent systems Conference

作者： Lak, Arezou Stilling, Denise S. (Derby) Mehrandezh, Mehran Univ Regina Fac Engn & Appl Sci Ind Syst Engn Regina SK S4S 0A2 Canada

ISBN: (纸本)9783031663352;9783031663369

An investigation of the linear kinematics of fertilizer granules for estimating fertilizer distribution using a centrifugal spreader was motivated for precision agriculture using vision-guided control. An image acquisition system captured frames of free fall particles exploring various testing conditions that included frame rate, lighting conditions scenarios, and background colors for optimizing image quality. Datasets with 500 images of the granules were analyzed using two distinct methods: Particle Image Velocimetry (PIV), implemented with PIVLab (Particle Image Velocimetry Lab) with FFT cross-correlation computations, and CNN-based optical flow estimation. The latter method used the pre-trained models, RAFT (Recurrent All-Pairs Field Transforms) and LiteFlowNet. These models have generalization abilities. Innovatively, these networks were integrated with YOLOv5 as a detector, transforming per-pixel estimation into particle-pixel estimation by localizing particles, flow vectors corresponding to particles from the per-pixel flow vectors were extracted using bounding box pixels which enabled trajectory and velocity estimation. The results provided valuable insight into the motion of the fertilizer granules for future applications in controlling fertilizer application.

关键词： Fertilizer PIV (Particle Image Velocimetry) CNN

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Deploying Humanoid Robots in a Social Environment 21st

Deploying Humanoid Robots in a Social Environment

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21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Adolfsson, Kristoffer Kuvaja Tigerstedt, Christa Bistrom, Dennis Espinosa-Leal, Leonardo Arcada Univ Appl Sci Grad Sch & Res Jan Magnus Janssons Pl 1 Helsinki 00560 Finland Arcada Univ Appl Sci Sch Engn Culture & Wellbeing Jan Magnus Janssons Pl 1 Helsinki 00560 Finland

ISBN: (纸本)9783031619045;9783031619052

From early 2020 Arcada has pursued state-of-the-art research in human-robot-interaction (HRI). Deploying robots in a multitude of use cases and designing applications with experts from their respective fields. However, getting a robot service from the drafting stages in a lab out into a real-world professional situation is a complex endeavour that takes management, communication, and a multitude of talents. The paper aims to suggest a method, divided into three-stages for deploying humanoids in social environments. By participatory research with autoethnographic influences, this method is proposed through the experience, insight and knowledge of the authors. Having worked with field experts and customers to deploy humanoid robots in a multitude of environments over the past five years in different research projects. The three-stage method suggested (discovery, development, and deployment) aims to further other HRI projects.

关键词： Human Robot Interaction Humanoid robots Interaction design

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Azure Kubernetes Services WebRouting AddOn 12th

Azure Kubernetes Services WebRouting AddOn

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Ciobanu, Andra-Isabela-Elena Borcoci, Eugen Vochin, Marius Constantin Obreja, Serban Georgica Natl Univ Sci & Technol Politehn Bucharest Bucharest Romania

ISBN: (纸本)9783031602269;9783031602276

In an era of cloud and data centers, compact and versatile data are important and needful. This paper proposes and develops an experiment as a demonstration of a web routing application inside Azure cloud, more specifically on a web routing AddOn feature on Azure Kubernetes Service. The goal of this add-on is to make it easier for any user to expose their application to the world in a secure manner while reducing some of the operational overhead that goes into that. This paper's contribution is useful for developers working in the domain of implementing Azure Kubernetes Services.

关键词： Containers Kubernetes Azure Cloud Computing Ingress Web Application Managed Identity DNS SSL/TLS authentication

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Facial Expression Recognition in ATM Surveillance System 5th

Facial Expression Recognition in ATM Surveillance System

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Sable, Saili Angadi, Sanjeevkumar Shinde, Ashwini Mehta, Shital Patil, Uma Nutan Coll Engn & Res Dept Comp Sci & Engn Pune Maharashtra India Nutan Coll Engn & Res Dept Comp Sci & Engn Artificial Intelligence Pune Maharashtra India

ISBN: (纸本)9789819720811;9789819720828

Facial expression analysis is a novel and challenging field with significant applications in data-driven animation and human-computer interaction, among other fields. Facial emotion identification requires a vital step called efficient facial representation, which is created from the original face images. In this paper, Face Expression Recognition (FER) is determined to understand the situation of a human in an ATM environment that may lead to suspicious events in the worst case. A realistic evaluation of Local Binary Patterns (LBP), a facial representation method based on statistical local features. A number of machine learning techniques were investigated in detail on various databases. To extract histograms and Local Binary Patterns (LBP), the face area is first separated into small areas. This feature vector makes a face easily recognizable and helps to find similarities between images. Support Vector Machine (SVM) was used to configure the FER system for multi-class facial expression categorization mode.

关键词： Feature Extraction LBP SVM Video Surveillance

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Empirical Investigation of Real Exchange Rate Misalignments in Morocco: A Machine Learning Approach 4th

Empirical Investigation of Real Exchange Rate Misalignments ...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Baya, Souad Fadlallah, Abdellali El Baraka, Hamza Mouhil, Ismail Natl Inst Stat & Appl Econ Rabat Morocco Mohamed V Univ Rabat Morocco

ISBN: (纸本)9783031686597;9783031686603

Machine learning techniques such as Random Forests and LASSO have become increasingly popular for forecasting time series dynamics. These methods have demonstrated superior predictive accuracy, particularly when dealing with a large number of explanatory variables. The primary goal of this study is to evaluate the effectiveness of these techniques in forecasting Real Exchange Rate misalignments, which refer to the disparity between the Real Effective Exchange Rate and the equilibrium or steady-state Real Exchange Rate. Our dataset consists of around one hundred variables encompassing economic, financial, social, and global environmental indicators. The application of these methods indicates that the predictive performance is satisfactory and aligns with findings from the International Monetary Fund, particularly those generated by the LASSO algorithm. Projections suggest an overvaluation in the second quarter and throughout 2021, with additional forecasts for 2022 indicating a 0.22% overvaluation of the REER.

关键词： Misalignment Equilibrium Real Exchange Rate LASSO Prediction Regularization parameter lambda

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Separating DNA Sequences of Microbes Using Apache Spark 7th

Separating DNA Sequences of Microbes Using Apache Spark

引用

7th International Conference on Green Technology and Sustainable Development

作者： Thanh Tung Pham Tuan Hiep Pham Van Vinh Le Bach Khoa Saigon Coll Ho Chi Minh City Vietnam HCMC Univ Technol & Educ Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

Metagenomic projects focus on analyzing microbial communities which contain a huge amount of DNA sequences. Therefore, utilizing high performance computing for analysis of microbial data is essential to enhance data processing efficiency. This study proposes a parallelized approach based on Apache Spark technology for clustering sequences of microbes in which DNA sequences are separated into groups of different organisms. The proposed method is derived from an available algorithm and designed to exploit the strength of high performance systems. Experimental results demonstrate the effectiveness of the proposed method which achieves significantly improved processing speed compared to its single mode algorithm.

关键词： Clustering Metagenomic High Performance Computing Apache Spark

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The Probabilistic Characteristics of the Logarithmic Ratio of APSK Signal Adjacent Symbol Amplitudes 13th

The Probabilistic Characteristics of the Logarithmic Ratio o...

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13th Computer Science Online Conference

作者： Chernoyarov, Oleg Golpaiegany, Leila Litvinenko, Vladimir Litvinenko, Yuliya Tishukov, Boris Natl Res Univ MPEI Krasnokazarmennaya Str 14 Moscow 111250 Russia Univ Ayatollah Ozma Boroujerdi Boroujerd *** Iran Voronezh State Tech Univ Moscow Ave 14 Moscow 394026 Russia

ISBN: (纸本)9783031702846;9783031702853

To implement high-speed systems for transmitting discrete information, amplitude and phase-shift keyed signals are widely used. Differential shift keying of the ratio of the amplitudes of adjacent symbols makes it possible to abandon the calibration of the amplitude characteristics of the reception path, while determining the probabilistic characteristics of this ratio and especially its log-rhyme is relevant. In this paper it is shown that the probabilistic characteristics of the logarithm of the ratio of the amplitudes of adjacent symbols under the influence of Gaussian noise do not depend on the absolute levels of the signal and noise, but are determined only by their ratio. The corresponding approximate expressions are obtained, and the influence of the type of amplitude and phase-shift keyed signal constellation is studied and its parameters. Statistical simulation is carried out. It is noted that the research results can be used in the design of communication systems.

关键词： Radio signal Amplitude and phase-shift keying Differential shift keying Ratio of the amplitudes of adjacent symbols Amplitude code demodulation

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Leveraging Deep Collaborative Filtering for Advanced Recommender systems 4th

Leveraging Deep Collaborative Filtering for Advanced Recomme...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Abakarim, Sana Qassimi, Sara Rakrak, Said Cadi Ayyad Univ Fac Sci & Tech L2IS Lab Marrakech Morocco

ISBN: (纸本)9783031686498;9783031686504

In the field of personalized recommendations, collaborative filtering techniques have evolved from traditional methods to advanced recommendation systems powered by deep learning. This study thoroughly investigates this transformative evolution, delving into the com- plex workings of recommender systems. Beyond theoretical discussions, our investigation involves a practical evaluation of these techniques on a variety of datasets, including the Amazon and Movie-Lens datasets, representing the e-commerce and entertainment domains. Through this perspective, we better understand how recommendation models dynamically adapt, strategically leveraging deep learning to build advanced recommendation systems adapted to various real-world contexts. To assess the predictive capabilities of these models, we use metrics such as RMSE (Root Mean Square Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error), offering qualified insight into the effective- ness of each technique, with a particular focus on the strategic use of deep learning for the development of advanced recommender systems. This analysis transcends exploration, offering practical implications for researchers and data scientists. Contributing to the current debate in the field, our study serves as a definitive guide for the design of recommender systems that strategically exploit deep learning, ensuring their relevance and effectiveness in a variety of application scenarios.

关键词： Recommender systems Comparative study Deep Learning Collaborative filtering Deep collaborative filtering

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Localizing Scan Targets from Human Pose for Autonomous Lung Ultrasound Imaging

Localizing Scan Targets from Human Pose for Autonomous Lung ...

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Intelligent systems Conference

作者： Long, Jianzhi Cai, Jicang Al-Battal, Abdullah Jin, Shiwei Zhang, Jing Tao, Dacheng Lerman, Imanuel Truong Nguyen Univ Sydney Sydney NSW 2006 Australia Univ Calif San Diego San Diego CA 92093 USA

ISBN: (纸本)9783031664274;9783031664281

Ultrasound is progressing toward becoming an affordable and versatile solution to medical imaging. With the advent of COVID-19 global pandemic, there is a need to fully automate ultrasound imaging as it requires trained operators in close proximity to patients for a long period of time, therefore increasing risk of infection. In this work, we investigate the important yet seldom-studied problem of ultrasound scan target localization, under the setting of lung ultrasound imaging. Existing works either lack human verification or generalization capability, by conducting experiments on phantom objects or relying domain-specific prior of the specific scan target. To address these issues, we develop a purely vision-based and data-driven method inspired by research in human pose estimation. We test the proposed method on 30 human subjects, and attain an accuracy level of 16.00 +/- 9.79 mm for probe positioning and 4.44 +/- 3.75. for probe orientation, with a success rate above 80% under an error threshold of 25 mm for all scan targets. Moreover, our approach can serve as a general solution to other types of ultrasound modalities. The code for implementation has been released.

关键词： Computer vision Robotics Autonomous ultrasound imaging Ultrasound target localization Human pose estimation

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Low Lightness Image Enhancement Using HSV Color Based on DCP with Color Restoration and Lightning Stretch 4th

Low Lightness Image Enhancement Using HSV Color Based on DCP...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Kadhim, Taqwa Q. Daway, Hazim G. Kadhim, Ahlam M. Mustansiriyah Univ Coll Sci Dept Phys Baghdad Iraq

ISBN: (纸本)9783031686498;9783031686504

The poor visibility of low-light images makes them unsuitable for computer vision algorithms and human observation. Many image-enhancing methods have been suggested as techniques for this problem. But it does not restore colors and improve lighting properly. Therefore, in this study, we propose an algorithm that restores colors and improves lightness for low-light images using LOL data, which contains 485 low-light images. The proposed algorithm includes three main steps. The first is to use the brightness low light area technique, and the second is to restore colors by applying two filters, the third is to improve the lighting component based on the HSV color model, where the color components are isolated from the lighting, and then maps are drawn using lightness stretch and adaptive histogram equalization. We have used reference quality standards Peak Signal to Noise Ratio (PSNR) and non-reference quality metrics Entropy (EN)' Average Gradient (AG) and Natural Image Quality Evaluator(NIQE). Through analyzing the results, the proposed method succeeded in improving low-light images and color restoration, and the quality standards reached EN (7.004), AG (11.286), NIQE(3.502), and PSNR (14.013) values.

关键词： DCP CLAHE HSV color space contrast enhancement Lightning stretch

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