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arXiv 2021年

作者： Afifi, Mahmoud Nasser Mohammed Graduate Program in Electrical Engineering and Computer Science York University TorontoON Canada

This thesis presents methods and approaches to image color correction, color enhancement, and color editing. To begin, we study the color correction problem from the standpoint of the camera's image signal processor (ISP). A camera's ISP is hardware that applies a series of in-camera image processing and color manipulation steps, many of which are nonlinear in nature, to render the initial sensor image to its final photo-finished representation saved in the 8-bit standard RGB (sRGB) color space. As white balance (WB) is one of the major procedures applied by the ISP for color correction, this thesis presents two different methods for ISP white balancing. Afterwards, we discuss another scenario of correcting and editing image colors, where we present a set of methods to correct and edit WB settings for images that have been improperly white-balanced by the ISP. Then, we explore another factor that has a significant impact on the quality of camera-rendered colors, in which we outline two different methods to correct exposure errors in camera-rendered images. Lastly, we discuss post-capture auto color editing and manipulation. In particular, we propose auto image recoloring methods to generate different realistic versions of the same camera-rendered image with new colors. Through extensive evaluations, we demonstrate that our methods provide superior solutions compared to existing alternatives targeting color correction, color enhancement, and color editing. © 2021, CC BY.

关键词： Color

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Ivan Alexander Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Departement Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers.

关键词： Training Sentiment analysis Accuracy Social networking (online) Text categorization Nearest neighbor methods Naive Bayes methods Random forests Robots Airline industry

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Downstream Task Performance of Pre-Trained Model For Outdoor Audio Event Classification

Downstream Task Performance of Pre-Trained Model For Outdoor...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Theresia Herlina Rochadiani Yaya Heryadi Ilvico Sonata Lukas Wawan Suparta BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Our surroundings’ auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models—three, four, and five classes—with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively.

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A conceptual framework of deploying a trained CNN model for mobile breast self-screening 18

A conceptual framework of deploying a trained CNN model for ...

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18th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Roslidar Muchamad, Masduki Khamdan Arnia, Fitri Syukri, Maimun Munadi, Khairul Universitas Syiah Kuala Doctoral Program School of Engineering Post Graduate Program Banda Aceh23111 Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Medical Faculty Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia

ISBN: (纸本)9780738111278

In this article, we propose a conceptual framework of mobile self-screening to detect breast cancer at the initial stage using temperature characteristics. A trained convolutional neural network (CNN) is implemented to extract and classify the breast thermogram. Moreover, a histogram of temperature distribution is also provided to support a recommendation to the user. As we consider the user's data privacy, reliability, low latency, and low cost, we also suggest some recommendations in inferencing the CNN model on the mobile device. Finally, we describe the application overview to demonstrate the feature function. © 2021 IEEE.

关键词： Neural network models

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Mechanical Wave Detection Through Polyurethane Foam Using Optical Fiber Sensors

Mechanical Wave Detection Through Polyurethane Foam Using Op...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Beatriz Brusamarello Uilian J. Dreyer Erik T. Silveira Carlos A. Bavastri Gilson A. Brunetto Luis F. P. Melegari Guilherme H. Weber Danilo F. Gomes Cicero Martelli Jean Carlos Cardozo da Silva Graduate Program in Electrical and Computer Engineering (CPGEI-UTFPR) Universidade Tecnologica Federal do Parana Curitiba Parana Brazil Graduate Program in Energy Systems (PPGSE-UTFPR) Universidade Tecnologica Federal do Parana Curitiba Parana Brazil Graduate Program in Mechanical Engineering (PGMEC-UFPR) Universidade Tecnologica Federal do Parana Curitiba Parana Brazil CPFL Energia Brasil Universidade Tecnologica Federal do Parana Curitiba Parana Brazil

In recent years, distributed acoustic sensing has garnered significant interest in seismic monitoring due to its sensitivity to low frequencies, continuity of measurement in both time and space and the capacity to provide vast amounts of data regarding the conditions of large structures. This study aims to develop a metrological characterization system to evaluate the performance of the distributed acoustic sensing systems to reconstruct the mechanical wavefront and measure its propagation speed in polyurethane foams. The wave propagation speed measured using distributed acoustic sensing is compared with optical fiber Bragg grating sensors and piezoelectric accelerometer, and the difference is less than 1.5%. The results demonstrate that it is possible to reconstruct the mechanical wavefront using both instrumented sensors, thus validating the application of a distributed acoustic sensing system for mechanical wave measurement in polyurethane foams.

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Interferometry Among Strongly Driven Quasiparticles with Distinct Dynamical and Geometrical Phases

Interferometry Among Strongly Driven Quasiparticles with Dis...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Seamus O'Hara Joe Costello Qi Tian Qile Wu Ken West Loren Pfeiffer Mark Sherwin Liang Wu Department of Physics and Institute of Terahertz Science and Technology University of California Santa Barbara Santa Barbara CA Intelligence Community Post-Doctoral Research Fellowship Program Philadelphia PA Department of Physics University of Pennsylvania Philadelphia PA Department of Electrical and Computer Engineering Princeton University Princeton NJ

ISBN: (纸本)9798350369311

In this proceeding, we present polarization data of sidebands emitted from strongly driven quasiparticles, demonstrate the dependence of these data on various parameters of the external driving field, and describe the data using an analytical expression, dependent on driving field parameters and the effective Hamiltonian of the system. We propose using this method on spin-momentum locked systems, such as altermagnets, to observe and reconstruct sidebands which are influenced by the topology of individual bands.

关键词： Electro-optic effects Amplitude modulation Topology Interferometry

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Adaptive Environment-Aware Robotic Arm Reaching Based on a Bio-Inspired Neurodynamical Computational Framework

Adaptive Environment-Aware Robotic Arm Reaching Based on a B...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Dimitrios Chatziparaschis Shan Zhong Vasileios Christopoulos Konstantinos Karydis Dept. of Electrical and Computer Engineering Univ. of California Riverside Riverside CA USA Neuroscience Graduate Program Univ. of California Riverside Riverside CA USA Dept. of Bioengineering Univ. of California Riverside Riverside CA USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Bio-inspired robotic systems are capable of adaptive learning, scalable control, and efficient information processing. Enabling real-time decision-making for such systems is critical to respond to dynamic changes in the environment. We focus on dynamic target tracking in open areas using a robotic six-degree-of-freedom manipulator with a bird-eye view camera for visual feedback, and by deploying the Neurodynamical Computational Framework (NeuCF). NeuCF is a recently developed bio-inspired model for target tracking based on Dynamic Neural Fields (DNFs) and Stochastic Optimal Control (SOC) theory. It has been trained for reaching actions on a planar surface toward localized visual beacons, and it can retarget or generate stop signals on the fly based on changes in the environment (e.g., a new target has emerged, or an existing one has been removed). We evaluated our system over various target-reaching scenarios. In all experiments, NeuCF had high end-effector positional accuracy, generated smooth trajectories, and provided reduced path lengths compared with a baseline cubic polynomial trajectory generator. In all, the developed system offers a robust and dynamic-aware robotic manipulation approach that affords real-time decision-making.

关键词： Visualization Accuracy Target tracking Robot vision systems Real-time systems Polynomials Generators Trajectory Robots Manipulator dynamics

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Systolic Array for Parallel Solution of the Robust Kalman Filter Used for Attitude and Position Estimations in UAVs

Systolic Array for Parallel Solution of the Robust Kalman Fi...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leandro José Evilásio Campos Marco Henrique Terra Ricardo Menotti Roberto Santos Inoue Graduate Program in Electrical Engineering Federal University of São Carlos São Carlos SP Brazil Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos SP Brazil Department of Computing Federal University of São Carlos

The efficient Kalman filter has been widely used in recent decades to obtain air navigation information in UAVs. However, for a good performance of the Kalman filter, the model that describes the system dynamics must not contain uncertainties. This paper presents the implementation of a robust Kalman filter to estimate the attitude, velocity, and position of UAVs. The robust filter considers uncertainties in the sensor models. A mathematical structure based on the solution of linear systems synthesizes the predictor-corrector robust estimation algorithm. The main contribution of this study is the proposed QR decomposition based on Givens rotation to solve the linear system. The simulated experiments used sensory data collected in Zürich-Switzerland and ground truth referencing attitude, velocity, and position. The offline simulation results express the effectiveness of the robust Kalman filter for this application, with a reduction of up to 18.9% in the estimation error, in relation to the standard Kalman filter. The proposal to use systolic arrays for numerical solutions has shown promise for implementation in parallel processing platforms, such as FPGAs.

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Non-Hermitian metasurface with non-trivial topology

Non-Hermitian metasurface with non-trivial topology

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Prasad, Ciril S. Yang, Frank Li, Weijian Lach, Rosemary Everitt, Henry O. Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States U.S. Army DEVCOM Army Research Laboratory-South HoustonTX United States

The synergy between topology and non-Hermiticity in photonics holds potential for optical devices that are robust against defects. We demonstrate a non-Hermitian plasmonic-dielectric metasurface in the visible with no... 详细信息

ISBN: (纸本)9781557528209

关键词： Topology

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Hidayaturrahman Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Computer Science Bina Nusatara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.

关键词： computer science Adaptation models Neural networks Feature extraction Data models Data mining Information technology

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