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Using Deep Learning dept. Maps to Improve Monocular SLAM 38

Using Deep Learning Depth Maps to Improve Monocular SLAM

引用

38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Hollows, Simon Green, Richard University of Canterbury Dept. Mechatronics Engineering Christchurch New Zealand University of Canterbury Dept. Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes the use of a neural network to generate dept. maps from 2D frames, offering an alternative to dept. cameras. The goal is to address the accuracy limitations of monocular SLAM by gaining more accurate dept. data through deep learning. This approach combines the simplicity and cost-effectiveness of a single camera setup with the enhanced accuracy of RGB-D ***-SLAM running with RGB-D input was used as the SLAM framework for this method. This allowed for accurate comparisons between dept. sensor dept. maps in test datasets and the generated dept. maps. The MiDaS neural network was used to generate estimated dept. maps. These were then scaled to create consistency over the entire test sequence. The TUM RGB-D dataset was used for *** generated dept. maps were able to compare with the TUM RGB-D data in accuracy. On one dataset, the TUM dept. maps had a root mean square error (RMSE) or 0.24 m while the generated dept. maps had a RMSE of 0.18 m. In general, the proposed method lacks consistency and the lack of good fusion between dept. maps causes this approach to fail in slow-moving scenes. © 2023 IEEE.

关键词： Cost effectiveness

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Driving Behavior Classification Using Smartphone Sensor Data 8

Driving Behavior Classification Using Smartphone Sensor Data

引用

8th International Conference on computer Science and engineering, UBMK 2023

作者： Dikbiyik, Deniz Alagoz, Fatih Boǧaziçi University Dept. of Software Engineering İstanbul Turkey Boǧaziçi University Dept. of Computer Engineering İstanbul Turkey

ISBN: (纸本)9798350340815

Given the substantial number of accidents because of human error, the development of automatic driver behavior monitoring systems has become a pressing need. By providing real-time monitoring and analysis of driving behavior, these systems have the potential to decrease the incidence of accidents and improve overall road safety. This study presents a novel analysis framework for classifying driving behavior based on data gathered from passengers' smartphones. The data were collected using our mobile application installed on the smartphones and processed using machine learning algorithms. The study utilized several machine learning classification t echniques, w ith a focus on developing a Long Short Term Memory (LSTM) algorithm for improved accuracy. A Federated Learning algorithm was also developed to collect the data not into one area and train a global model to apply for labeling the driving behavior. The results show the efficacy of the proposed approach in accurately classifying driving behavior based on data obtained from smartphones. © 2023 IEEE.

关键词： Smartphones

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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Posture Recognition-Based Robot Control Using Media Pipe Pose

Posture Recognition-Based Robot Control Using Media Pipe Pos...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Woo Kim, Jong Yeong Mun, Seo Park, BoJeong Choi, Younglim Kim, Hyunseok Dong-A University Dept. of Electronic Engineering Busan Korea Republic of Dong-A University Dept.of Mechanical Engineering Busan Korea Republic of Dong-A University Dept. of Software Innovation Center Busan Korea Republic of Dong-A University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331510756

This study proposed a body movement-based system for efficiently controlling robots in large indoor environments. Traditional hand gesture recognition systems experience significant accuracy loss in large indoor spaces, particularly as the distance between the user and the camera grows. To address this issue, clear and large body movements were used as control signals instead of hand movements. For example, participants could give commands to the robot by raising both hands or lifting one hand in a specific direction. The system utilized MediaPipe Pose to track the user's posture in real time and communicated commands from the server to the robot via TCP/IP socket communication. This structure ensured stable data transmission, enabling smooth robot control even in expansive spaces. Consequently, this research demonstrated the feasibility of effective robot control in large indoor areas, such as screen golf courses, and suggested the potential for system expansion in various indoor environments. © 2025 IEEE.

关键词： Human robot interaction

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Predicting Analysis of the Multi-Sensor Signals in Terms of Time Series

Predicting Analysis of the Multi-Sensor Signals in Terms of ...

引用

2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Zuev, Sergei Kabalyants, Petr Belgorod State Technological University Named after V. G. Shoukhov Dept. of Computer Software and Automated Systems Belgorod Russia

ISBN: (纸本)9798350395044

This study is aimed to present a new way to predict multi-sensor signals values based on the given values registered before. The method is based on the generalised serialisation method, proposed by the author earlier, and autoregression with adjustable window width. The given signals have been received as a sequence of values related to a random instances of measurements. However there exist some patterns that imply the values to be not random in fact. It is natural in this proposition to consider all the signals came from the object to be predictable up to some precision due to the pattern for every signal exists. It is shown in the paper how to make such unification of the signals to receive the time series;there are various methods of prediction for the time series, and autoregression is chosen for this study;the generalised serialisation method allows to transform the value predicted by autoregression to the multi-values needed for prediction. © 2024 IEEE.

关键词： Time series

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Efficient Hardware Implementation of Seizure Detector Using Machine Learning

Efficient Hardware Implementation of Seizure Detector Using ...

引用

2024 International Conference on Microelectronics, ICM 2024

作者： El-Tanbadawy, Mariam K. Salem, Mohammed A. Megeed Abd Elghany, Mohamed A. German University in Cairo Computer Engineering Dept. Egypt German University in Cairo Electronics Engineering Dept. Egypt Tu Darmstadt Integrated Electronic Systems Lab Germany

ISBN: (纸本)9798350379396

In the past few decades, Seizure Detection has been a vital cornerstone in biomedical research. Both hardware and software implementations have been explored to improve algorithms, detection performance and resources. In this paper, three handcrafted features composed of Coastline Function, Hurst Exponent and Fractal Dimension are used as training data for supervised learning models to build an efficient and sensitive seizure detector. Random Forest classifier exhibits the best accuracy at 98%. Hardware efficiency, too, is exploited through a modified Feature Extractor that combines the aforementioned features. The features are adjusted to reduce their hardware resources on Virtex-7 FPGA. The area utilization is improved by 10%, power utilization is improved by 20% and latency decreased by 32%. © 2024 IEEE.

关键词： Fractal dimension

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A Hybrid Flood Forecasting Approach Using Hidden Markov Model and Machine Learning Techniques 7

A Hybrid Flood Forecasting Approach Using Hidden Markov Mode...

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Sri Sakthi, L. Chitra, P. Harini Sree, M.S. Subbulakshmi, P. Umayal, C. Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350304268

Floods are one of the most common and adverse disasters that the world faces. In India, floods are seemed as the disaster that happens often and pose extreme risk to lives and belongings. Forecasting floods can greatly reduce the risk and effect of the forthcoming flood. A large quantity of parameters is answerable for flood occurrence. The amount of rainfall and temperature are the most vital parameters for inflicting floods. Hence rainfall and temperature are used as determinants inside the look at. Machine learning model such as logistic regression, Naive bayes, k-Nearest neighbours, support vector machines (SVM) and random forests generally generate a model or computation method to label the results. Further, in latest days, hybridization techniques are expected to yield better results, wherein multiple models are combined to provide a new model. The proposed work is to develop a hybrid model called HMM-ML which includes Hidden Markov model with the above cited ML models. as a result, HMM acts as a first-class-tuner to stimulate the model. The developed model is validated against the region Kozhikode located in the State of Kerala, India which gets the maximum amount of rainfall during the period between June - August. © 2023 IEEE.

关键词： Flood prediction Hidden Markov Model(HMM) Hybridization Machine Learning (ML) Rainfall Temperature

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Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection 24

Advancements in Dental Diagnostics: YOLOv3-Powered Machine L...

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Odeh, Suhail Samara, Anas Zreineh, Nicola Obeid, Seif Ibrieghieth, Maen Obaid, Mahmoud Bethlehem University Software Engineering Dept. Palestine Arab American University Computer System Engineering Jenin Palestine

ISBN: (纸本)9798350384307

This paper presents a rigorous investigation into the application of state-of-the-art machine learning techniques for the automated detection of dental issues, utilizing the YOLOv3 Algorithm, a cutting-edge one-stage object detection method. The study encompasses the meticulous annotation and utilization of a carefully curated dataset featuring 126 panoramic X-ray images, illustrating a diverse range of dental conditions. Among these conditions, the primary emphasis is placed on the precise detection of six specific dental problems. Employing advanced computer vision methodologies, the model demonstrates exceptional accuracy in the identification and precise localization of these targeted dental issues. The implications of these findings are profound for the field of dental healthcare, as the automated detection of dental problems holds the potential to significantly enhance the diagnostic and treatment planning capabilities of dental professionals. The results presented in this research represent a notable stride in the ongoing evolution of machine learning and underscore its capacity to revolutionize the landscape of dental diagnostics, ultimately contributing to the advancement of oral health management and patient care. © 2023 IEEE.

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Downlink Covariance Estimation in URA FDD Massive MIMO systems 48

Downlink Covariance Estimation in URA FDD Massive MIMO Syste...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bameri, Salime Almahrog, Khalid Gohary, Ramy H. El-Keyi, Amr Ahmed, Yahia Carleton University Dept. of Systems and Computer Engineering Canada Ericsson Canada Canada

ISBN: (纸本)9781728163277

We propose a low-complexity downlink channel covariance matrix estimation for massive multiple-input multiple-output systems in which the base station (BS) is equipped with a uniform rectangular antenna array (URA). This scheme can be expressed in the form of an affine transformation which depends only on the uplink and downlink carrier frequencies, and the BS array configurations. An upper bound on the estimation error is derived, which shows that the accuracy of the proposed scheme increases with the number of URA antennas and the compactness and differentiability class of the periodic extension of a non-linearly transformed version of the angular power spread. The performance superiority of the proposed scheme over its existing counterparts is confirmed through simulations. © 2023 IEEE.

关键词： angular power spectrum channel reciprocity estimation error Two-dimensional Fourier series

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Utilizing Deep Neural Networks to Improve Intrusion Detection System(IDS)

Utilizing Deep Neural Networks to Improve Intrusion Detectio...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Saad, Ahmed Mahmoud Meekay, Mohammed Awad El Sayed, Mostafa Samy Mansoura University Mansour Electronics and Computer and Systems Engineering Dept. Cairo Egypt Mansoura University Mansour Electronics and Communications Dept. Cairo Egypt

ISBN: (纸本)9798350351408

Internet services are now accessible everywhere in the world due to the rapid advancement of networks and technology in recent decades. The development of information security solutions that can recognize novel threats is now critical due to the surge in piracy and the compromising of many modern systems. An intrusion detection system (IDS) is one of the most important information security technologies. It uses machine learning and deep learning algorithms to locate anomalies in the network. The main goal of this paper is to identify the unknown assault package by utilizing a novel intrusion detection method that offers excellent network performance. In this model, two techniques-binary classification and multiclass classification-that make use of a deep neural network algorithm are used to detect attacks. The suggested's outstanding accuracy. © 2024 IEEE.

关键词： Network intrusion

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