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Visual Navigation for Autonomous Vehicles: An Open-source Hands-on Robotics Course at MIT 12

Visual Navigation for Autonomous Vehicles: An Open-source Ha...

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12th IEEE Integrated STEM Education Conference, ISEC 2022

作者： Carlone, Luca Khosoussi, Kasra Tzoumas, Vasileios Habibi, Golnaz Ryll, Markus Talak, Rajat Shi, Jingnan Antonante, Pasquale Dept. of Aeronautics and Astronautics Massachusetts Institute of Technology United States Senior Research Scientist Data61 Csiro Australia Dept. of Aerospace Engineering University of Michigan United States School of Computer Science University of Oklahoma United States Dept. of Aerospace and Geodesy Technical University of Munich Germany

ISBN: (纸本)9781665484299

This paper reports on the development, execution, and open-sourcing of a new robotics course at MIT. The course is a modern take on 'Visual Navigation for Autonomous Vehicles' (VNAV) and targets first-year graduate students and senior undergraduates with prior exposure to robotics. VNAV has the goal of preparing the students to perform research in robotics and vision-based navigation, with emphasis on drones and self-driving cars. The course spans the entire autonomous navigation pipeline;as such, it covers a broad set of topics, including geometric control and trajectory optimization, 2D and 3D computer vision, visual and visual-inertial odometry, place recognition, simultaneous localization and mapping, and geometric deep learning for perception. VNAV has three key features. First, it bridges traditional computer vision and robotics courses by exposing the challenges that are specific to embodied intelligence, e.g., limited computation and need for just-in-time and robust perception to close the loop over control and decision making. Second, it strikes a balance between dept. and breadth by combining rigorous technical notes (including topics that are less explored in typical robotics courses, e.g., on-manifold optimization) with slides and videos showcasing the latest research results. Third, it provides a compelling approach to hands-on robotics education by leveraging a physical drone platform (mostly suitable for small residential courses) and a photo-realistic Unity-based simulator (open-source and scalable to large online courses). VNAV has been offered at MIT in the Falls of 2018-2021 and is now publicly available on MIT OpenCourseWare (OCW) and at ***/. © 2022 IEEE.

关键词： Computer vision

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Uncertainty Aware Implicit Image Function for Arbitrary-Scale Super-Resolution

Uncertainty Aware Implicit Image Function for Arbitrary-Scal...

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IEEE International Conference on Image Processing

作者： Swastik Jena Saptarshi Panda Bunil Kumar Balabantaray Rajashree Nayak Dept. of Computer Science and Engineering National Institute of Technology Meghalaya India Dept. of Civil Engineering National Institute of Technology Meghalaya India Centre for Data Science JIS Institute of Advanced Studies and Research Kolkata India

The most integral step in computer vision and image processing tasks is the representation of images. Recently, continuous image parameterization using Implicit Neural Representations (INR) has shown a great advantage over discrete representations due to their spatial invariance. This property immediately finds application in the context of single-image super-resolution (SISR) at an arbitrary scale. However, most super-resolution models, including the INR-based Local Implicit Image Function (LIIF), produce only a single output, failing to address the ill-posedness of SISR. Moreover, these models tend to optimize a mean-squared-error (MSE) based loss function which causes blurring and structural distortion in regions exhibiting a high degree of variance (details). Our work proposes a novel uncertainty-aware self-supervised methodology (U-LIIF) that extends on LIIF, to reduce the blurriness and deals with the ill-posedness of SISR. Our U-LIIF does not require any re-training and yields diversified high-resolution images by leveraging model uncertainty. The efficacy of the proposed method is validated by substantial experiments on various benchmark datasets.

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Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT

Time Series Anomaly Detection with CNN for Environmental Sen...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Mirza Akhi Khatun Mangolika Bhattacharya Ciarán Eising Lubna Luxmi Dhirani Dept. Electronic & Computer Engineering SFI CRT Foundations in Data Science University of Limerick Limerick Ireland School of Information Technology Illinois State University Illinois USA Dept. Electronic & Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This research develops a new method to detect anomalies in time series data using Convolutional Neural Net-works (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an loT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in time series data, resulting in a 92 % accuracy in identifying possible attacks.

关键词： Temperature sensors Wireless sensor networks Temperature distribution Accuracy Time series analysis Medical services Humidity

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A Hybrid Machine Learning and Deep Learning Approach for Diabetes Prediction With a Tkinter–Based User Interface

A Hybrid Machine Learning and Deep Learning Approach for Dia...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Sai Bhargav Kasetty Issac Neha Margret K. Sai Madhuri Rajesh Kumar Ojha Nuthalapati Sudha K. Rajakumar SCOPE V.I.T University Vellore Tamilnadu India dept. of CSE Data Science BVRIT Narsapur Hyderabad Telangana India dept. of Computer Science & Engineering Silicon University Bhubaneswar Odisha India School of Engineering Mahindra University Hyderabad Telangana India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Diabetes prediction is a critical task in healthcare that can significantly benefit from advancements in machine learning and deep learning technologies. This paper presents a hybrid approach combining deep learning and traditional machine learning models for diabetes prediction. The proposed system integrates a Tkinter-based Graphical User Interface (GUI) to make it easier for users to interact and make predictions. In our discussion, we will cover the design and implementation of the system, and evaluate the performance of different models using precision, F1 score, recall, and, accuracy to provide insights into their effectiveness. Results demonstrate the efficacy of the hybrid approach, with a focus on model accuracy and user experience.

关键词： Deep learning Technological innovation Accuracy Computational modeling Medical services Predictive models User experience Diabetes Informatics Graphical user interfaces

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A Fine-Grained Attribute Pre-Labeling Method Based on Label Dependency and Feature Similarity Dynamics

A Fine-Grained Attribute Pre-Labeling Method Based on Label ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao-Chiang Shao Yu-Hsien Lin Chia-Wen Lin Inst. Data Science and Information Computing National Chung Hsing University Taiwan Dept. Electrical Engineering National Tsing Hua University Taiwan

In this paper, we proposed a fine-grained attribute pre-labeling method based on the multi-label recovery techniques. Given a fine-grained image dataset with overlooked attributes in its annotation vectors, our method can predict those missing attribute labels by learning the between-label dependency based on the estimated similarity between known attributes and the similarity of extracted deep image features. Furthermore, to prevent the learnable label dependency matrix from converging to a trivial solution, we designed a trace-loss to penalize the self-dependency of attributes. Comprehensive experiments on the CUB-200-2011 dataset show that, given a training set with 40% of attribute labels randomly dropped: i) our approach achieves a pre-labeling performance with an mAP value of 30.7 on a blind testing set, and ii) the missing attributes in the training set can be corrected with an accuracy of 89%. Our method can effectively and robustly perform the fine-grained pre-labeling task.

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment

Image based and Point Cloud based Methods for 3D View Recons...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Arya Agrawal Teena Sharma Nishchal K. Verma Shantaram Vasikarla School of Computer Science and Engineering Vellore Institute of Technology Vellore India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati India Dept. of Electrical Engineering Indian Institute of Technology Kanpur India Dept. of Computer Science California State University Northridge CA

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more.

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Risk estimation in a Markov cost process: lower and upper bounds 24

Risk estimation in a Markov cost process: lower and upper bo...

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Proceedings of the 41st International Conference on Machine Learning

作者： Gugan Thoppe Prashanth L. A. Sanjay P. Bhat Dept. of Computer Science and Automation Indian Institute of Science (IISc) Bengaluru India and Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ε-accuracy, either in expected or high-probability sense, requires at least Ω(1/ε2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs' mean. Even in that case, our lower bound of Ω(1/ε2) improves upon the existing Ω(1/ε) bound (Metelli et al., 2023).

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An Artificial Neural Network Model to Forecast Grain Crops in North-Kazakstan Agricultural Experimental Station

An Artificial Neural Network Model to Forecast Grain Crops i...

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2023 IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2023

作者： Mimenbayeva, Aigul Omirtay, Maylen Niyazova, Rozamgul Bekmagambetova, Gulmira Suleimenova, Raya Tursumbayeva, Ainur Astana It University Dept. of Computational and Data Sciences Astana Kazakhstan Astana It University Specialty Smart Technologies Astana Kazakhstan L. Gumilyov Eurasian National University Dept. of Artificial Intelligence Technologies Astana Kazakhstan S. Seifullin Kazakh Agrotechnical Research University Dept. of Computer Science Astana Kazakhstan Eurasian Technological University School of Engineering and Information Technology Almaty Kazakhstan S. Seifullin Kazakh Agrotechnical Research University Dept. of Information Systems Astana Kazakhstan

ISBN: (纸本)9798350336917

The paper analyzes and predicts the yield of grain crops of LLP 'North-Kazakhstan Agricultural Experimental Station' for the last 30 years. The class of artificial neural networks-multilayer perceptron (MLP) was used as a research tool. The process of training of a model of a neural network consisting of 30 networks was performed on the basis of 70% training and 30% control samples. Based on the analysis of residuals histogram and scatter plot of the target and output function, the best neural network models MLP-1-7-1 (p=0.92), MLP-1-8-1 (p=0.97), MLP-1-5-1 (p=0.82) were selected. Next, using the best MLP 1-8-1 network, the model was tested the given data. Using time series projection, a graph was illustrated and a table of predicted grain yield values for the coming years was tabulated. The absolute error showed the high accuracy of the obtained best MLP 1-8-1 network for forecasting the amount of grain crop yield. The obtained model of artificial network can be applied in research and monitoring of agricultural production development. © 2023 IEEE.

关键词： Multilayer neural networks

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Internet of Animal Health Things (IoAHT) Framework for Clinical Mastitis Detection in Dairy Cows

Internet of Animal Health Things (IoAHT) Framework for Clini...

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Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： Ankitha K Venugopala P S Harish Kunder Ankitha Shetty Dept. of Artificial Intelligence and Data Science NMAMIT Nitte Karkala India Dept. of Artificial Intelligence and Machine Learning Alvas Institute of Engineering and Technology Moodbidri India

The Earth serves as a shared habitat not just for humans but also for an array of animal species and organisms. Throughout history, animals have played a crucial role in preserving the natural ecosystem balance and meeting diverse human needs. They contribute significantly to various sectors, such as Agriculture, Dairy, Poultry, and animal husbandry, thus bolstering a country's economy. Human responsibility lies in ensuring the well-being of these animals, which is overseen by Veterinary science. In India, a major milk and milk product producer, the dairy industry actively contributes to the nation's economy. Among the primary milk sources-cows, buffaloes, goats, and sheep-cows hold a revered place in different faiths. Particularly in the dairy industry, cows are vital for milk production. However, monitoring cow health poses challenges. Clinical Mastitis, an inflammation affecting cows' mammary glands, hampers milk production. Automatic milking machines are commonly used, but identifying and segregating mastitis-infected cows from healthy ones remains difficult. The contagious nature of the disease increases the risk of widespread infection among cows. To combat this issue, we propose a solution called the Internet of Animal Health Things (IoAHT), based on the Internet of Things (IoT). IoAHT employs IoT technology for animal health, aiming to accurately identify cases of clinical mastitis in cows. The implementation of IoAHT has the potential to revolutionize health monitoring in the dairy industry, safeguarding cow well-being and ensuring sustainable milk production.

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AI-Powered Personalized Deep Learning Framework for Early Detection of Glioblastoma Using Multi-Modal MRI data

AI-Powered Personalized Deep Learning Framework for Early De...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sunitha T Veeramalai Sankaradass V.K Manindra Manish Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The prognosis of glioblastoma, which stands as the most malignant variant of a brain tumour, is clearly unfavourable due to the late diagnosis and the severe heterogeneity it is known to possess. Standard diagnostic techniques nowadays that employ MRI data quite often have a low-level performance in terms of diagnosing glioblastoma during its early stages owing to the diverse manner in which the tumours may grow in various patients. In light of this background, we present a new deep learning framework suitable for multi-modal MRI data and aimed at improving glioblastoma prognosis at an early stage. The framework utilizes convolutional neural networks (CNNs) to obtain features from T1, T2, and FLAIR MRI images, and combines this with an AI-guided individualized diagnostic system. This model utilizes relevant clinical data such as age and genetic data in a more individualized manner to meet patient’s needs during diagnosis. One more novel feature of our approach is the ensemble learning that combines CNNs with RNNs to detect minor tumour deposits more effectively over time. The proposed framework delivered an AUC of 0.94 on large glioblastoma MRI databases, thus progressing the early-stage diagnosis of the tumour type which was the aim. This advanced personalized method not only allows for superior diagnosis but also improves on the treatment plans that are available for such patients. Further development will concern clinical verification as well as increasing the capacity of the framework.

关键词： Deep learning Accuracy Sensitivity Magnetic resonance imaging Genetics Feature extraction History Prognostics and health management Standards Tumors

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