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Achieving Efficient and Realistic Full-Radar Simulations and Automatic data Annotation by exploiting Ray Meta data of a Radar Ray Tracing Simulator

arXiv

引用

arXiv 2023年

作者： Schüßler, Christian Hoffmann, Marcel Wirth, Vanessa Eskofier, Björn Weyrich, Tim Stamminger, Marc Vossiek, Martin Institute of Microwaves and Photonics Germany Chair of Visual Computing Germany Chair of Digital Reality Germany Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

In this work a novel radar simulation concept is introduced that allows to simulate realistic radar data for Range, Doppler, and for arbitrary antenna positions in an efficient way. Further, it makes it possible to automatically annotate the simulated radar signal by allowing to decompose it into different parts. This approach allows not only almost perfect annotations possible, but also allows the annotation of exotic effects, such as multi-path effects or to label signal parts originating from different parts of an object. This is possible by adapting the computation process of a Monte Carlo shooting and bouncing rays (SBR) simulator. By considering the hits of each simulated ray, various meta data can be stored such as hit position, mesh pointer, object IDs, and many more. This collected meta data can then be utilized to predict the change of path lengths introduced by object motion to obtain Doppler information or to apply specific ray filter rules in order obtain radar signals that only fulfil specific conditions, such as multiple bounces or containing specific object IDs. Using this approach, perfect and otherwise almost impossible annotations schemes can be realized. Copyright © 2023, The Authors. All rights reserved.

关键词： Metadata

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Unsupervised harmonic frequency-based gait sequence detection for Parkinson's disease

Unsupervised harmonic frequency-based gait sequence detectio...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Martin Ullrich Julius Hannink Heiko Gaßner Jochen Klucken Bjoern M. Eskofier Felix Kluge Machine Learning and Data Analytics Lab University of Erlangen-Nürnberg (FAU) Erlangen Germany Portabiles HealthCare Technologies GmbH Erlangen Germany Department of Molecular Neurology University Hospital Erlangen Erlangen Germany

Sensor-based gait analysis is a valuable tool in diagnosis and assessment of Parkinson's disease. Especially for large data sets, efficient analysis pipelines are required. Pre-segmentation of long time series into chunks of interest is a possible approach to increase efficiency. Therefore, we developed an unsupervised algorithm for the detection of gait sequences from continuous sensor signals. In the proposed method, gyroscope signals representing the angular rate of the feet are analyzed in the frequency domain using moving windows. A gait sequence was detected, if the frequency spectrum of a given window contained harmonic frequencies. The approach was tested on two data sets that differed in the ratio of clinical gait and cyclic movement tests. Sensitivity in both data sets was higher than 99% in a stride-to-stride comparison with ground truth. The specificity was measured with 76.1% (data set 1) and 94.5% (data set 2) for tests against sequences of other cyclic movements. In conclusion, the algorithm offers a reliable and efficient approach for the detection of gait sequences in time series data and is also promising for the application in long-term home-monitoring scenarios.

关键词： Harmonic analysis Sensitivity Foot Legged locomotion Frequency measurement Parkinson's disease Hospitals

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Deep CNN: A machine learning approach for driver drowsiness detection based on eye state

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Revue d'Intelligence Artificielle 2019年第6期33卷 461-466页

作者： Reddy Chirra, Venkata Rami Uyyala, Srinivasulu Reddy Kishore Kolli, Venkata Krishna Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Department of Computer Science and Engineering VFSTR Guntur522213 India

Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN. © 2019 Lavoisier. All rights reserved.

关键词： Face recognition

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Predicting Influential Higher-Order Patterns in Temporal Network data

Predicting Influential Higher-Order Patterns in Temporal Net...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Christoph Gote Vincenzo Perri Ingo Scholtes Data Analytics Group University of Zurich Zurich Switzerland Chair of Systems Design ETH Zurich Switzerland Chair of Machine Learning for Complex Networks Julius-Maximilians-Universität Würzburg Würzburg Germany

Networks are frequently used to model complex systems comprised of interacting elements. While edges capture the topology of direct interactions, the true complexity of many systems originates from higher-order patterns in paths by which nodes can indirectly influence each other. Path data, representing ordered sequences of consecutive direct interactions, can be used to model these patterns. On the one hand, to avoid overfitting, such models should only consider those higher-order patterns for which the data provide sufficient statistical evidence. On the other hand, we hypothesise that network models, which capture only direct interactions, underfit higher-order patterns present in data. Consequently, both approaches are likely to misidentify influential nodes in complex networks. We contribute to this issue by proposing five centrality measures based on MOGen, a multi-order generative model that accounts for all indirect influences up to a maximum distance but disregards influences at higher distances. We compare MOGen-based centralities to equivalent measures for network models and path data in a prediction experiment where we aim to identify influential nodes in out-of-sample data. Our results show strong evidence supporting our hypothesis. MOGen consistently outperforms both the network model and path-based prediction. We further show that the performance difference between MOGen and the path-based approach disappears if we have sufficient observations, confirming that the error is due to overfitting.

关键词： Social networking (online) Computational modeling Complex networks Predictive models Length measurement data models Topology

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Development of water flood model for oil production enhancement

Development of water flood model for oil production enhancem...

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Science and Artificial Intelligence conference (S.A.I.ence)

作者： Jetina J. Tsvaki Dmitry O. Tailakov Evgeniy N. Pavlovskiy Novosibirsk State University Novosibirsk Russia Laboratory for Fundamentals of Energy Technologies Kutateladze Institute of Thermophysics SB RAS Novosibirsk Russia Stream Data Analytics and Machine Learning Laboratory Novosibirsk State University Novosibirsk Russia

ISBN: (数字)9780738131115

ISBN: (纸本)9781665415439

Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity.

关键词： Production Oils Numerical models Computational modeling Recurrent neural networks Genetic algorithms data mining

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Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions

Watch Your Up-Convolution: CNN Based Generative Deep Neural ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ricard Durall Margret Keuper Janis Keuper Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany IWR University of Heidelberg Germany Data and Web Science Group University of Mannheim Germany Institute for Machine Learning and Analytics Offenburg University Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.

关键词： Gallium nitride Convolution Distortion Neural networks Training Generative adversarial networks

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ROBUST APPROXIMATE CHARACTERIZATION OF SINGLE-CELL HETEROGENEITY IN MICROBIAL GROWTH

arXiv

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arXiv 2024年

作者： Paul, Richard Seiffarth, Johannes Scharr, Hanno Nöh, Katharina IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany IBG-1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany RWTH Aachen University Aachen Germany

Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results. © 2024, CC BY.

关键词： Stochastic models

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FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

FewSOME: One-Class Few Shot Anomaly Detection with Siamese N...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Niamh Belton Misgina Tsighe Hagos Aonghus Lawlor Kathleen M. Curran Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose ‘Few Shot anOMaly dEtection’ (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on ‘few’ examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, ‘Stop Loss’, improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at stateof-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available; https://***/niamhbelton/FewSOME.

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data-driven Energy Consumption Modelling for Electric Micromobility using an Open dataset

Data-driven Energy Consumption Modelling for Electric Microm...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Yue Ding Sen Yan Maqsood Hussain Shah Hongyuan Fang Ji Li Mingming Liu SFI Centre for Research Training in Machine Learning (ML-Labs) Dublin City University SFI Insight Centre for Data Analytics and the School of Electronic Engineering Dublin City University Dublin Ireland Department of Mechanical Engineering University of Birmingham

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

The escalating challenges of traffic congestion and environmental degradation underscore the critical importance of embracing E-Mobility solutions in urban spaces. In particular, micro E-Mobility tools such as E-scooters and E-bikes, play a pivotal role in this transition, offering sustainable alternatives for urban commuters. However, the energy consumption patterns for these tools are a critical aspect that impacts their effectiveness in real-world scenarios and is essential for trip planning and boosting user confidence in using these. To this effect, recent studies have utilised physical models customised for specific mobility tools and conditions, but these models struggle with generalization and effectiveness in real-world scenarios due to a notable absence of open datasets for thorough model evaluation and verification. To fill this gap, our work presents an open dataset, collected in Dublin, Ireland, specifically designed for energy modelling research related to E-Scooters and E-Bikes. Furthermore, we provide a comprehensive analysis of energy consumption modelling based on the dataset using a set of representative machine learning algorithms and compare their performance against the contemporary mathematical models as a baseline. Our results demonstrate a notable advantage for data-driven models in comparison to the corresponding mathematical models for estimating energy consumption. Specifically, data-driven models outperform physical models in accuracy by up to 83.83% for E-Bikes and 82.16% for E-Scooters based on an in-depth analysis of the dataset under certain assumptions.

关键词： Energy consumption Analytical models machine learning algorithms Transportation Environmental degradation Electrification Boosting

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Trade Reliability for Security: Leakage-Failure Probability Minimization for machine-Type Communications in URLLC

arXiv

引用

arXiv 2023年

作者： Zhu, Yao Yuan, Xiaopeng Hu, Yulin Schaefer, Rafael F. Schmeink, Anke School of Electronic Information Wuhan University Wuhan430072 China Information Theory and Data Analytics RWTH Aachen University Aachen52074 Germany Information Theory and Machine Learning Technische Universität Dresden Dresden01062 Germany

How to provide information security while fulfilling ultra reliability and low-latency requirements is one of the major concerns for enabling the next generation of ultra-reliable and low-latency communications service (xURLLC), specially in machine-type communications. In this work, we investigate the reliability-security tradeoff via defining the leakage-failure probability, which is a metric that jointly characterizes both reliability and security performances for short-packet transmissions. We discover that the system performance can be enhanced by counter-intuitively allocating fewer resources for the transmission with finite blocklength (FBL) codes. In order to solve the corresponding optimization problem for the joint resource allocation, we propose an optimization framework, that leverages lower-bounded approximations for the decoding error probability in the FBL regime. We characterize the convexity of the reformulated problem and establish an efficient iterative searching method, the convergence of which is guaranteed. To show the extendability of the framework, we further discuss the blocklength allocation schemes with practical requirements of reliable-secure performance, as well as the transmissions with the statistical channel state information (CSI). Numerical results verify the accuracy of the proposed approach and demonstrate the reliability-security tradeoff under various setups. Copyright © 2023, The Authors. All rights reserved.

关键词： Reliability

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