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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Oliver Urbann Simon Camphausen Arne Moos Ingmar Schwarz Sören Kerner Maximilian Otten Automation and Embedded Systems Fraunhofer IML Dortmund Germany Robotics Research Institute TU Dortmund University Dortmund Germany Software and Information Engineering Fraunhofer IML Dortmund Germany

ISBN: (数字)9781728196152

ISBN: (纸本)9781728196169

Inference of Convolutional Neural Networks in time critical applications usually requires a GPU. In robotics or embedded devices these are often not available due to energy, space and cost constraints. Furthermore, installation of a deep learning framework or even a native compiler on the target platform is not possible. This paper presents a neural network code generator (NNCG) that generates from a trained CNN a plain ANSI C code file that encapsulates the inference in single a function. It can easily be included in existing projects and due to lack of dependencies, cross compilation is usually possible. Additionally, the code generation is optimized based on the known trained CNN and target platform following four design principles. The system is evaluated utilizing small CNN designed for this application. Compared to TensorFlow XLA and Glow speed-ups of up to 11.81 can be shown and even GPUs are outperformed regarding latency.

关键词： Robots Artificial neural networks Machine learning Libraries Generators embedded systems Optimization

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Clustering of the Scenario Space for the Assessment of Automated Driving

Clustering of the Scenario Space for the Assessment of Autom...

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IEEE Symposium on Intelligent Vehicle

作者： Jonas Kerber Sebastian Wagner Korbinian Groh Dominik Notz Thomas Kühbeck Daniel Watzenig Alois Knoll Chair of Robotics Artificial Intelligence and Embedded Systems Technische Universität München Munich Germany BMW NA Mountain View USA Institute of Automation and Control at Graz University of Technology Graz Austria

ISBN: (数字)9781728166735

ISBN: (纸本)9781728166742

Assessment and testing are among the biggest challenges for the release of automated driving. Up to this date, the exact procedure to achieve homologation is not settled. Current research focuses on scenario-based approaches that represent driving scenarios as test cases within a scenario space. This avoids redundancies in testing, enables the inclusion of virtual testing into the process, and makes a statement about test coverage possible. However, it is unclear how to define such a scenario space and the coverage criterion. This work presents a novel approach to the definition of the scenario space. Spatiotemporal filtering on naturalistic highway driving data provides a large amount of driving scenarios as a foundation. A custom distance measure between scenarios enables hierarchical agglomerative clustering, categorizing the scenarios into subspaces. The members of a resulting cluster found through this approach reveal a common structure that is visually observable. We discuss a data-driven solution to define the necessary test coverage for the assessment of automated driving. Finally, the contribution of the findings to achieve homologation is elaborated.

关键词： Testing Road transportation Data mining Trajectory Spatiotemporal phenomena Redundancy Filtering

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Trend Statistics Network and Channel invariant EEG Network for sleep arousal study

Trend Statistics Network and Channel invariant EEG Network f...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： M.V. Achuth Rao Prasanta Kumar Ghosh Tanuka Bhattacharjee Anirban Dutta Choudhury Indian Institute of Science Bangalore India TCS Research and Innovation India Embedded Systems and Robotics TCS Research and Innovation India

Sleep is a very important part of life. Lack of sleep or sleep disorder can cause a negative impact on day to day life and can have long term serious consequences. In this work, we propose an end-to-end trainable neural network for automated sleep arousal scoring. The network consists of two main parts. Firstly, a trend statistics network computes the moving average of the filtered signals at different scales. Secondly, we propose a channel invariant EEG network to detect the arousals in any Electroencephalography (EEG) channel. Finally, we combine the features from various channels through a convolution network and a bi-directional long short-term memory to predict the probability of arousal. Further, we propose an objective function that uses only respiratory effort related arousal (RERA) and non-arousal regions to optimize the network. We also propose a method to estimate the respiratory disturbance index (RDI) from the probability predicted by the network. Evaluation on Physionet Challenge 2018 database shows that the proposed method detects RERA with mean area under the precision-recall curve (AUPRC) of 0.50 in a 10-fold cross validation setup. The mean absolute error of RDI prediction is 6.11, while a two-class RDI severity prediction yields a specificity of 75% and a sensitivity of 83%.

关键词： Electroencephalography Feature extraction Market research Sleep apnea Neural networks Standards

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Precision of sung notes in carnatic music 19

Precision of sung notes in carnatic music

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19th International Society for Music Information Retrieval Conference, ISMIR 2018

作者： Viraraghavan, Venkata Subramanian Murthy, Hema A. Aravind, R. TCS Research and Innovation Embedded Systems and Robotics Bangalore India Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9782954035123

Carnatic music is replete with continuous pitch movement called gamakas and can be viewed as consisting of constant-pitch notes (CPNs) and transients. The stationary points (STAs) of transients – points where the pitch curve changes direction – also carry melody information. In this paper, the precision of sung notes in Carnatic music is studied in detail by treating CPNs and STAs separately. There is variation among the nineteen musicians considered, but on average, the precision of CPNs increases exponentially with duration and settles at about 10 cents for CPNs longer than 0.5 seconds. For analyzing STAs, in contrast to Western music, rāga (melody) information is found to be necessary, and errors in STAs show a significantly larger standard deviation of about 60 cents. To corroborate these observations, the music was automatically transcribed and re-synthesized using CPN and STA information using two interpolation techniques. The results of perceptual tests clearly indicate that the grammar is highly flexible. We also show that the precision errors are not due to poor pitch tracking, singer deficiencies or delay in auditory feedback. © V S Viraraghavan, H A Murthy, R Aravind.

关键词： Music

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A Heart Rate Driven Kalman Filter for Continuous Arousal Trend Monitoring

A Heart Rate Driven Kalman Filter for Continuous Arousal Tre...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Tanuka Bhattacharjee Shreyasi Datta Deepan Das Anirban Dutta Choudhury Arpan Pal Prasanta Kumar Ghosh Embedded Systems and Robotics TCS Research and Innovation India Indian Institute of Science Bangalore Karnataka IN

This paper proposes a continuous and unsupervised approach of monitoring the arousal trend of an individual from his heart rate using Kalman Filter. The state-space model of the filter characterizes the baseline arousal condition. Deviations from this baseline model are used to recognize the arousal trend. A publicly available dataset, DECAF, comprising the physiological responses of 30 subjects while watching 36 movie clips inducing different emotions, is used to validate the proposed technique. For each clip, annotations of arousal given by experts per second are used to quantify the ground truth of arousal change. Experimental results suggest that the proposed algorithm achieves a median correlation of 0.53 between the computed and expected arousal levels which is significantly higher than that achievable by the state-of-the-art technique.

关键词： Market research Heart rate Monitoring Motion pictures Biomedical monitoring Correlation Noise measurement

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Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals

arXiv

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arXiv 2018年

作者： Gupta, Kavya Bhowmick, Brojeshwar Majumdar, Angshul Embedded Systems and Robotics TCS Research and Innovation India Indraprastha Institute of Information Technology Delhi India

This work addresses the problem of reconstructing biomedical signals from their lower dimensional projections. Traditionally Compressed Sensing (CS) based techniques have been employed for this task. These are transductive inversion processes;the problem with these approaches is that the inversion is time-consuming and hence not suitable for real-time applications. With the recent advent of deep learning, Stacked Sparse Denoising Autoencoder (SSDAE) has been used for learning inversion in an inductive setup. The training period for inductive learning is large but is very fast during application – capable of real-time speed. This work proposes a new approach for inductive learning of the inversion process. It is based on Coupled Analysis Dictionary Learning. Results on Biomedical signal reconstruction show that our proposed approach is very fast and yields result far better than CS and SSDAE. Copyright © 2018, The Authors. All rights reserved.

关键词： Inverse problems

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MOTION BLUR REMOVAL VIA COUPLED AUTOENCODER

arXiv

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arXiv 2018年

作者： Gupta, Kavya Bhowmick, Brojeshwar Majumdar, Angshul Embedded Systems and Robotics TCS Research and Innovation India Indraprastha Institute of Information Technology Delhi India

In this paper a joint optimization technique has been proposed for coupled autoencoder which learns the autoencoder weights and coupling map (between source and target) simultaneously. The technique is applicable to any transfer learning problem. In this work, we propose a new formulation that recasts deblurring as a transfer learning problem;it is solved using the proposed coupled autoencoder. The proposed technique can operate on-the-fly;since it does not require solving any costly inverse problem. Experiments have been carried out on state-of-the-art techniques;our method yields better quality images in shorter operating times. Copyright © 2018, The Authors. All rights reserved.

关键词： Inverse problems

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Safe Stop Trajectory Planning for Highly Automated Vehicles: An Optimal Control Problem Formulation

Safe Stop Trajectory Planning for Highly Automated Vehicles:...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Svensson, Lars Masson, Lola Mohan, Naveen Ward, Erik Brenden, Anna Pernestål Feng, Lei Törngren, Martin Mechatronics and Embedded Control Systems KTH Royal Institute of Technology StockholmSE-10044 Sweden LAAS-CNRS University of Toulouse Toulouse France Robotics Perception and Learning KTH Royal Institute of Technology Sweden Integrated Transport Research Lab KTH Royal Institute of Technology Sweden

ISBN: (纸本)9781538644522

Highly automated road vehicles need the capability of stopping safely in a situation that disrupts continued normal operation, e.g. due to internal system faults. Motion planning for safe stop differs from nominal motion planning, since there is not a specific goal location. Rather, the desired behavior is that the vehicle should reach a stopped state, preferably outside of active lanes. Also, the functionality to stop safely needs to be of high integrity. The first contribution of this paper is to formulate the safe stop problem as a benchmark optimal control problem, which can be solved by dynamic programming. However, this solution method cannot be used in real-time. The second contribution is to develop a real-time safe stop trajectory planning algorithm, based on selection from a precomputed set of trajectories. By exploiting the particular properties of the safe stop problem, the cardinality of the set is decreased, making the algorithm computationally efficient. Furthermore, a monitoring based architecture concept is proposed, that ensures dependability of the safe stop function. Finally, a proof of concept simulation using the proposed architecture and the safe stop trajectory planner is presented. © 2018 IEEE.

关键词： Motion planning

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Methods for Improving the Accuracy of the Virtual Assessment of Autonomous Driving

Methods for Improving the Accuracy of the Virtual Assessment...

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International Conference on Connected Vehicles and Expo (ICCVE)

作者： Dominik Notz Martin Sigl Thomas Kühbeck Sebastian Wagner Korbinian Groh Christoph Schütz Daniel Watzenig BMW of North America Mountain View CA USA BMW AG München Germany Department of Informatics Chair of Robotics Artificial Intelligence and Embedded Systems Technische Universität Munich München Germany Institute of Automation and Control Graz University of Technology Graz Austria

ISBN: (纸本)9781728101439

Assessing the safety of an autonomous vehicle is an open problem within the research domain for autonomous vehicles. Next to real-world driving tests, simulation and reprocessing of recordings play a crucial role in validating the correct and safe behavior. Current state-of-the-art methods for function reprocessing suffer from several sources of error and hence, might lead to incorrect results. In this work, an overview of the most recent reprocessing methods is given and their shortcomings are described. We suggest the derivation of explicit sensor models and the learning of behavior models for traffic objects. An overview of different levels of sensor and different kinds of agent models is given along with a discussion for the need for statistical and machine learning based models. Furthermore, a novel method, based on infrastructure sensors, to collect the data needed for the derivation of the models is presented.

关键词： Autonomous vehicles Machine learning Intelligent agents Vehicle detection Sensor systems Data collection Simulation Data reprocessing Autonomous Vehicles Vehicle detection Sensor systems Intelligent agents Data Collection Methods reprocessing simulating Artificial Intelligence Machine Learning Error Sources derivation Sensors

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Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

arXiv

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

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