scholarly journals Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 852 ◽  
Author(s):  
Ievgeniia Maksymova ◽  
Christian Steger ◽  
Norbert Druml
Keyword(s):  
Point Cloud ◽  
Storage Capacity ◽  
Sensor Data ◽  
Future Research ◽  
Operating Environment ◽  
Compression Algorithms ◽  
Computing Unit ◽  
Safety Regulations ◽  
Electronic Computing ◽  
On Chip

Due to specific dynamics of the operating environment and required safety regulations, the amount of acquired data of an automotive LiDAR sensor that has to be processed is reaching several Gbit/s. Therefore, data compression is much-needed to enable future multi-sensor automated vehicles. Numerous techniques have been developed to compress LiDAR raw data; however, these techniques are primarily targeting a compression of 3D point cloud, while the way data is captured and transferred from a sensor to an electronic computing unit (ECU) was left out. The purpose of this paper is to discuss and evaluate how various low-level compression algorithms could be used in the automotive LiDAR sensor in order to optimize on-chip storage capacity and link bandwidth. We also discuss relevant parameters that affect amount of collected data per second and what are the associated issues. After analyzing compressing approaches and identifying their limitations, we conclude several promising directions for future research.

scholarly journals Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

2021 ◽  
Author(s):  
Alexander Bigalke ◽  
Lasse Hansen ◽  
Jasper Diesel ◽  
Mattias P. Heinrich
Keyword(s):  
Point Cloud ◽  
Drug Dosage ◽  
Sensor Data ◽  
Patient Specific ◽  
Point Cloud Data ◽  
Weight Estimation ◽  
Visual Sensor ◽  
Cloud Data ◽  
Emergency Situations ◽  
3D Cnn

Abstract Purpose Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data. Methods We propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient’s volumetric surface without a cover. Second, the patient’s weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression. Results We evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to $${16}{\%}$$ 16 % and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to $${52}{\%}$$ 52 % . Conclusion We present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.

scholarly journals Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4370
Author(s):  
Yongze Jin ◽  
Guo Xie ◽  
Yankai Li ◽  
Xiaohui Zhang ◽  
Ning Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expectation Maximization ◽  
Monitoring Data ◽  
Sensor Data ◽  
Operating Environment ◽  
Mass Model ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Mass ◽  
Parameter Identifications

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

Validation of Wearable Sensor Technology to Measure Social Proximity of Young Children With Autism Spectrum Disorders

2021 ◽  
pp. 108835762110282
Author(s):  
Sarah N. Douglas ◽  
Yan Shi ◽  
Saptarshi Das ◽  
Subir Biswas
Keyword(s):  
Autism Spectrum Disorders ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Sensor System ◽  
Sensor Data ◽  
Sensor Technology ◽  
Future Research ◽  
Social Proximity ◽  
Children With Asd ◽  
Spectrum Disorders

Children with autism spectrum disorders (ASD) struggle to develop appropriate social skills, which can lead to later social rejection, isolation, and mental health concerns. Educators play an important role in supporting and monitoring social skill development for children with ASD, but the tools used by educators are often tedious, lack suitable sensitivity, provide limited information to plan interventions, and are time-consuming. Therefore, we conducted a study to evaluate the use of a sensor system to measure social proximity between three children with ASD and their peers in an inclusive preschool setting. We compared video-coded data with sensor data using point-by-point agreement to measure the accuracy of the sensor system. Results suggest that the sensor system can adequately measure social proximity between children with ASD and their peers. The next steps for sensor system validation are discussed along with clinical and educational implications, limitations, and future research directions.

Dimensionality Reduction With Multi-Fold Deep Denoising Autoencoder

2020 ◽  
pp. 154-165
Author(s):  
Pattabiraman V. ◽  
Parvathi R.
Keyword(s):  
Large Scale ◽  
Curse Of Dimensionality ◽  
Data Sources ◽  
Sensor Data ◽  
Future Research ◽  
Computational Power ◽  
Nonlinear Methods ◽  
Research Areas ◽  
Learning Techniques ◽  
Natural Data

Natural data erupting directly out of various data sources, such as text, image, video, audio, and sensor data, comes with an inherent property of having very large dimensions or features of the data. While these features add richness and perspectives to the data, due to sparsity associated with them, it adds to the computational complexity while learning, unable to visualize and interpret them, thus requiring large scale computational power to make insights out of it. This is famously called “curse of dimensionality.” This chapter discusses the methods by which curse of dimensionality is cured using conventional methods and analyzes its performance for given complex datasets. It also discusses the advantages of nonlinear methods over linear methods and neural networks, which could be a better approach when compared to other nonlinear methods. It also discusses future research areas such as application of deep learning techniques, which can be applied as a cure for this curse.

scholarly journals Statistical Analysis of Vertical and Torsional Whipping Response Based on Full-Scale Measurement of a Large Container Ship

2020 ◽  
Vol 10 (8) ◽  
pp. 2978
Author(s):  
Ryo Hanada ◽  
Tetsuo Okada ◽  
Yasumi Kawamura ◽  
Tetsuji Miyashita
Keyword(s):  
Sensor Data ◽  
Future Research ◽  
Container Ship ◽  
Stress Monitoring ◽  
Sea State ◽  
Operational Conditions ◽  
Hull Girder ◽  
Future Design ◽  
Vibrational Response ◽  
Large Container

In this study, as a preliminary attempt to reveal the whipping response of large container ships in actual seaways, the stress monitoring data of an 8600 TEU large container ship were analyzed. The measurement lasted approximately five years, and using a large amount of data, we investigated how the sea state and operational conditions affected the whipping response. In addition, the midship longitudinal stresses were decomposed into hull girder vertical bending, horizontal bending, and torsional and axial components. Thereafter, we found that the whipping magnitude on the torsional and horizontal bending components is much smaller than that on the vertical bending component. Future research would include the analysis of a larger amount of data, analysis of other sensor data, and effects of various patterns of vibrational response on the ultimate strength and fatigue strength. The obtained results will benefit the future design and operation of large container ships for safer navigation.

scholarly journals PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1573 ◽  
Author(s):  
Haojie Liu ◽  
Kang Liao ◽  
Chunyu Lin ◽  
Yao Zhao ◽  
Meiqin Liu
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Interpolation Method ◽  
Low Frequency ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Sensor Data ◽  
Navigation Systems ◽  
Intermediate Point ◽  
Cascade Structure

LiDAR sensors can provide dependable 3D spatial information at a low frequency (around 10 Hz) and have been widely applied in the field of autonomous driving and unmanned aerial vehicle (UAV). However, the camera with a higher frequency (around 20 Hz) has to be decreased so as to match with LiDAR in a multi-sensor system. In this paper, we propose a novel Pseudo-LiDAR interpolation network (PLIN) to increase the frequency of LiDAR sensor data. PLIN can generate temporally and spatially high-quality point cloud sequences to match the high frequency of cameras. To achieve this goal, we design a coarse interpolation stage guided by consecutive sparse depth maps and motion relationship. We also propose a refined interpolation stage guided by the realistic scene. Using this coarse-to-fine cascade structure, our method can progressively perceive multi-modal information and generate accurate intermediate point clouds. To the best of our knowledge, this is the first deep framework for Pseudo-LiDAR point cloud interpolation, which shows appealing applications in navigation systems equipped with LiDAR and cameras. Experimental results demonstrate that PLIN achieves promising performance on the KITTI dataset, significantly outperforming the traditional interpolation method and the state-of-the-art video interpolation technique.

scholarly journals Scalable Implementation of Hippocampal Network on Digital Neuromorphic System towards Brain-Inspired Intelligence

2020 ◽  
Vol 10 (8) ◽  
pp. 2857
Author(s):  
Wei Sun ◽  
Jiang Wang ◽  
Nan Zhang ◽  
Shuangming Yang
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Critical Role ◽  
Future Research ◽  
Calculation Algorithm ◽  
Field Programmable ◽  
Innovative System ◽  
Hippocampal Network ◽  
Single Field ◽  
On Chip

In this paper, an expanded digital hippocampal spurt neural network (HSNN) is innovatively proposed to simulate the mammalian cognitive system and to perform the neuroregulatory dynamics that play a critical role in the cognitive processes of the brain, such as memory and learning. The real-time computation of a large-scale peak neural network can be realized by the scalable on-chip network and parallel topology. By exploring the latest research in the field of neurons and comparing with the results of this paper, it can be found that the implementation of the hippocampal neuron model using the coordinate rotation numerical calculation algorithm can significantly reduce the cost of hardware resources. In addition, the rational use of on-chip network technology can further improve the performance of the system, and even significantly improve the network scalability on a single field programmable gate array chip. The neuromodulation dynamics are considered in the proposed system, which can replicate more relevant biological dynamics. Based on the analysis of biological theory and the theory of hardware integration, it is shown that the innovative system proposed in this paper can reproduce the biological characteristics of the hippocampal network and may be applied to brain-inspired intelligent subjects. The study in this paper will have an unexpected effect on the future research of digital neuromorphic design of spike neural network and the dynamics of the hippocampal network.

scholarly journals Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5012 ◽  
Author(s):  
Bilal Arshad ◽  
Robert Ogie ◽  
Johan Barthelemy ◽  
Biswajeet Pradhan ◽  
Nicolas Verstaevel ◽  
...  
Keyword(s):  
Systematic Review ◽  
Computer Vision ◽  
Coastal Lagoons ◽  
Early Warning Systems ◽  
Flood Management ◽  
Sensor Data ◽  
Future Research ◽  
Warning Systems ◽  
Flood Monitoring ◽  
Computer Vision Applications

Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

Sensor data fusion in the context of electric vehicles charging stations using a Network-on-Chip

2018 ◽  
Vol 56 ◽  
pp. 134-143 ◽  
Author(s):  
Ivan Stoychev ◽  
Philipp Wehner ◽  
Jens Rettkowski ◽  
Tobias Kalb ◽  
Patrick Wichert ◽  
...  
Keyword(s):  
Data Fusion ◽  
Electric Vehicles ◽  
Network On Chip ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
On Chip ◽  
Charging Stations
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