The risk of infection of investigators working on the biomechanics of human bone from a variety of modern pathogens including the human immunodeficiency virus or the hepatitis B virus has increased recently. New safety procedures are needed to reduce that risk. The procedure we follow in our laboratory employs brief (< 3 h) fixation in formaldehyde, and we report here the effects it has on some mechanical properties of bovine bone. Results in quasistatic loading tests were almost unaffected by our fixation protocol, but a significant decrease in impact strength was found. These results indicate that there may be some interaction between fixation and strain rate dependent effects and, therefore, some caution is needed when using common biomechanical measurement methods on fixed bone material.

Effect of formaldehydefixation on some mechanical properties of bovine bone

Research in Autonomous Driving is taking momentum due to the inherent advantages of autonomous driving systems. The main advantage being the disassociation of the driver from the vehicle reducing the human intervention. However, the Autonomous Driving System involves many subsystems which need to be integrated as a whole system. Some of the tasks include Motion Planning, Vehicle Localization, Pedestrian Detection, Traffic Sign Detection, Road-marking Detection, Automated Parking, Vehicle Cybersecurity, and System Fault Diagnosis. This paper aims to the overview of various Machine Learning and Deep Learning Algorithms used in Autonomous Driving Architectures for different tasks like Motion Planning, Vehicle Localization, Pedestrian Detection, Traffic Sign Detection, Road-marking Detection, Automated Parking, Vehicle Cybersecurity and Fault Diagnosis. This paper surveys the technical aspects of Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems. Comparison of these algorithms is done based on the metrics like mean Intersect in over Union (mIoU), Average Precision (AP)missed detection rate, miss rate False Positives Per Image (FPPI), and average number for false frame detection. This study contributes to picture a review of the Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems and is organized based on the different tasks of the system.

Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms

Though it is known that the water content of biological soft tissues alters mechanical properties, little attempt has been made to adjust the tissue water content prior to biomechanical testing as part of standardization procedures. The objective of this study was to examine the effects of altered water content on the macro and micro scale mechanical tissues properties. Human iliotibial band samples were obtained during autopsies to osmotically adapt their water content. Macro mechanical tensile testing of the samples was conducted with digital image correlation, and micro mechanical tests using atomic force microscopy. Analyses were conducted for elastic moduli, tensile strength, and strain at maximum force, and correlations for water content, anthropometric data, and post-mortem interval. Different mechanical properties exist at different water concentrations. Correlations to anthropometric data are more likely to be found at water concentrations close to the native state. These data underline the need for adapting the water content of soft tissues for macro and micro biomechanical experiments to optimize their validity. The osmotic stress protocol provides a feasible and reliable standardization approach to adjust for water content-related differences induced by age at death, post-mortem interval and tissue processing time with known impact on the stress-strain properties.

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Water-content related alterations in macro and micro scale tendon biomechanics

This paper provides a starting point for researchers and practitioners from biology, medicine, physics and engineering who can benefit from an up-to-date literature survey on patient-specific bone fracture modelling, simulation and risk analysis. This survey hints at a framework for devising realistic patient-specific bone fracture simulations. This paper has 18 sections: Section 1 presents the main interested parties; Section 2 explains the organzation of the text; Section 3 motivates further work on patient-specific bone fracture simulation; Section 4 motivates this survey; Section 5 concerns the collection of bibliographical references; Section 6 motivates the physico-mathematical approach to bone fracture; Section 7 presents the modelling of bone as a continuum; Section 8 categorizes the surveyed literature into a continuum mechanics framework; Section 9 concerns the computational modelling of bone geometry; Section 10 concerns the estimation of bone mechanical properties; Section 11 concerns the selection of boundary conditions representative of bone trauma; Section 12 concerns bone fracture simulation; Section 13 presents the multiscale structure of bone; Section 14 concerns the multiscale mathematical modelling of bone; Section 15 concerns the experimental validation of bone fracture simulations; Section 16 concerns bone fracture risk assessment. Lastly, glossaries for symbols, acronyms, and physico-mathematical terms are provided.

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Patient-Specific Bone Multiscale Modelling, Fracture Simulation and Risk Analysis—A Survey

Machining-induced thermal damage in cortical bone: necrosis and micro-mechanical integrity

Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Convolutional neural networks (CNNs) exhibit excellent performance on the head pose estimation problem under controllable conditions, but their generalization ability in the wild needs to be improved. To address this issue, we propose an approach involving the introduction of facial landmark information into the task simplifier and landmark heatmap generator constructed before the feed-forward neural network, which can use this information to normalize the face shape into a canonical shape and generate a landmark heatmap based on the transformed facial landmarks to assist in feature extraction, for enhancing generalization ability in the wild. Our method was trained on 300W-LP and tested on AFLW2000-3D. The result shows that for the same feed-forward neural network when our method is used to introduce facial landmark information into a CNN, accuracy improves from 88.5% to 99.0% and mean average error decreases from 5.94° to 1.46° on AFLW2000-3D. Furthermore, we evaluate our method on several datasets used for pose estimation and compare the result with AFLW2000-3D, finding that the features extracted by a CNN could not reflect the head pose efficiently, which limits the performance of the CNN on the head pose estimation problem in wild. By introducing facial landmarks, the CNN could extract features that reflect head pose more efficiently, thereby significantly improving the accuracy of head pose estimation in the wild.

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Head Pose Estimation in the Wild Assisted by Facial Landmarks Based on Convolutional Neural Networks

Due to the complex visual environment and incomplete display of parking slots on around-view images, vision-based parking slot detection is a major challenge. Previous studies in this field mostly use the existing models to solve the problem, the steps of which are cumbersome. In this paper, we propose a parking slot detection method that uses directional entrance line regression and classification based on a deep convolutional neural network (DCNN) to make it robust and simple. For parking slots with different shapes and observed from different angles, we represent the parking slot as a directional entrance line. Subsequently, we design a DCNN detector to simultaneously obtain the type, position, length, and direction of the entrance line. After that, the complete parking slot can be easily inferred using the detection results and prior geometric information. To verify our method, we conduct experiments on the public ps2.0 dataset and self-annotated parking slot dataset with 2,135 images. The results show that our method not only outperforms state-of-the-art competitors with a precision rate of 99.68% and a recall rate of 99.41% on the ps2.0 dataset but also performs a satisfying generalization on the self-annotated dataset. Moreover, it achieves a real-time detection speed of 13 ms per frame on Titan Xp. By converting the parking slot into a directional entrance line, the specially designed DCNN detector can quickly and effectively detect various types of parking slots.

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Parking Slot Detection on Around-View Images Using DCNN.

The invention discloses a vehicle positioning method. The method includes the following steps of obtaining initial first position information when a positioned vehicle enters a road, obtaining a lane change state through a visual system in the driving process, updating the first position information according to the lane change state, sending the first position information to a road side unit by the positioned vehicle through vehicle-road coordinative communication, obtaining second position information through signal analysis by the road side unit, fusing the first position information, the second position information and prestored road mathematic information through the road side unit, obtaining vehicle position information of the positioned vehicle, feeding final position information back to the positioned vehicle through real-time communication by the road side unit, and obtaining the specific position of the vehicle in a map. The vehicle positioning method is not limited by geography, accurate positioning can be completed in the urban environment with unsatisfactory GPS/DGPS accuracy, and the requirements of the intelligent vehicle for collision avoidance, autonomous driving and other application are met.

Vehicle positioning method

Most current vision-based fatigue detection methods don’t have high-performance and robust face detector. They detect driver fatigue using single detection feature and cannot achieve real-time effi...

Libo Cao

Papers

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Head Pose Estimation in the Wild Assisted by Facial Landmarks Based on Convolutional Neural Networks

Parking Slot Detection on Around-View Images Using DCNN.

Vehicle positioning method

Driver fatigue detection based on convolutional neural network and face alignment for edge computing device