A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications.

The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements.

Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data.

In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles.

Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup.

Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM.

Resource-Constrained Onboard Inference of 3D Object Detection and Localisation in Point Clouds Targeting Self-Driving Applications.

Research about deep learning applied in object detection tasks in LiDAR data has been massively widespread in recent years, achieving notable developments, namely in improving precision and inference speed performances. These improvements have been facilitated by powerful GPU servers, taking advantage of their capacity to train the networks in reasonable periods and their parallel architecture that allows for high performance and real-time inference. However, these features are limited in autonomous driving due to space, power capacity, and inference time constraints, and onboard devices are not as powerful as their counterparts used for training.

Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries.

Remote sensing technology, such as hyperspectral imaging, in combination with machine learning algorithms, has emerged as a viable tool for rapid and nondestructive assessment of wine grape ripeness. However, the differences in terroir, together with the climatic variations and the variability exhibited by different grape varieties, have a considerable impact on the grape ripening stages within a vintage and between vintages and, consequently, on the robustness of the predictive models. To address this challenge, we present a novel one-dimensional convolutional neural network architecture-based model for the prediction of sugar content and pH, using reflectance hyperspectral data from different vintages.

Radiographic assessment of humeroulnar congruity in a medium and a large breed of dog.

Elbow joint incongruity is recognized as an important factor in the development, treatment, and prognosis of canine elbow dysplasia. Elbow incongruity has been measured based on radiographic joint space widths, however these values can be affected by the degree of elbow joint flexion. Recent studies have reported radiographic curvature radii as more precise measures of humeroulnar congruity in dogs.

Computed tomography and radiographic assessment of congruity between the ulnar trochlear notch and humeral trochlea in large breed dogs.

Objective: Assess the fit between the ulnar trochlear notch (UTN) and humeral trochlea (HT) in elbow radiographs and computed tomography (CT) images by measuring the curvature radii in normal joints.

Methods: Mediolateral extended view (MLE) and CT were performed on 16 elbows from cadaveric dogs weighing over 20 kg. Curves were traced at the subchondral level from the UTN central ridge and the HT sagittal groove related to the UTN on ~132° joint extension.

Characterization of neural network generalization in the determination of pH and anthocyanin content of wine grape in new vintages and varieties.

The generalization ability of hyperspectral imaging combined with neural networks (NN) in estimating pH and anthocyanin content during ripening was evaluated for vintages and varieties not employed in the NN creation. A NN, from a previously published work, trained with grape samples of Touriga Franca (TF) variety harvested in 2012 was tested with TF from 2013 and two new varieties, Touriga Nacional (TN) and Tinta Barroca (TB) from 2013. Each sample contained a small number of whole berries.

Use of Visible and Short-Wave Near-Infrared Hyperspectral Imaging To Fingerprint Anthocyanins in Intact Grape Berries.

In red grape berries, anthocyanins account for about 50% of the skin phenols and are responsible for the final wine color. Individual anthocyanin levels and compositional profiles vary with cultivar, maturity, season, region, and yield and have been proposed as chemical markers to differentiate wines and to provide valuable information regarding the adulteration of musts and wines. A fast, easy, solvent-free, nondestructive method based on visible, short-wave, and near-infrared hyperspectral imaging (HSI) in intact grape berries to fingerprint the color pigments in eight different grape varieties was developed and tested against HPLC.

Curvature Radius Measurements From the Ulnar Trochlear Notch in Large Dogs.

Assessing the ulnar trochlear notch (UTN) radiographic anatomy has been considered important, but difficult, in the diagnosis of elbow dysplasia. The purpose of this study was to evaluate UTN curvature of natural elbows in radiographs, using a methodology applied to disarticulated joints. The methodology was implemented and validated using dedicated software created by the authors.

Automatic histogram threshold using fuzzy measures.

In this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided.

Comparison of clinical, radiographic, computed tomographic, and magnetic resonance imaging methods for early prediction of canine hip laxity and dysplasia.

The purpose of the study was to use two palpation methods (Bardens and Ortolani), a radiographic distraction view, three computed tomography (CT) measurements (dorsolateral subluxation score, the lateral center-edge angle, and acetabular ventroversion angle) and two magnetic resonance (MR) imaging hip studies (synovial fluid and acetabular depth indices) in the early monitoring of hip morphology and laxity in 7-9 week old puppies; and in a follow-up study to compare their accuracy in predicting later hip laxity and dysplasia. The MR imaging study was performed with the dog in dorsal recumbency and the CT study with the animal in a weight-bearing position. There was no association between clinical laxity with later hip laxity or dysplasia.

Dynamic joint stiffness of the ankle during walking: gender-related differences.

Objectives: To characterize and compare dynamic joint stiffness (DJS) of the ankle in the sagittal plane during natural cadence walking in both genders.

Design: Observation, cross-sectional and matched pairs.

Participants: Twenty-one males (mean age=27+/-4.

Passive hip laxity in Estrela Mountain Dog--distraction index, heritability and breeding values.

Two hundred and fifteen Estrela Mountain Dogs (EMD) were examined using the PennHIP method between 2002 and 2006. Passive hip laxity (PHL) was estimated calculating the distraction index (DI). Pedigree information was obtained from the Portuguese Kennel Club.

A comparison of two-dimensional and three-dimensional techniques for the determination of hindlimb kinematics during treadmill locomotion in rats following spinal cord injury.

Of all the detrimental effects of spinal cord injury (SCI), one of the most devastating effects is the disruption of the ability to walk. Therefore, much effort has been focused on developing several methods to document the recovery of locomotor function after experimental SCI. Computerized rat gait analysis is becoming increasingly popular in the SCI research community.

A comparison analysis of hindlimb kinematics during overground and treadmill locomotion in rats.

The convenience of the motor-driven treadmill makes it an attractive instrument for investigating rat locomotion. However, no data are available to indicate whether hindlimb treadmill kinematic findings may be compared or generalized to overground locomotion. In this investigation, we compared overground and treadmill locomotion for differences in the two-dimensional angular kinematics and temporal and spatial measurements for the hindlimb.

Effect of skin movement on the analysis of hindlimb kinematics during treadmill locomotion in rats.

In rat gait kinematics, the method most frequently used for measuring hindlimb movement involves placing markers on the skin surface overlying the joints being analyzed. Soft tissue movement around the knee joint has been considered the principle source of error when estimating hindlimb joint kinematics in rodents. However, the motion of knee marker was never quantified, nor the different variations in joint angle associated with this gait analysis system.

Functional and morphological assessment of a standardized rat sciatic nerve crush injury with a non-serrated clamp.

Peripheral nerve researchers frequently use the rat sciatic nerve crush as a model for axonotmesis. Unfortunately, studies from various research groups report results from different crush techniques and by using a variety of evaluation tools, making comparisons between studies difficult. The purpose of this investigation was to determine the sequence of functional and morphologic changes after an acute sciatic nerve crush injury with a non-serrated clamp, giving a final standardized pressure of p = 9 MPa.

Methods for the experimental functional assessment of rat sciatic nerve regeneration.

In experimental peripheral nerve studies, the rat sciatic nerve model is widely used to examine functional changes after different surgical repairs or pharmacological treatments, following nerve injury. The number and diversity of tests which have been used to assess functional recovery after experimental interventions often makes it difficult to recommend any particular indicator of nerve regeneration. Functional assessment after sciatic nerve lesion has long been focused on walking track analysis, therefore, this article describes in more detail the method to obtain and measure the walking tracks in order to calculate the sciatic functional index (SFI).

Toe out angle: a functional index for the evaluation of sciatic nerve recovery in the rat model.

In experimental peripheral nerve studies, the rat sciatic nerve model is widely used to examine functional outcome following nerve injury and repair. A variety of evaluation methods exist in the literature, but an adequate selection continues to be a critical point for the researcher. Rats with sciatic nerve injury typically ambulate with an external rotation of the foot.

Motion of the foot and ankle during the stance phase in rats.

Computerized analysis of rat gait is becoming an invaluable technique used by some peripheral nerve investigators for the evaluation of function. In this article we describe the use of a biomechanical model of the foot and ankle that allows a quantitative assessment and description of the ankle angle, reflecting plantarflexion and dorsiflexion during the stance phase of gait. Kinematic data of 144 trial walks from 36 normal rats were recorded with a high-speed digital image camera at 225 images per second.