TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality

Evidence-based investigations and treatments of recurrent pregnancy loss.

OBJECTIVE : To give an overview of currently used investigations and treatments offered to women with recurrent pregnancy loss (RPL) and, from an evidence-based point of view, to evaluate the usefulness of these interventions. DESIGN : Ten experts on epidemiologic, genetic, anatomic, endocrinologic, thrombophilic, immunologic, and immunogenetic aspects of RPL discussed methodologic problems threatening the validity of research in RPL during and after an international workshop on the evidence-based management of RPL. CONCLUSION(S) : Most RPL patients have several risk factors for miscarriage, and an extensive investigation for all major factors should always be undertaken. There is an urgent need for agreement concerning the thresholds for detecting what is normal and abnormal, irrespective of whether laboratory tests or uterine abnormalities are concerned. A series of lifestyle factors should be reported in future studies of RPL because they might modify the effect of laboratory or anatomic risk factors. More and larger randomized controlled trials, including trials of surgical procedures, are urgently needed, and to achieve this objective multiple centers have to collaborate. Current meta-analyses evaluating the efficacy of treatments of RPL are generally pooling very heterogeneous patient populations and treatments. It is recommended that future meta-analyses look at subsets of patients and treatment protocols that are more combinable.</p

Nuclear modification factor for charged pions and protons at forward rapidity in central Au plus Au collisions at 200 GeV

We present spectra of charged pions and protons in 0–10% central Au + Au collisions at View the MathML sourcesNN=200 GeV at mid-rapidity (y=0y=0) and forward pseudorapidity (η=2.2η=2.2) measured with the BRAHMS experiment at RHIC. The spectra are compared to spectra from p+pp+p collisions at the same energy scaled by the number of binary collisions. The resulting nuclear modification factors for central Au + Au collisions at both y=0y=0 and η=2.2η=2.2 exhibit suppression for charged pions but not for (anti-) protons at intermediate pTpT. The View the MathML sourcep¯/π− ratios have been measured up to pT∼3 GeV/cpT∼3 GeV/c at the two rapidities and the results indicate that a significant fraction of the charged hadrons produced at intermediate pTpT range are (anti-) protons at both mid-rapidity and η=2.2η=2.2