AWPP: A New Scheme for Wireless Access Control Proportional to Traffic Priority and Rate

Cutting-edge wireless networking approaches are required to efficiently differentiate traffic and handle it according to its special characteristics. The current Medium Access Control (MAC) scheme which is expected to be sufficiently supported by well-known networking vendors comes from the IEEE 802.11e workgroup. The standardized solution is the Hybrid Coordination Function (HCF), that includes the mandatory Enhanced Distributed Channel Access (EDCA) protocol and the optional Hybrid Control Channel Access (HCCA) protocol. These two protocols greatly differ in nature and they both have significant limitations. The objective of this work is the development of a high-performance MAC scheme for wireless networks, capable of providing predictable Quality of Service (QoS) via an efficient traffic differentiation algorithm in proportion to the traffic priority and generation rate. The proposed Adaptive Weighted and Prioritized Polling (AWPP) protocol is analyzed, and its superior deterministic operation is revealed

Isolation and fine mapping of Rps6: An intermediate host resistance gene in barley to wheat stripe rust

A plant may be considered a nonhost of a pathogen if all known genotypes of a plant species are resistant to all known isolates of a pathogen species. However, if a small number of genotypes are susceptible to some known isolates of a pathogen species this plant maybe considered an intermediate host. Barley (Hordeum vulgare) is an intermediate host for Puccinia striiformis f. sp. tritici (Pst), the causal agent of wheat stripe rust. We wanted to understand the genetic architecture underlying resistance to Pst and to determine whether any overlap exists with resistance to the host pathogen, Puccinia striiformis f. sp. hordei (Psh). We mapped Pst resistance to chromosome 7H and show that host and intermediate host resistance is genetically uncoupled. Therefore, we designate this resistance locus Rps6. We used phenotypic and genotypic selection on F2:3 families to isolate Rps6 and fine mapped the locus to a 0.1 cM region. Anchoring of the Rps6 locus to the barley physical map placed the region on two adjacent fingerprinted contigs. Efforts are now underway to sequence the minimal tiling path and to delimit the physical region harbouring Rps6. This will facilitate additional marker development and permit identification of candidate genes in the region

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Detection and classification of opened and closed flowers in grape inflorescences using Mask R-CNN

Accurate measurements of the change in total flower count, and the ratio of opened to closed flowers per inflorescence with time, play an important role in studying phenological changes of inflorescences over time. The duration of flowering has an important role in the resulting fruitset and yield. Automation of the flower counting process with inflorescence images, using image processing and morphological tools, is a challenging problem. This is because it involves the processing of images with varying image qualities, and also because of the close similarity in images between the two classes of interests, opened and closed flowers. Our aim is to build a system with one of the most promising deep learning object detection networks, Mask R-CNN, to detect the individual instances of the above two classes separately using the images with no prior alterations. The system should be tested with the images taken with different illumination levels, different backgrounds, and with different scales. Our system was tested with images taken in three consecutive flowering seasons (2018, 2019 and 2020) and showed promising results. These tests also highlighted areas that can be improved to ensure better accuracy

Fusion of thermal and visible colour images for robust detection of people in forests

Safe operation of automated robotic platforms in environments where humans also work require on-board sensors that can accurately and robustly detect humans in the environment so that appropriate action can be taken. This is a challenging problem in unstructured outdoor environments as most sensors are negatively affected by changing environmental conditions like ambient light and moisture. Our aim is to use a combination of thermal and visible colour images to detect humans in forest environments. The system should be able to work through dense foliage and should not be confused by other objects that generate heat like machines or other animals. We developed and tested a system on a data-set of sensor data collected in a similar outdoor environment but with synthetic targets added to highlight the ability of the system to be robust to severe optical occlusion in dense vegetation and to the presence of hot machines that could fool the thermal sensor. Our initial results show promise and also highlight where improvements can be made with further testing in more realistic forest environments

Detection and classification of opened and closed flowers in grape inflorescences using Mask R-CNN

Accurate measurements of the change in total flower count, and the ratio of opened to closed flowers per inflorescence with time, play an important role in studying phenological changes of inflorescences over time. The duration of flowering has an important role in the resulting fruitset and yield. Automation of the flower counting process with inflorescence images, using image processing and morphological tools, is a challenging problem. This is because it involves the processing of images with varying image qualities, and also because of the close similarity in images between the two classes of interests, opened and closed flowers. Our aim is to build a system with one of the most promising deep learning object detection networks, Mask R-CNN, to detect the individual instances of the above two classes separately using the images with no prior alterations. The system should be tested with the images taken with different illumination levels, different backgrounds, and with different scales. Our system was tested with images taken in three consecutive flowering seasons (2018, 2019 and 2020) and showed promising results. These tests also highlighted areas that can be improved to ensure better accuracy

Detection, classification, and collaborative tracking of multiple targets using video sensors

The study of collaborative, distributed, real-time sensor networks is an emerging research area. Such networks are expected to play an essential role in a number of applications such as, surveillance and tracking of vehicles in the battlefield of the future. This paper proposes an approach to detect and classify multiple targets, and collaboratively track their position and velocity utilizing video cameras. Arbitrarily placed cameras collaboratively perform selfcalibration and provide complete battlefield coverage. If some of the cameras are equipped with a GPS system, they are able to metrically reconstruct the scene and determine the absolute coordinates of the tracked targets. A background subtraction scheme combined with a Markov random field based approach is used to detect the target even when it becomes stationary. Targets are continuously tracked using a distributed Kalman filter approach. As the targets move the coverage is handed over to the "best" neighboring cluster of sensors. This paper demonstrates the potential for the development of distributed optical sensor networks and addresses problems and tradeoffs associated with this particular implementation