Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Level Features

In this paper, an approach is developed for segmenting an image into major surfaces and potential objects using RGBD images and 3D point cloud data retrieved from a Kinect sensor. In the proposed segmentation algorithm, depth and RGB data are mapped together. Color, texture, XYZ world coordinates, and normal-, surface-, and graph-based segmentation index features are then generated for each pixel point. These attributes are used to cluster similar points together and segment the image. The inclusion of new depth-related features provided improved segmentation performance over RGB-only algorithms by resolving illumination and occlusion problems that cannot be handled using graph-based segmentation algorithms, as well as accurately identifying pixels associated with the main structure components of rooms (walls, ceilings, floors). Since each segment is a potential object or structure, the output of this algorithm is intended to be used for object recognition. The algorithm has been tested on commercial building images and results show the usability of the algorithm in real time applications

Gesture based Human Computer Interaction for Athletic Training

The invention of depth sensors for mobile devices, has led to availability of relatively inexpensive high-resolution depth and visual (RGB) sensing for a wide range of applications. The complementary nature of the depth and visual information opens up new opportunities to solve fundamental problems in object and activity recognition, people tracking, 3D mapping and localization, etc. One of the most interesting challenges that can be tackled by using these sensors is tracking the body movements of athletes and providing natural interaction as a result. In this study depth sensors and gesture recognition tools will be used to analyze the position and angle of an athlete’s body parts thought out an exercise. The goal is to assess the training performance of an athlete and decrease injury risk by giving warnings when the trainer is performing a high risk activity

Optimizing the best play in basketball using deep learning

In a close game of basketball, victory or defeat can depend on a single shot. Being able to identify the best player and play scenario for a given opponent’s defense can increase the likelihood of victory. Progress in technology has resulted in an increase in the popularity of sports analytics over the last two decades, where data can be used by teams and individuals to their advantage. A popular data analytic technique in sports is deep learning. Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. The process relies on a raw dataset for training purposes. It can be utilized in sports by using deep learning to read the data and provide a better understanding of where players can be the most successful. In this study the data used were on division I women’s basketball games of a private university in a conference featuring top 25 teams. Deep learning was applied to optimize the best offensive play in a game scenario for a given set of features. The system is used to predict the play that would lead to the highest probability of a made shot

Assessment of Levee Erosion using Image Processing and Contextual Cueing

Soil erosion is one of the most severe land degradation problems afflicting many parts of the world where topography of the land is relatively steep. Due to inaccessibility to steep terrain, such as slopes in levees and forested mountains, advanced data processing techniques can be used to identify and assess high risk erosion zones. Unlike existing methods that require human observations, which can be expensive and error-prone, the proposed approach uses a fully automated algorithm to indicate when an area is at risk of erosion; this is accomplished by processing Landsat and aerial images taken using drones. In this paper the image processing algorithm is presented, which can be used to identify the scene of an image by classifying it in one of six categories: levee, mountain, forest, degraded forest, cropland, grassland or orchard. This paper focuses on automatic scene detection using global features with local representations to show the gradient structure of an image. The output of this work counts as a contextual cueing and can be used in erosion assessment, which can be used to predict erosion risks in levees. We also discuss the environmental implications of deferred erosion control in levees

Evaluating the toxicity of permeability enhanchers of polyethylene glycol brij ethers surfactants group on cellular membranes and some of their physicochemical properties

The aim of this study is to evaluate the effect of polyethylene glycol brij ethers surfactants group on red blood cells as a model for biological membranes. Also in this study, physicochemical properties including emulsification index (E24), foam producing activity (Fh) and critical micelle concentration (cmc) were studied. Surfactant solutions were prepared in McIvan’s buffer in specific concentrations. 0.2 ml of red blood cells (RBC) was mixed with 0.2 ml of each surfactant solution. The four surfactant solutions had each been incubated differently at two different temperatures for three different times. Each test was done six times. The results were presented as mean absorbance ± the standard deviation. E24, Fh and cmc were also determined for each surfactant solution. All of the surfactant solutions showed hemolytic activity. In comparison with the four studied surfactants, brij 56 had the highest hemolytic effect and brij 72 the lowest. The values of E24 and Fh had good correlation with hydrophilic-lipophilic balance values. According to the results of this study, brijs should be used at concentrations lower than cmc in formulations. Also, according to the results, the use of brijs with low hemolytic effect such as brij 72, is preferred in pharmaceutical preparations.Key words: Brij, biological membrane, hemolysis, hydrophile-lipophile balance (HLB)

‘It is not a quick fix’ structural and contextual issues that affect implementation of integrated health and well-being services: a qualitative study from North East England

Objective The objective of this article is to examine the factors affecting the design, commissioning and delivery of integrated health and well-being services (IHWSs), which seek to address multiple health-related behaviours, improve well-being and tackle health inequalities using holistic approaches. Study design Qualitative studies embedded within iterative process evaluations. Methods Semi-structured interviews conducted with 16 key informants as part of two separate evaluations of IHWSs in North East England, supplemented by informal observations of service delivery. Transcripts and fieldnotes were analysed thematically. Results The study findings identify a challenging organisational context in which to implement innovative service redesign, as a result of budget cuts and changes in NHS and local authority capacity. Pressures to demonstrate outcomes affected the ability to negotiate the practicalities of joint working. Progress is at risk of being undermined by pressures to disinvest before the long-term benefits to population health and well-being are realised. The findings raise important questions about contract management and relationships between commissioners and providers involved in implementing these new ways of working. Conclusions These findings provide useful learning in terms of the delivery and commissioning of similar IHWSs, contributing to understanding of the benefits and challenges of this model of working

The synthetic triterpenoid RTA dh404 (CDDO-dhTFEA) restores endothelial function impaired by reduced Nrf2 activity in chronic kidney disease

AbstractChronic kidney disease (CKD) is associated with endothelial dysfunction and accelerated cardiovascular disease, which are largely driven by systemic oxidative stress and inflammation. Oxidative stress and inflammation in CKD are associated with and, in part, due to impaired activity of the cytoprotective transcription factor Nrf2. RTA dh404 is a synthetic oleanane triterpenoid compound which potently activates Nrf2 and inhibits the pro-inflammatory transcription factor NF-κB. This study was designed to test the effects of RTA dh404 on endothelial function, inflammation, and the Nrf2-mediated antioxidative system in the aorta of rats with CKD induced by 5/6 nephrectomy. Sham-operated rats served as controls. Subgroups of CKD rats were treated orally with RTA dh404 (2mg/kg/day) or vehicle for 12 weeks. The aortic rings from untreated CKD rats exhibited a significant reduction in the acetylcholine-induced relaxation response which was restored by RTA dh404 administration. Impaired endothelial function in the untreated CKD rats was accompanied by significant reduction of Nrf2 activity (nuclear translocation) and expression of its cytoprotective target genes, as well as accumulation of nitrotyrosine and upregulation of NAD(P)H oxidases, 12-lipoxygenase, MCP-1, and angiotensin II receptors in the aorta. These abnormalities were ameliorated by RTA dh404 administration, as demonstrated by the full or partial restoration of the expression of all the above analytes to sham control levels. Collectively, the data demonstrate that endothelial dysfunction in rats with CKD induced by 5/6 nephrectomy is associated with impaired Nrf2 activity in arterial tissue, which can be reversed with long term administration of RTA dh404

Deep Eutectic Mixtures as Reaction Media for the Enantioselective Organocatalyzed α-Amination of 1,3-Dicarbonyl Compounds

The enantioselective α-amination of 1,3-dicarbonyl compounds has been performed using deep eutectic solvents (DES) as a reaction media and chiral 2-amino benzimidazole-derived compounds as a catalytic system. With this procedure, the use of toxic volatile organic compounds (VOCs) as reaction media is avoided. Furthermore, highly functionalized chiral molecules, which are important intermediates for the natural product synthesis, are synthetized by an efficient and stereoselective protocol. Moreover, the reaction can be done on a preparative scale, with the recycling of the catalytic system being possible for at least five consecutive reaction runs. This procedure represents a cheap, simple, clean, and scalable method that meets most of the principles to be considered a green and sustainable process.Financial support from the University of Alicante (UAUSTI16-03, UAUSTI16-10, VIGROB-173), the Spanish Ministerio de Economía, Industria y Competitividad (CTQ2015-66624-P) is acknowledged

OpenBDLM, an Open-Source Software for Structural Health Monitoring using Bayesian Dynamic Linear Models

During the last decade, the rise of sensing technologies fostered the development of new data-driven Structural Health Monitoring (SHM) techniques. Among them, Bayesian Dynamic Linear Models (BDLMs) are capable of isolating the baseline responses of civil infrastructure from external effects, thus allowing to interpret the intrinsic behavior of civil structure and to detect anomalies. The generalization of BDLMs for SHM borrows tools from many fields, and there is currently no standalone software allowing BDLMs to be used routinely by practioners. This study intends to bridge this gap by introducing OpenBDLM, a Matlab open-source software specifically developed to use BDLMs for SHM. In this paper, synthetic dataset is examinated to illustrate the functionalities of the software, from data pre-processing to results visualization

A Kernel-Based Method for Modeling Non-harmonic Periodic Phenomena in Bayesian Dynamic Linear Models

Modeling periodic phenomena with accuracy is a key aspect to detect abnormal behavior in time series for the context of Structural Health Monitoring. Modeling complex non-harmonic periodic pattern currently requires sophisticated techniques and significant computational resources. To overcome these limitations, this paper proposes a novel approach that combines the existing Bayesian Dynamic Linear Models with a kernel-based method for handling periodic patterns in time series. The approach is applied to model the traffic load on the Tamar Bridge and the piezometric pressure under a dam. The results show that the proposed method succeeds in modeling the stationary and non-stationary periodic patterns for both case studies. Also, it is computationally efficient, versatile, self-adaptive to changing conditions, and capable of handling observations collected at irregular time intervals