Vance Holcomb (foamburst7)

Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively.Performing network-based analysis on medical and biological data makes a wide variety of machine learning tools available. Clustering, which can be used for classification, presents opportunities for identifying hard-to-reach groups for the development of customized health interventions. Due to a desire to convert abundant DNA gene co-expression data into networks, many graph inference methods have been developed. Likewise there are many clustering and classification tools. This paper presents a comparison of techniques for graph inference and clustering, using different numbers of features, in order to select the best tuple of graph inference method, clustering method, and number of features according to a particular phenotype. An extensive machine learning based analysis of the REGARDS dataset is conducted, evaluating the CoNet and K-Nearest Neighbors (KNN) network inference methods, along with the Louvain, Leiden and NBR-Clust clustering techniques. Results from analysis involving five internal cluster evaluation indices show the traditional KNN inference method and NBR-Clust and Louvain clustering produce the most promising clusters with medical phenotype data. It is also shown that visualization can aid in interpreting the clusters, and that the clusters produced can identify meaningful groups indicating customized interventions.Red blood cell (RBC) segmentation and classification from microscopic images is a crucial step for the diagnosis of sickle cell disease (SCD). In this work, we adopt a deep learning based semantic segmentation framework to solve the RBC classification task. A major challenge for robust segmentation and classification is the large variations on the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address these challenges, we apply deformable convolution layers to the classic U-Net structure and implement the deformable U-Net (dU-Net). U-Net architecture has been shown to offer accurate localization for image semantic segmentation. Moreover, deformable convolution enables free-form deformation of the feature learning process, thus making the network more robust to various cell morphologies and image settings. dU-Net is tested on microscopic red blood cell images from patients with sickle cell disease. Results show that dU-Net can achieve highest accuracy for both binary segmentation and multi-class semantic segmentation tasks, comparing with both unsupervised and state-of-the-art deep learning based supervised segmentation methods. Through detailed investigation of the segmentation results, we further conclude that the performance improvement is mainly caused by the deformable convolution layer, which has better ability to separate the touching cells, discriminate the background noise and predict correct cell shapes without any shape priors.For an uncertain multiagent system, distributed cooperative learning control exerting the learning capability of the control system in a cooperative way is one of the most important and challenging issues. selleck products This article aims to address this issue for an uncertain high-order nonlinear multiagent system with guaranteed transient performance and preserved initial connectivity under an undirected and static communication topology. The considered multiagent system has an identical structure and the uncertain agent dynamics are estimated by localized radial basis function (RBF) neural networks (NNs) in a cooperative way. The NN weight estimates are rigorously proven to converge to small neighborhoods of their