Target geo-localization based on camera vision simulation of UAV.

This paper presents a simulation study on estimating the Geo-Location of a target based on multiple image of the target taken from a gimbaled camera mounted on a unmanned aerial vehicle (UAV), which orbits around the target with a radius such that the target is always in the field of camera vision. The Camera Vision Simulation of the UAV is implemented by using an ortho Geo-TIFF (Geo-Spatial Tagged Information File Format) as imagery reference, positional and attitude attributes of UAV, Gimbal and Camera and internal characteristic of the simulated Camera. Target is localized using the simulation images taken from multiple bearing waypoints by applying the Geo-Location algorithm using the simulation parameters as reference. For improving the accuracy of the estimation, error reduction techniques like true average, moving average and recursive least square are also suggested and implemented

Automated Cardiac Health Diagnosis: A Time-Domain Approach.

Cardiological problems are one of the leading causes of human fatality. Electrocardiogram is a major noninvasive tool for monitoring heart conditions. The human vision is not suitable to identify the minute changes in Electrocardiogram wave amplitude and time intervals; hence an automatic diagnostic tool is necessary for precise abnormality detection. This paper presents a classification method to classify seven heartbeat conditions-normal and six classes of abnormalities. The algorithm implements a time domain approach to obtain the statistical features from the Electrocardiogram beats extracted from the arrhythmia database. This objective of this work is to find the suitability of time domain features to arrhythmia classification with machine learning. The statistical features are extracted from raw ECG signal, the time derivative, time integral and 5-point first derivative stencil of the ECG data. The cardiac abnormality classification is implemented with Support Vector Machine. The attained classification accuracy is upto 93% for chosen input feature pairs for binary Support Vector Machine

UAS Simulator: A Laboratory Set-Up

This work describes the procedure for laboratory setup for Unmanned Aerial System (UAS) Simulator. UAS has three major components viz., UAV (or drone), Pilot and the system in place that connects both of them or interface. This simulator not only helps pilot for training purposes, designers for testing new models, mission planners in planning missions in a different environment but also helps in testing and development of the Synthetic Vision System (SVS). SVS generates a rendered image or 3D image of the flying environment and aware operator using an onboard database of terrain, obstacles, and relevant cultural features

Stress detection from EEG using power ratio.

Stress correlates itself as a mental conscious and emotion within a person that influences mental ability and decision-making skills, which results in an inappropriate work. Studies have recently developed to detect the stress in a person while performing different tasks. One of the methods is through Electroencephalograph (EEG). These are the bioelectrical signals generated in a human body while performing the tasks and thus describes the activity of the brain. Any action taken by a person changes the properties of these signals. This present work focuses on the classification of baseline (relax) and stress detection using EEG sub-band power ratio as features. Support vector machine (SVM) classifier with different kernel function parameters and K-nearest neighbor (KNN) classifier with a different number of neighbors with holdout and 10-fold cross-validation technique were used to classify power ratio features in order to detect stress. To evaluate the classifier performance various performance metrics were used. It is observed that KNN with a number of neighbors as one, with Euclidean distance gives better performance in both validation techniques and also anterior frontal channel Fpl that is placed at the left side of the brain itself gives a good accuracy of 99.42%. The performance of the proposed method is verified on a publicly available mental arithmetic dataset where stress is induced while performing the mental cognitive workload i.e., mental serial subtraction

Overexpression of CDX2 perturbs HOX gene expression in murine progenitors depending on its N-terminal domain and is closely correlated with deregulated HOX gene expression in human acute myeloid leukemia.

The mechanisms underlying deregulation of HOX gene expression in AML are poorly understood. The ParaHox gene CDX2 was shown to act as positive upstream regulator of several HOX genes. In this study, constitutive expression of Cdx2 caused perturbation of leukemogenic Hox genes such as Hoxa10 and Hoxb8 in murine hematopoietic progenitors. Deletion of the N-terminal domain of Cdx2 abrogated its ability to perturb Hox gene expression and to cause acute myeloid leukemia (AML) in mice. In contrast inactivation of the putative Pbx interacting site of Cdx2 did not change the leukemogenic potential of the gene. In an analysis of 115 patients with AML, expression levels of CDX2 were closely correlated with deregulated HOX gene expression. Patients with normal karyotype showed a 14-fold higher expression of CDX2 and deregulated HOX gene expression compared with patients with chromosomal translocations such as t(8:21) or t(15;17). All patients with AML with normal karyotype tested were negative for CDX1 and CDX4 expression. These data link the leukemogenic potential of Cdx2 to its ability to dysregulate Hox genes. They furthermore correlate the level of CDX2 expression with HOX gene expression in human AML and support a potential role of CDX2 in the development of human AML with aberrant Hox gene expression