Preprocessing of Prithvi Post Flight data for Parameter Identification

This document presents the methods of preprocessing flight data . The data acquired during flight test contain data spikes and- noise, due to various reasons and requires processing . of data to remove spikes and noise before they are used for any analysis. For this purpose a finite difference method for editing the flight data 'to remove the spikes and Fast Fourier Transfort (FFT) method for filtering the flight data to remove the noise have been used . A software package "PREVRD' incorporating these methods has been developed . The spectral analysis via FFT to select cut-off frequency for filtering has also been incorporated in the PREPRO . The package is validated . using test example and is used successfully for processing PRITHVI post flight data

Factorization filtering algorithm with colored noise for tracking

In this paper factorization filtering algorithms are described and used for processing13; data from a typical flight test range. Specifically U-D (unit upper triangular-diagonal)13; factorization based Kalman filtering algorithms are considered. The algorithms are13; validated using simulated data and implemented in MATLAB and alpha DEC machines.13; UDP protocols are used to transfer data from one DEC machine to another where the13; UD filter algorithm is activated to process the data. A very brief description of the fusion13; scheme in which the UD filtering algorithms are being used is given

Spontaneously opening GABA receptors play a significant role in neuronal signal filtering and integration

Acknowledgements This work was supported by The Rosetrees Trust Research Grant A1066, RS MacDonald Seedcorn Award and Wellcome Trust—UoE ISSF Award to S.S. The authors thank Prof. David Wyllie (University of Edinburgh) and Prof. Dmitri Rusakov (UCL) for their valuable suggestions on paper preparation.Peer reviewedPublisher PD

Progress in gene therapy for neurological disorders

Diseases of the nervous system have devastating effects and are widely distributed among the population, being especially prevalent in the elderly. These diseases are often caused by inherited genetic mutations that result in abnormal nervous system development, neurodegeneration, or impaired neuronal function. Other causes of neurological diseases include genetic and epigenetic changes induced by environmental insults, injury, disease-related events or inflammatory processes. Standard medical and surgical practice has not proved effective in curing or treating these diseases, and appropriate pharmaceuticals do not exist or are insufficient to slow disease progression. Gene therapy is emerging as a powerful approach with potential to treat and even cure some of the most common diseases of the nervous system. Gene therapy for neurological diseases has been made possible through progress in understanding the underlying disease mechanisms, particularly those involving sensory neurons, and also by improvement of gene vector design, therapeutic gene selection, and methods of delivery. Progress in the field has renewed our optimism for gene therapy as a treatment modality that can be used by neurologists, ophthalmologists and neurosurgeons. In this Review, we describe the promising gene therapy strategies that have the potential to treat patients with neurological diseases and discuss prospects for future development of gene therapy

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Pc Based Program For Estimation Of Aerodynamic Derivatives From Aeroballistics Range Trajectory

This Document Describes The Development And Galidation Of Interactive PC Based Parameter Estimation Code For Determination Of Aerodynamic Derivatives From Aero Ballistic Range Trajectories . It Also Contains The Typical Results And The User Manual With Relevant Listings Of The Software And Computer Print Out Of The Case Study

Seeker Filter and Fusion for Maneuvering Target using IMM

This project proposal is aimed at developing a seeker filter to track the air breathing target (ABT) using interacting multiple model (IMM) Kalman filter in the presence of seeker noise and glint noise. The proposed seeker filter incorporates identified maneuver models, glint noise model, seeker noise model with eclipsing and RCS fluctuation effect for estimating the following with minimum time lag and high attenuation of noise : i. LOS angles and rates ii. Target acceleration iii. Range and range rate iv. Closing Velocity v. Gimbal Angle

SENSOR DATA CHARACTERISATION AND FUSION FOR TARGET TRACKING APPLICATIONS

Data Fusion based on data from several ground based target tracking radars, EOTs and INS sensors is a complex problem as the data contains different systematic errors, time stamps and time delays. This paper presents some practical solutions to correcting the sensor errors like bias, estimation of measurement and process noise, time stamp and time delay error handling. The solutions are implemented in a data fusion scheme for a tracking application. The data fusion scheme is tested on simulated data of a moving target and also applied to real data of an aircraft tracked by three ground based radars

Integrated Multi Sensor Multi Target Data Fusion Analysis Software - MsmtDat

This paper presents the features and performance of the integrated multi sensor multi target tracking and fusion software ‘MsmtDat.’ developed at NAL. The software is highly interactive and user friendly and has GUI based front panel for interaction. Depending on the requirement, user can tune the software for handling multi sensor single target (MSST) or multi sensor multi target (MSMT) data with or without clutter by appropriate flag setting in the GUI panel. Also provisions are made to handle maneuvering targets using interacting multiple model (IMM) technique in combination with MSST or MSMT. Software is designed keeping in view the requirements of scientists and engineers who are engaged in target tracking and radar data processing

Interacting Multiple Model Extended Kalman Filter for Tracking Target Executing Evasive Maneuvers

A 3-model interacting multiple model extended Kalman filter with constant velocity, constant acceleration and constant turn models (IMMEKF-VAT) is proposed for tracking a maneuvering target undergoing acceleration as well as turn maneuvers in the Cartesian zx− plane. Its performance is compared with 2-model IMMEKF-VA. Performance comparison indicates that the IMMEKF-VAT provides smoother estimates of target states only during turn maneuver at the cost of extra computation. IMMEKF-VA should suffice for tracking targets executing constant acceleration as well as constant turn maneuvers