GUNSHOT DIRECTION OF ARRIVAL DETERMINATION USING BIO-INSPIRED MEMS SENSORS

A key component of battle space awareness is direction of arrival (DoA) determination of gunshots. In the initial stages of an engagement, quick and reliable DoA determination enhances a Marine’s ability to execute the observe-orient-decide-act (OODA) loop, increasing chances of survival and mission success. Naval Postgraduate School (NPS) has developed a novel, biomimetic acoustic sensor modeled after the auditory system of the Ormia Ochracea fly. This microelectromechanical system (MEMS)-based directional sound sensor, which consists of two wings connected to a substrate using two torsional legs in the middle, is well documented in previous NPS theses. Each sensor has a uniform dipole beam pattern. By combining two crossed MEMS sensors (crossed-dipoles) with an omni-directional microphone, 360° DoA determination can be fully resolved. The objective of this thesis is to evaluate, optimize, and develop DoA estimators for gunshots in the time- and frequency-domain, specifically for the crossed-dipoles sensors plus an omni-directional microphone configuration.ONR, Arlington, VA 22203Outstanding ThesisEnsign, United States NavyApproved for public release. Distribution is unlimited

Test of reward contingent precall

Precall represents improved memory for material practised after the recall test. Such behaviour has been suggested to serve the needs/motives of the individual. However, attempts to examine this have met with limited success, possibly reflecting the value of the reward. The current pre-registered study took the original approach of identifying a motivating reward: a cash reward of £10. The main study then examined the effect of offering this reward contingent upon precall performance. Two confirmatory predictions were made: first, that post recall practise will lead to greater precall. Second, that a contingent reward will elicit greater precall. A repeated measures design had participants randomly presented with 20 arousing images, after which they were given a surprise recall task. Following this a sub-set of the images was presented twice allowing them to practice. Precall scores represented the number of correctly recalled images that were subsequently repeated and baseline scores the number of correctly recalled images not repeated. Analysis showed precall scores were significantly higher than baseline, however the contingent reward had no effect. This may indicate a Type I error or an anomalous precognitive effect. Hence, some speculative ideas are proposed in an attempt to account for the pattern of data

Effects of Tank Gun Structural Components on the First Shot Hit Probability

Fire power for a main battle tank is one of the most important performance parameters like survivability and mobility. Fire power effectiveness is directly related to the first shot hit probability, performance of main gun, second armament, gun and turret drive system, fire control system, automatic target tracker, commander and gunner sight etc. First shot hit probability (a measure of cumulative effects of errors) is affected by the variations of the projectile parameters, the main gun structure uncertainties, fire control system errors, interaction between the projectile and the gun barrel and the unpredictable environmental changes. These errors and variations can be eliminated or minimised by understanding and simulating the firing event properly, manufacturing the related parts in high precision, using advanced fire control algorithms, and accurate sensors. In this review study, the effects of main gun structural components on the first shot hit probability are investigated taking into account all of the associated error sources. In order for a main battle tank to have both high and repetitive first shot hit probability under all battlefield conditions the gun structure should respond in a similar manner in successive firings without causing any abrupt change in performance. In this study, first the dynamic behaviour of gun/projectile system is discussed and then the design recommendations for the main gun components such as bearings, gun barrel, recoil system etc. to achieve higher first shot hit probability are reviewed

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Aluminium Process Fault Detection and Diagnosis

The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments

Development and application of an optogenetic platform for controlling and imaging a large number of individual neurons

The understanding and treatment of brain disorders as well as the development of intelligent machines is hampered by the lack of knowledge of how the brain fundamentally functions. Over the past century, we have learned much about how individual neurons and neural networks behave, however new tools are critically needed to interrogate how neural networks give rise to complex brain processes and disease conditions. Recent innovations in molecular techniques, such as optogenetics, have enabled neuroscientists unprecedented precision to excite, inhibit and record defined neurons. The impressive sensitivity of currently available optogenetic sensors and actuators has now enabled the possibility of analyzing a large number of individual neurons in the brains of behaving animals. To promote the use of these optogenetic tools, this thesis integrates cutting edge optogenetic molecular sensors which is ultrasensitive for imaging neuronal activity with custom wide field optical microscope to analyze a large number of individual neurons in living brains. Wide-field microscopy provides a large field of view and better spatial resolution approaching the Abbe diffraction limit of fluorescent microscope. To demonstrate the advantages of this optical platform, we imaged a deep brain structure, the Hippocampus, and tracked hundreds of neurons over time while mouse was performing a memory task to investigate how those individual neurons related to behavior. In addition, we tested our optical platform in investigating transient neural network changes upon mechanical perturbation related to blast injuries. In this experiment, all blasted mice show a consistent change in neural network. A small portion of neurons showed a sustained calcium increase for an extended period of time, whereas the majority lost their activities. Finally, using optogenetic silencer to control selective motor cortex neurons, we examined their contributions to the network pathology of basal ganglia related to Parkinson’s disease. We found that inhibition of motor cortex does not alter exaggerated beta oscillations in the striatum that are associated with parkinsonianism. Together, these results demonstrate the potential of developing integrated optogenetic system to advance our understanding of the principles underlying neural network computation, which would have broad applications from advancing artificial intelligence to disease diagnosis and treatment

Naturalistic paradigms for neuroimaging and bedside measures of conscious awareness

Complex, naturalistic stimuli can test for covert awareness in behaviourally non-responsive patients. For patients with poor visual function, this thesis aimed to identify an auditory-only stimulus that could evaluate executive function. Also, it assessed if Galvanic Skin Response could be a suitable bedside testing method. Healthy individuals listened to 4 auditory stimuli in the fMRI scanner. During Galvanic Skin Response recording, an independent group of controls listened to an audio narrative and watched a movie. Behaviourally non-responsive patients were also tested during movie viewing. Using fMRI, an audio narrative was identified that produced widespread brain synchronization between healthy participants, critically in the frontoparietal network. Healthy controls showed highly similar GSR to a suspenseful movie. A locked-in syndrome patient had a similar GSR to controls during movie viewing. The audio narrative can be used for future patient testing, and GSR can be used to test for consciousness at the bedside

Identification of control chart patterns using neural networks

To produce products with consistent quality, manufacturing processes need to be closely monitored for any deviations in the process. Proper analysis of control charts that are used to determine the state of the process not only requires a thorough knowledge and understanding of the underlying distribution theories associated with control charts, but also the experience of an expert in decision making. The present work proposes a modified backpropagation neural network methodology to identify and interpret various patterns of variations that can occur in a manufacturing process. Control charts primarily in the form of X-bar chart are widely used to identify the situations when control actions will be needed for manufacturing systems. Various types of patterns are observed in control charts. Identification of these control chart patterns (CCPs) can provide clues to potential quality problems in the manufacturing process. Each type of control chart pattern has its own geometric shape and various related features can represent this shape. This project formulates Shewhart mean (X-bar) and range (R) control charts for diagnosis and interpretation by artificial neural networks. Neural networks are trained to discriminate between samples from probability distributions considered within control limits and those which have shifted in both location and variance. Neural networks are also trained to recognize samples and predict future points from processes which exhibit long term or cyclical drift. The advantages and disadvantages of neural control charts compared to traditional statistical process control are iscussed. In processes, the causes of variations may be categorized as chance (unassignable) causes and special (assignable) causes. The variations due to chance causes are inevitable, and difficult to detect and identify. On the other hand, the variations due to special causes prevent the process being a stable and predictable. Such variations should be determined effectively and eliminated from the process by taking the necessary corrective actions to maintain the process in control and improve the quality of the products as well. In this study, a multilayered neural network trained with a back propagation algorithm was applied to pattern recognition on control charts. The neural network was experimented on a set of generated data