Physical Realization of an Optical Automatic Target Recognition System

In the modern warfare there is an active development of a new trend connected with a robotic warfare. One of the critical elements of robotics warfare systems is an automatic target recognition system, allowing to recognize objects, based on the data received from sensors. This work considers aspects of optical realization of such a system by means of NIR target scanning at fixed wavelengths. An algorithm was designed, an experimental setup was built and samples of various modern gear and apparel materials were tested. For pattern testing the samples of actively arm engaged armies camouflages were chosen. Tests were performed both in clear atmosphere and in the artificial extremely humid and hot atmosphere to simulate field conditions.

Role of polarisation in automatic target recognition using resonance descriptions

The role of polarisation in late time complex resonance based target identification is investigated numerically for the case of an L-shaped wire. While repeated extraction of the resonances for varying polarisation allows for better signal-to-noise immunity, it is also found that there are preferred polarisations for each complex resonance. The first few of these polarisations are extracted for the sample target.

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

Estimation of nonparametric probability density functions with applications to automatic speech recognition

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2007

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Automatic landslide recognition through Optimum-Path Forest

In this paper we shed light over the problem of landslide automatic recognition using supervised classification, and we also introduced the OPF classifier in this context. We employed two images acquired from Geoeye-MS satellite at March-2010 in the northwest (high steep areas) and north sides (pipeline area) covering the area of Duque de Caxias city, Rio de Janeiro State, Brazil. The landslide recognition rate has been assessed through a cross-validation with 10 runnings. In regard to the classifiers, we have used OPF against SVM with Radial Basis Function for kernel mapping and a Bayesian classifier. We can conclude that OPF, Bayes and SVM achieved high recognition rates, being OPF the fastest approach. © 2012 IEEE.

The natural killer gene complex genetic locus Chok encodes Ly-49D, a target recognition receptor that activates natural killing

Previously, we established that natural killer (NK) cells from C57BL/6 (B6), but not BALB/c, mice lysed Chinese hamster ovary (CHO) cells, and we mapped the locus that determines this differential CHO-killing capacity to the NK gene complex on chromosome 6. The localization of Chok in the NK gene complex suggested that it may encode either an activating or an inhibitory receptor. Here, results from a lectin-facilitated lysis assay predicted that Chok is an activating B6 NK receptor. Therefore, we immunized BALB/c mice with NK cells from BALB.B6–Cmv1r congenic mice and generated a mAb, designated 4E4, that blocked B6-mediated CHO lysis. mAb 4E4 also redirected lysis of Daudi targets, indicating its reactivity with an activating NK cell receptor. Furthermore, only the 4E4+ B6 NK cell subset mediated CHO killing, and this lysis was abrogated by preincubation with mAb 4E4. Flow cytometric analysis indicated that mAb 4E4 specifically reacts with Ly-49D but not Ly-49A, B, C, E, G, H, or I transfectants. Finally, gene transfer of Ly-49DB6 into BALB/c NK cells conferred cytotoxic capacity against CHO cells, thus establishing that the Ly-49D receptor is sufficient to activate NK cells to lyse this target. Hence, Ly-49D is the Chok gene product and is a mouse NK cell receptor capable of directly triggering natural killing.

Tn5/IS50 target recognition

This communication reports an analysis of Tn5/IS50 target site selection by using an extensive collection of Tn5 and IS50 insertions in two relatively small regions of DNA (less than 1 kb each). For both regions data were collected resulting from in vitro and in vivo transposition events. Since the data sets are consistent and transposase was the only protein present in vitro, this demonstrates that target selection is a property of only transposase. There appear to be two factors governing target selection. A target consensus sequence, which presumably reflects the target selection of individual pairs of Tn5/IS50 bound transposase protomers, was deduced by analyzing all insertion sites. The consensus Tn5/IS50 target site is A-GNTYWRANC-T. However, we observed that independent insertion sites tend to form groups of closely located insertions (clusters), and insertions very often were spaced in a 5-bp periodic fashion. This suggests that Tn5/IS50 target selection is facilitated by more than two transposase protomers binding to the DNA, and, thus, for a site to be a good target, the overlapping neighboring DNA should be a good target, too. Synthetic target sequences were designed and used to test and confirm this model.

Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Thesis (Ph.D.)--University of Washington, 2016-06

The human factors of automatic speech recognition in control room systems

This thesis addresses the viability of automatic speech recognition for control room systems; with careful system design, automatic speech recognition (ASR) devices can be useful means for human computer interaction in specific types of task. These tasks can be defined as complex verbal activities, such as command and control, and can be paired with spatial tasks, such as monitoring, without detriment. It is suggested that ASR use be confined to routine plant operation, as opposed the critical incidents, due to possible problems of stress on the operators' speech.  It is proposed that using ASR will require operators to adapt a commonly used skill to cater for a novel use of speech. Before using the ASR device, new operators will require some form of training. It is shown that a demonstration by an experienced user of the device can lead to superior performance than instructions. Thus, a relatively cheap and very efficient form of operator training can be supplied by demonstration by experienced ASR operators. From a series of studies into speech based interaction with computers, it is concluded that the interaction be designed to capitalise upon the tendency of operators to use short, succinct, task specific styles of speech. From studies comparing different types of feedback, it is concluded that operators be given screen based feedback, rather than auditory feedback, for control room operation. Feedback will take two forms: the use of the ASR device will require recognition feedback, which will be best supplied using text; the performance of a process control task will require task feedback integrated into the mimic display. This latter feedback can be either textual or symbolic, but it is suggested that symbolic feedback will be more beneficial. Related to both interaction style and feedback is the issue of handling recognition errors. These should be corrected by simple command repetition practices, rather than use error handling dialogues. This method of error correction is held to be non intrusive to primary command and control operations. This thesis also addresses some of the problems of user error in ASR use, and provides a number of recommendations for its reduction.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.