Planar optical waveguide based sandwich assay sensors and processes for the detection of biological targets including protein markers, pathogens and cellular debris

DOEpatents

Martinez, Jennifer S [Santa Fe, NM; Swanson, Basil I [Los Alamos, NM; Grace, Karen M [Los Alamos, NM; Grace, Wynne K [Los Alamos, NM; Shreve, Andrew P [Santa Fe, NM

2009-06-02

An assay element is described including recognition ligands bound to a film on a single mode planar optical waveguide, the film from the group of a membrane, a polymerized bilayer membrane, and a self-assembled monolayer containing polyethylene glycol or polypropylene glycol groups therein and an assay process for detecting the presence of a biological target is described including injecting a biological target-containing sample into a sensor cell including the assay element, with the recognition ligands adapted for binding to selected biological targets, maintaining the sample within the sensor cell for time sufficient for binding to occur between selected biological targets within the sample and the recognition ligands, injecting a solution including a reporter ligand into the sensor cell; and, interrogating the sample within the sensor cell with excitation light from the waveguide, the excitation light provided by an evanescent field of the single mode penetrating into the biological target-containing sample to a distance of less than about 200 nanometers from the waveguide thereby exciting the fluorescent-label in any bound reporter ligand within a distance of less than about 200 nanometers from the waveguide and resulting in a detectable signal.

Laser range profiling for small target recognition

NASA Astrophysics Data System (ADS)

Steinvall, Ove; Tulldahl, Michael

2016-05-01

The detection and classification of small surface and airborne targets at long ranges is a growing need for naval security. Long range ID or ID at closer range of small targets has its limitations in imaging due to the demand on very high transverse sensor resolution. It is therefore motivated to look for 1D laser techniques for target ID. These include vibrometry, and laser range profiling. Vibrometry can give good results but is also sensitive to certain vibrating parts on the target being in the field of view. Laser range profiling is attractive because the maximum range can be substantial, especially for a small laser beam width. A range profiler can also be used in a scanning mode to detect targets within a certain sector. The same laser can also be used for active imaging when the target comes closer and is angular resolved. The present paper will show both experimental and simulated results for laser range profiling of small boats out to 6-7 km range and a UAV mockup at close range (1.3 km). We obtained good results with the profiling system both for target detection and recognition. Comparison of experimental and simulated range waveforms based on CAD models of the target support the idea of having a profiling system as a first recognition sensor and thus narrowing the search space for the automatic target recognition based on imaging at close ranges. The naval experiments took place in the Baltic Sea with many other active and passive EO sensors beside the profiling system. Discussion of data fusion between laser profiling and imaging systems will be given. The UAV experiments were made from the rooftop laboratory at FOI.

Infrared vehicle recognition using unsupervised feature learning based on K-feature

NASA Astrophysics Data System (ADS)

Lin, Jin; Tan, Yihua; Xia, Haijiao; Tian, Jinwen

2018-02-01

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

Unsupervised learning in persistent sensing for target recognition by wireless ad hoc networks of ground-based sensors

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve beyond the period of system operation in which the training data are representative. To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines (SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based location and tracking of detected targets by active nodes. The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection, recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the dimension of samples used in the target recognition and tracking routines. Target tracking is based on simplified versions of Kalman filtration. Accuracy of recognition and tracking of implemented versions of the proposed suite of unsupervised algorithms is somewhat degraded from the ideal. Target recognition and tracking by supervised routines and by unsupervised SVM and CLNN routines in the ground-based WSN is evaluated in simulations using published system values and sensor data from vehicular targets in ground-surveillance scenarios. Results are compared with previously published performance for the system of the ground-based sensor network (GSN) and UAV swarm.

A new FOD recognition algorithm based on multi-source information fusion and experiment analysis

NASA Astrophysics Data System (ADS)

Li, Yu; Xiao, Gang

2011-08-01

Foreign Object Debris (FOD) is a kind of substance, debris or article alien to an aircraft or system, which would potentially cause huge damage when it appears on the airport runway. Due to the airport's complex circumstance, quick and precise detection of FOD target on the runway is one of the important protections for airplane's safety. A multi-sensor system including millimeter-wave radar and Infrared image sensors is introduced and a developed new FOD detection and recognition algorithm based on inherent feature of FOD is proposed in this paper. Firstly, the FOD's location and coordinate can be accurately obtained by millimeter-wave radar, and then according to the coordinate IR camera will take target images and background images. Secondly, in IR image the runway's edges which are straight lines can be extracted by using Hough transformation method. The potential target region, that is, runway region, can be segmented from the whole image. Thirdly, background subtraction is utilized to localize the FOD target in runway region. Finally, in the detailed small images of FOD target, a new characteristic is discussed and used in target classification. The experiment results show that this algorithm can effectively reduce the computational complexity, satisfy the real-time requirement and possess of high detection and recognition probability.

Advanced miniature processing handware for ATR applications

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor); Daud, Taher (Inventor); Thakoor, Anikumar (Inventor)

2003-01-01

A Hybrid Optoelectronic Neural Object Recognition System (HONORS), is disclosed, comprising two major building blocks: (1) an advanced grayscale optical correlator (OC) and (2) a massively parallel three-dimensional neural-processor. The optical correlator, with its inherent advantages in parallel processing and shift invariance, is used for target of interest (TOI) detection and segmentation. The three-dimensional neural-processor, with its robust neural learning capability, is used for target classification and identification. The hybrid optoelectronic neural object recognition system, with its powerful combination of optical processing and neural networks, enables real-time, large frame, automatic target recognition (ATR).

Human target acquisition performance

NASA Astrophysics Data System (ADS)

Teaney, Brian P.; Du Bosq, Todd W.; Reynolds, Joseph P.; Thompson, Roger; Aghera, Sameer; Moyer, Steven K.; Flug, Eric; Espinola, Richard; Hixson, Jonathan

2012-06-01

The battlefield has shifted from armored vehicles to armed insurgents. Target acquisition (identification, recognition, and detection) range performance involving humans as targets is vital for modern warfare. The acquisition and neutralization of armed insurgents while at the same time minimizing fratricide and civilian casualties is a mounting concern. U.S. Army RDECOM CERDEC NVESD has conducted many experiments involving human targets for infrared and reflective band sensors. The target sets include human activities, hand-held objects, uniforms & armament, and other tactically relevant targets. This paper will define a set of standard task difficulty values for identification and recognition associated with human target acquisition performance.

Redox polymer and probe DNA tethered to gold electrodes for enzyme-amplified amperometric detection of DNA hybridization.

PubMed

Kavanagh, Paul; Leech, Dónal

2006-04-15

The detection of nucleic acids based upon recognition surfaces formed by co-immobilization of a redox polymer mediator and DNA probe sequences on gold electrodes is described. The recognition surface consists of a redox polymer, [Os(2,2'-bipyridine)2(polyvinylimidazole)(10)Cl](+/2+), and a model single DNA strand cross-linked and tethered to a gold electrode via an anchoring self-assembled monolayer (SAM) of cysteamine. Hybridization between the immobilized probe DNA of the recognition surface and a biotin-conjugated target DNA sequence (designed from the ssrA gene of Listeria monocytogenes), followed by addition of an enzyme (glucose oxidase)-avidin conjugate, results in electrical contact between the enzyme and the mediating redox polymer. In the presence of glucose, the current generated due to the catalytic oxidation of glucose to gluconolactone is measured, and a response is obtained that is binding-dependent. The tethering of the probe DNA and redox polymer to the SAM improves the stability of the surface to assay conditions of rigorous washing and high salt concentration (1 M). These conditions eliminate nonspecific interaction of both the target DNA and the enzyme-avidin conjugate with the recognition surfaces. The sensor response increases linearly with increasing concentration of target DNA in the range of 1 x 10(-9) to 2 x 10(-6) M. The detection limit is approximately 1.4 fmol, (corresponding to 0.2 nM of target DNA). Regeneration of the recognition surface is possible by treatment with 0.25 M NaOH solution. After rehybridization of the regenerated surface with the target DNA sequence, >95% of the current is recovered, indicating that the redox polymer and probe DNA are strongly bound to the surface. These results demonstrate the utility of the proposed approach.

Key features for ATA / ATR database design in missile systems

NASA Astrophysics Data System (ADS)

Özertem, Kemal Arda

2017-05-01

Automatic target acquisition (ATA) and automatic target recognition (ATR) are two vital tasks for missile systems, and having a robust detection and recognition algorithm is crucial for overall system performance. In order to have a robust target detection and recognition algorithm, an extensive image database is required. Automatic target recognition algorithms use the database of images in training and testing steps of algorithm. This directly affects the recognition performance, since the training accuracy is driven by the quality of the image database. In addition, the performance of an automatic target detection algorithm can be measured effectively by using an image database. There are two main ways for designing an ATA / ATR database. The first and easy way is by using a scene generator. A scene generator can model the objects by considering its material information, the atmospheric conditions, detector type and the territory. Designing image database by using a scene generator is inexpensive and it allows creating many different scenarios quickly and easily. However the major drawback of using a scene generator is its low fidelity, since the images are created virtually. The second and difficult way is designing it using real-world images. Designing image database with real-world images is a lot more costly and time consuming; however it offers high fidelity, which is critical for missile algorithms. In this paper, critical concepts in ATA / ATR database design with real-world images are discussed. Each concept is discussed in the perspective of ATA and ATR separately. For the implementation stage, some possible solutions and trade-offs for creating the database are proposed, and all proposed approaches are compared to each other with regards to their pros and cons.

Pattern recognition for passive polarimetric data using nonparametric classifiers

NASA Astrophysics Data System (ADS)

Thilak, Vimal; Saini, Jatinder; Voelz, David G.; Creusere, Charles D.

2005-08-01

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.

Feature-based RNN target recognition

NASA Astrophysics Data System (ADS)

Bakircioglu, Hakan; Gelenbe, Erol

1998-09-01

Detection and recognition of target signatures in sensory data obtained by synthetic aperture radar (SAR), forward- looking infrared, or laser radar, have received considerable attention in the literature. In this paper, we propose a feature based target classification methodology to detect and classify targets in cluttered SAR images, that makes use of selective signature data from sensory data, together with a neural network technique which uses a set of trained networks based on the Random Neural Network (RNN) model (Gelenbe 89, 90, 91, 93) which is trained to act as a matched filter. We propose and investigate radial features of target shapes that are invariant to rotation, translation, and scale, to characterize target and clutter signatures. These features are then used to train a set of learning RNNs which can be used to detect targets within clutter with high accuracy, and to classify the targets or man-made objects from natural clutter. Experimental data from SAR imagery is used to illustrate and validate the proposed method, and to calculate Receiver Operating Characteristics which illustrate the performance of the proposed algorithm.

Virtual reality method to analyze visual recognition in mice.

PubMed

Young, Brent Kevin; Brennan, Jayden Nicole; Wang, Ping; Tian, Ning

2018-01-01

Behavioral tests have been extensively used to measure the visual function of mice. To determine how precisely mice perceive certain visual cues, it is necessary to have a quantifiable measurement of their behavioral responses. Recently, virtual reality tests have been utilized for a variety of purposes, from analyzing hippocampal cell functionality to identifying visual acuity. Despite the widespread use of these tests, the training requirement for the recognition of a variety of different visual targets, and the performance of the behavioral tests has not been thoroughly characterized. We have developed a virtual reality behavior testing approach that can essay a variety of different aspects of visual perception, including color/luminance and motion detection. When tested for the ability to detect a color/luminance target or a moving target, mice were able to discern the designated target after 9 days of continuous training. However, the quality of their performance is significantly affected by the complexity of the visual target, and their ability to navigate on a spherical treadmill. Importantly, mice retained memory of their visual recognition for at least three weeks after the end of their behavioral training.

Lifting wavelet method of target detection

NASA Astrophysics Data System (ADS)

Han, Jun; Zhang, Chi; Jiang, Xu; Wang, Fang; Zhang, Jin

2009-11-01

Image target recognition plays a very important role in the areas of scientific exploration, aeronautics and space-to-ground observation, photography and topographic mapping. Complex environment of the image noise, fuzzy, all kinds of interference has always been to affect the stability of recognition algorithm. In this paper, the existence of target detection in real-time, accuracy problems, as well as anti-interference ability, using lifting wavelet image target detection methods. First of all, the use of histogram equalization, the goal difference method to obtain the region, on the basis of adaptive threshold and mathematical morphology operations to deal with the elimination of the background error. Secondly, the use of multi-channel wavelet filter wavelet transform of the original image de-noising and enhancement, to overcome the general algorithm of the noise caused by the sensitive issue of reducing the rate of miscarriage of justice will be the multi-resolution characteristics of wavelet and promotion of the framework can be designed directly in the benefits of space-time region used in target detection, feature extraction of targets. The experimental results show that the design of lifting wavelet has solved the movement of the target due to the complexity of the context of the difficulties caused by testing, which can effectively suppress noise, and improve the efficiency and speed of detection.

Target recognition and phase acquisition by using incoherent digital holographic imaging

NASA Astrophysics Data System (ADS)

Lee, Munseob; Lee, Byung-Tak

2017-05-01

In this study, we proposed the Incoherent Digital Holographic Imaging (IDHI) for recognition and phase information of dedicated target. Although recent development of a number of target recognition techniques such as LIDAR, there have limited success in target discrimination, in part due to low-resolution, low scanning speed, and computation power. In the paper, the proposed system consists of the incoherent light source, such as LED, Michelson interferometer, and digital CCD for acquisition of four phase shifting image. First of all, to compare with relative coherence, we used a source as laser and LED, respectively. Through numerical reconstruction by using the four phase shifting method and Fresnel diffraction method, we recovered the intensity and phase image of USAF resolution target apart from about 1.0m distance. In this experiment, we show 1.2 times improvement in resolution compared to conventional imaging. Finally, to confirm the recognition result of camouflaged targets with the same color from background, we carry out to test holographic imaging in incoherent light. In this result, we showed the possibility of a target detection and recognition that used three dimensional shape and size signatures, numerical distance from phase information of obtained holographic image.

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

Target recognition of ladar range images using slice image: comparison of four improved algorithms

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jingya; Wang, Liang; Zhai, Yu; Cheng, Yang

2017-07-01

Compared with traditional 3-D shape data, ladar range images possess properties of strong noise, shape degeneracy, and sparsity, which make feature extraction and representation difficult. The slice image is an effective feature descriptor to resolve this problem. We propose four improved algorithms on target recognition of ladar range images using slice image. In order to improve resolution invariance of the slice image, mean value detection instead of maximum value detection is applied in these four improved algorithms. In order to improve rotation invariance of the slice image, three new improved feature descriptors-which are feature slice image, slice-Zernike moments, and slice-Fourier moments-are applied to the last three improved algorithms, respectively. Backpropagation neural networks are used as feature classifiers in the last two improved algorithms. The performance of these four improved recognition systems is analyzed comprehensively in the aspects of the three invariances, recognition rate, and execution time. The final experiment results show that the improvements for these four algorithms reach the desired effect, the three invariances of feature descriptors are not directly related to the final recognition performance of recognition systems, and these four improved recognition systems have different performances under different conditions.

Oxytocin increases bias, but not accuracy, in face recognition line-ups.

PubMed

Bate, Sarah; Bennetts, Rachel; Parris, Benjamin A; Bindemann, Markus; Udale, Robert; Bussunt, Amanda

2015-07-01

Previous work indicates that intranasal inhalation of oxytocin improves face recognition skills, raising the possibility that it may be used in security settings. However, it is unclear whether oxytocin directly acts upon the core face-processing system itself or indirectly improves face recognition via affective or social salience mechanisms. In a double-blind procedure, 60 participants received either an oxytocin or placebo nasal spray before completing the One-in-Ten task-a standardized test of unfamiliar face recognition containing target-present and target-absent line-ups. Participants in the oxytocin condition outperformed those in the placebo condition on target-present trials, yet were more likely to make false-positive errors on target-absent trials. Signal detection analyses indicated that oxytocin induced a more liberal response bias, rather than increasing accuracy per se. These findings support a social salience account of the effects of oxytocin on face recognition and indicate that oxytocin may impede face recognition in certain scenarios. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Detection of target phonemes in spontaneous and read speech.

PubMed

Mehta, G; Cutler, A

1988-01-01

Although spontaneous speech occurs more frequently in most listeners' experience than read speech, laboratory studies of human speech recognition typically use carefully controlled materials read from a script. The phonological and prosodic characteristics of spontaneous and read speech differ considerably, however, which suggests that laboratory results may not generalise to the recognition of spontaneous speech. In the present study listeners were presented with both spontaneous and read speech materials, and their response time to detect word-initial target phonemes was measured. Responses were, overall, equally fast in each speech mode. However, analysis of effects previously reported in phoneme detection studies revealed significant differences between speech modes. In read speech but not in spontaneous speech, later targets were detected more rapidly than targets preceded by short words. In contrast, in spontaneous speech but not in read speech, targets were detected more rapidly in accented than in unaccented words and in strong than in weak syllables. An explanation for this pattern is offered in terms of characteristic prosodic differences between spontaneous and read speech. The results support claims from previous work that listeners pay great attention to prosodic information in the process of recognising speech.

Enzyme-Encapsulated Liposome-Linked Immunosorbent Assay Enabling Sensitive Personal Glucose Meter Readout for Portable Detection of Disease Biomarkers.

PubMed

Lin, Bingqian; Liu, Dan; Yan, Jinmao; Qiao, Zhi; Zhong, Yunxin; Yan, Jiawei; Zhu, Zhi; Ji, Tianhai; Yang, Chaoyong James

2016-03-23

There is considerable demand for sensitive, selective, and portable detection of disease-associated proteins, particularly in clinical practice and diagnostic applications. Portable devices are highly desired for detection of disease biomarkers in daily life due to the advantages of being simple, rapid, user-friendly, and low-cost. Herein we report an enzyme-encapsulated liposome-linked immunosorbent assay for sensitive detection of proteins using personal glucose meters (PGM) for portable quantitative readout. Liposomes encapsulating a large amount of amyloglucosidase or invertase are surface-coated with recognition elements such as aptamers or antibodies for target recognition. By translating molecular recognition signal into a large amount of glucose with the encapsulated enzyme, disease biomarkers such as thrombin or C-reactive protein (CRP) can be quantitatively detected by a PGM with a high detection limit of 1.8 or 0.30 nM, respectively. With the advantages of portability, ease of use, and low-cost, the method reported here has potential for portable and quantitative detection of various targets for different POC testing scenarios, such as rapid diagnosis in clinic offices, health monitoring at the bedside, and chemical/biochemical safety control in the field.

Modeling guidance and recognition in categorical search: bridging human and computer object detection.

PubMed

Zelinsky, Gregory J; Peng, Yifan; Berg, Alexander C; Samaras, Dimitris

2013-10-08

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery.

Modeling guidance and recognition in categorical search: Bridging human and computer object detection

PubMed Central

Zelinsky, Gregory J.; Peng, Yifan; Berg, Alexander C.; Samaras, Dimitris

2013-01-01

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery. PMID:24105460

Simultaneous detection of multiple DNA targets by integrating dual-color graphene quantum dot nanoprobes and carbon nanotubes.

PubMed

Qian, Zhaosheng; Shan, Xiaoyue; Chai, Lujing; Chen, Jianrong; Feng, Hui

2014-12-01

Simultaneous detection of multiple DNA targets was achieved based on a biocompatible graphene quantum dots (GQDs) and carbon nanotubes (CNTs) platform through spontaneous assembly between dual-color GQD-based probes and CNTs and subsequently self-recognition between DNA probes and targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The rational development of molecularly imprinted polymer-based sensors for protein detection.

PubMed

Whitcombe, Michael J; Chianella, Iva; Larcombe, Lee; Piletsky, Sergey A; Noble, James; Porter, Robert; Horgan, Adrian

2011-03-01

The detection of specific proteins as biomarkers of disease, health status, environmental monitoring, food quality, control of fermenters and civil defence purposes means that biosensors for these targets will become increasingly more important. Among the technologies used for building specific recognition properties, molecularly imprinted polymers (MIPs) are attracting much attention. In this critical review we describe many methods used for imprinting recognition for protein targets in polymers and their incorporation with a number of transducer platforms with the aim of identifying the most promising approaches for the preparation of MIP-based protein sensors (277 references).

A robust recognition and accurate locating method for circular coded diagonal target

NASA Astrophysics Data System (ADS)

Bao, Yunna; Shang, Yang; Sun, Xiaoliang; Zhou, Jiexin

2017-10-01

As a category of special control points which can be automatically identified, artificial coded targets have been widely developed in the field of computer vision, photogrammetry, augmented reality, etc. In this paper, a new circular coded target designed by RockeTech technology Corp. Ltd is analyzed and studied, which is called circular coded diagonal target (CCDT). A novel detection and recognition method with good robustness is proposed in the paper, and implemented on Visual Studio. In this algorithm, firstly, the ellipse features of the center circle are used for rough positioning. Then, according to the characteristics of the center diagonal target, a circular frequency filter is designed to choose the correct center circle and eliminates non-target noise. The precise positioning of the coded target is done by the correlation coefficient fitting extreme value method. Finally, the coded target recognition is achieved by decoding the binary sequence in the outer ring of the extracted target. To test the proposed algorithm, this paper has carried out simulation experiments and real experiments. The results show that the CCDT recognition and accurate locating method proposed in this paper can robustly recognize and accurately locate the targets in complex and noisy background.

Schizophrenia alters intra-network functional connectivity in the caudate for detecting speech under informational speech masking conditions.

PubMed

Zheng, Yingjun; Wu, Chao; Li, Juanhua; Li, Ruikeng; Peng, Hongjun; She, Shenglin; Ning, Yuping; Li, Liang

2018-04-04

Speech recognition under noisy "cocktail-party" environments involves multiple perceptual/cognitive processes, including target detection, selective attention, irrelevant signal inhibition, sensory/working memory, and speech production. Compared to health listeners, people with schizophrenia are more vulnerable to masking stimuli and perform worse in speech recognition under speech-on-speech masking conditions. Although the schizophrenia-related speech-recognition impairment under "cocktail-party" conditions is associated with deficits of various perceptual/cognitive processes, it is crucial to know whether the brain substrates critically underlying speech detection against informational speech masking are impaired in people with schizophrenia. Using functional magnetic resonance imaging (fMRI), this study investigated differences between people with schizophrenia (n = 19, mean age = 33 ± 10 years) and their matched healthy controls (n = 15, mean age = 30 ± 9 years) in intra-network functional connectivity (FC) specifically associated with target-speech detection under speech-on-speech-masking conditions. The target-speech detection performance under the speech-on-speech-masking condition in participants with schizophrenia was significantly worse than that in matched healthy participants (healthy controls). Moreover, in healthy controls, but not participants with schizophrenia, the strength of intra-network FC within the bilateral caudate was positively correlated with the speech-detection performance under the speech-masking conditions. Compared to controls, patients showed altered spatial activity pattern and decreased intra-network FC in the caudate. In people with schizophrenia, the declined speech-detection performance under speech-on-speech masking conditions is associated with reduced intra-caudate functional connectivity, which normally contributes to detecting target speech against speech masking via its functions of suppressing masking-speech signals.

Learning target masks in infrared linescan imagery

NASA Astrophysics Data System (ADS)

Fechner, Thomas; Rockinger, Oliver; Vogler, Axel; Knappe, Peter

1997-04-01

In this paper we propose a neural network based method for the automatic detection of ground targets in airborne infrared linescan imagery. Instead of using a dedicated feature extraction stage followed by a classification procedure, we propose the following three step scheme: In the first step of the recognition process, the input image is decomposed into its pyramid representation, thus obtaining a multiresolution signal representation. At the lowest three levels of the Laplacian pyramid a neural network filter of moderate size is trained to indicate the target location. The last step consists of a fusion process of the several neural network filters to obtain the final result. To perform this fusion we use a belief network to combine the various filter outputs in a statistical meaningful way. In addition, the belief network allows the integration of further knowledge about the image domain. By applying this multiresolution recognition scheme, we obtain a nearly scale- and rotational invariant target recognition with a significantly decreased false alarm rate compared with a single resolution target recognition scheme.

An evolution based biosensor receptor DNA sequence generation algorithm.

PubMed

Kim, Eungyeong; Lee, Malrey; Gatton, Thomas M; Lee, Jaewan; Zang, Yupeng

2010-01-01

A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements.

An Underwater Target Detection System for Electro-Optical Imagery Data

DTIC Science & Technology

2010-06-01

detection and segmentation of underwater mine-like objects in the EO images captured with a CCD-based image sensor. The main focus of this research is to...develop a robust detection algorithm that can be used to detect low contrast and partial underwater objects from the EO imagery with low false alarm rate...underwater target detection I. INTRODUCTION Automatic detection and recognition of underwater objects from EO imagery poses a serious challenge due to poor

Object recognition with hierarchical discriminant saliency networks.

PubMed

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and computer vision literatures. This demonstrates benefits for all the functional enhancements of the HDSN, the class tuning inherent to discriminant saliency, and saliency layers based on templates of increasing target selectivity and invariance. Altogether, these experiments suggest that there are non-trivial benefits in integrating attention and recognition.

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter.

PubMed

Ye, Tao; Zhou, Fuqiang

2015-04-10

When imaged by detectors, space targets (including satellites and debris) and background stars have similar point-spread functions, and both objects appear to change as detectors track targets. Therefore, traditional tracking methods cannot separate targets from stars and cannot directly recognize targets in 2D images. Consequently, we propose an autonomous space target recognition and tracking approach using a star sensor technique and a Kalman filter (KF). A two-step method for subpixel-scale detection of star objects (including stars and targets) is developed, and the combination of the star sensor technique and a KF is used to track targets. The experimental results show that the proposed method is adequate for autonomously recognizing and tracking space targets.

Evaluating structural pattern recognition for handwritten math via primitive label graphs

NASA Astrophysics Data System (ADS)

Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

2013-01-01

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

A study of payload specialist station monitor size constraints. [space shuttle orbiters

NASA Technical Reports Server (NTRS)

Kirkpatrick, M., III; Shields, N. L., Jr.; Malone, T. B.

1975-01-01

Constraints on the CRT display size for the shuttle orbiter cabin are studied. The viewing requirements placed on these monitors were assumed to involve display of imaged scenes providing visual feedback during payload operations and display of alphanumeric characters. Data on target recognition/resolution, target recognition, and range rate detection by human observers were utilized to determine viewing requirements for imaged scenes. Field-of-view and acuity requirements for a variety of payload operations were obtained along with the necessary detection capability in terms of range-to-target size ratios. The monitor size necessary to meet the acuity requirements was established. An empirical test was conducted to determine required recognition sizes for displayed alphanumeric characters. The results of the test were used to determine the number of characters which could be simultaneously displayed based on the recognition size requirements using the proposed monitor size. A CRT display of 20 x 20 cm is recommended. A portion of the display area is used for displaying imaged scenes and the remaining display area is used for alphanumeric characters pertaining to the displayed scene. The entire display is used for the character alone mode.

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles †

PubMed Central

López-Rodríguez, Patricia; Escot-Bocanegra, David; Fernández-Recio, Raúl; Bravo, Ignacio

2015-01-01

Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising. PMID:25551484

EzyAmp signal amplification cascade enables isothermal detection of nucleic acid and protein targets.

PubMed

Linardy, Evelyn M; Erskine, Simon M; Lima, Nicole E; Lonergan, Tina; Mokany, Elisa; Todd, Alison V

2016-01-15

Advancements in molecular biology have improved the ability to characterize disease-related nucleic acids and proteins. Recently, there has been an increasing desire for tests that can be performed outside of centralised laboratories. This study describes a novel isothermal signal amplification cascade called EzyAmp (enzymatic signal amplification) that is being developed for detection of targets at the point of care. EzyAmp exploits the ability of some restriction endonucleases to cleave substrates containing nicks within their recognition sites. EzyAmp uses two oligonucleotide duplexes (partial complexes 1 and 2) which are initially cleavage-resistant as they lack a complete recognition site. The recognition site of partial complex 1 can be completed by hybridization of a triggering oligonucleotide (Driver Fragment 1) that is generated by a target-specific initiation event. Binding of Driver Fragment 1 generates a completed complex 1, which upon cleavage, releases Driver Fragment 2. In turn, binding of Driver Fragment 2 to partial complex 2 creates completed complex 2 which when cleaved releases additional Driver Fragment 1. Each cleavage event separates fluorophore quencher pairs resulting in an increase in fluorescence. At this stage a cascade of signal production becomes independent of further target-specific initiation events. This study demonstrated that the EzyAmp cascade can facilitate detection and quantification of nucleic acid targets with sensitivity down to aM concentration. Further, the same cascade detected VEGF protein with a sensitivity of 20nM showing that this universal method for amplifying signal may be linked to the detection of different types of analytes in an isothermal format. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

The Monitoring, Detection, Isolation and Assessment of Information Warfare Attacks Through Multi-Level, Multi-Scale System Modeling and Model Based Technology

DTIC Science & Technology

2004-01-01

login identity to the one under which the system call is executed, the parameters of the system call execution - file names including full path...Anomaly detection COAST-EIMDT Distributed on target hosts EMERALD Distributed on target hosts and security servers Signature recognition Anomaly...uses a centralized architecture, and employs an anomaly detection technique for intrusion detection. The EMERALD project [80] proposes a

Improved detection probability of low level light and infrared image fusion system

NASA Astrophysics Data System (ADS)

Luo, Yuxiang; Fu, Rongguo; Zhang, Junju; Wang, Wencong; Chang, Benkang

2018-02-01

Low level light(LLL) image contains rich information on environment details, but is easily affected by the weather. In the case of smoke, rain, cloud or fog, much target information will lose. Infrared image, which is from the radiation produced by the object itself, can be "active" to obtain the target information in the scene. However, the image contrast and resolution is bad, the ability of the acquisition of target details is very poor, and the imaging mode does not conform to the human visual habit. The fusion of LLL and infrared image can make up for the deficiency of each sensor and give play to the advantages of single sensor. At first, we show the hardware design of fusion circuit. Then, through the recognition probability calculation of the target(one person) and the background image(trees), we find that the trees detection probability of LLL image is higher than that of the infrared image, and the person detection probability of the infrared image is obviously higher than that of LLL image. The detection probability of fusion image for one person and trees is higher than that of single detector. Therefore, image fusion can significantly enlarge recognition probability and improve detection efficiency.

Trends in Correlation-Based Pattern Recognition and Tracking in Forward-Looking Infrared Imagery

PubMed Central

Alam, Mohammad S.; Bhuiyan, Sharif M. A.

2014-01-01

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. PMID:25061840

Electrochemical impedimetric sensor based on molecularly imprinted polymers/sol-gel chemistry for methidathion organophosphorous insecticide recognition.

PubMed

Bakas, Idriss; Hayat, Akhtar; Piletsky, Sergey; Piletska, Elena; Chehimi, Mohamed M; Noguer, Thierry; Rouillon, Régis

2014-12-01

We report here a novel method to detect methidathion organophosphorous insecticides. The sensing platform was architected by the combination of molecularly imprinted polymers and sol-gel technique on inexpensive, portable and disposable screen printed carbon electrodes. Electrochemical impedimetric detection technique was employed to perform the label free detection of the target analyte on the designed MIP/sol-gel integrated platform. The selection of the target specific monomer by electrochemical impedimetric methods was consistent with the results obtained by the computational modelling method. The prepared electrochemical MIP/sol-gel based sensor exhibited a high recognition capability toward methidathion, as well as a broad linear range and a low detection limit under the optimized conditions. Satisfactory results were also obtained for the methidathion determination in waste water samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Modeling Human Visual Perception for Target Detection in Military Simulations

DTIC Science & Technology

2009-06-01

incorrectly, is a subject for future research. Possibly, one could exploit the Recognition-by-Components theory of Biederman (1987) and decompose the...Psychophysiscs, 55, 485-496. Biederman , I. (1987). Recognition-by-components: A theory of human image understand- ing. Psychological Review, 94, 115-147

Multi-layer cube sampling for liver boundary detection in PET-CT images.

PubMed

Liu, Xinxin; Yang, Jian; Song, Shuang; Song, Hong; Ai, Danni; Zhu, Jianjun; Jiang, Yurong; Wang, Yongtian

2018-06-01

Liver metabolic information is considered as a crucial diagnostic marker for the diagnosis of fever of unknown origin, and liver recognition is the basis of automatic diagnosis of metabolic information extraction. However, the poor quality of PET and CT images is a challenge for information extraction and target recognition in PET-CT images. The existing detection method cannot meet the requirement of liver recognition in PET-CT images, which is the key problem in the big data analysis of PET-CT images. A novel texture feature descriptor called multi-layer cube sampling (MLCS) is developed for liver boundary detection in low-dose CT and PET images. The cube sampling feature is proposed for extracting more texture information, which uses a bi-centric voxel strategy. Neighbour voxels are divided into three regions by the centre voxel and the reference voxel in the histogram, and the voxel distribution information is statistically classified as texture feature. Multi-layer texture features are also used to improve the ability and adaptability of target recognition in volume data. The proposed feature is tested on the PET and CT images for liver boundary detection. For the liver in the volume data, mean detection rate (DR) and mean error rate (ER) reached 95.15 and 7.81% in low-quality PET images, and 83.10 and 21.08% in low-contrast CT images. The experimental results demonstrated that the proposed method is effective and robust for liver boundary detection.

Crowding by a single bar: probing pattern recognition mechanisms in the visual periphery.

PubMed

Põder, Endel

2014-11-06

Whereas visual crowding does not greatly affect the detection of the presence of simple visual features, it heavily inhibits combining them into recognizable objects. Still, crowding effects have rarely been directly related to general pattern recognition mechanisms. In this study, pattern recognition mechanisms in visual periphery were probed using a single crowding feature. Observers had to identify the orientation of a rotated T presented briefly in a peripheral location. Adjacent to the target, a single bar was presented. The bar was either horizontal or vertical and located in a random direction from the target. It appears that such a crowding bar has very strong and regular effects on the identification of the target orientation. The observer's responses are determined by approximate relative positions of basic visual features; exact image-based similarity to the target is not important. A version of the "standard model" of object recognition with second-order features explains the main regularities of the data. © 2014 ARVO.

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

Protein Detection via Direct Enzymatic Amplification of Short DNA Aptamers

PubMed Central

Fischer, Nicholas O.; Tarasow, Theodore M.; Tok, Jeffrey B.-H.

2008-01-01

Aptamers are single-stranded nucleic acids that fold into defined tertiary structures to bind target molecules with high specificities and affinities. DNA aptamers have garnered much interest as recognition elements for biodetection and diagnostic applications due to their small size, ease of discovery and synthesis, and chemical and thermal stability. Herein, we describe the design and application of a short DNA molecule capable of both protein target binding and amplifiable bioreadout processes. As both recognition and readout capabilities are incorporated into a single DNA molecule, tedious conjugation procedures required for protein-DNA hybrids can be omitted. The DNA aptamer is designed to be amplified directly by either the polymerase chain reaction (PCR) or rolling circle amplification (RCA) processes, taking advantage of real-time amplification monitoring techniques for target detection. A combination of both RCA and PCR provides a wide protein target dynamic range (1 μM to 10 pM). PMID:17980857

Research on infrared dim-point target detection and tracking under sea-sky-line complex background

NASA Astrophysics Data System (ADS)

Dong, Yu-xing; Li, Yan; Zhang, Hai-bo

2011-08-01

Target detection and tracking technology in infrared image is an important part of modern military defense system. Infrared dim-point targets detection and recognition under complex background is a difficulty and important strategic value and challenging research topic. The main objects that carrier-borne infrared vigilance system detected are sea-skimming aircrafts and missiles. Due to the characteristics of wide field of view of vigilance system, the target is usually under the sea clutter. Detection and recognition of the target will be taken great difficulties .There are some traditional point target detection algorithms, such as adaptive background prediction detecting method. When background has dispersion-decreasing structure, the traditional target detection algorithms would be more useful. But when the background has large gray gradient, such as sea-sky-line, sea waves etc .The bigger false-alarm rate will be taken in these local area .It could not obtain satisfactory results. Because dim-point target itself does not have obvious geometry or texture feature ,in our opinion , from the perspective of mathematics, the detection of dim-point targets in image is about singular function analysis .And from the perspective image processing analysis , the judgment of isolated singularity in the image is key problem. The foregoing points for dim-point targets detection, its essence is a separation of target and background of different singularity characteristics .The image from infrared sensor usually accompanied by different kinds of noise. These external noises could be caused by the complicated background or from the sensor itself. The noise might affect target detection and tracking. Therefore, the purpose of the image preprocessing is to reduce the effects from noise, also to raise the SNR of image, and to increase the contrast of target and background. According to the low sea-skimming infrared flying small target characteristics , the median filter is used to eliminate noise, improve signal-to-noise ratio, then the multi-point multi-storey vertical Sobel algorithm will be used to detect the sea-sky-line ,so that we can segment sea and sky in the image. Finally using centroid tracking method to capture and trace target. This method has been successfully used to trace target under the sea-sky complex background.

Testing Saliency Parameters for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Pandya, Sagar

2012-01-01

A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.

Applications of independent component analysis in SAR images

NASA Astrophysics Data System (ADS)

Huang, Shiqi; Cai, Xinhua; Hui, Weihua; Xu, Ping

2009-07-01

The detection of faint, small and hidden targets in synthetic aperture radar (SAR) image is still an issue for automatic target recognition (ATR) system. How to effectively separate these targets from the complex background is the aim of this paper. Independent component analysis (ICA) theory can enhance SAR image targets and improve signal clutter ratio (SCR), which benefits to detect and recognize faint targets. Therefore, this paper proposes a new SAR image target detection algorithm based on ICA. In experimental process, the fast ICA (FICA) algorithm is utilized. Finally, some real SAR image data is used to test the method. The experimental results verify that the algorithm is feasible, and it can improve the SCR of SAR image and increase the detection rate for the faint small targets.

Local structure preserving sparse coding for infrared target recognition

PubMed Central

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa

2017-01-01

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824

Detection of facilities in satellite imagery using semi-supervised image classification and auxiliary contextual observables

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harvey, Neal R; Ruggiero, Christy E; Pawley, Norma H

2009-01-01

Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervisedmore » assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.« less

Hetero-enzyme-based two-round signal amplification strategy for trace detection of aflatoxin B1 using an electrochemical aptasensor.

PubMed

Zheng, Wanli; Teng, Jun; Cheng, Lin; Ye, Yingwang; Pan, Daodong; Wu, Jingjing; Xue, Feng; Liu, Guodong; Chen, Wei

2016-06-15

An electrochemical aptasensor for trace detection of aflatoxin B1 (AFB1) was developed by using an aptamer as the recognition unit while adopting the telomerase and EXO III based two-round signal amplification strategy as the signal enhancement units. The telomerase amplification was used to elongate the ssDNA probes on the surface of gold nanoparticles, by which the signal response range of the signal-off model electrochemical aptasensor could be correspondingly enlarged. Then, the EXO III amplification was used to hydrolyze the 3'-end of the dsDNA after the recognition of target AFB1, which caused the release of bounded AFB1 into the sensing system, where it participated in the next recognition-sensing cycle. With this two-round signal amplified electrochemical aptasensor, target AFB1 was successfully measured at trace concentrations with excellent detection limit of 0.6*10(-4)ppt and satisfied specificity due to the excellent affinity of the aptamer against AFB1. Based on this designed two-round signal amplification strategy, both the sensing range and detection limit were greatly improved. This proposed ultrasensitive electrochemical aptasensor method was also validated by comparison with the classic instrumental methods. Importantly, this hetero-enzyme based two-round signal amplified electrochemical aptasensor offers a great promising protocol for ultrasensitive detection of AFB1 and other mycotoxins by replacing the core recognition sequence of the aptamer. Copyright © 2016 Elsevier B.V. All rights reserved.

Thin membrane sensor with biochemical switch

NASA Technical Reports Server (NTRS)

Worley, III, Jennings F. (Inventor); Case, George D. (Inventor)

1994-01-01

A modular biosensor system for chemical or biological agent detection utilizes electrochemical measurement of an ion current across a gate membrane triggered by the reaction of the target agent with a recognition protein conjugated to a channel blocker. The sensor system includes a bioresponse simulator or biochemical switch module which contains the recognition protein-channel blocker conjugate, and in which the detection reactions occur, and a transducer module which contains a gate membrane and a measuring electrode, and in which the presence of agent is sensed electrically. In the poised state, ion channels in the gate membrane are blocked by the recognition protein-channel blocker conjugate. Detection reactions remove the recognition protein-channel blocker conjugate from the ion channels, thus eliciting an ion current surge in the gate membrane which subsequently triggers an output alarm. Sufficiently large currents are generated that simple direct current electronics are adequate for the measurements. The biosensor has applications for environmental, medical, and industrial use.

Application of passive imaging polarimetry in the discrimination and detection of different color targets of identical shapes using color-blind imaging sensors

NASA Astrophysics Data System (ADS)

El-Saba, A. M.; Alam, M. S.; Surpanani, A.

2006-05-01

Important aspects of automatic pattern recognition systems are their ability to efficiently discriminate and detect proper targets with low false alarms. In this paper we extend the applications of passive imaging polarimetry to effectively discriminate and detect different color targets of identical shapes using color-blind imaging sensor. For this case of study we demonstrate that traditional color-blind polarization-insensitive imaging sensors that rely only on the spatial distribution of targets suffer from high false detection rates, especially in scenarios where multiple identical shape targets are present. On the other hand we show that color-blind polarization-sensitive imaging sensors can successfully and efficiently discriminate and detect true targets based on their color only. We highlight the main advantages of using our proposed polarization-encoded imaging sensor.

Improved detection sensitivity of γ-aminobutyric acid based on graphene oxide interface on an optical microfiber.

PubMed

Zhou, Jun; Huang, Yunyun; Chen, Chaoyan; Xiao, Aoxiang; Guo, Tuan; Guan, Bai-Ou

2018-05-11

Interfacing bio-recognition elements to optical materials is a longstanding challenge to manufacture sensitive biosensors and inexpensive diagnostic devices. In this work, a graphene oxide (GO) interface has been constructed between silica microfiber and bio-recognition elements to develop an improved γ-aminobutyric acid (GABA) sensing approach. The GO interface, which was located at the site with the strongest evanescent field on the microfiber surface, improved the detection sensitivity by providing a larger platform for more bio-recognition element immobilization, and amplifying surface refractive index change caused by combination between bio-recognition elements and target molecules. Owing to the interface improvement, the microfiber has a three times improved sensitivity of 1.03 nm/log M for GABA detection, and hence a lowest limit of detection of 2.91 × 10-18 M, which is 7 orders of magnitude higher than that without the GO interface. Moreover, the micrometer-sized footprint and non-radioactive nature enable easy implantation in human brains for in vivo applications.

A unique dual recognition hairpin probe mediated fluorescence amplification method for sensitive detection of uracil-DNA glycosylase and endonuclease IV activities.

PubMed

Wu, Yushu; Yan, Ping; Xu, Xiaowen; Jiang, Wei

2016-03-07

Uracil-DNA glycosylase (UDG) and endonuclease IV (Endo IV) play cooperative roles in uracil base-excision repair (UBER) and inactivity of either will interrupt the UBER to cause disease. Detection of UDG and Endo IV activities is crucial to evaluate the UBER process in fundamental research and diagnostic application. Here, a unique dual recognition hairpin probe mediated fluorescence amplification method was developed for sensitively and selectively detecting UDG and Endo IV activities. For detecting UDG activity, the uracil base in the probe was excised by the target enzyme to generate an apurinic/apyrimidinic (AP) site, achieving the UDG recognition. Then, the AP site was cleaved by a tool enzyme Endo IV, releasing a primer to trigger rolling circle amplification (RCA) reaction. Finally, the RCA reaction produced numerous repeated G-quadruplex sequences, which interacted with N-methyl-mesoporphyrin IX to generate an enhanced fluorescence signal. Alternatively, for detecting Endo IV activity, the uracil base in the probe was first converted into an AP site by a tool enzyme UDG. Next, the AP site was cleaved by the target enzyme, achieving the Endo IV recognition. The signal was then generated and amplified in the same way as those in the UDG activity assay. The detection limits were as low as 0.00017 U mL(-1) for UDG and 0.11 U mL(-1) for Endo IV, respectively. Moreover, UDG and Endo IV can be well distinguished from their analogs. This method is beneficial for properly evaluating the UBER process in function studies and disease prognoses.

Sensor and Processing COI (Briefing Charts)

DTIC Science & Technology

2014-05-27

Persistent Surveillance • Target Detection, Recognition & ID at Standoff Ranges • Force/Platform/Sensor Protection • Target Tracking • Early Warning • BDA ...inhomogeneous and complex media is also a foundational challenge for President’s BRAIN initiative. 38 Explore Advanced Sensors And Processing

Strategic Deployment of Orthographic Knowledge in Phoneme Detection

ERIC Educational Resources Information Center

Cutler, Anne; Treiman, Rebecca; van Ooijen, Brit

2010-01-01

The phoneme detection task is widely used in spoken-word recognition research. Alphabetically literate participants, however, are more used to explicit representations of letters than of phonemes. The present study explored whether phoneme detection is sensitive to how target phonemes are, or may be, orthographically realized. Listeners detected…

Influenza sensor

DOEpatents

Swanson, Basil I.; Song, Xuedong; Unkefer, Clifford; Silks, III, Louis A.; Schmidt, Jurgen G.

2003-09-30

A sensor for the detection of tetrameric multivalent neuraminidase within a sample is disclosed, where a positive detection indicates the presence of a target virus within the sample. Also disclosed is a trifunctional composition of matter including a trifunctional linker moiety with groups bonded thereto including (a) an alkyl chain adapted for attachment to a substrate, (b) a fluorescent moiety capable of generating a fluorescent signal, and (c) a recognition moiety having a spacer group of a defined length thereon, the recognition moiety capable of binding with tetrameric multivalent neuraminidase.

Influenza Sensor

DOEpatents

Swanson, Basil I.; Song, Xuedong; Unkefer, Clifford; Silks, III, Louis A.; Schmidt, Jurgen G.

2006-03-28

A sensor for the detection of tetrameric multivalent neuraminidase within a sample is disclosed, where a positive detection indicates the presence of a target virus within the sample. Also disclosed is a trifunctional composition of matter including a trifunctional linker moiety with groups bonded thereto including (a) an alkyl chain adapted for attachment to a substrate, (b) a fluorescent moiety capable of generating a fluorescent signal, and (c) a recognition moiety having a spacer group of a defined length thereon, the recognition moiety capable of binding with tetrameric multivalent neuraminidase.

Influenza Sensor

DOEpatents

Swanson, Basil I.; Song, Xuedong; Unkefer, Clifford; Silks, III, Louis A.; Schmidt, Jurgen G.

2005-05-17

A sensor for the detection of tetrameric multivalent neuraminidase within a sample is disclosed, where a positive detection indicates the presence of a target virus within the sample. Also disclosed is a trifunctional composition of matter including a trifunctional linker moiety with groups bonded thereto including (a) an alkyl chain adapted for attachment to a substrate, (b) a fluorescent moiety capable of generating a fluorescent signal, and (c) a recognition moiety having a spacer group of a defined length thereon, the recognition moiety capable of binding with tetrameric multivalent neuraminidase.

Hierarchical Context Modeling for Video Event Recognition.

PubMed

Wang, Xiaoyang; Ji, Qiang

2016-10-11

Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.

An algorithm for automatic target recognition using passive radar and an EKF for estimating aircraft orientation

NASA Astrophysics Data System (ADS)

Ehrman, Lisa M.

2005-07-01

Rather than emitting pulses, passive radar systems rely on "illuminators of opportunity," such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, further scaling the RCS. A Rician likelihood model compares the scaled RCS of the illuminated aircraft with those of the potential targets. To improve the robustness of the result, the algorithm jointly optimizes over feasible orientation profiles and target types via dynamic programming.

Effects of Extended Camera Baseline and Image Magnification on Target Detection Time and Target Recognition with a Stereoscopic TV System.

DTIC Science & Technology

1986-02-01

rates. Target detection times were calculated by averaging response times for all trials within a treatment condition, excluding trials in which...responses in a treatment condition by the total number of valid trials within that condition. Each of these dependent measures was subjected to its own...or 1905 mm). Comparisons among the various treatment means for this effect revealed 13 4~ ~ q-. ~ ~ .-..... 2.309 A EXPERIMENT ONE. 2.29- 0 0 z 2.19

Study on multispectral imaging detection and recognition

NASA Astrophysics Data System (ADS)

Jun, Wang; Na, Ding; Gao, Jiaobo; Yu, Hu; Jun, Wu; Li, Junna; Zheng, Yawei; Fei, Gao; Sun, Kefeng

2009-07-01

Multispectral imaging detecting technology use target radiation character in spectral spatial distribution and relation between spectral and image to detect target and remote sensing measure. Its speciality is multi channel, narrow bandwidth, large amount of information, high accuracy. The ability of detecting target in environment of clutter, camouflage, concealment and beguilement is improved. At present, spectral imaging technology in the range of multispectral and hyperspectral develop greatly. The multispectral imaging equipment of unmanned aerial vehicle can be used in mine detection, information, surveillance and reconnaissance. Spectral imaging spectrometer operating in MWIR and LWIR has already been applied in the field of remote sensing and military in the advanced country. The paper presents the technology of multispectral imaging. It can enhance the reflectance, scatter and radiation character of the artificial targets among nature background. The targets among complex background and camouflage/stealth targets can be effectively identified. The experiment results and the data of spectral imaging is obtained.

Face shape and face identity processing in behavioral variant fronto-temporal dementia: A specific deficit for familiarity and name recognition of famous faces.

PubMed

De Winter, François-Laurent; Timmers, Dorien; de Gelder, Beatrice; Van Orshoven, Marc; Vieren, Marleen; Bouckaert, Miriam; Cypers, Gert; Caekebeke, Jo; Van de Vliet, Laura; Goffin, Karolien; Van Laere, Koen; Sunaert, Stefan; Vandenberghe, Rik; Vandenbulcke, Mathieu; Van den Stock, Jan

2016-01-01

Deficits in face processing have been described in the behavioral variant of fronto-temporal dementia (bvFTD), primarily regarding the recognition of facial expressions. Less is known about face shape and face identity processing. Here we used a hierarchical strategy targeting face shape and face identity recognition in bvFTD and matched healthy controls. Participants performed 3 psychophysical experiments targeting face shape detection (Experiment 1), unfamiliar face identity matching (Experiment 2), familiarity categorization and famous face-name matching (Experiment 3). The results revealed group differences only in Experiment 3, with a deficit in the bvFTD group for both familiarity categorization and famous face-name matching. Voxel-based morphometry regression analyses in the bvFTD group revealed an association between grey matter volume of the left ventral anterior temporal lobe and familiarity recognition, while face-name matching correlated with grey matter volume of the bilateral ventral anterior temporal lobes. Subsequently, we quantified familiarity-specific and name-specific recognition deficits as the sum of the celebrities of which respectively only the name or only the familiarity was accurately recognized. Both indices were associated with grey matter volume of the bilateral anterior temporal cortices. These findings extent previous results by documenting the involvement of the left anterior temporal lobe (ATL) in familiarity detection and the right ATL in name recognition deficits in fronto-temporal lobar degeneration.

Glucose enhancement of a facial recognition task in young adults.

PubMed

Metzger, M M

2000-02-01

Numerous studies have reported that glucose administration enhances memory processes in both elderly and young adult subjects. Although these studies have utilized a variety of procedures and paradigms, investigations of both young and elderly subjects have typically used verbal tasks (word list recall, paragraph recall, etc.). In the present study, the effect of glucose consumption on a nonverbal, facial recognition task in young adults was examined. Lemonade sweetened with either glucose (50 g) or saccharin (23.7 mg) was consumed by college students (mean age of 21.1 years) 15 min prior to a facial recognition task. The task consisted of a familiarization phase in which subjects were presented with "target" faces, followed immediately by a recognition phase in which subjects had to identify the targets among a random array of familiar target and novel "distractor" faces. Statistical analysis indicated that there were no differences on hit rate (target identification) for subjects who consumed either saccharin or glucose prior to the test. However, further analyses revealed that subjects who consumed glucose committed significantly fewer false alarms and had (marginally) higher d-prime scores (a signal detection measure) compared to subjects who consumed saccharin prior to the test. These results parallel a previous report demonstrating glucose enhancement of a facial recognition task in probable Alzheimer's patients; however, this is believed to be the first demonstration of glucose enhancement for a facial recognition task in healthy, young adults.

A novel "signal-on/off" sensing platform for selective detection of thrombin based on target-induced ratiometric electrochemical biosensing and bio-bar-coded nanoprobe amplification strategy.

PubMed

Wang, Lanlan; Ma, Rongna; Jiang, Liushan; Jia, Liping; Jia, Wenli; Wang, Huaisheng

2017-06-15

A novel dual-signal ratiometric electrochemical aptasensor for highly sensitive and selective detection of thrombin has been designed on the basis of signal-on and signal-off strategy. Ferrocene labeled hairpin probe (Fc-HP), thrombin aptamer and methyl blue labeled bio-bar-coded AuNPs (MB-P3-AuNPs) were rationally introduced for the construction of the assay platform, which combined the advantages of the recognition of aptamer, the amplification of bio-bar-coded nanoprobe, and the ratiometric signaling readout. In the presence of thrombin, the interaction between thrombin and the aptamer leads to the departure of MB-P3-AuNPs from the sensing interface, and the conformation of the single stranded Fc-HP to a hairpin structure to take the Fc confined near the electrode surface. Such conformational changes resulted in the oxidation current of Fc increased and that of MB decreased. Therefore, the recognition event of the target can be dual-signal ratiometric electrochemical readout in both the "signal-off" of MB and the "signal-on" of Fc. The proposed strategy showed a wide linear detection range from 0.003 to 30nM with a detection limit of 1.1 pM. Moreover, it exhibits good performance of excellent selectivity, good stability, and acceptable fabrication reproducibility. By changing the recognition probe, this protocol could be easily expanded into the detection of other targets, showing promising potential applications in disease diagnostics and bioanalysis. Copyright © 2016. Published by Elsevier B.V.

Binding-induced DNA walker for signal amplification in highly selective electrochemical detection of protein.

PubMed

Ji, Yuhang; Zhang, Lei; Zhu, Longyi; Lei, Jianping; Wu, Jie; Ju, Huangxian

2017-10-15

A binding-induced DNA walker-assisted signal amplification was developed for highly selective electrochemical detection of protein. Firstly, the track of DNA walker was constructed by self-assembly of the high density ferrocene (Fc)-labeled anchor DNA and aptamer 1 on the gold electrode surface. Sequentially, a long swing-arm chain containing aptamer 2 and walking strand DNA was introduced onto gold electrode through aptamers-target specific recognition, and thus initiated walker strand sequences to hybridize with anchor DNA. Then, the DNA walker was activated by the stepwise cleavage of the hybridized anchor DNA by nicking endonuclease to release multiple Fc molecules for signal amplification. Taking thrombin as the model target, the Fc-generated electrochemical signal decreased linearly with logarithm value of thrombin concentration ranging from 10pM to 100nM with a detection limit of 2.5pM under the optimal conditions. By integrating the specific recognition of aptamers to target with the enzymatic cleavage of nicking endonuclease, the aptasensor showed the high selectivity. The binding-induced DNA walker provides a promising strategy for signal amplification in electrochemical biosensor, and has the extensive applications in sensitive and selective detection of the various targets. Copyright © 2017 Elsevier B.V. All rights reserved.

User acceptance of intelligent avionics: A study of automatic-aided target recognition

NASA Technical Reports Server (NTRS)

Becker, Curtis A.; Hayes, Brian C.; Gorman, Patrick C.

1991-01-01

User acceptance of new support systems typically was evaluated after the systems were specified, designed, and built. The current study attempts to assess user acceptance of an Automatic-Aided Target Recognition (ATR) system using an emulation of such a proposed system. The detection accuracy and false alarm level of the ATR system were varied systematically, and subjects rated the tactical value of systems exhibiting different performance levels. Both detection accuracy and false alarm level affected the subjects' ratings. The data from two experiments suggest a cut-off point in ATR performance below which the subjects saw little tactical value in the system. An ATR system seems to have obvious tactical value only if it functions at a correct detection rate of 0.7 or better with a false alarm level of 0.167 false alarms per square degree or fewer.

Association of impaired facial affect recognition with basic facial and visual processing deficits in schizophrenia.

PubMed

Norton, Daniel; McBain, Ryan; Holt, Daphne J; Ongur, Dost; Chen, Yue

2009-06-15

Impaired emotion recognition has been reported in schizophrenia, yet the nature of this impairment is not completely understood. Recognition of facial emotion depends on processing affective and nonaffective facial signals, as well as basic visual attributes. We examined whether and how poor facial emotion recognition in schizophrenia is related to basic visual processing and nonaffective face recognition. Schizophrenia patients (n = 32) and healthy control subjects (n = 29) performed emotion discrimination, identity discrimination, and visual contrast detection tasks, where the emotionality, distinctiveness of identity, or visual contrast was systematically manipulated. Subjects determined which of two presentations in a trial contained the target: the emotional face for emotion discrimination, a specific individual for identity discrimination, and a sinusoidal grating for contrast detection. Patients had significantly higher thresholds (worse performance) than control subjects for discriminating both fearful and happy faces. Furthermore, patients' poor performance in fear discrimination was predicted by performance in visual detection and face identity discrimination. Schizophrenia patients require greater emotional signal strength to discriminate fearful or happy face images from neutral ones. Deficient emotion recognition in schizophrenia does not appear to be determined solely by affective processing but is also linked to the processing of basic visual and facial information.

24/7 security system: 60-FPS color EMCCD camera with integral human recognition

NASA Astrophysics Data System (ADS)

Vogelsong, T. L.; Boult, T. E.; Gardner, D. W.; Woodworth, R.; Johnson, R. C.; Heflin, B.

2007-04-01

An advanced surveillance/security system is being developed for unattended 24/7 image acquisition and automated detection, discrimination, and tracking of humans and vehicles. The low-light video camera incorporates an electron multiplying CCD sensor with a programmable on-chip gain of up to 1000:1, providing effective noise levels of less than 1 electron. The EMCCD camera operates in full color mode under sunlit and moonlit conditions, and monochrome under quarter-moonlight to overcast starlight illumination. Sixty frame per second operation and progressive scanning minimizes motion artifacts. The acquired image sequences are processed with FPGA-compatible real-time algorithms, to detect/localize/track targets and reject non-targets due to clutter under a broad range of illumination conditions and viewing angles. The object detectors that are used are trained from actual image data. Detectors have been developed and demonstrated for faces, upright humans, crawling humans, large animals, cars and trucks. Detection and tracking of targets too small for template-based detection is achieved. For face and vehicle targets the results of the detection are passed to secondary processing to extract recognition templates, which are then compared with a database for identification. When combined with pan-tilt-zoom (PTZ) optics, the resulting system provides a reliable wide-area 24/7 surveillance system that avoids the high life-cycle cost of infrared cameras and image intensifiers.

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units

PubMed Central

Liu, Kui; Wei, Sixiao; Chen, Zhijiang; Jia, Bin; Chen, Genshe; Ling, Haibin; Sheaff, Carolyn; Blasch, Erik

2017-01-01

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions. PMID:28208684

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units.

PubMed

Liu, Kui; Wei, Sixiao; Chen, Zhijiang; Jia, Bin; Chen, Genshe; Ling, Haibin; Sheaff, Carolyn; Blasch, Erik

2017-02-12

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions.

Target-responsive DNA-capped nanocontainer used for fabricating universal detector and performing logic operations

PubMed Central

Wu, Li; Ren, Jinsong; Qu, Xiaogang

2014-01-01

Nucleic acids have become a powerful tool in nanotechnology because of their controllable diverse conformational transitions and adaptable higher-order nanostructure. Using single-stranded DNA probes as the pore-caps for various target recognition, here we present an ultrasensitive universal electrochemical detection system based on graphene and mesoporous silica, and achieve sensitivity with all of the major classes of analytes and simultaneously realize DNA logic gate operations. The concept is based on the locking of the pores and preventing the signal-reporter molecules from escape by target-induced the conformational change of the tailored DNA caps. The coupling of ‘waking up’ gatekeeper with highly specific biochemical recognition is an innovative strategy for the detection of various targets, able to compete with classical methods which need expensive instrumentation and sophisticated experimental operations. The present study has introduced a new electrochemical signal amplification concept and also adds a new dimension to the function of graphene-mesoporous materials hybrids as multifunctional nanoscale logic devices. More importantly, the development of this approach would spur further advances in important areas, such as point-of-care diagnostics or detection of specific biological contaminations, and hold promise for use in field analysis. PMID:25249622

Automatic target recognition and detection in infrared imagery under cluttered background

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Koç, Aykut; Alatan, A. Aydın.

2017-10-01

Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

Geometric shapes inversion method of space targets by ISAR image segmentation

NASA Astrophysics Data System (ADS)

Huo, Chao-ying; Xing, Xiao-yu; Yin, Hong-cheng; Li, Chen-guang; Zeng, Xiang-yun; Xu, Gao-gui

2017-11-01

The geometric shape of target is an effective characteristic in the process of space targets recognition. This paper proposed a method of shape inversion of space target based on components segmentation from ISAR image. The Radon transformation, Hough transformation, K-means clustering, triangulation will be introduced into ISAR image processing. Firstly, we use Radon transformation and edge detection to extract space target's main body spindle and solar panel spindle from ISAR image. Then the targets' main body, solar panel, rectangular and circular antenna are segmented from ISAR image based on image detection theory. Finally, the sizes of every structural component are computed. The effectiveness of this method is verified using typical targets' simulation data.

Human detection in sensitive security areas through recognition of omega shapes using MACH filters

NASA Astrophysics Data System (ADS)

Rehman, Saad; Riaz, Farhan; Hassan, Ali; Liaquat, Muwahida; Young, Rupert

2015-03-01

Human detection has gained considerable importance in aggravated security scenarios over recent times. An effective security application relies strongly on detailed information regarding the scene under consideration. A larger accumulation of humans than the number of personal authorized to visit a security controlled area must be effectively detected, amicably alarmed and immediately monitored. A framework involving a novel combination of some existing techniques allows an immediate detection of an undesirable crowd in a region under observation. Frame differencing provides a clear visibility of moving objects while highlighting those objects in each frame acquired by a real time camera. Training of a correlation pattern recognition based filter on desired shapes such as elliptical representations of human faces (variants of an Omega Shape) yields correct detections. The inherent ability of correlation pattern recognition filters caters for angular rotations in the target object and renders decision regarding the existence of the number of persons exceeding an allowed figure in the monitored area.

Effects of pre-experimental knowledge on recognition memory.

PubMed

Bird, Chris M; Davies, Rachel A; Ward, Jamie; Burgess, Neil

2011-01-01

The influence of pre-experimental autobiographical knowledge on recognition memory was investigated using as memoranda faces that were either personally known or unknown to the participant. Under a dual process theory, such knowledge boosted both recollection- and familiarity-based recognition judgements. Under an unequal variance signal detection model, pre-experimental knowledge increased both the variance and the separation of the target and foil memory strength distributions, boosting hits and correct rejections. Thus, pre-experimental knowledge has profound effects on the multiple, interacting processes that subserve recognition memory, and likely in the neural systems that underpin them.

Strip biosensor for amplified detection of nerve growth factor-beta based on a molecular translator and catalytic DNA circuit.

PubMed

Liu, Jun; Lai, Ting; Mu, Kejie; Zhou, Zheng

2014-10-07

We have demonstrated a new visual detection approach based on a molecular translator and a catalytic DNA circuit for the detection of nerve growth factor-beta (NGF-β). In this assay, a molecular translator based on the binding-induced DNA strand-displacement reaction was employed to convert the input protein to an output DNA signal. The molecular translator is composed of a target recognition element and a signal output element. Target recognition is achieved by the binding of the anti-NGF-β antibody to the target protein. Polyclonal anti-NGF-β antibody is conjugated to DNA1 and DNA2. The antibody conjugated DNA1 is initially hybridized to DNA3 to form a stable DNA1/DNA3 duplex. In the presence of NGF-β, the binding of the same target protein brings DNA1 and DNA2 into close proximity, resulting in an increase in their local effective concentration. This process triggers the strand-displacement reaction between DNA2 and DNA3 and releases the output DNA3. The released DNA3 is further amplified by a catalytic DNA circuit. The product of the catalytic DNA circuit is detected by a strip biosensor. This proposed assay has high sensitivity and selectivity with a dynamic response ranging from 10 fM to 10 pM, and its detection limit is 10 fM of NGF-β. This work provides a sensitive, enzyme-free, and universal strategy for the detection of other proteins.

Summary of tracking and identification methods

NASA Astrophysics Data System (ADS)

Blasch, Erik; Yang, Chun; Kadar, Ivan

2014-06-01

Over the last two decades, many solutions have arisen to combine target tracking estimation with classification methods. Target tracking includes developments from linear to non-linear and Gaussian to non-Gaussian processing. Pattern recognition includes detection, classification, recognition, and identification methods. Integrating tracking and pattern recognition has resulted in numerous approaches and this paper seeks to organize the various approaches. We discuss the terminology so as to have a common framework for various standards such as the NATO STANAG 4162 - Identification Data Combining Process. In a use case, we provide a comparative example highlighting that location information (as an example) with additional mission objectives from geographical, human, social, cultural, and behavioral modeling is needed to determine identification as classification alone does not allow determining identification or intent.

Fast iterative censoring CFAR algorithm for ship detection from SAR images

NASA Astrophysics Data System (ADS)

Gu, Dandan; Yue, Hui; Zhang, Yuan; Gao, Pengcheng

2017-11-01

Ship detection is one of the essential techniques for ship recognition from synthetic aperture radar (SAR) images. This paper presents a fast iterative detection procedure to eliminate the influence of target returns on the estimation of local sea clutter distributions for constant false alarm rate (CFAR) detectors. A fast block detector is first employed to extract potential target sub-images; and then, an iterative censoring CFAR algorithm is used to detect ship candidates from each target blocks adaptively and efficiently, where parallel detection is available, and statistical parameters of G0 distribution fitting local sea clutter well can be quickly estimated based on an integral image operator. Experimental results of TerraSAR-X images demonstrate the effectiveness of the proposed technique.

Road sign recognition with fuzzy adaptive pre-processing models.

PubMed

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance.

Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

PubMed Central

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. PMID:22778650

A label-free ultrasensitive fluorescence detection of viable Salmonella enteritidis using enzyme-induced cascade two-stage toehold strand-displacement-driven assembly of G-quadruplex DNA.

PubMed

Zhang, Peng; Liu, Hui; Ma, Suzhen; Men, Shuai; Li, Qingzhou; Yang, Xin; Wang, Hongning; Zhang, Anyun

2016-06-15

The harm of Salmonella enteritidis (S. enteritidis ) to public health mainly by contaminating fresh food and water emphasizes the urgent need for rapid detection techniques to help control the spread of the pathogen. In this assay, an newly designed capture probe complex that contained specific S. enteritidis-aptamer and hybridized signal target sequence was used for viable S. enteritidis recognition directly. In the presence of the target S. enteritidis, single-stranded target sequences were liberated and initiated the replication-cleavage reaction, producing numerous G-quadruplex structures with a linker on the 3'-end. And then, the sensing system took innovative advantage of quadratic linker-induced strand-displacement for the first time to release target sequence in succession, leading to the cyclic reuse of the target sequences and cascade signal amplification, thereby achieving the successive production of G-quadruplex structures. The fluorescent dye, N-Methyl mesoporphyrin IX, binded to these G-quadruplex structures and generated significantly enhanced fluorescent signals to achieve highly sensitive detection of S. enteritidis down to 60 CFU/mL with a linear range from 10(2) to 10(7)CFU/mL. By coupling the cascade two-stage target sequences-recyclable toehold strand-displacement with aptamer-based target recognition successfully, it is the first report on a novel non-label, modification-free and DNA extraction-free ultrasensitive fluorescence biosensor for detecting viable S. enteritidis directly, which can discriminate from dead S. enteritidis. Copyright © 2016 Elsevier B.V. All rights reserved.

[Representation of letter position in visual word recognition process].

PubMed

Makioka, S

1994-08-01

Two experiments investigated the representation of letter position in visual word recognition process. In Experiment 1, subjects (12 undergraduates and graduates) were asked to detect a target word in a briefly-presented probe. Probes consisted of two kanji words. The latters which formed targets (critical letters) were always contained in probes. (e.g. target: [symbol: see text] probe: [symbol: see text]) High false alarm rate was observed when critical letters occupied the same within-word relative position (left or right within the word) in the probe words as in the target word. In Experiment 2 (subject were ten undergraduates and graduates), spaces adjacent to probe words were replaced by randomly chosen hiragana letters (e.g. [symbol: see text]), because spaces are not used to separate words in regular Japanese sentences. In addition to the effect of within-word relative position as in Experiment 1, the effect of between-word relative position (left or right across the probe words) was observed. These results suggest that information about within-word relative position of a letter is used in word recognition process. The effect of within-word relative position was explained by a connectionist model of word recognition.

DBSCAN-based ROI extracted from SAR images and the discrimination of multi-feature ROI

NASA Astrophysics Data System (ADS)

He, Xin Yi; Zhao, Bo; Tan, Shu Run; Zhou, Xiao Yang; Jiang, Zhong Jin; Cui, Tie Jun

2009-10-01

The purpose of the paper is to extract the region of interest (ROI) from the coarse detected synthetic aperture radar (SAR) images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most difficult tasks in the SAR-image automatic target recognition system. The density-based spatial clustering of applications with noise (DBSCAN) relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features: only two insensitivity parameters (radius of neighborhood and minimum number of points) are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the calculation time and memory can be reduced. In the multi-feature ROI discrimination scheme, we extract several target features which contain the geometry features such as the area discriminator and Radon-transform based target profile discriminator, the distribution characteristics such as the EFF discriminator, and the EM scattering property such as the PPR discriminator. The synthesized judgment effectively eliminates the false alarms.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.

PubMed

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Experiments and Analysis of Close-Shot Identification of On-Branch Citrus Fruit with RealSense

PubMed Central

Liu, Jizhan; Yuan, Yan; Zhou, Yao; Zhu, Xinxin

2018-01-01

Fruit recognition based on depth information has been a hot topic due to its advantages. However, the present equipment and methods cannot meet the requirements of rapid and reliable recognition and location of fruits in close shot for robot harvesting. To solve this problem, we propose a recognition algorithm for citrus fruit based on RealSense. This method effectively utilizes depth-point cloud data in a close-shot range of 160 mm and different geometric features of the fruit and leaf to recognize fruits with a intersection curve cut by the depth-sphere. Experiments with close-shot recognition of six varieties of fruit under different conditions were carried out. The detection rates of little occlusion and adhesion were from 80–100%. However, severe occlusion and adhesion still have a great influence on the overall success rate of on-branch fruits recognition, the rate being 63.8%. The size of the fruit has a more noticeable impact on the success rate of detection. Moreover, due to close-shot near-infrared detection, there was no obvious difference in recognition between bright and dark conditions. The advantages of close-shot limited target detection with RealSense, fast foreground and background removal and the simplicity of the algorithm with high precision may contribute to high real-time vision-servo operations of harvesting robots. PMID:29751594

The Binaural Masking-Level Difference of Mandarin Tone Detection and the Binaural Intelligibility-Level Difference of Mandarin Tone Recognition in the Presence of Speech-Spectrum Noise

PubMed Central

Ho, Cheng-Yu; Li, Pei-Chun; Chiang, Yuan-Chuan; Young, Shuenn-Tsong; Chu, Woei-Chyn

2015-01-01

Binaural hearing involves using information relating to the differences between the signals that arrive at the two ears, and it can make it easier to detect and recognize signals in a noisy environment. This phenomenon of binaural hearing is quantified in laboratory studies as the binaural masking-level difference (BMLD). Mandarin is one of the most commonly used languages, but there are no publication values of BMLD or BILD based on Mandarin tones. Therefore, this study investigated the BMLD and BILD of Mandarin tones. The BMLDs of Mandarin tone detection were measured based on the detection threshold differences for the four tones of the voiced vowels /i/ (i.e., /i1/, /i2/, /i3/, and /i4/) and /u/ (i.e., /u1/, /u2/, /u3/, and /u4/) in the presence of speech-spectrum noise when presented interaurally in phase (S0N0) and interaurally in antiphase (SπN0). The BILDs of Mandarin tone recognition in speech-spectrum noise were determined as the differences in the target-to-masker ratio (TMR) required for 50% correct tone recognitions between the S0N0 and SπN0 conditions. The detection thresholds for the four tones of /i/ and /u/ differed significantly (p<0.001) between the S0N0 and SπN0 conditions. The average detection thresholds of Mandarin tones were all lower in the SπN0 condition than in the S0N0 condition, and the BMLDs ranged from 7.3 to 11.5 dB. The TMR for 50% correct Mandarin tone recognitions differed significantly (p<0.001) between the S0N0 and SπN0 conditions, at –13.4 and –18.0 dB, respectively, with a mean BILD of 4.6 dB. The study showed that the thresholds of Mandarin tone detection and recognition in the presence of speech-spectrum noise are improved when phase inversion is applied to the target speech. The average BILDs of Mandarin tones are smaller than the average BMLDs of Mandarin tones. PMID:25835987

Very-Long-Distance Remote Hearing and Vibrometry

NASA Technical Reports Server (NTRS)

Maleki, Lute; Yu, Nan; Matsko, Andrey; Savchenkov, Anatoliy

2009-01-01

A proposed development of laser-based instrumentation systems would extend the art of laser Doppler vibrometry beyond the prior limits of laser-assisted remote hearing and industrial vibrometry for detecting defects in operating mechanisms. A system according to the proposal could covertly measure vibrations of objects at distances as large as thousands of kilometers and could process the measurement data to enable recognition of vibrations characteristic of specific objects of interest, thereby enabling recognition of the objects themselves. A typical system as envisioned would be placed in orbit around the Earth for use as a means of determining whether certain objects on or under the ground are of interest as potential military targets. Terrestrial versions of these instruments designed for airborne or land- or sea-based operation could be similarly useful for military or law-enforcement purposes. Prior laser-based remote-hearing systems are not capable of either covert operation or detecting signals beyond modest distances when operated at realistic laser power levels. The performances of prior systems for recognition of objects by remote vibrometry are limited by low signal-to-noise ratios and lack of filtering of optical signals returned from targets. The proposed development would overcome these limitations. A system as proposed would include a narrow-band laser as its target illuminator, a lock-in-detection receiver subsystem, and a laser-power-control subsystem that would utilize feedback of the intensity of background illumination of the target to adjust the laser power. The laser power would be set at a level high enough to enable the desired measurements but below the threshold of detectability by an imaginary typical modern photodetector located at the target and there exposed to the background illumination. The laser beam would be focused tightly on the distant target, such that the receiving optics would be exposed to only one speckle. The return signal would be extremely-narrow-band filtered (to sub-kilohertz bandwidth) in the optical domain by a whispering-gallery- mode filter so as to remove most of the background illumination. The filtered optical signal would be optically amplified. This combination of optical filtering and optical amplification would provide an optical signal that would be strong enough to be detectable but not so strong as to saturate the detector in the lock-in detection subsystem.

Linear high-boost fusion of Stokes vector imagery for effective discrimination and recognition of real targets in the presence of multiple identical decoys

NASA Astrophysics Data System (ADS)

El-Saba, Aed; Sakla, Wesam A.

2010-04-01

Recently, the use of imaging polarimetry has received considerable attention for use in automatic target recognition (ATR) applications. In military remote sensing applications, there is a great demand for sensors that are capable of discriminating between real targets and decoys. Accurate discrimination of decoys from real targets is a challenging task and often requires the fusion of various sensor modalities that operate simultaneously. In this paper, we use a simple linear fusion technique known as the high-boost fusion method for effective discrimination of real targets in the presence of multiple decoys. The HBF assigns more weight to the polarization-based imagery in forming the final fused image that is used for detection. We have captured both intensity and polarization-based imagery from an experimental laboratory arrangement containing a mixture of sand/dirt, rocks, vegetation, and other objects for the purpose of simulating scenery that would be acquired in a remote sensing military application. A target object and three decoys that are identical in physical appearance (shape, surface structure and color) and different in material composition have also been placed in the scene. We use the wavelet-filter joint transform correlation (WFJTC) technique to perform detection between input scenery and the target object. Our results show that use of the HBF method increases the correlation performance metrics associated with the WFJTC-based detection process when compared to using either the traditional intensity or polarization-based images.

Analytically Sensitive Protein Detection in Microtiter Plates by Proximity Ligation with Rolling Circle Amplification.

PubMed

Ebai, Tonge; Souza de Oliveira, Felipe Marques; Löf, Liza; Wik, Lotta; Schweiger, Caroline; Larsson, Anders; Keilholtz, Ulrich; Haybaeck, Johannes; Landegren, Ulf; Kamali-Moghaddam, Masood

2017-09-01

Detecting proteins at low concentrations in plasma is crucial for early diagnosis. Current techniques in clinical routine, such as sandwich ELISA, provide sensitive protein detection because of a dependence on target recognition by pairs of antibodies, but detection of still lower protein concentrations is often called for. Proximity ligation assay with rolling circle amplification (PLARCA) is a modified proximity ligation assay (PLA) for analytically specific and sensitive protein detection via binding of target proteins by 3 antibodies, and signal amplification via rolling circle amplification (RCA) in microtiter wells, easily adapted to instrumentation in use in hospitals. Proteins captured by immobilized antibodies were detected using a pair of oligonucleotide-conjugated antibodies. Upon target recognition these PLA probes guided oligonucleotide ligation, followed by amplification via RCA of circular DNA strands that formed in the reaction. The RCA products were detected by horseradish peroxidase-labeled oligonucleotides to generate colorimetric reaction products with readout in an absorbance microplate reader. We compared detection of interleukin (IL)-4, IL-6, IL-8, p53, and growth differentiation factor 15 (GDF-15) by PLARCA and conventional sandwich ELISA or immuno-RCA. PLARCA detected lower concentrations of proteins and exhibited a broader dynamic range compared to ELISA and iRCA using the same antibodies. IL-4 and IL-6 were detected in clinical samples at femtomolar concentrations, considerably lower than for ELISA. PLARCA offers detection of lower protein levels and increased dynamic ranges compared to ELISA. The PLARCA procedure may be adapted to routine instrumentation available in hospitals and research laboratories. © 2017 American Association for Clinical Chemistry.

Application of infrared uncooled cameras in surveillance systems

NASA Astrophysics Data System (ADS)

Dulski, R.; Bareła, J.; Trzaskawka, P.; PiÄ tkowski, T.

2013-10-01

The recent necessity to protect military bases, convoys and patrols gave serious impact to the development of multisensor security systems for perimeter protection. One of the most important devices used in such systems are IR cameras. The paper discusses technical possibilities and limitations to use uncooled IR camera in a multi-sensor surveillance system for perimeter protection. Effective ranges of detection depend on the class of the sensor used and the observed scene itself. Application of IR camera increases the probability of intruder detection regardless of the time of day or weather conditions. It also simultaneously decreased the false alarm rate produced by the surveillance system. The role of IR cameras in the system was discussed as well as technical possibilities to detect human being. Comparison of commercially available IR cameras, capable to achieve desired ranges was done. The required spatial resolution for detection, recognition and identification was calculated. The simulation of detection ranges was done using a new model for predicting target acquisition performance which uses the Targeting Task Performance (TTP) metric. Like its predecessor, the Johnson criteria, the new model bounds the range performance with image quality. The scope of presented analysis is limited to the estimation of detection, recognition and identification ranges for typical thermal cameras with uncooled microbolometer focal plane arrays. This type of cameras is most widely used in security systems because of competitive price to performance ratio. Detection, recognition and identification range calculations were made, and the appropriate results for the devices with selected technical specifications were compared and discussed.

Image and Sensor Data Processing for Target Acquisition and Recognition.

DTIC Science & Technology

1980-11-01

technological, cost, size and weight constraints. The critical problem seems to be detecting the target with an acceptable false alarm rate, rather than...for its operation, loss of detail can result from adjacent differing pixels being forced into the same displayed grey-level. This may be detected in...a) Enhanced (b) Fig.1 High contrast scene demonstrating loss of detail in a local area of low contrast Luminance T V Black Peak white wavelength 1

The research of multi-frame target recognition based on laser active imaging

NASA Astrophysics Data System (ADS)

Wang, Can-jin; Sun, Tao; Wang, Tin-feng; Chen, Juan

2013-09-01

Laser active imaging is fit to conditions such as no difference in temperature between target and background, pitch-black night, bad visibility. Also it can be used to detect a faint target in long range or small target in deep space, which has advantage of high definition and good contrast. In one word, it is immune to environment. However, due to the affect of long distance, limited laser energy and atmospheric backscatter, it is impossible to illuminate the whole scene at the same time. It means that the target in every single frame is unevenly or partly illuminated, which make the recognition more difficult. At the same time the speckle noise which is common in laser active imaging blurs the images . In this paper we do some research on laser active imaging and propose a new target recognition method based on multi-frame images . Firstly, multi pulses of laser is used to obtain sub-images for different parts of scene. A denoising method combined homomorphic filter with wavelet domain SURE is used to suppress speckle noise. And blind deconvolution is introduced to obtain low-noise and clear sub-images. Then these sub-images are registered and stitched to combine a completely and uniformly illuminated scene image. After that, a new target recognition method based on contour moments is proposed. Firstly, canny operator is used to obtain contours. For each contour, seven invariant Hu moments are calculated to generate the feature vectors. At last the feature vectors are input into double hidden layers BP neural network for classification . Experiments results indicate that the proposed algorithm could achieve a high recognition rate and satisfactory real-time performance for laser active imaging.

Enzyme-triggered Gelation: Targeting Proteases with Internal Cleavage Sites

PubMed Central

Bremmer, Steven C.

2014-01-01

A generalizable method for detecting protease activity via gelation is described. A recognition sequence is used to target the protease of interest while a second protease is used to remove the residual residues from the gelator scaffold. Using this approach, selective assays for both MMP-9 and PSA are demonstrated. PMID:24394494

Sensitive fluorescence detection of nucleic acids based on isothermal circular strand-displacement polymerization reaction.

PubMed

Guo, Qiuping; Yang, Xiaohai; Wang, Kemin; Tan, Weihong; Li, Wei; Tang, Hongxing; Li, Huimin

2009-02-01

Here we have developed a sensitive DNA amplified detection method based on isothermal strand-displacement polymerization reaction. This method takes advantage of both the hybridization property of DNA and the strand-displacement property of polymerase. Importantly, we demonstrate that our method produces a circular polymerization reaction activated by the target, which essentially allows it to self-detect. Functionally, this DNA system consists of a hairpin fluorescence probe, a short primer and polymerase. Upon recognition and hybridization with the target ssDNA, the stem of the hairpin probe is opened, after which the opened probe anneals with the primer and triggers the polymerization reaction. During this process of the polymerization reaction, a complementary DNA is synthesized and the hybridized target is displaced. Finally, the displaced target recognizes and hybridizes with another probe, triggering the next round of polymerization reaction, reaching a target detection limit of 6.4 x 10(-15) M.

Enzyme Functionalized AuNPs and Glucometer-based Protein Detection

NASA Astrophysics Data System (ADS)

Dai, Tao; Fang, Jie; Yu, Wen; Xie, Guoming

2017-12-01

We here developed a novel method for protein detection by using protein aptamer-functionalized magnetic beads for protein recognition and invertase-functionalized AuNPs catalyze sucrose generate glucose that can be detected by a glucometer. First, the invertase and DNA probe P2 are immobilized onto the gold nanoparticles (I.P2@AuNPs). Next protein aptamer P1 are immobilized onto the streptavidin-coated Magnetic beads (P1@MB). P1 and P2 can complementary to form double-stranded DNA. When target protein presence, P1 combine with target and release I/P2@AuNPs. Then magnetic separation, take supernatant fluid and add sucrose after a period of reaction, detection of glucose concentration by glucometer, thus achieve the sensitive and selective detection of the target protein.

Non-Enzymatic Detection of Bacterial Genomic DNA Using the Bio-Barcode Assay

PubMed Central

Hill, Haley D.; Vega, Rafael A.; Mirkin, Chad A.

2011-01-01

The detection of bacterial genomic DNA through a non-enzymatic nanomaterials based amplification method, the bio-barcode assay, is reported. The assay utilizes oligonucleotide functionalized magnetic microparticles to capture the target of interest from the sample. A critical step in the new assay involves the use of blocking oligonucleotides during heat denaturation of the double stranded DNA. These blockers bind to specific regions of the target DNA upon cooling, and prevent the duplex DNA from re-hybridizing, which allows the particle probes to bind. Following target isolation using the magnetic particles, oligonucleotide functionalized gold nanoparticles act as target recognition agents. The oligonucleotides on the nanoparticle (barcodes) act as amplification surrogates. The barcodes are then detected using the Scanometric method. The limit of detection for this assay was determined to be 2.5 femtomolar, and this is the first demonstration of a barcode type assay for the detection of double stranded, genomic DNA. PMID:17927207

State Recognition of High Voltage Isolation Switch Based on Background Difference and Iterative Search

NASA Astrophysics Data System (ADS)

Xu, Jiayuan; Yu, Chengtao; Bo, Bin; Xue, Yu; Xu, Changfu; Chaminda, P. R. Dushantha; Hu, Chengbo; Peng, Kai

2018-03-01

The automatic recognition of the high voltage isolation switch by remote video monitoring is an effective means to ensure the safety of the personnel and the equipment. The existing methods mainly include two ways: improving monitoring accuracy and adopting target detection technology through equipment transformation. Such a method is often applied to specific scenarios, with limited application scope and high cost. To solve this problem, a high voltage isolation switch state recognition method based on background difference and iterative search is proposed in this paper. The initial position of the switch is detected in real time through the background difference method. When the switch starts to open and close, the target tracking algorithm is used to track the motion trajectory of the switch. The opening and closing state of the switch is determined according to the angle variation of the switch tracking point and the center line. The effectiveness of the method is verified by experiments on different switched video frames of switching states. Compared with the traditional methods, this method is more robust and effective.

Design of a sensitive aptasensor based on magnetic microbeads-assisted strand displacement amplification and target recycling.

PubMed

Li, Ying; Ji, Xiaoting; Song, Weiling; Guo, Yingshu

2013-04-03

A cross-circular amplification system for sensitive detection of adenosine triphosphate (ATP) in cancer cells was developed based on aptamer-target interaction, magnetic microbeads (MBs)-assisted strand displacement amplification and target recycling. Here we described a new recognition probe possessing two parts, the ATP aptamer and the extension part. The recognition probe was firstly immobilized on the surface of MBs and hybridized with its complementary sequence to form a duplex. When combined with ATP, the probe changed its conformation, revealing the extension part in single-strand form, which further served as a toehold for subsequent target recycling. The released complementary sequence of the probe acted as the catalyst of the MB-assisted strand displacement reaction. Incorporated with target recycling, a large amount of biotin-tagged MB complexes were formed to stimulate the generation of chemiluminescence (CL) signal in the presence of luminol and H2O2 by incorporating with streptavidin-HRP, reaching a detection limit of ATP as low as 6.1×10(-10)M. Moreover, sample assays of ATP in Ramos Burkitt's lymphoma B cells were performed, which confirmed the reliability and practicality of the protocol. Copyright © 2013 Elsevier B.V. All rights reserved.

High-speed railway real-time localization auxiliary method based on deep neural network

NASA Astrophysics Data System (ADS)

Chen, Dongjie; Zhang, Wensheng; Yang, Yang

2017-11-01

High-speed railway intelligent monitoring and management system is composed of schedule integration, geographic information, location services, and data mining technology for integration of time and space data. Assistant localization is a significant submodule of the intelligent monitoring system. In practical application, the general access is to capture the image sequences of the components by using a high-definition camera, digital image processing technique and target detection, tracking and even behavior analysis method. In this paper, we present an end-to-end character recognition method based on a deep CNN network called YOLO-toc for high-speed railway pillar plate number. Different from other deep CNNs, YOLO-toc is an end-to-end multi-target detection framework, furthermore, it exhibits a state-of-art performance on real-time detection with a nearly 50fps achieved on GPU (GTX960). Finally, we realize a real-time but high-accuracy pillar plate number recognition system and integrate natural scene OCR into a dedicated classification YOLO-toc model.

Multi-Stage System for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

The striking similarities between standard, distractor-free, and target-free recognition

PubMed Central

Dobbins, Ian G.

2012-01-01

It is often assumed that observers seek to maximize correct responding during recognition testing by actively adjusting a decision criterion. However, early research by Wallace (Journal of Experimental Psychology: Human Learning and Memory 4:441–452, 1978) suggested that recognition rates for studied items remained similar, regardless of whether or not the tests contained distractor items. We extended these findings across three experiments, addressing whether detection rates or observer confidence changed when participants were presented standard tests (targets and distractors) versus “pure-list” tests (lists composed entirely of targets or distractors). Even when observers were made aware of the composition of the pure-list test, the endorsement rates and confidence patterns remained largely similar to those observed during standard testing, suggesting that observers are typically not striving to maximize the likelihood of success across the test. We discuss the implications for decision models that assume a likelihood ratio versus a strength decision axis, as well as the implications for prior findings demonstrating large criterion shifts using target probability manipulations. PMID:21476108

Human recognition based on head-shoulder contour extraction and BP neural network

NASA Astrophysics Data System (ADS)

Kong, Xiao-fang; Wang, Xiu-qin; Gu, Guohua; Chen, Qian; Qian, Wei-xian

2014-11-01

In practical application scenarios like video surveillance and human-computer interaction, human body movements are uncertain because the human body is a non-rigid object. Based on the fact that the head-shoulder part of human body can be less affected by the movement, and will seldom be obscured by other objects, in human detection and recognition, a head-shoulder model with its stable characteristics can be applied as a detection feature to describe the human body. In order to extract the head-shoulder contour accurately, a head-shoulder model establish method with combination of edge detection and the mean-shift algorithm in image clustering has been proposed in this paper. First, an adaptive method of mixture Gaussian background update has been used to extract targets from the video sequence. Second, edge detection has been used to extract the contour of moving objects, and the mean-shift algorithm has been combined to cluster parts of target's contour. Third, the head-shoulder model can be established, according to the width and height ratio of human head-shoulder combined with the projection histogram of the binary image, and the eigenvectors of the head-shoulder contour can be acquired. Finally, the relationship between head-shoulder contour eigenvectors and the moving objects will be formed by the training of back-propagation (BP) neural network classifier, and the human head-shoulder model can be clustered for human detection and recognition. Experiments have shown that the method combined with edge detection and mean-shift algorithm proposed in this paper can extract the complete head-shoulder contour, with low calculating complexity and high efficiency.

Space-based infrared sensors of space target imaging effect analysis

NASA Astrophysics Data System (ADS)

Dai, Huayu; Zhang, Yasheng; Zhou, Haijun; Zhao, Shuang

2018-02-01

Target identification problem is one of the core problem of ballistic missile defense system, infrared imaging simulation is an important means of target detection and recognition. This paper first established the space-based infrared sensors ballistic target imaging model of point source on the planet's atmosphere; then from two aspects of space-based sensors camera parameters and target characteristics simulated atmosphere ballistic target of infrared imaging effect, analyzed the camera line of sight jitter, camera system noise and different imaging effects of wave on the target.

Traffic Signs in Complex Visual Environments

DOT National Transportation Integrated Search

1982-11-01

The effects of sign luminance on detection and recognition of traffic control devices is mediated through contrast with the immediate surround. Additionally, complex visual scenes are known to degrade visual performance with targets well above visual...

Salient man-made structure detection in infrared images

NASA Astrophysics Data System (ADS)

Li, Dong-jie; Zhou, Fu-gen; Jin, Ting

2013-09-01

Target detection, segmentation and recognition is a hot research topic in the field of image processing and pattern recognition nowadays, among which salient area or object detection is one of core technologies of precision guided weapon. Many theories have been raised in this paper; we detect salient objects in a series of input infrared images by using the classical feature integration theory and Itti's visual attention system. In order to find the salient object in an image accurately, we present a new method to solve the edge blur problem by calculating and using the edge mask. We also greatly improve the computing speed by improving the center-surround differences method. Unlike the traditional algorithm, we calculate the center-surround differences through rows and columns separately. Experimental results show that our method is effective in detecting salient object accurately and rapidly.

A Portable and Autonomous Magnetic Detection Platform for Biosensing

PubMed Central

Germano, José; Martins, Verónica C.; Cardoso, Filipe A.; Almeida, Teresa M.; Sousa, Leonel; Freitas, Paulo P.; Piedade, Moisés S.

2009-01-01

This paper presents a prototype of a platform for biomolecular recognition detection. The system is based on a magnetoresistive biochip that performs biorecognition assays by detecting magnetically tagged targets. All the electronic circuitry for addressing, driving and reading out signals from spin-valve or magnetic tunnel junctions sensors is implemented using off-the-shelf components. Taking advantage of digital signal processing techniques, the acquired signals are processed in real time and transmitted to a digital analyzer that enables the user to control and follow the experiment through a graphical user interface. The developed platform is portable and capable of operating autonomously for nearly eight hours. Experimental results show that the noise level of the described platform is one order of magnitude lower than the one presented by the previously used measurement set-up. Experimental results also show that this device is able to detect magnetic nanoparticles with a diameter of 250 nm at a concentration of about 40 fM. Finally, the biomolecular recognition detection capabilities of the platform are demonstrated by performing a hybridization assay using complementary and non-complementary probes and a magnetically tagged 20mer single stranded DNA target. PMID:22408516

Ballistic missile precession frequency extraction based on the Viterbi & Kalman algorithm

NASA Astrophysics Data System (ADS)

Wu, Longlong; Xie, Yongjie; Xu, Daping; Ren, Li

2015-12-01

Radar Micro-Doppler signatures are of great potential for target detection, classification and recognition. In the mid-course phase, warheads flying outside the atmosphere are usually accompanied by precession. Precession may induce additional frequency modulations on the returned radar signal, which can be regarded as a unique signature and provide additional information that is complementary to existing target recognition methods. The main purpose of this paper is to establish a more actual precession model of conical ballistic missile warhead and extract the precession parameters by utilizing Viterbi & Kalman algorithm, which improving the precession frequency estimation accuracy evidently , especially in low SNR.

Automated night/day standoff detection, tracking, and identification of personnel for installation protection

NASA Astrophysics Data System (ADS)

Lemoff, Brian E.; Martin, Robert B.; Sluch, Mikhail; Kafka, Kristopher M.; McCormick, William; Ice, Robert

2013-06-01

The capability to positively and covertly identify people at a safe distance, 24-hours per day, could provide a valuable advantage in protecting installations, both domestically and in an asymmetric warfare environment. This capability would enable installation security officers to identify known bad actors from a safe distance, even if they are approaching under cover of darkness. We will describe an active-SWIR imaging system being developed to automatically detect, track, and identify people at long range using computer face recognition. The system illuminates the target with an eye-safe and invisible SWIR laser beam, to provide consistent high-resolution imagery night and day. SWIR facial imagery produced by the system is matched against a watch-list of mug shots using computer face recognition algorithms. The current system relies on an operator to point the camera and to review and interpret the face recognition results. Automation software is being developed that will allow the system to be cued to a location by an external system, automatically detect a person, track the person as they move, zoom in on the face, select good facial images, and process the face recognition results, producing alarms and sharing data with other systems when people are detected and identified. Progress on the automation of this system will be presented along with experimental night-time face recognition results at distance.

Robust human detection, tracking, and recognition in crowded urban areas

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2014-06-01

In this paper, we present algorithms we recently developed to support an automated security surveillance system for very crowded urban areas. In our approach for human detection, the color features are obtained by taking the difference of R, G, B spectrum and converting R, G, B to HSV (Hue, Saturation, Value) space. Morphological patch filtering and regional minimum and maximum segmentation on the extracted features are applied for target detection. The human tracking process approach includes: 1) Color and intensity feature matching track candidate selection; 2) Separate three parallel trackers for color, bright (above mean intensity), and dim (below mean intensity) detections, respectively; 3) Adaptive track gate size selection for reducing false tracking probability; and 4) Forward position prediction based on previous moving speed and direction for continuing tracking even when detections are missed from frame to frame. The Human target recognition is improved with a Super-Resolution Image Enhancement (SRIE) process. This process can improve target resolution by 3-5 times and can simultaneously process many targets that are tracked. Our approach can project tracks from one camera to another camera with a different perspective viewing angle to obtain additional biometric features from different perspective angles, and to continue tracking the same person from the 2nd camera even though the person moved out of the Field of View (FOV) of the 1st camera with `Tracking Relay'. Finally, the multiple cameras at different view poses have been geo-rectified to nadir view plane and geo-registered with Google- Earth (or other GIS) to obtain accurate positions (latitude, longitude, and altitude) of the tracked human for pin-point targeting and for a large area total human motion activity top-view. Preliminary tests of our algorithms indicate than high probability of detection can be achieved for both moving and stationary humans. Our algorithms can simultaneously track more than 100 human targets with averaged tracking period (time length) longer than the performance of the current state-of-the-art.

A facile approach to construct versatile signal amplification system for bacterial detection.

PubMed

Qi, Peng; Zhang, Dun; Wan, Yi; Lv, Dandan

2014-01-01

In this work, a facile approach to design versatile signal amplification system for bacterial detection has been presented. Bio-recognition elements and signaling molecules can be immobilized on the surface of Fe₃O₄@MnO₂ nanomaterials with the help of bioinspired polydopamine (PDA). Fe₃O₄@MnO₂ nanoplates were chosen as carrier for bio-recognizing and signaling molecules because this kind of nanomaterial was superparamagnetic and the existence of MnO₂ could enhance the polymerization of dopamine due to its strong oxidative ability. This nanocomposite system was versatile because PDA around Fe₃O₄@MnO₂ nanoplates provided a stable and convenient platform for immobilization of biological and chemical materials, and various kinds of bio-recognizing and signaling molecules could be immobilized by reaction with pendant amino groups of dopamine to meet different detection requirements. Since a substantial amount of signaling molecules were immobilized on the surface of the nanocomposites, so the sensitivity of detection would be improved when the prepared nanocomposites were selectively conjugated with target pathogen. In the experimental section, a sandwich-type electrochemical biosensor was developed to verify the amplified bacterial detection sensitivity. Concanavalin A (conA) and ferrocene (Fc) were chosen as bio-recognition elements and signaling molecules for detection of Desulforibrio caledoiensis, respectively. The conA and Fc modified nanocomposites were conjugated on electrode by the selective recognition between conA and target bacteria, and the bacterial population was obtained by quantification of the electrochemical signal of Fc moieties. The experimental results showed that the detection sensitivity for D. caledoiensis was improved by taking advantage of this signal amplification system. © 2013 Elsevier B.V. All rights reserved.

Infrared telephoto lenses design for joint transform correlator

NASA Astrophysics Data System (ADS)

Chen, Yu; Huo, Furong; Zheng, Liqin

2014-11-01

Joint transform correlator (JTC) is quite useful for pattern recognition in many fields, which can realize automatic real-time recognition of target in cluttered background with high precision. For military application, JTC can also be applied for thermo target recognition especially at night. To make JTC recognize thermo targets, an infrared telephoto lens is designed in this paper. Long focal length and short tube length are required for this usage. So the structure of a positive lens group and a negative lens group are adopted. Besides, the effective focal length and relative aperture should be large enough to ensure the distant targets can be detected with adequate illumination. In this paper, the working waveband of adopted infrared CCD detector is 8-12μm. According to Nyquist law, the characteristic frequency of the system is 14lp/mm. The optional materials are very few for infrared optical systems, in which only several kinds of materials such as Germanium, ZnSe, ZnS are commonly used. Various aberrations are not easy to be corrected. So it is very difficult to design a good infrared optical system. Besides, doublet or triplet should be avoided to be used in infrared optical system considering possible cracking for different thermal expansion coefficients of different infrared materials. The original configuration is composed of three lenses. After optimization, the image quality can get limit diffraction. The root mean square (RMS) radii of three fields are 6.754μm, 7.301μm and 12.158μm respectively. They are all less than the Airy spot diameter 48.8μm. Wavefront aberration at 0.707 field of view (FOV) is only 0.1wavelength. After adjusting the radius to surface templates, setting tolerances and giving element drawings, this system has been fabricated successfully. Optical experimental results of infrared target recognition using JTC are given in this paper. The correlation peaks can be detected and located easily, which confirms the good image quality of the designed infrared telephoto lens.

An automatic target recognition system based on SAR image

NASA Astrophysics Data System (ADS)

Li, Qinfu; Wang, Jinquan; Zhao, Bo; Luo, Furen; Xu, Xiaojian

2009-10-01

In this paper, an automatic target recognition (ATR) system based on synthetic aperture radar (SAR) is proposed. This ATR system can play an important role in the simulation of up-to-data battlefield environment and be used in ATR research. To establish an integral and available system, the processing of SAR image was divided into four main stages which are de-noise, detection, cluster-discrimination and segment-recognition, respectively. The first three stages are used for searching region of interest (ROI). Once the ROIs are extracted, the recognition stage will be taken to compute the similarity between the ROIs and the templates in the electromagnetic simulation software National Electromagnetic Scattering Code (NESC). Due to the lack of the SAR raw data, the electromagnetic simulated images are added to the measured SAR background to simulate the battlefield environment8. The purpose of the system is to find the ROIs which can be the artificial military targets such as tanks, armored cars and so on and to categorize the ROIs into the right classes according to the existing templates. From the results we can see that the proposed system achieves a satisfactory result.

A Novel Malware Target Recognition Architecture for Enhanced Cyberspace Situation Awareness

DTIC Science & Technology

2011-09-01

71 3.1 Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 CRISP - DM Process...71 3.3 Execution of the CRISP - DM Process...64 10. Adaptation of CRISP - DM process (from [13]). . . . . . . . . . . . . . . . . . . . . . . 72 11. Comparison of detection

Target detection cycle criteria when using the targeting task performance metric

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Jacobs, Eddie L.; Vollmerhausen, Richard H.

2004-12-01

The US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate of the US Army (NVESD) has developed a new target acquisition metric to better predict the performance of modern electro-optical imagers. The TTP metric replaces the Johnson criteria. One problem with transitioning to the new model is that the difficulty of searching in a terrain has traditionally been quantified by an "N50." The N50 is the number of Johnson criteria cycles needed for the observer to detect the target half the time, assuming that the observer is not time limited. In order to make use of this empirical data base, a conversion must be found relating Johnson cycles for detection to TTP cycles for detection. This paper describes how that relationship is established. We have found that the relationship between Johnson and TTP is 1:2.7 for the recognition and identification tasks.

Analytical applications of aptamers

NASA Astrophysics Data System (ADS)

Tombelli, S.; Minunni, M.; Mascini, M.

2007-05-01

Aptamers are single stranded DNA or RNA ligands which can be selected for different targets starting from a library of molecules containing randomly created sequences. Aptamers have been selected to bind very different targets, from proteins to small organic dyes. Aptamers are proposed as alternatives to antibodies as biorecognition elements in analytical devices with ever increasing frequency. This in order to satisfy the demand for quick, cheap, simple and highly reproducible analytical devices, especially for protein detection in the medical field or for the detection of smaller molecules in environmental and food analysis. In our recent experience, DNA and RNA aptamers, specific for three different proteins (Tat, IgE and thrombin), have been exploited as bio-recognition elements to develop specific biosensors (aptasensors). These recognition elements have been coupled to piezoelectric quartz crystals and surface plasmon resonance (SPR) devices as transducers where the aptamers have been immobilized on the gold surface of the crystals electrodes or on SPR chips, respectively.

Age, cognitive style, and traffic signs.

PubMed

Lambert, L D; Fleury, M

1994-04-01

This study assessed the efficiency with which young and older adults of varying field dependence extract information from traffic signs. It also identified some visual attributes of signs which affect recognition time. Two experiments were conducted. In Exp. 1, digitized signs, embedded in rural and urban backgrounds, were presented on a computer monitor. Subjects indicated on which side a target sign had appeared. Analysis showed that recognition times were dependent on age and field-dependence scores. Also, visual backgrounds and spatial frequency of pictographs affected RTs. In Exp. 2, recognition RT to 2 signs with redesigned pictographs was measured as well as time taken to detect signs. The signs showing reduced spatial frequency were the fastest to recognize, although no effect was noticed during detection. The subjects who showed the worst performance when facing the original signs benefitted the most from the modifications.

Fire flame detection based on GICA and target tracking

NASA Astrophysics Data System (ADS)

Rong, Jianzhong; Zhou, Dechuang; Yao, Wei; Gao, Wei; Chen, Juan; Wang, Jian

2013-04-01

To improve the video fire detection rate, a robust fire detection algorithm based on the color, motion and pattern characteristics of fire targets was proposed, which proved a satisfactory fire detection rate for different fire scenes. In this fire detection algorithm: (a) a rule-based generic color model was developed based on analysis on a large quantity of flame pixels; (b) from the traditional GICA (Geometrical Independent Component Analysis) model, a Cumulative Geometrical Independent Component Analysis (C-GICA) model was developed for motion detection without static background and (c) a BP neural network fire recognition model based on multi-features of the fire pattern was developed. Fire detection tests on benchmark fire video clips of different scenes have shown the robustness, accuracy and fast-response of the algorithm.

Driver landmark and traffic sign identification in early Alzheimer's disease.

PubMed

Uc, E Y; Rizzo, M; Anderson, S W; Shi, Q; Dawson, J D

2005-06-01

To assess visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with Alzheimer's disease. 33 drivers with probable Alzheimer's disease of mild severity and 137 neurologically normal older adults underwent a battery of visual and cognitive tests and were asked to report detection of specific landmarks and traffic signs along a segment of an experimental drive. The drivers with mild Alzheimer's disease identified significantly fewer landmarks and traffic signs and made more at-fault safety errors during the task than control subjects. Roadside target identification performance and safety errors were predicted by scores on standardised tests of visual and cognitive function. Drivers with Alzheimer's disease are impaired in a task of visual search and recognition of roadside targets; the demands of these targets on visual perception, attention, executive functions, and memory probably increase the cognitive load, worsening driving safety.

Molecularly Imprinted Sol-Gel-Based QCM Sensor Arrays for the Detection and Recognition of Volatile Aldehydes.

PubMed

Liu, Chuanjun; Wyszynski, Bartosz; Yatabe, Rui; Hayashi, Kenshi; Toko, Kiyoshi

2017-02-16

The detection and recognition of metabolically derived aldehydes, which have been identified as important products of oxidative stress and biomarkers of cancers; are considered as an effective approach for early cancer detection as well as health status monitoring. Quartz crystal microbalance (QCM) sensor arrays based on molecularly imprinted sol-gel (MISG) materials were developed in this work for highly sensitive detection and highly selective recognition of typical aldehyde vapors including hexanal (HAL); nonanal (NAL) and bezaldehyde (BAL). The MISGs were prepared by a sol-gel procedure using two matrix precursors: tetraethyl orthosilicate (TEOS) and tetrabutoxytitanium (TBOT). Aminopropyltriethoxysilane (APT); diethylaminopropyltrimethoxysilane (EAP) and trimethoxy-phenylsilane (TMP) were added as functional monomers to adjust the imprinting effect of the matrix. Hexanoic acid (HA); nonanoic acid (NA) and benzoic acid (BA) were used as psuedotemplates in view of their analogous structure to the target molecules as well as the strong hydrogen-bonding interaction with the matrix. Totally 13 types of MISGs with different components were prepared and coated on QCM electrodes by spin coating. Their sensing characters towards the three aldehyde vapors with different concentrations were investigated qualitatively. The results demonstrated that the response of individual sensors to each target strongly depended on the matrix precursors; functional monomers and template molecules. An optimization of the 13 MISG materials was carried out based on statistical analysis such as principle component analysis (PCA); multivariate analysis of covariance (MANCOVA) and hierarchical cluster analysis (HCA). The optimized sensor array consisting of five channels showed a high discrimination ability on the aldehyde vapors; which was confirmed by quantitative comparison with a randomly selected array. It was suggested that both the molecularly imprinting (MIP) effect and the matrix effect contributed to the sensitivity and selectivity of the optimized sensor array. The developed MISGs were expected to be promising materials for the detection and recognition of volatile aldehydes contained in exhaled breath or human body odor.

Molecularly Imprinted Sol-Gel-Based QCM Sensor Arrays for the Detection and Recognition of Volatile Aldehydes

PubMed Central

Liu, Chuanjun; Wyszynski, Bartosz; Yatabe, Rui; Hayashi, Kenshi; Toko, Kiyoshi

2017-01-01

The detection and recognition of metabolically derived aldehydes, which have been identified as important products of oxidative stress and biomarkers of cancers; are considered as an effective approach for early cancer detection as well as health status monitoring. Quartz crystal microbalance (QCM) sensor arrays based on molecularly imprinted sol-gel (MISG) materials were developed in this work for highly sensitive detection and highly selective recognition of typical aldehyde vapors including hexanal (HAL); nonanal (NAL) and bezaldehyde (BAL). The MISGs were prepared by a sol-gel procedure using two matrix precursors: tetraethyl orthosilicate (TEOS) and tetrabutoxytitanium (TBOT). Aminopropyltriethoxysilane (APT); diethylaminopropyltrimethoxysilane (EAP) and trimethoxy-phenylsilane (TMP) were added as functional monomers to adjust the imprinting effect of the matrix. Hexanoic acid (HA); nonanoic acid (NA) and benzoic acid (BA) were used as psuedotemplates in view of their analogous structure to the target molecules as well as the strong hydrogen-bonding interaction with the matrix. Totally 13 types of MISGs with different components were prepared and coated on QCM electrodes by spin coating. Their sensing characters towards the three aldehyde vapors with different concentrations were investigated qualitatively. The results demonstrated that the response of individual sensors to each target strongly depended on the matrix precursors; functional monomers and template molecules. An optimization of the 13 MISG materials was carried out based on statistical analysis such as principle component analysis (PCA); multivariate analysis of covariance (MANCOVA) and hierarchical cluster analysis (HCA). The optimized sensor array consisting of five channels showed a high discrimination ability on the aldehyde vapors; which was confirmed by quantitative comparison with a randomly selected array. It was suggested that both the molecularly imprinting (MIP) effect and the matrix effect contributed to the sensitivity and selectivity of the optimized sensor array. The developed MISGs were expected to be promising materials for the detection and recognition of volatile aldehydes contained in exhaled breath or human body odor. PMID:28212347

Familiarity facilitates feature-based face processing.

PubMed

Visconti di Oleggio Castello, Matteo; Wheeler, Kelsey G; Cipolli, Carlo; Gobbini, M Ida

2017-01-01

Recognition of personally familiar faces is remarkably efficient, effortless and robust. We asked if feature-based face processing facilitates detection of familiar faces by testing the effect of face inversion on a visual search task for familiar and unfamiliar faces. Because face inversion disrupts configural and holistic face processing, we hypothesized that inversion would diminish the familiarity advantage to the extent that it is mediated by such processing. Subjects detected personally familiar and stranger target faces in arrays of two, four, or six face images. Subjects showed significant facilitation of personally familiar face detection for both upright and inverted faces. The effect of familiarity on target absent trials, which involved only rejection of unfamiliar face distractors, suggests that familiarity facilitates rejection of unfamiliar distractors as well as detection of familiar targets. The preserved familiarity effect for inverted faces suggests that facilitation of face detection afforded by familiarity reflects mostly feature-based processes.

Photonics: From target recognition to lesion detection

NASA Technical Reports Server (NTRS)

Henry, E. Michael

1994-01-01

Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.

Discriminating Famous from Fictional Names Based on Lifetime Experience: Evidence in Support of a Signal-Detection Model Based on Finite Mixture Distributions

ERIC Educational Resources Information Center

Bowles, Ben; Harlow, Iain M.; Meeking, Melissa M.; Kohler, Stefan

2012-01-01

It is widely accepted that signal-detection mechanisms contribute to item-recognition memory decisions that involve discriminations between targets and lures based on a controlled laboratory study episode. Here, the authors employed mathematical modeling of receiver operating characteristics (ROC) to determine whether and how a signal-detection…

Planar optical waveguide based sandwich assay sensors and processes for the detection of biological targets including early detection of cancers

DOEpatents

Martinez, Jennifer S [Santa Fe, NM; Swanson, Basil I [Los Alamos, NM; Shively, John E [Arcadia, CA; Li, Lin [Monrovia, CA

2009-06-02

An assay element is described including recognition ligands adapted for binding to carcinoembryonic antigen (CEA) bound to a film on a single mode planar optical waveguide, the film from the group of a membrane, a polymerized bilayer membrane, and a self-assembled monolayer containing polyethylene glycol or polypropylene glycol groups therein and an assay process for detecting the presence of CEA is described including injecting a possible CEA-containing sample into a sensor cell including the assay element, maintaining the sample within the sensor cell for time sufficient for binding to occur between CEA present within the sample and the recognition ligands, injecting a solution including a reporter ligand into the sensor cell; and, interrogating the sample within the sensor cell with excitation light from the waveguide, the excitation light provided by an evanescent field of the single mode penetrating into the biological target-containing sample to a distance of less than about 200 nanometers from the waveguide thereby exciting any bound reporter ligand within a distance of less than about 200 nanometers from the waveguide and resulting in a detectable signal.

Tensor Fukunaga-Koontz transform for small target detection in infrared images

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Wang, Jingzhuo; Yang, Huizhen; Gong, Chenglong; Zhou, Yuanshen; Liu, Lipeng; Zhang, Zhen; Shen, Shuli

2016-09-01

Infrared small targets detection plays a crucial role in warning and tracking systems. Some novel methods based on pattern recognition technology catch much attention from researchers. However, those classic methods must reshape images into vectors with the high dimensionality. Moreover, vectorizing breaks the natural structure and correlations in the image data. Image representation based on tensor treats images as matrices and can hold the natural structure and correlation information. So tensor algorithms have better classification performance than vector algorithms. Fukunaga-Koontz transform is one of classification algorithms and it is a vector version method with the disadvantage of all vector algorithms. In this paper, we first extended the Fukunaga-Koontz transform into its tensor version, tensor Fukunaga-Koontz transform. Then we designed a method based on tensor Fukunaga-Koontz transform for detecting targets and used it to detect small targets in infrared images. The experimental results, comparison through signal-to-clutter, signal-to-clutter gain and background suppression factor, have validated the advantage of the target detection based on the tensor Fukunaga-Koontz transform over that based on the Fukunaga-Koontz transform.

Neural network for intelligent query of an FBI forensic database

NASA Astrophysics Data System (ADS)

Uvanni, Lee A.; Rainey, Timothy G.; Balasubramanian, Uma; Brettle, Dean W.; Weingard, Fred; Sibert, Robert W.; Birnbaum, Eric

1997-02-01

Examiner is an automated fired cartridge case identification system utilizing a dual-use neural network pattern recognition technology, called the statistical-multiple object detection and location system (S-MODALS) developed by Booz(DOT)Allen & Hamilton, Inc. in conjunction with Rome Laboratory. S-MODALS was originally designed for automatic target recognition (ATR) of tactical and strategic military targets using multisensor fusion [electro-optical (EO), infrared (IR), and synthetic aperture radar (SAR)] sensors. Since S-MODALS is a learning system readily adaptable to problem domains other than automatic target recognition, the pattern matching problem of microscopic marks for firearms evidence was analyzed using S-MODALS. The physics; phenomenology; discrimination and search strategies; robustness requirements; error level and confidence level propagation that apply to the pattern matching problem of military targets were found to be applicable to the ballistic domain as well. The Examiner system uses S-MODALS to rank a set of queried cartridge case images from the most similar to the least similar image in reference to an investigative fired cartridge case image. The paper presents three independent tests and evaluation studies of the Examiner system utilizing the S-MODALS technology for the Federal Bureau of Investigation.

Colocalization recognition-activated cascade signal amplification strategy for ultrasensitive detection of transcription factors.

PubMed

Zhu, Desong; Wang, Lei; Xu, Xiaowen; Jiang, Wei

2017-03-15

Transcription factors (TFs) bind to specific double-stranded DNA (dsDNA) sequences in the regulatory regions of genes to regulate the process of gene transcription. Their expression levels sensitively reflect cell developmental situation and disease state. TFs have become potential diagnostic markers and therapeutic targets of cancers and some other diseases. Hence, high sensitive detection of TFs is of vital importance for early diagnosis of diseases and drugs development. The traditional exonucleases-assisted signal amplification methods suffered from the false positives caused by incomplete digestion of excess recognition probes. Herein, based on a new recognition way-colocalization recognition (CR)-activated dual signal amplification, an ultrasensitive fluorescent detection strategy for TFs was developed. TFs-induced the colocalization of three split recognition components resulted in noticeable increases of local effective concentrations and hybridization of three split components, which activated the subsequent cascade signal amplification including strand displacement amplification (SDA) and exponential rolling circle amplification (ERCA). This strategy eliminated the false positive influence and achieved ultra-high sensitivity towards the purified NF-κB p50 with detection limit of 2.0×10 -13 M. Moreover, NF-κB p50 can be detected in as low as 0.21ngμL -1 HeLa cell nuclear extracts. In addition, this proposed strategy could be used for the screening of NF-κB p50 activity inhibitors and potential anti-NF-κB p50 drugs. Finally, our proposed strategy offered a potential method for reliable detection of TFs in medical diagnosis and treatment research of cancers and other related diseases. Copyright © 2016 Elsevier B.V. All rights reserved.

Foliage penetration by using 4-D point cloud data

NASA Astrophysics Data System (ADS)

Méndez Rodríguez, Javier; Sánchez-Reyes, Pedro J.; Cruz-Rivera, Sol M.

2012-06-01

Real-time awareness and rapid target detection are critical for the success of military missions. New technologies capable of detecting targets concealed in forest areas are needed in order to track and identify possible threats. Currently, LAser Detection And Ranging (LADAR) systems are capable of detecting obscured targets; however, tracking capabilities are severely limited. Now, a new LADAR-derived technology is under development to generate 4-D datasets (3-D video in a point cloud format). As such, there is a new need for algorithms that are able to process data in real time. We propose an algorithm capable of removing vegetation and other objects that may obfuscate concealed targets in a real 3-D environment. The algorithm is based on wavelets and can be used as a pre-processing step in a target recognition algorithm. Applications of the algorithm in a real-time 3-D system could help make pilots aware of high risk hidden targets such as tanks and weapons, among others. We will be using a 4-D simulated point cloud data to demonstrate the capabilities of our algorithm.

Does aging impair first impression accuracy? Differentiating emotion recognition from complex social inferences.

PubMed

Krendl, Anne C; Rule, Nicholas O; Ambady, Nalini

2014-09-01

Young adults can be surprisingly accurate at making inferences about people from their faces. Although these first impressions have important consequences for both the perceiver and the target, it remains an open question whether first impression accuracy is preserved with age. Specifically, could age differences in impressions toward others stem from age-related deficits in accurately detecting complex social cues? Research on aging and impression formation suggests that young and older adults show relative consensus in their first impressions, but it is unknown whether they differ in accuracy. It has been widely shown that aging disrupts emotion recognition accuracy, and that these impairments may predict deficits in other social judgments, such as detecting deceit. However, it is unclear whether general impression formation accuracy (e.g., emotion recognition accuracy, detecting complex social cues) relies on similar or distinct mechanisms. It is important to examine this question to evaluate how, if at all, aging might affect overall accuracy. Here, we examined whether aging impaired first impression accuracy in predicting real-world outcomes and categorizing social group membership. Specifically, we studied whether emotion recognition accuracy and age-related cognitive decline (which has been implicated in exacerbating deficits in emotion recognition) predict first impression accuracy. Our results revealed that emotion recognition accuracy did not predict first impression accuracy, nor did age-related cognitive decline impair it. These findings suggest that domains of social perception outside of emotion recognition may rely on mechanisms that are relatively unimpaired by aging. PsycINFO Database Record (c) 2014 APA, all rights reserved.

The simulation study on optical target laser active detection performance

NASA Astrophysics Data System (ADS)

Li, Ying-chun; Hou, Zhao-fei; Fan, Youchen

2014-12-01

According to the working principle of laser active detection system, the paper establishes the optical target laser active detection simulation system, carry out the simulation study on the detection process and detection performance of the system. For instance, the performance model such as the laser emitting, the laser propagation in the atmosphere, the reflection of optical target, the receiver detection system, the signal processing and recognition. We focus on the analysis and modeling the relationship between the laser emitting angle and defocus amount and "cat eye" effect echo laser in the reflection of optical target. Further, in the paper some performance index such as operating range, SNR and the probability of the system have been simulated. The parameters including laser emitting parameters, the reflection of the optical target and the laser propagation in the atmosphere which make a great influence on the performance of the optical target laser active detection system. Finally, using the object-oriented software design methods, the laser active detection system with the opening type, complete function and operating platform, realizes the process simulation that the detection system detect and recognize the optical target, complete the performance simulation of each subsystem, and generate the data report and the graph. It can make the laser active detection system performance models more intuitive because of the visible simulation process. The simulation data obtained from the system provide a reference to adjust the structure of the system parameters. And it provides theoretical and technical support for the top level design of the optical target laser active detection system and performance index optimization.

Research on infrared ship detection method in sea-sky background

NASA Astrophysics Data System (ADS)

Tang, Da; Sun, Gang; Wang, Ding-he; Niu, Zhao-dong; Chen, Zeng-ping

2013-09-01

An approach to infrared ship detection based on sea-sky-line(SSL) detection, ROI extraction and feature recognition is proposed in this paper. Firstly, considering that far ships are expected to be adjacent to the SSL, SSL is detected to find potential target areas. Radon transform is performed on gradient image to choose candidate SSLs, and detection result is given by fuzzy synthetic evaluation values. Secondly, in view of recognizable condition that there should be enough differences between target and background in infrared image, two gradient masks have been created and improved as practical guidelines in eliminating false alarm. Thirdly, extract ROI near the SSL by using multi-grade segmentation and fusion method after image sharpening, and unsuitable candidates are screened out according to the gradient masks and ROI shape. Finally, we segment the rest of ROIs by two-stage modified OTSU, and calculate target confidence as a standard measuring the facticity of target. Compared with other ship detection methods, proposed method is suitable for bipolar targets, which offers a good practicability and accuracy, and achieves a satisfying detection speed. Detection experiments with 200 thousand frames show that the proposed method is widely applicable, powerful in resistance to interferences and noises with a detection rate of above 95%, which satisfies the engineering needs commendably.

Unveiling NIR Aza-Boron-Dipyrromethene (BODIPY) Dyes as Raman Probes: Surface-Enhanced Raman Scattering (SERS)-Guided Selective Detection and Imaging of Human Cancer Cells.

PubMed

Adarsh, Nagappanpillai; Ramya, Adukkadan N; Maiti, Kaustabh Kumar; Ramaiah, Danaboyina

2017-10-12

The development of new Raman reporters has attracted immense attention in diagnostic research based on surface enhanced Raman scattering (SERS) techniques, which is a well established method for ultrasensitive detection through molecular fingerprinting and imaging. Herein, for the first time, we report the unique and efficient Raman active features of the selected aza-BODIPY dyes 1-6. These distinctive attributes could be extended at the molecular level to allow detection through SERS upon adsorption onto nano-roughened gold surface. Among the newly revealed Raman reporters, the amino substituted derivative 4 showed high signal intensity at very low concentrations (ca. 0.4 μm for 4-Au). Interestingly, an efficient nanoprobe has been constructed by using gold nanoparticles as SERS substrate, and 4 as the Raman reporter (4-Au@PEG), which unexpectedly showed efficient recognition of three human cancer cells (lung: A549, cervical: HeLa, Fibrosarcoma: HT-1080) without any specific surface marker. We observed well reflected and resolved Raman mapping and characteristic signature peaks whereas, such recognition was not observed in normal fibroblast (3T3L1) cells. To confirm these findings, a SERS nanoprobe was conjugated with a specific tumour targeting marker, EGFR (Epidermal Growth Factor Receptor), a well known targeted agent for Human Fibrosarcoma (HT1080). This nanoprobe efficiently targeted the surface marker of HT1080 cells, threreby demonstrating its use as an ultrasensitive Raman probe for detection and targeted imaging, leaving normal cells unaffected. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Divided attention enhances the recognition of emotional stimuli: evidence from the attentional boost effect.

PubMed

Rossi-Arnaud, Clelia; Spataro, Pietro; Costanzi, Marco; Saraulli, Daniele; Cestari, Vincenzo

2018-01-01

The present study examined predictions of the early-phase-elevated-attention hypothesis of the attentional boost effect (ABE), which suggests that transient increases in attention at encoding, as instantiated in the ABE paradigm, should enhance the recognition of neutral and positive items (whose encoding is mostly based on controlled processes), while having small or null effects on the recognition of negative items (whose encoding is primarily based on automatic processes). Participants were presented a sequence of negative, neutral and positive stimuli (pictures in Experiment 1, words in Experiment 2) associated to target (red) squares, distractor (green) squares or no squares (baseline condition). They were told to attend to the pictures/words and simultaneously press the spacebar of the computer when a red square appeared. In a later recognition task, stimuli associated to target squares were recognised better than stimuli associated to distractor squares, replicating the standard ABE. More importantly, we also found that: (a) the memory enhancement following target detection occurred with all types of stimuli (neutral, negative and positive) and (b) the advantage of negative stimuli over neutral stimuli was intact in the DA condition. These findings suggest that the encoding of negative stimuli depends on both controlled (attention-dependent) and automatic (attention-independent) processes.

Target-triggering multiple-cycle signal amplification strategy for ultrasensitive detection of DNA based on QCM and SPR.

PubMed

Song, Weiling; Yin, Wenshuo; Sun, Wenbo; Guo, Xiaoyan; He, Peng; Yang, Xiaoyan; Zhang, Xiaoru

2018-04-24

Detection of ultralow concentrations of nucleic acid sequences is a central challenge in the early diagnosis of genetic diseases. Herein, we developed a target-triggering cascade multiple cycle amplification for ultrasensitive DNA detection using quartz crystal microbalance (QCM) and surface plasmon resonance (SPR). It was based on the exonuclease Ⅲ (Exo Ⅲ)-assisted signal amplification and the hybridization chain reaction (HCR). The streptavidin-coated Au-NPs (Au-NPs-SA) were assembled on the HCR products as recognition element. Upon sensing of target DNA, the duplex DNA probe triggered the Exo Ⅲ cleavage process, accompanied by generating a new secondary target DNA and releasing target DNA. The released target DNA and the secondary target DNA were recycled. Simultaneously, numerous single strands were liberated and acted as the trigger of HCR to generate further signal amplification, resulting in the immobilization of abundant Au-NPs-SA on the gold substrate. The QCM sensor results were found to be comparable to that achieved using a SPR sensor platform. This method exhibited a high sensitivity toward target DNA with a detection limit of 0.70 fM. The high sensitivity and specificity make this method a great potential for detecting DNA with trace amounts in bioanalysis and clinical biomedicine. Copyright © 2018 Elsevier Inc. All rights reserved.

Detection of buried mines with seismic sonar

NASA Astrophysics Data System (ADS)

Muir, Thomas G.; Baker, Steven R.; Gaghan, Frederick E.; Fitzpatrick, Sean M.; Hall, Patrick W.; Sheetz, Kraig E.; Guy, Jeremie

2003-10-01

Prior research on seismo-acoustic sonar for detection of buried targets [J. Acoust. Soc. Am. 103, 2333-2343 (1998)] has continued with examination of the target strengths of buried test targets as well as targets of interest, and has also examined detection and confirmatory classification of these, all using arrays of seismic sources and receivers as well as signal processing techniques to enhance target recognition. The target strengths of two test targets (one a steel gas bottle, the other an aluminum powder keg), buried in a sand beach, were examined as a function of internal mass load, to evaluate theory developed for seismic sonar target strength [J. Acoust. Soc. Am. 103, 2344-2353 (1998)]. The detection of buried naval and military targets of interest was achieved with an array of 7 shaker sources and 5, three-axis seismometers, at a range of 5 m. Vector polarization filtering was the main signal processing technique for detection. It capitalizes on the fact that the vertical and horizontal components in Rayleigh wave echoes are 90 deg out of phase, enabling complex variable processing to obtain the imaginary component of the signal power versus time, which is unique to Rayleigh waves. Gabor matrix processing of this signal component was the main technique used to determine whether the target was man-made or just a natural target in the environment. [Work sponsored by ONR.

Biosensors based on DNA-Functionalized Graphene

NASA Astrophysics Data System (ADS)

Vishnubhotla, Ramya; Ping, Jinglei; Vrudhula, Amey; Johnson, A. T. Charlie

Since its discovery, graphene has been used for sensing applications due to its outstanding electrical properties and biocompatibility. Here, we demonstrate the capabilities of field effect transistors (FETs) based on CVD-grown graphene functionalized with commercially obtained DNA oligomers and aptamers for detection of various biomolecular targets (e.g., complementary DNA and small molecule drug targets). Graphene FETs were created with a scalable photolithography process that produces arrays consisting of 50-100 FETs with a layout suitable for multiplexed detection of four molecular targets. FETs were characterized via AFM to confirm the presence of the aptamer. From the measured electrical characteristics, it was determined that binding of molecular targets by the DNA chemical recognition element led to a reproducible, concentration-dependent shift in the Dirac voltage. This biosensor class is potentially suitable for applications in drug detection. This work is funded by NIH through the Center for AIDS Research at the University of Pennsylvania.

Diode/magnetic tunnel junction cell for fully scalable matrix-based biochip

NASA Astrophysics Data System (ADS)

Cardoso, F. A.; Ferreira, H. A.; Conde, J. P.; Chu, V.; Freitas, P. P.; Vidal, D.; Germano, J.; Sousa, L.; Piedade, M. S.; Costa, B. A.; Lemos, J. M.

2006-04-01

Magnetoresistive biochips have been recently introduced for the detection of biomolecular recognition. In this work, the detection site incorporates a thin-film diode in series with a magnetic tunnel junction (MTJ), leading to a matrix-based biochip that can be easily scaled up to screen large numbers of different target analytes. The fabricated 16×16 cell matrix integrates hydrogenated amorphous silicon (a-Si:H) diodes with aluminum oxide barrier MTJ. Each detection site also includes a U-shaped current line for magnetically assisted target concentration at probe sites. The biochip is being integrated in a portable, credit card size electronics control platform. Detection of 250 nm diameter magnetic nanoparticles by one of the matrix cells is demonstrated.

Foldable polymers as probes

DOEpatents

Li, Alexander D. Q. [Pullman, WA; Wang, Wei [Pullman, WA

2007-07-03

Disclosed herein are novel probes, which can be used to detect and identify target molecules of interest in a sample. The disclosed probes can be used to monitor conformational changes induced by molecular recognition events in addition to providing signaling the presence and/or identity of a target molecule. Methods, including solid phase synthesis techniques, for making probe molecules that exhibit changes in their optical properties upon target molecule binding are described in the disclosure. Also disclosed herein are novel chromophore moieties, which have tailored fluorescent emission spectra.

Foldable polymers as probes

DOEpatents

Li, Alexander D. Q. [Pullman, WA; Wang, Wei [Pullman, WA

2009-07-07

Disclosed herein are novel probes, which can be used to detect and identify target molecules of interest in a sample. The disclosed probes can be used to monitor conformational changes induced by molecular recognition events in addition to providing signaling the presence and/or identity of a target molecule. Methods, including solid phase synthesis techniques, for making probe molecules that exhibit changes in their optical properties upon target molecule binding are described in the disclosure. Also disclosed herein are novel chromophore moieties, which have tailored fluorescent emission spectra.

Electrochemiluminescence aptasensor for adenosine triphosphate detection using host-guest recognition between metallocyclodextrin complex and aptamer.

PubMed

Chen, Hong; Chen, Qiong; Zhao, Yingying; Zhang, Fan; Yang, Fan; Tang, Jie; He, Pingang

2014-04-01

A sensitive and label-free electrochemiluminescence (ECL) aptasensor for the detection of adenosine triphosphate (ATP) was successfully designed using host-guest recognition between a metallocyclodextrin complex, i.e., tris(bipyridine)ruthenium(II)-β-cyclodextrin [tris(bpyRu)-β-CD], and an ATP-binding aptamer. In the protocol, the NH2-terminated aptamer was immobilized on a glassy carbon electrode (GCE) by a coupling interaction. After host-guest recognition between tris(bpyRu)-β-CD and aptamer, the tris(bpyRu)-β-CD/aptamer/GCE produced a strong ECL signal as a result of the photoactive properties of tris(bpyRu)-β-CD. However, in the presence of ATP, the ATP/aptamer complex was formed preferentially, which restricted host-guest recognition, and therefore less tris(bpyRu)-β-CD was attached to the GCE surface, resulting in an obvious decrease in the ECL intensity. Under optimal determination conditions, an excellent logarithmic linear relationship between the ECL decrease and ATP concentration was obtained in the range 10.0-0.05 nM, with a detection limit of 0.01 nM at the S/N ratio of 3. The proposed ECL-based ATP aptasensor exhibited high sensitivity and selectivity, without time-consuming signal-labeling procedures, and is considered to be a promising model for detection of aptamer-specific targets. Copyright © 2014. Published by Elsevier B.V.

Label-Free Aptasensors for the Detection of Mycotoxins

PubMed Central

Rhouati, Amina; Catanante, Gaelle; Nunes, Gilvanda; Hayat, Akhtar; Marty, Jean-Louis

2016-01-01

Various methodologies have been reported in the literature for the qualitative and quantitative monitoring of mycotoxins in food and feed samples. Based on their enhanced specificity, selectivity and versatility, bio-affinity assays have inspired many researchers to develop sensors by exploring bio-recognition phenomena. However, a significant problem in the fabrication of these devices is that most of the biomolecules do not generate an easily measurable signal upon binding to the target analytes, and signal-generating labels are required to perform the measurements. In this context, aptamers have been emerged as a potential and attractive bio-recognition element to design label-free aptasensors for various target analytes. Contrary to other bioreceptor-based approaches, the aptamer-based assays rely on antigen binding-induced conformational changes or oligomerization states rather than binding-assisted changes in adsorbed mass or charge. This review will focus on current designs in label-free conformational switchable design strategies, with a particular focus on applications in the detection of mycotoxins. PMID:27999353

Identification of novel antibody-reactive detection sites for comprehensive gluten monitoring.

PubMed

Röckendorf, Niels; Meckelein, Barbara; Scherf, Katharina A; Schalk, Kathrin; Koehler, Peter; Frey, Andreas

2017-01-01

Certain cereals like wheat, rye or barley contain gluten, a protein mixture that can trigger celiac disease (CD). To make gluten-free diets available for affected individuals the gluten content of foodstuff must be monitored. For this purpose, antibody-based assays exist which rely on the recognition of certain linear gluten sequence motifs. Yet, not all CD-active gluten constituents and fragments formed during food processing/fermentation may be covered by those tests. In this study, we therefore assayed the coverage of reportedly CD-active gluten components by currently available detection antibodies and determined the antibody-inducing capacity of wheat gluten constituents in order to provide novel diagnostic targets for comprehensive gluten quantitation. Immunizations of outbred mice with purified gliadins and glutenins were conducted and the linear target recognition profile of the sera was recorded using synthetic peptide arrays that covered the sequence space of gluten constituents present in those preparations. The resulting murine immunorecognition profile of gluten demonstrated that further linear binding sites beyond those recognized by the monoclonal antibodies α20, R5 and G12 exist and may be exploitable as diagnostic targets. We conclude that the safety of foodstuffs for CD patients can be further improved by complementing current tests with antibodies directed against additional CD-active gluten components. Currently unrepresented linear gluten detection sites in glutenins and α-gliadins suggest sequences QQQYPS, PQQSFP, QPGQGQQG and QQPPFS as novel targets for antibody generation.

Identification of novel antibody-reactive detection sites for comprehensive gluten monitoring

PubMed Central

Röckendorf, Niels; Meckelein, Barbara; Scherf, Katharina A.; Schalk, Kathrin; Koehler, Peter

2017-01-01

Certain cereals like wheat, rye or barley contain gluten, a protein mixture that can trigger celiac disease (CD). To make gluten-free diets available for affected individuals the gluten content of foodstuff must be monitored. For this purpose, antibody-based assays exist which rely on the recognition of certain linear gluten sequence motifs. Yet, not all CD-active gluten constituents and fragments formed during food processing/fermentation may be covered by those tests. In this study, we therefore assayed the coverage of reportedly CD-active gluten components by currently available detection antibodies and determined the antibody-inducing capacity of wheat gluten constituents in order to provide novel diagnostic targets for comprehensive gluten quantitation. Immunizations of outbred mice with purified gliadins and glutenins were conducted and the linear target recognition profile of the sera was recorded using synthetic peptide arrays that covered the sequence space of gluten constituents present in those preparations. The resulting murine immunorecognition profile of gluten demonstrated that further linear binding sites beyond those recognized by the monoclonal antibodies α20, R5 and G12 exist and may be exploitable as diagnostic targets. We conclude that the safety of foodstuffs for CD patients can be further improved by complementing current tests with antibodies directed against additional CD-active gluten components. Currently unrepresented linear gluten detection sites in glutenins and α-gliadins suggest sequences QQQYPS, PQQSFP, QPGQGQQG and QQPPFS as novel targets for antibody generation. PMID:28759621

Modeling peripheral vision for moving target search and detection.

PubMed

Yang, Ji Hyun; Huston, Jesse; Day, Michael; Balogh, Imre

2012-06-01

Most target search and detection models focus on foveal vision. In reality, peripheral vision plays a significant role, especially in detecting moving objects. There were 23 subjects who participated in experiments simulating target detection tasks in urban and rural environments while their gaze parameters were tracked. Button responses associated with foveal object and peripheral object (PO) detection and recognition were recorded. In an urban scenario, pedestrians appearing in the periphery holding guns were threats and pedestrians with empty hands were non-threats. In a rural scenario, non-U.S. unmanned aerial vehicles (UAVs) were considered threats and U.S. UAVs non-threats. On average, subjects missed detecting 2.48 POs among 50 POs in the urban scenario and 5.39 POs in the rural scenario. Both saccade reaction time and button reaction time can be predicted by peripheral angle and entrance speed of POs. Fast moving objects were detected faster than slower objects and POs appearing at wider angles took longer to detect than those closer to the gaze center. A second-order mixed-effect model was applied to provide each subject's prediction model for peripheral target detection performance as a function of eccentricity angle and speed. About half the subjects used active search patterns while the other half used passive search patterns. An interactive 3-D visualization tool was developed to provide a representation of macro-scale head and gaze movement in the search and target detection task. An experimentally validated stochastic model of peripheral vision in realistic target detection scenarios was developed.

Host-Guest Recognition-Assisted Electrochemical Release: Its Reusable Sensing Application Based on DNA Cross Configuration-Fueled Target Cycling and Strand Displacement Reaction Amplification.

PubMed

Chang, Yuanyuan; Zhuo, Ying; Chai, Yaqin; Yuan, Ruo

2017-08-15

In this work, an elegantly designed host-guest recognition-assisted electrochemical release was established and applied in a reusable electrochemical biosensor for the detection of microRNA-182-5p (miRNA-182-5p), a prostate cancer biomarker in prostate cancer, based on the DNA cross configuration-fueled target cycling and strand displacement reaction (SDR) amplification. With such a design, the single target miRNA input could be converted to large numbers of single-stranded DNA (S1-Trp and S2-Trp) output, which could be trapped by cucurbit[8]uril methyl viologen (CB-8-MV 2+ ) based on the host-guest recognition, significantly enhancing the sensitivity for miRNA detection. Moreover, the nucleic acids products obtained from the process of cycling amplification could be utilized sufficiently, avoiding the waste and saving the experiment cost. Impressively, by resetting a settled voltage, the proposed biosensor could release S1-Trp and S2-Trp from the electrode surface, attributing that the guest ion methyl viologen (MV 2+ ) was reduced to MV +· under this settled voltage and formed a more-stable CB-8-MV +· -MV +· complex. Once O 2 was introduced in this system, MV +· could be oxidized to MV 2+ , generating the complex of CB-8-MV 2+ for capturing S1-Trp and S2-Trp again in only 5 min. As a result, the simple and fast regeneration of biosensor for target detection was realized on the base of electrochemical redox-driven assembly and release, overcoming the challenges of time-consuming, burdensome operations and expensive experimental cost in traditional reusable biosensors and updating the construction method for a reusable bisensor. Furthermore, the biosensor could be reused for more than 10 times with a regeneration rate of 93.20%-102.24%. After all, the conception of this work provides a novel thought for the construction of effective reusable biosensor to detect miRNA and other biomarkers and has great potential application in the area requiring the release of nucleic acids or proteins.

Development of structure switching aptamer assay for detection of aflatoxin M1 in milk sample.

PubMed

Sharma, Atul; Catanante, Gaëlle; Hayat, Akhtar; Istamboulie, Georges; Ben Rejeb, Ines; Bhand, Sunil; Marty, Jean Louis

2016-09-01

The discovery of in-vitro systematic evolution of ligands by exponential enrichment (SELEX) process has considerably broaden the utility of aptamer as bio-recognition element, providing the high binding affinity and specificity against the target analytes. Recent research has focused on the development of structure switching signaling aptamer assay, transducing the aptamer- target recognition event into an easily detectable signal. In this paper, we demonstrate the development of structure switching aptamer assay for determination of aflatoxin M1 (AFM1) employing the quenching-dequenching mechanism. Hybridization of fluorescein labelled anti-AFM1 aptamer (F-aptamer) with TAMRA labelled complementary sequences (Q-aptamer) brings the fluorophore and the quencher into close proximity, which results in maximum fluorescence quenching. On addition of AFM1, the target induced conformational formation of antiparallel G-quadruplex aptamer-AFM1 complex results in fluorescence recovery. Under optimized experimental conditions, the developed method showed the good linearity with limit of detection (LOD) at 5.0ngkg(-1) for AFM1. The specificity of the sensing platform was carefully investigated against aflatoxin B1 (AFB1) and ochratoxin A (OTA). The developed assay platform showed the high specificity towards AFM1. The practical application of the developed aptamer assay was verified for detection of AFM1 in spiked milk samples. Good recoveries were obtained in the range from 94.40% to 95.28% (n=3) from AFM1 spiked milk sample. Copyright © 2016. Published by Elsevier B.V.

Recognition during recall failure: Semantic feature matching as a mechanism for recognition of semantic cues when recall fails.

PubMed

Cleary, Anne M; Ryals, Anthony J; Wagner, Samantha R

2016-01-01

Research suggests that a feature-matching process underlies cue familiarity-detection when cued recall with graphemic cues fails. When a test cue (e.g., potchbork) overlaps in graphemic features with multiple unrecalled studied items (e.g., patchwork, pitchfork, pocketbook, pullcork), higher cue familiarity ratings are given during recall failure of all of the targets than when the cue overlaps in graphemic features with only one studied target and that target fails to be recalled (e.g., patchwork). The present study used semantic feature production norms (McRae et al., Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005) to examine whether the same holds true when the cues are semantic in nature (e.g., jaguar is used to cue cheetah). Indeed, test cues (e.g., cedar) that overlapped in semantic features (e.g., a_tree, has_bark, etc.) with four unretrieved studied items (e.g., birch, oak, pine, willow) received higher cue familiarity ratings during recall failure than test cues that overlapped in semantic features with only two (also unretrieved) studied items (e.g., birch, oak), which in turn received higher familiarity ratings during recall failure than cues that did not overlap in semantic features with any studied items. These findings suggest that the feature-matching theory of recognition during recall failure can accommodate recognition of semantic cues during recall failure, providing a potential mechanism for conceptually-based forms of cue recognition during target retrieval failure. They also provide converging evidence for the existence of the semantic features envisaged in feature-based models of semantic knowledge representation and for those more concretely specified by the production norms of McRae et al. (Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005).

A molecular beacon based on DNA-templated silver nanoclusters for the highly sensitive and selective multiplexed detection of virulence genes.

PubMed

Han, Dan; Wei, Chunying

2018-05-01

In this work, we develop a fluorescent molecular beacon based on the DNA-templated silver nanoclusters (DNA-Ag NCs). The skillfully designed molecular beacon can be conveniently used for detection of diverse virulence genes as long as the corresponding recognition sequences are embedded. Importantly, the constructed detection system allows simultaneous detection of multiple nucleic acids, which is attributed to non-overlapping emission spectra of the as-synthesized silver nanoclusters. Based on the target-induced fluorescence enhancement, three infectious disease-related genes HIV, H1N1, and H5N1 are detected, and the corresponding detection limits are 3.53, 0.12 and 3.95nM, respectively. This design allows specific, versatile and simultaneous detection of diverse targets with easy operation and low cost. Copyright © 2017 Elsevier B.V. All rights reserved.

Feature Extraction and Selection Strategies for Automated Target Recognition

NASA Technical Reports Server (NTRS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Feature extraction and selection strategies for automated target recognition

NASA Astrophysics Data System (ADS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-04-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Error Rates in Users of Automatic Face Recognition Software

PubMed Central

White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

2015-01-01

In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

Automatically Detecting Likely Edits in Clinical Notes Created Using Automatic Speech Recognition

PubMed Central

Lybarger, Kevin; Ostendorf, Mari; Yetisgen, Meliha

2017-01-01

The use of automatic speech recognition (ASR) to create clinical notes has the potential to reduce costs associated with note creation for electronic medical records, but at current system accuracy levels, post-editing by practitioners is needed to ensure note quality. Aiming to reduce the time required to edit ASR transcripts, this paper investigates novel methods for automatic detection of edit regions within the transcripts, including both putative ASR errors but also regions that are targets for cleanup or rephrasing. We create detection models using logistic regression and conditional random field models, exploring a variety of text-based features that consider the structure of clinical notes and exploit the medical context. Different medical text resources are used to improve feature extraction. Experimental results on a large corpus of practitioner-edited clinical notes show that 67% of sentence-level edits and 45% of word-level edits can be detected with a false detection rate of 15%. PMID:29854187

Intelligent Classification in Huge Heterogeneous Data Sets

DTIC Science & Technology

2015-06-01

Competencies DoD Department of Defense GMTI Ground Moving Target Indicator ISR Intelligence, Surveillance and Reconnaissance NCD Noncoherent Change...Detection OCR Optical Character Recognition PCA Principal Component Analysis SAR Synthetic Aperture Radar SVD Singular Value Decomponsition USPS United States Postal Service 8 Approved for Public Release; Distribution Unlimited.

DNA nanotechnology-enabled biosensors.

PubMed

Chao, Jie; Zhu, Dan; Zhang, Yinan; Wang, Lianhui; Fan, Chunhai

2016-02-15

Biosensors employ biological molecules to recognize the target and utilize output elements which can translate the biorecognition event into electrical, optical or mass-sensitive signals to determine the quantities of the target. DNA-based biosensors, as a sub-field to biosensor, utilize DNA strands with short oligonucleotides as probes for target recognition. Although DNA-based biosensors have offered a promising alternative for fast, simple and cheap detection of target molecules, there still exist key challenges including poor stability and reproducibility that hinder their competition with the current gold standard for DNA assays. By exploiting the self-recognition properties of DNA molecules, researchers have dedicated to make versatile DNA nanostructures in a highly rigid, controllable and functionalized manner, which offers unprecedented opportunities for developing DNA-based biosensors. In this review, we will briefly introduce the recent advances on design and fabrication of static and dynamic DNA nanostructures, and summarize their applications for fabrication and functionalization of DNA-based biosensors. Copyright © 2015 Elsevier B.V. All rights reserved.

Multiplex detection of quality indicator molecule targets in urine using programmable hairpin probes based on a simple double-T type microchip electrophoresis platform and isothermal polymerase-catalyzed target recycling.

PubMed

Zhou, Lingying; Gan, Ning; Wu, Yongxiang; Hu, Futao; Lin, Jianyuan; Cao, Yuting; Wu, Dazhen

2018-05-29

Recently, it has been crucial to be able to detect and quantify small molecular targets simultaneously in biological samples. Herein, a simple and conventional double-T type microchip electrophoresis (MCE) based platform for the multiplex detection of quality indicator molecule targets in urine, using ampicillin (AMPI), adenosine triphosphate (ATP) and estradiol (E2) as models, was developed. Several programmable hairpin probes (PHPs) were designed for detecting different targets and triggering isothermal polymerase-catalyzed target recycling (IPCTR) for signal amplification. Based on the target-responsive aptamer structure of PHP (Domain I), target recognition can induce PHP conformational transition and produce extension duplex DNA (dsDNA), assisted by primers & Bst polymerase. Afterwards, the target can be displaced to react with another PHP and initiate the next cycle. After several rounds of reaction, the dsDNA can be produced in large amounts by IPCTR. Three targets can be simultaneously converted to dsDNA fragments with different lengths, which can be separated and detected using MCE. Thus, a simple double-T type MCE based platform was successfully built for the homogeneous detection of multiplex targets in one channel. Under optimal conditions, the assay exhibited high throughput (48 samples per hour at most, not including reaction time) and sensitivity to three targets in urine with a detection limit of 1 nM (ATP), 0.05 nM (AMPI) and 0.1 nM (E2) respectively. The multiplex assay was successfully employed for the above three targets in several urine samples and combined the advantages of the high specificity of programmable hairpin probes, the excellent signal amplification of IPCTR, and the high through-put of MCE which can be employed for screening in biochemical analysis.

Enzyme-free homogeneous electrochemical biosensor for DNA assay using toehold-triggered strand displacement reaction coupled with host-guest recognition of Fe3O4@SiO2@β-CD nanocomposites.

PubMed

Jiang, Jingjing; Lin, Xinyi; Ding, Dong; Diao, Guowang

2018-04-17

Taking advantages of the toehold-triggered strand displacement reaction (TSDR) and host-guest interaction of β-cyclodextrin (β-CD), a facile enzyme-free and homogeneous electrochemical sensing strategy was designed for the sensitive assay of target DNA using Fe 3 O 4 @SiO 2 @β-CD nanocomposites and ferrocene-labeled hairpin DNA (H-1) as the capture and electrochemical probes, respectively. Upon addition of target molecule, the initiated TSDR process induced the conformational change of H-1, and subsequently stimulated the dynamic assembly of assist probes (A-1 and A-2) to generate H-1:A-1:A-2 duplex along with the release of target sequence. The released target could drive the next TSDR recycling and finally result in the formation of numerous DNA duplex. After the molecular recognition of Fe 3 O 4 @SiO 2 @β-CD nanocomposites, a large number of duplex were easily separated from the supernatant solution under an external magnetic field, which led to a decreased H-1 concentration in residual solution, concomitant with a remarkable reduction of peak current. Under the optimized conditions, wide linear range (1-5000 pM), low detection limit (0.3 pM), desirable reproducibility, good selectivity, and satisfactory practical analysis were obtained by the combination of the superior recognition capability of β-CD, TSDR-induced signal amplification, and homogeneous electroanalytical method. The proposed detection strategy could offer a universal approach for the monitoring of various biological analytes via the rational design of probe sequences. Copyright © 2018 Elsevier B.V. All rights reserved.

Directing an artificial zinc finger protein to new targets by fusion to a non-DNA-binding domain.

PubMed

Lim, Wooi F; Burdach, Jon; Funnell, Alister P W; Pearson, Richard C M; Quinlan, Kate G R; Crossley, Merlin

2016-04-20

Transcription factors are often regarded as having two separable components: a DNA-binding domain (DBD) and a functional domain (FD), with the DBD thought to determine target gene recognition. While this holds true for DNA bindingin vitro, it appears thatin vivoFDs can also influence genomic targeting. We fused the FD from the well-characterized transcription factor Krüppel-like Factor 3 (KLF3) to an artificial zinc finger (AZF) protein originally designed to target the Vascular Endothelial Growth Factor-A (VEGF-A) gene promoter. We compared genome-wide occupancy of the KLF3FD-AZF fusion to that observed with AZF. AZF bound to theVEGF-Apromoter as predicted, but was also found to occupy approximately 25,000 other sites, a large number of which contained the expected AZF recognition sequence, GCTGGGGGC. Interestingly, addition of the KLF3 FD re-distributes the fusion protein to new sites, with total DNA occupancy detected at around 50,000 sites. A portion of these sites correspond to known KLF3-bound regions, while others contained sequences similar but not identical to the expected AZF recognition sequence. These results show that FDs can influence and may be useful in directing AZF DNA-binding proteins to specific targets and provide insights into how natural transcription factors operate. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Automatic recognition of ship types from infrared images using superstructure moment invariants

NASA Astrophysics Data System (ADS)

Li, Heng; Wang, Xinyu

2007-11-01

Automatic object recognition is an active area of interest for military and commercial applications. In this paper, a system addressing autonomous recognition of ship types in infrared images is proposed. Firstly, an approach of segmentation based on detection of salient features of the target with subsequent shadow removing is proposed, as is the base of the subsequent object recognition. Considering the differences between the shapes of various ships mainly lie in their superstructures, we then use superstructure moment functions invariant to translation, rotation and scale differences in input patterns and develop a robust algorithm of obtaining ship superstructure. Subsequently a back-propagation neural network is used as a classifier in the recognition stage and projection images of simulated three-dimensional ship models are used as the training sets. Our recognition model was implemented and experimentally validated using both simulated three-dimensional ship model images and real images derived from video of an AN/AAS-44V Forward Looking Infrared(FLIR) sensor.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

PubMed Central

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-01

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

Adding temporally localized noise can enhance the contribution of target knowledge on contrast detection.

PubMed

Silvestre, Daphné; Cavanagh, Patrick; Arleo, Angelo; Allard, Rémy

2017-02-01

External noise paradigms are widely used to characterize sensitivity by comparing the effect of a variable on contrast threshold when it is limited by internal versus external noise. A basic assumption of external noise paradigms is that the processing properties are the same in low and high noise. However, recent studies (e.g., Allard & Cavanagh, 2011; Allard & Faubert, 2014b) suggest that this assumption could be violated when using spatiotemporally localized noise (i.e., appearing simultaneously and at the same location as the target) but not when using spatiotemporally extended noise (i.e., continuously displayed, full-screen, dynamic noise). These previous findings may have been specific to the crowding and 0D noise paradigms that were used, so the purpose of the current study is to test if this violation of noise-invariant processing also occurs in a standard contrast detection task in white noise. The rationale of the current study is that local external noise triggers the use of recognition rather than detection and that a recognition process should be more affected by uncertainty about the shape of the target than one involving detection. To investigate the contribution of target knowledge on contrast detection, the effect of orientation uncertainty was evaluated for a contrast detection task in the absence of noise and in the presence of spatiotemporally localized or extended noise. A larger orientation uncertainty effect was observed with temporally localized noise than with temporally extended noise or with no external noise, indicating a change in the nature of the processing for temporally localized noise. We conclude that the use of temporally localized noise in external noise paradigms risks triggering a shift in process, invalidating the noise-invariant processing required for the paradigm. If, instead, temporally extended external noise is used to match the properties of internal noise, no such processing change occurs.

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method

PubMed Central

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-01-01

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets. PMID:27801795

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method.

PubMed

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-10-27

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

Strategic deployment of orthographic knowledge in phoneme detection.

PubMed

Cutler, Anne; Treiman, Rebecca; van Ooijen, Brit

2010-01-01

The phoneme detection task is widely used in spoken-word recognition research. Alphabetically literate participants, however, are more used to explicit representations of letters than of phonemes. The present study explored whether phoneme detection is sensitive to how target phonemes are, or may be, orthographically realized. Listeners detected the target sounds [b, m, t, f, s, k] in word-initial position in sequences of isolated English words. Response times were faster to the targets [b, m, t], which have consistent word-initial spelling, than to the targets [f, s, k], which are inconsistently spelled, but only when spelling was rendered salient by the presence in the experiment of many irregularly spelled filler words. Within the inconsistent targets [f, s, k], there was no significant difference between responses to targets in words with more usual (foam, seed, cattle) versus less usual (phone, cede, kettle) spellings. Phoneme detection is thus not necessarily sensitive to orthographic effects; knowledge of spelling stored in the lexical representations of words does not automatically become available as word candidates are activated. However, salient orthographic manipulations in experimental input can induce such sensitivity. We attribute this to listeners' experience of the value of spelling in everyday situations that encourage phonemic decisions (such as learning new names).

Aptamer-incorporated hydrogels for visual detection, controlled drug release, and targeted cancer therapy

PubMed Central

Liu, Jun; Kang, Huaizhi; Donovan, Michael; Zhu, Zhi

2017-01-01

Hydrogels are water-retainable materials, made from cross-linked polymers, that can be tailored to applications in bioanalysis and biomedicine. As technology advances, an increasing number of molecules have been used as the components of hydrogel systems. However, the shortcomings of these systems have prompted researchers to find new materials that can be incorporated into them. Among all of these emerging materials, aptamers have recently attracted substantial attention because of their unique properties, for example biocompatibility, selective binding, and molecular recognition, all of which make them promising candidates for target-responsive hydrogel engineering. In this work, we will review how aptamers have been incorporated into hydrogel systems to enable colorimetric detection, controlled drug release, and targeted cancer therapy. PMID:22052153

Automatic three-dimensional measurement of large-scale structure based on vision metrology.

PubMed

Zhu, Zhaokun; Guan, Banglei; Zhang, Xiaohu; Li, Daokui; Yu, Qifeng

2014-01-01

All relevant key techniques involved in photogrammetric vision metrology for fully automatic 3D measurement of large-scale structure are studied. A new kind of coded target consisting of circular retroreflective discs is designed, and corresponding detection and recognition algorithms based on blob detection and clustering are presented. Then a three-stage strategy starting with view clustering is proposed to achieve automatic network orientation. As for matching of noncoded targets, the concept of matching path is proposed, and matches for each noncoded target are found by determination of the optimal matching path, based on a novel voting strategy, among all possible ones. Experiments on a fixed keel of airship have been conducted to verify the effectiveness and measuring accuracy of the proposed methods.

Polarimetric Imaging System for Automatic Target Detection and Recognition

DTIC Science & Technology

2000-03-01

technique shown in Figure 4(b) can also be used to integrate polarizer arrays with other types of imaging sensors, such as LWIR cameras and uncooled...vertical stripe pattern in this φ image is caused by nonuniformities in the particular polarizer array used. 2. CIRCULAR POLARIZATION IMAGING USING

Recognition of Typhus Group Rickettsia-Infected Targets by Human Lymphokine-Activated Killer Cells

DTIC Science & Technology

1988-09-01

rick- Similar problems in detection of antigens of Theileria parva ettsia-specific cell surface antigens by performing polyacryl- (7) or influenza virus...infected with the protozoan parasite Theileria parva: workers in our laboratory are now in the process of cloning parasite strain specificity and class I

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

NASA Astrophysics Data System (ADS)

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution.

PubMed

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-06

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

PubMed Central

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining. PMID:28059147

Quantitative methods and detection techniques in hyperspectral imaging involving medical and other applications

NASA Astrophysics Data System (ADS)

Roy, Ankita

2007-12-01

This research using Hyperspectral imaging involves recognizing targets through spatial and spectral matching and spectral un-mixing of data ranging from remote sensing to medical imaging kernels for clinical studies based on Hyperspectral data-sets generated using the VFTHSI [Visible Fourier Transform Hyperspectral Imager], whose high resolution Si detector makes the analysis achievable. The research may be broadly classified into (I) A Physically Motivated Correlation Formalism (PMCF), which places both spatial and spectral data on an equivalent mathematical footing in the context of a specific Kernel and (II) An application in RF plasma specie detection during carbon nanotube growing process. (III) Hyperspectral analysis for assessing density and distribution of retinopathies like age related macular degeneration (ARMD) and error estimation enabling the early recognition of ARMD, which is treated as an ill-conditioned inverse imaging problem. The broad statistical scopes of this research are two fold-target recognition problems and spectral unmixing problems. All processes involve experimental and computational analysis of Hyperspectral data sets is presented, which is based on the principle of a Sagnac Interferometer, calibrated to obtain high SNR levels. PMCF computes spectral/spatial/cross moments and answers the question of how optimally the entire hypercube should be sampled and finds how many spatial-spectral pixels are required precisely for a particular target recognition. Spectral analysis of RF plasma radicals, typically Methane plasma and Argon plasma using VFTHSI has enabled better process monitoring during growth of vertically aligned multi-walled carbon nanotubes by instant registration of the chemical composition or density changes temporally, which is key since a significant correlation can be found between plasma state and structural properties. A vital focus of this dissertation is towards medical Hyperspectral imaging applied to retinopathies like age related macular degeneration targets taken with a Fundus imager, which is akin to the VFTHSI. Detection of the constituent components in the diseased hyper-pigmentation area is also computed. The target or reflectance matrix is treated as a highly ill-conditioned spectral un-mixing problem, to which methodologies like inverse techniques, principal component analysis (PCA) and receiver operating curves (ROC) for precise spectral recognition of infected area. The region containing ARMD was easily distinguishable from the spectral mesh plots over the entire band-pass area. Once the location was detected the PMCF coefficients were calculated by cross correlating a target of normal oxygenated retina with the deoxygenated one. The ROCs generated using PMCF shows 30% higher detection probability with improved accuracy than ROCs based on Spectral Angle Mapper (SAM). By spectral unmixing methods, the important endmembers/carotenoids of the MD pigment were found to be Xanthophyl and lutein, while beta-carotene which showed a negative correlation in the unconstrained inverse problem is a supplement given to ARMD patients to prevent the disease and does not occur in the eye. Literature also shows degeneration of meso-zeaxanthin. Ophthalmologists may assert the presence of ARMD and commence the diagnosis process if the Xanthophyl pigment have degenerated 89.9%, while the lutein has decayed almost 80%, as found deduced computationally. This piece of current research takes it to the next level of precise investigation in the continuing process of improved clinical findings by correlating the microanatomy of the diseased fovea and shows promise of an early detection of this disease.

Single-carbon discrimination by selected peptides for individual detection of volatile organic compounds

NASA Astrophysics Data System (ADS)

Ju, Soomi; Lee, Ki-Young; Min, Sun-Joon; Yoo, Yong Kyoung; Hwang, Kyo Seon; Kim, Sang Kyung; Yi, Hyunjung

2015-03-01

Although volatile organic compounds (VOCs) are becoming increasingly recognized as harmful agents and potential biomarkers, selective detection of the organic targets remains a tremendous challenge. Among the materials being investigated for target recognition, peptides are attractive candidates because of their chemical robustness, divergence, and their homology to natural olfactory receptors. Using a combinatorial peptide library and either a graphitic surface or phenyl-terminated self-assembled monolayer as relevant target surfaces, we successfully selected three interesting peptides that differentiate a single carbon deviation among benzene and its analogues. The heterogeneity of the designed target surfaces provided peptides with varying affinity toward targeted molecules and generated a set of selective peptides that complemented each other. Microcantilever sensors conjugated with each peptide quantitated benzene, toluene and xylene to sub-ppm levels in real time. The selection of specific receptors for a group of volatile molecules will provide a strong foundation for general approach to individually monitoring VOCs.

Recognition unit-free and self-cleaning photoelectrochemical sensing platform on TiO2 nanotube photonic crystals for sensitive and selective detection of dopamine release from mouse brain.

PubMed

Xin, Yanmei; Li, Zhenzhen; Wu, Wenlong; Fu, Baihe; Wu, Hongjun; Zhang, Zhonghai

2017-01-15

For implementing sensitive and selective detection of biological molecules, the biosensors are been designed more and more complicated. The exploration of detection platform in a simple way without loss their sensitivity and selectivity is always a big challenge. Herein, a prototype of recognition biomolecule unit-free photoelectrochemical (PEC) sensing platform with self-cleaning activity is proposed with TiO 2 nanotube photonic crystal (TiO 2 NTPCs) materials as photoelectrode, and dopamine (DA) molecule as both sensitizer and target analyte. The unique adsorption between DA and TiO 2 NTPCs induces the formation of charge transfer complex, which not only expends the optical absorption of TiO 2 into visible light region, thus significantly boosts the PEC performance under illumination of visible light, but also implements the selective detection of DA on TiO 2 photoelectrode. This simple but efficient PEC analysis platform presents a low detection limit of 0.15nm for detection of DA, which allows to realize the sensitive and selective determination of DA release from the mouse brain for its practical application after coupled with a microdialysis probe. The DA functionalized TiO 2 NTPCs PEC sensing platform opens up a new PEC detection model, without using extra-biomolecule auxiliary, just with target molecule naturally adsorbed on the electrode for sensitive and selective detection, and paves a new avenue for biosensors design with minimalism idea. Copyright © 2016 Elsevier B.V. All rights reserved.

Inside-the-wall detection of objects with low metal content using the GPR sensor: effects of different wall structures on the detection performance

NASA Astrophysics Data System (ADS)

Dogan, Mesut; Yesilyurt, Omer; Turhan-Sayan, Gonul

2018-04-01

Ground penetrating radar (GPR) is an ultra-wideband electromagnetic sensor used not only for subsurface sensing but also for the detection of objects which may be hidden behind a wall or inserted within the wall. Such applications of the GPR technology are used in both military and civilian operations such as mine or IED (improvised explosive device) detection, rescue missions after earthquakes and investigation of archeological sites. Detection of concealed objects with low metal content is known to be a challenging problem in general. Use of A-scan, B-scan and C-scan GPR data in combination provides valuable information for target recognition in such applications. In this paper, we study the problem of target detection for potentially explosive objects embedded inside a wall. GPR data is numerically simulated by using an FDTD-based numerical computation tool when dielectric targets and targets with low metal content are inserted into different types of walls. A small size plastic bottle filled with trinitrotoluene (TNT) is used as the target with and without a metal fuse in it. The targets are buried into two different types of wall; a homogeneous brick wall and an inhomogeneous wall constructed by bricks having periodically located air holes in it. Effects of using an inhomogeneous wall structure with internal boundaries are investigated as a challenging scenario, paying special attention to preprocessing.

Signal Amplification Technologies for the Detection of Nucleic Acids: from Cell-Free Analysis to Live-Cell Imaging.

PubMed

Fozooni, Tahereh; Ravan, Hadi; Sasan, Hosseinali

2017-12-01

Due to their unique properties, such as programmability, ligand-binding capability, and flexibility, nucleic acids can serve as analytes and/or recognition elements for biosensing. To improve the sensitivity of nucleic acid-based biosensing and hence the detection of a few copies of target molecule, different modern amplification methodologies, namely target-and-signal-based amplification strategies, have already been developed. These recent signal amplification technologies, which are capable of amplifying the signal intensity without changing the targets' copy number, have resulted in fast, reliable, and sensitive methods for nucleic acid detection. Working in cell-free settings, researchers have been able to optimize a variety of complex and quantitative methods suitable for deploying in live-cell conditions. In this study, a comprehensive review of the signal amplification technologies for the detection of nucleic acids is provided. We classify the signal amplification methodologies into enzymatic and non-enzymatic strategies with a primary focus on the methods that enable us to shift away from in vitro detecting to in vivo imaging. Finally, the future challenges and limitations of detection for cellular conditions are discussed.

Real Time Intelligent Target Detection and Analysis with Machine Vision

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

2000-01-01

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

Terrain feature recognition for synthetic aperture radar (SAR) imagery employing spatial attributes of targets

NASA Astrophysics Data System (ADS)

Iisaka, Joji; Sakurai-Amano, Takako

1994-08-01

This paper describes an integrated approach to terrain feature detection and several methods to estimate spatial information from SAR (synthetic aperture radar) imagery. Spatial information of image features as well as spatial association are key elements in terrain feature detection. After applying a small feature preserving despeckling operation, spatial information such as edginess, texture (smoothness), region-likeliness and line-likeness of objects, target sizes, and target shapes were estimated. Then a trapezoid shape fuzzy membership function was assigned to each spatial feature attribute. Fuzzy classification logic was employed to detect terrain features. Terrain features such as urban areas, mountain ridges, lakes and other water bodies as well as vegetated areas were successfully identified from a sub-image of a JERS-1 SAR image. In the course of shape analysis, a quantitative method was developed to classify spatial patterns by expanding a spatial pattern through the use of a series of pattern primitives.

Optimization of OT-MACH Filter Generation for Target Recognition

NASA Technical Reports Server (NTRS)

Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

Salience from the decision perspective: You know where it is before you know it is there.

PubMed

Zehetleitner, Michael; Müller, Hermann J

2010-12-31

In visual search for feature contrast ("odd-one-out") singletons, identical manipulations of salience, whether by varying target-distractor similarity or dimensional redundancy of target definition, had smaller effects on reaction times (RTs) for binary localization decisions than for yes/no detection decisions. According to formal models of binary decisions, identical differences in drift rates would yield larger RT differences for slow than for fast decisions. From this principle and the present findings, it follows that decisions on the presence of feature contrast singletons are slower than decisions on their location. This is at variance with two classes of standard models of visual search and object recognition that assume a serial cascade of first detection, then localization and identification of a target object, but also inconsistent with models assuming that as soon as a target is detected all its properties, spatial as well as non-spatial (e.g., its category), are available immediately. As an alternative, we propose a model of detection and localization tasks based on random walk processes, which can account for the present findings.

Feature Extraction Using Attributed Scattering Center Models for Model-Based Automatic Target Recognition (ATR)

DTIC Science & Technology

2005-10-01

section of the coiled arm. Right: measured realized total gain for a square spiral in free space with inductive treatment. . . . . . . . 154 8.5 Initial...appreciable velocities can often be easily separated from clutter returns, slow moving targets of more moderate cross sections can be very difficult to detect...electromagnetic radiation and measuring the energy scattered back. The data obtained as a result of this process is a finite-extent, noisy set of

Integrated Spintronic Platforms for Biomolecular Recognition Detection

NASA Astrophysics Data System (ADS)

Martins, V. C.; Cardoso, F. A.; Loureiro, J.; Mercier, M.; Germano, J.; Cardoso, S.; Ferreira, R.; Fonseca, L. P.; Sousa, L.; Piedade, M. S.; Freitas, P. P.

2008-06-01

This paper covers recent developments in magnetoresistive based biochip platforms fabricated at INESC-MN, and their application to the detection and quantification of pathogenic waterborn microorganisms in water samples for human consumption. Such platforms are intended to give response to the increasing concern related to microbial contaminated water sources. The presented results concern the development of biological active DNA chips and protein chips and the demonstration of the detection capability of the present platforms. Two platforms are described, one including spintronic sensors only (spin-valve based or magnetic tunnel junction based), and the other, a fully scalable platform where each probe site consists of a MTJ in series with a thin film diode (TFD). Two microfluidic systems are described, for cell separation and concentration, and finally, the read out and control integrated electronics are described, allowing the realization of bioassays with a portable point of care unit. The present platforms already allow the detection of complementary biomolecular target recognition with 1 pM concentration.

Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

NASA Astrophysics Data System (ADS)

Kim, Sungho

2017-06-01

Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

Synthesis of a multi-functional DNA nanosphere barcode system for direct cell detection.

PubMed

Han, Sangwoo; Lee, Jae Sung; Lee, Jong Bum

2017-09-28

Nucleic acid-based technologies have been applied to numerous biomedical applications. As a novel material for target detection, DNA has been used to construct a barcode system with a range of structures. This paper reports multi-functionalized DNA nanospheres (DNANSs) by rolling circle amplification (RCA) with several functionalized nucleotides. DNANSs with a barcode system were designed to exhibit fluorescence for coding enhanced signals and contain biotin for more functionalities, including targeting through the biotin-streptavidin (biotin-STA) interaction. Functionalized deoxynucleotide triphosphates (dNTPs) were mixed in the RCA process and functional moieties can be expressed on the DNANSs. The anti-epidermal growth factor receptor antibodies (anti-EGFR Abs) can be conjugated on DNANSs for targeting cancer cells specifically. As a proof of concept, the potential of the multi-functional DNANS barcode was demonstrated by direct cell detection as a simple detection method. The DNANS barcode provides a new route for the simple and rapid selective recognition of cancer cells.

Automatic Target Recognition Based on Cross-Plot

PubMed Central

Wong, Kelvin Kian Loong; Abbott, Derek

2011-01-01

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

A host-guest-recognition-based electrochemical aptasensor for thrombin detection.

PubMed

Fan, Hao; Li, Hui; Wang, Qingjiang; He, Pingang; Fang, Yuzhi

2012-05-15

A sensitive electrochemical aptasensor for thrombin detection is presented based on the host-guest recognition technique. In this sensing protocol, a 15 based thrombin aptamer (ab. TBA) was dually labeled with a thiol at its 3' end and a 4-((4-(dimethylamino)phenyl)azo) benzoic acid (dabcyl) at its 5' end, respectively, which was previously immobilized on one Au electrode surface by AuS bond and used as the thrombin probe during the protein sensing procedure. One special electrochemical marker was prepared by modifying CdS nanoparticle with β-cyclodextrins (ab. CdS-CDs), which employed as electrochemical signal provider and would conjunct with the thrombin probe modified electrode through the host-guest recognition of CDs to dabcyl. In the absence of thrombin, the probe adopted linear structure to conjunct with CdS-CDs. In present of thrombin, the TBA bond with thrombin and transformed into its special G-quarter structure, which forced CdS-CDs into the solution. Therefore, the target-TBA binding event can be sensitively transduced via detecting the electrochemical oxidation current signal of Cd of CdS nanoparticles in the solution. Using this method, as low as 4.6 pM thrombin had been detected. Copyright © 2012 Elsevier B.V. All rights reserved.

Combining functionalised nanoparticles and SERS for the detection of DNA relating to disease.

PubMed

Graham, Duncan; Stevenson, Ross; Thompson, David G; Barrett, Lee; Dalton, Colette; Faulds, Karen

2011-01-01

DNA functionalised nanoparticle probes offer new opportunities in analyte detection. Ultrasensitive, molecularly specific targeting of analytes is possible through the use of metallic nanoparticles and their ability to generate a surface enhanced Raman scattering (SERS) response. This is leading to a new range of diagnostic clinical probes based on SERS detection. Our approaches have shown how such probes can detect specific DNA sequences by using a biomolecular recognition event to 'turn on' a SERS response through a controlled assembly process of the DNA functionalised nanoparticles. Further, we have prepared DNA aptamer functionalised SERS probes and demonstrated how introduction of a protein target can change the aggregation state of the nanoparticles in a dose-dependant manner. These approaches are being used as methods to detect biomolecules that indicate a specific disease being present with a view to improving disease management.

Detection of Non-Nucleic Acid Targets with an Unmodified Aptamer and a Fluorogenic Competitor

PubMed Central

Li, Na

2010-01-01

Aptamers are oligonucleotides that can bind to various non-nucleic acid targets, ranging from proteins to small molecules, with a specificity and affinity comparable to that of antibodies. Most aptamer-based detection strategies require modification on the aptamer, which could lead to a significant loss in its affinity and specificity to the target. Here we reported a generic strategy to design aptamer-based optical probes. An unmodified aptamer specific to the target and a fluorogenic competitor complementary to the aptamer are utilized for target recognition and signal generation, respectively. The competitor is a hairpin oligonucleotide with a fluorophore attached on one end and a quencher attached on the other. When no target is present, the competitor binds to the aptamer. However, when the target is introduced, the competitor will be displaced from the aptamer by the target, thus resulting in a target-specific decrease in fluorescence signal. Successful application of this strategy to different types of targets (small molecules and proteins) as well as different types of aptamers (DNA and RNA) has been demonstrated. Furthermore, a thermodynamics-based prediction model was established to further rationalize the optimization process. Due to its rapidness and simplicity, this aptamer-based detection strategy holds great promise in high throughput applications. PMID:20563298

Hepatitis-Associated Liver Cancer: Gaps and Opportunities to Improve Care

PubMed Central

McMahon, Brian; Block, Timothy; Cohen, Chari; Evans, Alison A.; Hosangadi, Anu; London, W. Thomas; Sherman, Morris

2016-01-01

The global burden of hepatocellular carcinoma (HCC; primary liver cancer) is increasing. HCC is often unaccompanied by clear symptomatology, causing patients to be unaware of their disease. Moreover, effective treatment for those with advanced disease is lacking. As such, effective surveillance and early detection of HCC are essential. However, current screening and surveillance guidelines are not being fully implemented. Some at-risk populations fall outside of the guidelines, and patients who are screened are often not diagnosed at an early enough stage for treatment to be effective. From March 17 to 19, 2015, the Hepatitis B Foundation sponsored a workshop to identify gaps and limitations in current approaches to the detection and treatment of HCC and to define research priorities and opportunities for advocacy. In this Commentary, we summarize areas for further research and action that were discussed throughout the workshop to improve the recognition of liver disease generally, improve the recognition of liver cancer risk, and improve the recognition that screening for HCC makes a life-saving difference. Participants agreed that primary prevention of HCC relies on prevention and treatment of viral hepatitis and other underlying etiologies. Earlier diagnosis (secondary prevention) needs to be substantially improved. Areas for attention include increasing practitioner awareness, better definition of at-risk populations, and improved performance of screening approaches (ultrasound, biomarkers for detection, risk stratification, targeted therapies). The heterogeneous nature of HCC makes it unlikely that a single therapeutic agent will be universally effective. Medical management will benefit from the development of new, targeted treatment approaches. PMID:26626106

Optical demodulation system for digitally encoded suspension array in fluoroimmunoassay

NASA Astrophysics Data System (ADS)

He, Qinghua; Li, Dongmei; He, Yonghong; Guan, Tian; Zhang, Yilong; Shen, Zhiyuan; Chen, Xuejing; Liu, Siyu; Lu, Bangrong; Ji, Yanhong

2017-09-01

A laser-induced breakdown spectroscopy and fluorescence spectroscopy-coupled optical system is reported to demodulate digitally encoded suspension array in fluoroimmunoassay. It takes advantage of the plasma emissions of assembled elemental materials to digitally decode the suspension array, providing a more stable and accurate recognition to target biomolecules. By separating the decoding procedure of suspension array and adsorption quantity calculation of biomolecules into two independent channels, the cross talk between decoding and label signals in traditional methods had been successfully avoided, which promoted the accuracy of both processes and realized more sensitive quantitative detection of target biomolecules. We carried a multiplexed detection of several types of anti-IgG to verify the quantitative analysis performance of the system. A limit of detection of 1.48×10-10 M was achieved, demonstrating the detection sensitivity of the optical demodulation system.

Colorimetric Detection of Small Molecules in Complex Matrixes via Target-Mediated Growth of Aptamer-Functionalized Gold Nanoparticles.

PubMed

Soh, Jun Hui; Lin, Yiyang; Rana, Subinoy; Ying, Jackie Y; Stevens, Molly M

2015-08-04

A versatile and sensitive colorimetric assay that allows the rapid detection of small-molecule targets using the naked eye is demonstrated. The working principle of the assay integrates aptamer-target recognition and the aptamer-controlled growth of gold nanoparticles (Au NPs). Aptamer-target interactions modulate the amount of aptamer strands adsorbed on the surface of aptamer-functionalized Au NPs via desorption of the aptamer strands when target molecules bind with the aptamer. Depending on the resulting aptamer coverage, Au NPs grow into morphologically varied nanostructures, which give rise to different colored solutions. Au NPs with low aptamer coverage grow into spherical NPs, which produce red-colored solutions, whereas Au NPs with high aptamer coverage grow into branched NPs, which produce blue-colored solutions. We achieved visible colorimetric response and nanomolar detection limits for the detection of ochratoxin A (1 nM) in red wine samples, as well as cocaine (1 nM) and 17β-estradiol (0.2 nM) in spiked synthetic urine and saliva, respectively. The detection limits were well within clinically and physiologically relevant ranges, and below the maximum food safety limits. The assay is highly sensitive, specific, and able to detect an array of analytes rapidly without requiring sophisticated equipment, making it relevant for many applications, such as high-throughput drug and clinical screening, food sampling, and diagnostics. Furthermore, the assay is easily adapted as a chip-based platform for rapid and portable target detection.

Fluorescence self-quenching assay for the detection of target collagen sequences using a short probe peptide.

PubMed

Nian, Linge; Hu, Yue; Fu, Caihong; Song, Chen; Wang, Jie; Xiao, Jianxi

2018-01-01

The development of novel assays to detect collagen fragments is of utmost importance for diagnostic, prognostic and therapeutic decisions in various collagen-related diseases, and one essential question is to discover probe peptides that can specifically recognize target collagen sequences. Herein we have developed the fluorescence self-quenching assay as a convenient tool to screen the capability of a series of fluorescent probe peptides of variable lengths to bind with target collagen peptides. We have revealed that the targeting ability of probe peptides is length-dependent, and have discovered a relatively short probe peptide FAM-G(POG) 8 capable to identify the target peptide. We have further demonstrated that fluorescence self-quenching assay together with this short probe peptide can be applied to specifically detect the desired collagen fragment in complex biological media. Fluorescence self-quenching assay provides a powerful new tool to discover effective peptides for the recognition of collagen biomarkers, and it may have great potential to identify probe peptides for various protein biomarkers involved in pathological conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Investigating the potential of multiwalled carbon nanotubes based zinc nanocomposite as a recognition interface towards plant pathogen detection.

PubMed

Tahir, Muhammad Ali; Hameed, Sadaf; Munawar, Anam; Amin, Imran; Mansoor, Shahid; Khan, Waheed S; Bajwa, Sadia Zafar

2017-11-01

The emergence of nanotechnology has opened new horizons for constructing efficient recognition interfaces. This is the first report where the potential of a multiwalled carbon nanotube based zinc nanocomposite (MWCNTs-Zn NPs) investigated for the detection of an agricultural pathogen i.e. Chili leaf curl betasatellite (ChLCB). Atomic force microscope analyses revealed the presence of multiwalled carbon nanotubes (MWCNTs) having a diameter of 50-100nm with zinc nanoparticles (Zn-NPs) of 25-500nm. In this system, these bunches of Zn-NPs anchored along the whole lengths of MWCNTs were used for the immobilization of probe DNA strands. The electrochemical performance of DNA biosensor was assessed in the absence and presence of the complementary DNA during cyclic and differential pulse voltammetry scans. Target binding events occurring on the interface surface patterned with single-stranded DNA was quantitatively translated into electrochemical signals due to hybridization process. In the presence of complementary target DNA, as the result of duplex formation, there was a decrease in the peak current from 1.89×10 -04 to 5.84×10 -05 A. The specificity of this electrochemical DNA biosensor was found to be three times as compared to non-complementary DNA. This material structuring technique can be extended to design interfaces for the recognition of the other plant viruses and biomolecules. Copyright © 2017 Elsevier B.V. All rights reserved.

Real-time road detection in infrared imagery

NASA Astrophysics Data System (ADS)

Andre, Haritini E.; McCoy, Keith

1990-09-01

Automatic road detection is an important part in many scene recognition applications. The extraction of roads provides a means of navigation and position update for remotely piloted vehicles or autonomous vehicles. Roads supply strong contextual information which can be used to improve the performance of automatic target recognition (ATh) systems by directing the search for targets and adjusting target classification confidences. This paper will describe algorithmic techniques for labeling roads in high-resolution infrared imagery. In addition, realtime implementation of this structural approach using a processor array based on the Martin Marietta Geometric Arithmetic Parallel Processor (GAPPTh) chip will be addressed. The algorithm described is based on the hypothesis that a road consists of pairs of line segments separated by a distance "d" with opposite gradient directions (antiparallel). The general nature of the algorithm, in addition to its parallel implementation in a single instruction, multiple data (SIMD) machine, are improvements to existing work. The algorithm seeks to identify line segments meeting the road hypothesis in a manner that performs well, even when the side of the road is fragmented due to occlusion or intersections. The use of geometrical relationships between line segments is a powerful yet flexible method of road classification which is independent of orientation. In addition, this approach can be used to nominate other types of objects with minor parametric changes.

Goldstone field test activities: Target search

NASA Technical Reports Server (NTRS)

Tarter, J.

1986-01-01

In March of this year prototype SETI equipment was installed at DSS13, the 26 meter research and development antenna at NASA's Goldstone complex of satellite tracking dishes. The SETI equipment will remain at this site at least through the end of the summer so that the hardware and software developed for signal detection and recognition can be fully tested in a dynamic observatory environment. The field tests are expected to help understand which strategies for observing and which signal recognition algorithms perform best in the presence of strong man-made interfering signals (RFI) and natural astronomical sources.

Research on autonomous identification of airport targets based on Gabor filtering and Radon transform

NASA Astrophysics Data System (ADS)

Yi, Juan; Du, Qingyu; Zhang, Hong jiang; Zhang, Yao lei

2017-11-01

Target recognition is a leading key technology in intelligent image processing and application development at present, with the enhancement of computer processing ability, autonomous target recognition algorithm, gradually improve intelligence, and showed good adaptability. Taking the airport target as the research object, analysis the airport layout characteristics, construction of knowledge model, Gabor filter and Radon transform based on the target recognition algorithm of independent design, image processing and feature extraction of the airport, the algorithm was verified, and achieved better recognition results.

Detecting buried explosive hazards with handheld GPR and deep learning

NASA Astrophysics Data System (ADS)

Besaw, Lance E.

2016-05-01

Buried explosive hazards (BEHs), including traditional landmines and homemade improvised explosives, have proven difficult to detect and defeat during and after conflicts around the world. Despite their various sizes, shapes and construction material, ground penetrating radar (GPR) is an excellent phenomenology for detecting BEHs due to its ability to sense localized differences in electromagnetic properties. Handheld GPR detectors are common equipment for detecting BEHs because of their flexibility (in part due to the human operator) and effectiveness in cluttered environments. With modern digital electronics and positioning systems, handheld GPR sensors can sense and map variation in electromagnetic properties while searching for BEHs. Additionally, large-scale computers have demonstrated an insatiable appetite for ingesting massive datasets and extracting meaningful relationships. This is no more evident than the maturation of deep learning artificial neural networks (ANNs) for image and speech recognition now commonplace in industry and academia. This confluence of sensing, computing and pattern recognition technologies offers great potential to develop automatic target recognition techniques to assist GPR operators searching for BEHs. In this work deep learning ANNs are used to detect BEHs and discriminate them from harmless clutter. We apply these techniques to a multi-antennae, handheld GPR with centimeter-accurate positioning system that was used to collect data over prepared lanes containing a wide range of BEHs. This work demonstrates that deep learning ANNs can automatically extract meaningful information from complex GPR signatures, complementing existing GPR anomaly detection and classification techniques.

EOID Evaluation and Automated Target Recognition

DTIC Science & Technology

2002-09-30

Electro - Optic IDentification (EOID) sensors into shallow water littoral zone minehunting systems on towed, remotely operated, and autonomous platforms. These downlooking laser-based sensors operate at unparalleled standoff ranges in visible wavelengths to image and identify mine-like objects (MLOs) that have been detected through other sensing means such as magnetic induction and various modes of acoustic imaging. Our long term goal is to provide a robust automated target cueing and identification capability for use with these imaging sensors. It is also our goal to assist

EOID Evaluation and Automated Target Recognition

DTIC Science & Technology

2001-09-30

Electro - Optic IDentification (EOID) sensors into shallow water littoral zone minehunting systems on towed, remotely operated, and autonomous platforms. These downlooking laser-based sensors operate at unparalleled standoff ranges in visible wavelengths to image and identify mine-like objects that have been detected through other sensing means such as magnetic induction and various modes of acoustic imaging. Our long term goal is to provide a robust automated target cueing and identification capability for use with these imaging sensors. It is also our goal to assist the

Single-Stranded γPNAs for In Vivo Site-Specific Genome Editing via Watson-Crick Recognition

PubMed Central

Bahal, Raman; Quijano, Elias; McNeer, Nicole Ali; Liu, Yanfeng; Bhunia, Dinesh C.; López-Giráldez, Francesco; Fields, Rachel J.; Saltzman, W. Mark; Ly, Danith H.; Glazer, Peter M.

2014-01-01

Triplex-forming peptide nucleic acids (PNAs) facilitate gene editing by stimulating recombination of donor DNAs within genomic DNA via site-specific formation of altered helical structures that further stimulate DNA repair. However, PNAs designed for triplex formation are sequence restricted to homopurine sites. Herein we describe a novel strategy where next generation single-stranded gamma PNAs (γPNAs) containing miniPEG substitutions at the gamma position can target genomic DNA in mouse bone marrow at mixed-sequence sites to induce targeted gene editing. In addition to enhanced binding, γPNAs confer increased solubility and improved formulation into poly(lactic-co-glycolic acid) (PLGA) nanoparticles for efficient intracellular delivery. Single-stranded γPNAs induce targeted gene editing at frequencies of 0.8% in mouse bone marrow cells treated ex vivo and 0.1% in vivo via IV injection, without detectable toxicity. These results suggest that γPNAs may provide a new tool for induced gene editing based on Watson-Crick recognition without sequence restriction. PMID:25174576

Single-stranded γPNAs for in vivo site-specific genome editing via Watson-Crick recognition.

PubMed

Bahal, Raman; Quijano, Elias; McNeer, Nicole A; Liu, Yanfeng; Bhunia, Dinesh C; Lopez-Giraldez, Francesco; Fields, Rachel J; Saltzman, William M; Ly, Danith H; Glazer, Peter M

2014-01-01

Triplex-forming peptide nucleic acids (PNAs) facilitate gene editing by stimulating recombination of donor DNAs within genomic DNA via site-specific formation of altered helical structures that further stimulate DNA repair. However, PNAs designed for triplex formation are sequence restricted to homopurine sites. Herein we describe a novel strategy where next generation single-stranded gamma PNAs (γPNAs) containing miniPEG substitutions at the gamma position can target genomic DNA in mouse bone marrow at mixed-sequence sites to induce targeted gene editing. In addition to enhanced binding, γPNAs confer increased solubility and improved formulation into poly(lactic-co-glycolic acid) (PLGA) nanoparticles for efficient intracellular delivery. Single-stranded γPNAs induce targeted gene editing at frequencies of 0.8% in mouse bone marrow cells treated ex vivo and 0.1% in vivo via IV injection, without detectable toxicity. These results suggest that γPNAs may provide a new tool for induced gene editing based on Watson-Crick recognition without sequence restriction.

Molecular Machine Powered Surface Programmatic Chain Reaction for Highly Sensitive Electrochemical Detection of Protein.

PubMed

Zhu, Jing; Gan, Haiying; Wu, Jie; Ju, Huangxian

2018-04-17

A bipedal molecular machine powered surface programmatic chain reaction was designed for electrochemical signal amplification and highly sensitive electrochemical detection of protein. The bipedal molecular machine was built through aptamer-target specific recognition for the binding of one target protein with two DNA probes, which hybridized with surface-tethered hairpin DNA 1 (H1) via proximity effect to expose the prelocked toehold domain of H1 for the hybridization of ferrocene-labeled hairpin DNA 2 (H2-Fc). The toehold-mediated strand displacement reaction brought the electrochemical signal molecule Fc close to the electrode and meanwhile released the bipedal molecular machine to traverse the sensing surface by the surface programmatic chain reaction. Eventually, a large number of duplex structures of H1-H2 with ferrocene groups facing to the electrode were formed on the sensor surface to generate an amplified electrochemical signal. Using thrombin as a model target, this method showed a linear detection range from 2 pM to 20 nM with a detection limit of 0.76 pM. The proposed detection strategy was enzyme-free and allowed highly sensitive and selective detection of a variety of protein targets by using corresponding DNA-based affinity probes, showing potential application in bioanalysis.

Direct ultrasensitive electrochemical biosensing of pathogenic DNA using homogeneous target-initiated transcription amplification

PubMed Central

Yan, Yurong; Ding, Shijia; Zhao, Dan; Yuan, Rui; Zhang, Yuhong; Cheng, Wei

2016-01-01

Sensitive and specific methodologies for detection of pathogenic gene at the point-of-care are still urgent demands in rapid diagnosis of infectious diseases. This work develops a simple and pragmatic electrochemical biosensing strategy for ultrasensitive and specific detection of pathogenic nucleic acids directly by integrating homogeneous target-initiated transcription amplification (HTITA) with interfacial sensing process in single analysis system. The homogeneous recognition and specific binding of target DNA with the designed hairpin probe triggered circular primer extension reaction to form DNA double-strands which contained T7 RNA polymerase promoter and served as templates for in vitro transcription amplification. The HTITA protocol resulted in numerous single-stranded RNA products which could synchronously hybridized with the detection probes and immobilized capture probes for enzyme-amplified electrochemical detection on the biosensor surface. The proposed electrochemical biosensing strategy showed very high sensitivity and selectivity for target DNA with a dynamic response range from 1 fM to 100 pM. Using salmonella as a model, the established strategy was successfully applied to directly detect invA gene from genomic DNA extract. This proposed strategy presented a simple, pragmatic platform toward ultrasensitive nucleic acids detection and would become a versatile and powerful tool for point-of-care pathogen identification. PMID:26729209

Direct ultrasensitive electrochemical biosensing of pathogenic DNA using homogeneous target-initiated transcription amplification

NASA Astrophysics Data System (ADS)

Yan, Yurong; Ding, Shijia; Zhao, Dan; Yuan, Rui; Zhang, Yuhong; Cheng, Wei

2016-01-01

Sensitive and specific methodologies for detection of pathogenic gene at the point-of-care are still urgent demands in rapid diagnosis of infectious diseases. This work develops a simple and pragmatic electrochemical biosensing strategy for ultrasensitive and specific detection of pathogenic nucleic acids directly by integrating homogeneous target-initiated transcription amplification (HTITA) with interfacial sensing process in single analysis system. The homogeneous recognition and specific binding of target DNA with the designed hairpin probe triggered circular primer extension reaction to form DNA double-strands which contained T7 RNA polymerase promoter and served as templates for in vitro transcription amplification. The HTITA protocol resulted in numerous single-stranded RNA products which could synchronously hybridized with the detection probes and immobilized capture probes for enzyme-amplified electrochemical detection on the biosensor surface. The proposed electrochemical biosensing strategy showed very high sensitivity and selectivity for target DNA with a dynamic response range from 1 fM to 100 pM. Using salmonella as a model, the established strategy was successfully applied to directly detect invA gene from genomic DNA extract. This proposed strategy presented a simple, pragmatic platform toward ultrasensitive nucleic acids detection and would become a versatile and powerful tool for point-of-care pathogen identification.

Direct ultrasensitive electrochemical biosensing of pathogenic DNA using homogeneous target-initiated transcription amplification.

PubMed

Yan, Yurong; Ding, Shijia; Zhao, Dan; Yuan, Rui; Zhang, Yuhong; Cheng, Wei

2016-01-05

Sensitive and specific methodologies for detection of pathogenic gene at the point-of-care are still urgent demands in rapid diagnosis of infectious diseases. This work develops a simple and pragmatic electrochemical biosensing strategy for ultrasensitive and specific detection of pathogenic nucleic acids directly by integrating homogeneous target-initiated transcription amplification (HTITA) with interfacial sensing process in single analysis system. The homogeneous recognition and specific binding of target DNA with the designed hairpin probe triggered circular primer extension reaction to form DNA double-strands which contained T7 RNA polymerase promoter and served as templates for in vitro transcription amplification. The HTITA protocol resulted in numerous single-stranded RNA products which could synchronously hybridized with the detection probes and immobilized capture probes for enzyme-amplified electrochemical detection on the biosensor surface. The proposed electrochemical biosensing strategy showed very high sensitivity and selectivity for target DNA with a dynamic response range from 1 fM to 100 pM. Using salmonella as a model, the established strategy was successfully applied to directly detect invA gene from genomic DNA extract. This proposed strategy presented a simple, pragmatic platform toward ultrasensitive nucleic acids detection and would become a versatile and powerful tool for point-of-care pathogen identification.

Detecting and visualizing weak signatures in hyperspectral data

NASA Astrophysics Data System (ADS)

MacPherson, Duncan James

This thesis evaluates existing techniques for detecting weak spectral signatures from remotely sensed hyperspectral data. Algorithms are presented that successfully detect hard-to-find 'mystery' signatures in unknown cluttered backgrounds. The term 'mystery' is used to describe a scenario where the spectral target and background endmembers are unknown. Sub-Pixel analysis and background suppression are used to find deeply embedded signatures which can be less than 10% of the total signal strength. Existing 'mystery target' detection algorithms are derived and compared. Several techniques are shown to be superior both visually and quantitatively. Detection performance is evaluated using confidence metrics that are developed. A multiple algorithm approach is shown to improve detection confidence significantly. Although the research focuses on remote sensing applications, the algorithms presented can be applied to a wide variety of diverse fields such as medicine, law enforcement, manufacturing, earth science, food production, and astrophysics. The algorithms are shown to be general and can be applied to both the reflective and emissive parts of the electromagnetic spectrum. The application scope is a broad one and the final results open new opportunities for many specific applications including: land mine detection, pollution and hazardous waste detection, crop abundance calculations, volcanic activity monitoring, detecting diseases in food, automobile or airplane target recognition, cancer detection, mining operations, extracting galactic gas emissions, etc.

Target recognition in passive terahertz image of human body

NASA Astrophysics Data System (ADS)

Zhao, Ran; Zhao, Yuan-meng; Deng, Chao; Zhang, Cun-lin; Li, Yue

2014-11-01

THz radiation can penetrate through many nonpolar dielectric materials and can be used for nondestructive/noninvasive sensing and imaging of targets under nonpolar, nonmetallic covers or containers. Thus using THz systems to "see through" concealing barriers (i.e. packaging, corrugated cardboard, clothing) has been proposed as a new security screening method. Objects that can be detected by THz include concealed weapons, explosives, and chemical agents under clothing. Passive THz imaging system can detect THz wave from human body without transmit any electromagnetic wave, and the suspicious objects will become visible because the THz wave is blocked by this items. We can find out whether or not someone is carrying dangerous objects through this image. In this paper, the THz image enhancement, segmentation and contour extraction algorithms were studied to achieve effective target image detection. First, the terahertz images are enhanced and their grayscales are stretched. Then we apply global threshold segmentation to extract the target, and finally the targets are marked on the image. Experimental results showed that the algorithm proposed in this paper can extract and mark targets effectively, so that people can identify suspicious objects under clothing quickly. The algorithm can significantly improve the usefulness of the terahertz security apparatus.

The Automatic Recognition of the Abnormal Sky-subtraction Spectra Based on Hadoop

NASA Astrophysics Data System (ADS)

An, An; Pan, Jingchang

2017-10-01

The skylines, superimposing on the target spectrum as a main noise, If the spectrum still contains a large number of high strength skylight residuals after sky-subtraction processing, it will not be conducive to the follow-up analysis of the target spectrum. At the same time, the LAMOST can observe a quantity of spectroscopic data in every night. We need an efficient platform to proceed the recognition of the larger numbers of abnormal sky-subtraction spectra quickly. Hadoop, as a distributed parallel data computing platform, can deal with large amounts of data effectively. In this paper, we conduct the continuum normalization firstly and then a simple and effective method will be presented to automatic recognize the abnormal sky-subtraction spectra based on Hadoop platform. Obtain through the experiment, the Hadoop platform can implement the recognition with more speed and efficiency, and the simple method can recognize the abnormal sky-subtraction spectra and find the abnormal skyline positions of different residual strength effectively, can be applied to the automatic detection of abnormal sky-subtraction of large number of spectra.

Synthesis and bio-applications of targeted magnetic-fluorescent composite nanoparticles

NASA Astrophysics Data System (ADS)

Xia, Hui; Tong, Ruijie; Song, Yanling; Xiong, Fang; Li, Jiman; Wang, Shichao; Fu, Huihui; Wen, Jirui; Li, Dongze; Zeng, Ye; Zhao, Zhiwei; Wu, Jiang

2017-04-01

Magnetic-fluorescent nanoparticles have a tremendous potential in biology. As the benefits of these materials gained recognition, increasing attention has been given to the conjugation of magnetic-fluorescent nanoparticles with targeting ligands. The magnetic and fluorescent properties of nanoparticles offer several functionalities, including imaging, separation, and visualization, while the presence of a targeting ligand allows for selective cell and tissue targeting. In this review, methods for the synthesis of targeted magnetic-fluorescent nanoparticles are explored, and recent applications of these nanocomposites to the detection and separation of biomolecules, fluorescent and magnetic resonance imaging, and cancer diagnosis and treatment will be summarized. As these materials are further optimized, targeted magnetic-fluorescent nanoparticles hold great promise for the diagnosis and treatment of some diseases.

[Study on the hepatocytic cell targetability of liposomes].

PubMed

Hou, Xin-pu; Wang, Li; Wang, Xiang-tao; Li, Sha

2003-02-01

To target for hepatocytic cell, liposomes was modified by special ligand. Sterically stabilized liposomes (SSL) was conjugated with asialofeticin (AF), the ligand of asialoglycoprotein receptor (ASGP-R) of hepatocyte. ASGP-R-BLM is the ASGP-R reconstructed on bilayer lipid membrane (BLM). The recognition reaction between AF-SSL and ASGP-R-BLM can be monitored by the varieties of membrane electrical parameters. The targetability of AF-SSL mediated to hepatocyte was detected by radioisotopic labeled in vitro and in vivo. The therapeutic effect of antihepatocarcinoma was observed also. The lifetime of ASGP-R-BLM decreased with the added amount of AF-SSL. It was demonstrated that there was recognition reaction between AF-SSL and ASGP-R-BLM. The combination of AF-SSL with hepatocyte was significantly higher than that of SSL without AF-modified in vitro and in vivo. The survival time of rat for AF-SSL carriered ADM (adriamycin) group was much longer and the toxicities on heart, kidney and lung were lower than those SSL carried ADM group. It is possible to actively target the cell with specific receptor by ligand modified liposomes. The result prvide scientific basis of hepatocyte targeted liposomes.

Automated target recognition using passive radar and coordinated flight models

NASA Astrophysics Data System (ADS)

Ehrman, Lisa M.; Lanterman, Aaron D.

2003-09-01

Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are particularly attractive since they allow receivers to operate without emitting energy, rendering them covert. Many existing passive radar systems estimate the locations and velocities of targets. This paper focuses on adding an automatic target recognition (ATR) component to such systems. Our approach to ATR compares the Radar Cross Section (RCS) of targets detected by a passive radar system to the simulated RCS of known targets. To make the comparison as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. The estimated positions become inputs for an algorithm that uses a coordinated flight model to compute probable aircraft orientation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of several potential target classes as they execute the estimated maneuvers. The RCS is then scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. The Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, so that the RCS can be further scaled. The Rician model compares the RCS of the illuminated aircraft with those of the potential targets. This comparison results in target identification.

Object detection from images obtained through underwater turbulence medium

NASA Astrophysics Data System (ADS)

Furhad, Md. Hasan; Tahtali, Murat; Lambert, Andrew

2017-09-01

Imaging through underwater experiences severe distortions due to random fluctuations of temperature and salinity in water, which produces underwater turbulence through diffraction limited blur. Lights reflecting from objects perturb and attenuate contrast, making the recognition of objects of interest difficult. Thus, the information available for detecting underwater objects of interest becomes a challenging task as they have inherent confusion among the background, foreground and other image properties. In this paper, a saliency-based approach is proposed to detect the objects acquired through an underwater turbulent medium. This approach has drawn attention among a wide range of computer vision applications, such as image retrieval, artificial intelligence, neuro-imaging and object detection. The image is first processed through a deblurring filter. Next, a saliency technique is used on the image for object detection. In this step, a saliency map that highlights the target regions is generated and then a graph-based model is proposed to extract these target regions for object detection.

Evaluation of Malware Target Recognition Deployed in a Cloud-Based Fileserver Environment

DTIC Science & Technology

2012-03-01

many of these detection techniques could be evaded with simple obfuscation. Kolter and Maloof extend Schultz’s research in [KM04] and [KM06]. Their...69 [KM04] Jeremy Z. Kolter and Marcus A. Maloof. Learning to detect malicious executables in the wild. In Proceedings of the tenth ACM SIGKDD...international conference on Knowledge discovery and data mining, KDD ’04, pages 470–478, New York, NY, USA, 2004. ACM. [KM06] J.Z. Kolter and M.A. Maloof

Active Multimodal Sensor System for Target Recognition and Tracking

PubMed Central

Zhang, Guirong; Zou, Zhaofan; Liu, Ziyue; Mao, Jiansen

2017-01-01

High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent diversity of the available target-related data. This paper proposes an active multimodal sensor system for target recognition and tracking, consisting of a visible, an infrared, and a hyperspectral sensor. The system makes full use of its multisensor information collection abilities; furthermore, it can actively control different sensors to collect additional data, according to the needs of the real-time target recognition and tracking processes. This level of integration between hardware collection control and data processing is experimentally shown to effectively improve the accuracy and robustness of the target recognition and tracking system. PMID:28657609

The relationship study between image features and detection probability based on psychology experiments

NASA Astrophysics Data System (ADS)

Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

2011-04-01

Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

Responsive hybrid inorganic-organic system derived from lanthanide luminescence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Zhan; Zheng, Yuhui, E-mail: yhzheng78@scnu.edu.cn; Jiang, Lasheng

2016-05-15

Highlights: • A novel covalent hybrid material was used to detect hemoglobin. • All the recognition experiments were performed in buffer solution. • Porous nano-structures was extensively studied for the recognition. - Abstract: Terbium ions were incorporated into new organic-inorganic matrices to achieve intense green emissions. Hemoglobin (HB) interactions lead to dramatic changes in the luminescence emission intensities. Infrared spectra, morphological studies and photoluminescence give information for the speciation and process of hemoglobin additions. The porous material has a large specific surface area of 351 cm{sup 2}/g and the detection limit for HB (0.7 μM) was much lower than itsmore » physical doped material (8 μM). This promising hybrid material will lead to the design of versatile optical probes that are efficiently responding to the external targets.« less

Aging and Emotion Recognition: Not Just a Losing Matter

PubMed Central

Sze, Jocelyn A.; Goodkind, Madeleine S.; Gyurak, Anett; Levenson, Robert W.

2013-01-01

Past studies on emotion recognition and aging have found evidence of age-related decline when emotion recognition was assessed by having participants detect single emotions depicted in static images of full or partial (e.g., eye region) faces. These tests afford good experimental control but do not capture the dynamic nature of real-world emotion recognition, which is often characterized by continuous emotional judgments and dynamic multi-modal stimuli. Research suggests that older adults often perform better under conditions that better mimic real-world social contexts. We assessed emotion recognition in young, middle-aged, and older adults using two traditional methods (single emotion judgments of static images of faces and eyes) and an additional method in which participants made continuous emotion judgments of dynamic, multi-modal stimuli (videotaped interactions between young, middle-aged, and older couples). Results revealed an age by test interaction. Largely consistent with prior research, we found some evidence that older adults performed worse than young adults when judging single emotions from images of faces (for sad and disgust faces only) and eyes (for older eyes only), with middle-aged adults falling in between. In contrast, older adults did better than young adults on the test involving continuous emotion judgments of dyadic interactions, with middle-aged adults falling in between. In tests in which target stimuli differed in age, emotion recognition was not facilitated by an age match between participant and target. These findings are discussed in terms of theoretical and methodological implications for the study of aging and emotional processing. PMID:22823183

Robust and Effective Component-based Banknote Recognition for the Blind

PubMed Central

Hasanuzzaman, Faiz M.; Yang, Xiaodong; Tian, YingLi

2012-01-01

We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users. PMID:22661884

A robust algorithm for automated target recognition using precomputed radar cross sections

NASA Astrophysics Data System (ADS)

Ehrman, Lisa M.; Lanterman, Aaron D.

2004-09-01

Passive radar is an emerging technology that offers a number of unique benefits, including covert operation. Many such systems are already capable of detecting and tracking aircraft. The goal of this work is to develop a robust algorithm for adding automated target recognition (ATR) capabilities to existing passive radar systems. In previous papers, we proposed conducting ATR by comparing the precomputed RCS of known targets to that of detected targets. To make the precomputed RCS as accurate as possible, a coordinated flight model is used to estimate aircraft orientation. Once the aircraft's position and orientation are known, it is possible to determine the incident and observed angles on the aircraft, relative to the transmitter and receiver. This makes it possible to extract the appropriate radar cross section (RCS) from our simulated database. This RCS is then scaled to account for propagation losses and the receiver's antenna gain. A Rician likelihood model compares these expected signals from different targets to the received target profile. We have previously employed Monte Carlo runs to gauge the probability of error in the ATR algorithm; however, generation of a statistically significant set of Monte Carlo runs is computationally intensive. As an alternative to Monte Carlo runs, we derive the relative entropy (also known as Kullback-Liebler distance) between two Rician distributions. Since the probability of Type II error in our hypothesis testing problem can be expressed as a function of the relative entropy via Stein's Lemma, this provides us with a computationally efficient method for determining an upper bound on our algorithm's performance. It also provides great insight into the types of classification errors we can expect from our algorithm. This paper compares the numerically approximated probability of Type II error with the results obtained from a set of Monte Carlo runs.

Aggression and courtship in Drosophila: pheromonal communication and sex recognition.

PubMed

Fernández, María Paz; Kravitz, Edward A

2013-11-01

Upon encountering a conspecific in the wild, males have to rapidly detect, integrate and process the most relevant signals to evoke an appropriate behavioral response. Courtship and aggression are the most important social behaviors in nature for procreation and survival: for males, making the right choice between the two depends on the ability to identify the sex of the other individual. In flies as in most species, males court females and attack other males. Although many sensory modalities are involved in sex recognition, chemosensory communication mediated by specific molecules that serve as pheromones plays a key role in helping males distinguish between courtship and aggression targets. The chemosensory signals used by flies include volatile and non-volatile compounds, detected by the olfactory and gustatory systems. Recently, several putative olfactory and gustatory receptors have been identified that play key roles in sex recognition, allowing investigators to begin to map the neuronal circuits that convey this sensory information to higher processing centers in the brain. Here, we describe how Drosophila melanogaster males use taste and smell to make correct behavioral choices.

Aggression and Courtship in Drosophila: Pheromonal Communication and Sex Recognition

PubMed Central

Fernández, María Paz; Kravitz, Edward A.

2013-01-01

Upon encountering a conspecific in the wild, males have to rapidly detect, integrate and process the most relevant signals to evoke an appropriate behavioral response. Courtship and aggression are the most important social behaviors in nature for procreation and survival: for males, making the right choice between the two depends on the ability to identify the sex of the other individual. In flies as in most species, males court females and attack other males. Although many sensory modalities are involved in sex recognition, chemosensory communication mediated by specific molecules that serve as pheromones plays a key role in helping males distinguish between courtship and aggression targets. The chemosensory signals used by flies include volatile and non-volatile compounds, detected by the olfactory and gustatory systems. Recently, several putative olfactory and gustatory receptors have been identified that play key roles in sex recognition, allowing investigators to begin to map the neuronal circuits that convey this sensory information to higher processing centers in the brain. Here, we describe how Drosophila melanogaster males use taste and smell to make correct behavioral choices. PMID:24043358

Current Status and Future Prospects for Aptamer-Based Mycotoxin Detection.

PubMed

Ruscito, Annamaria; Smith, McKenzie; Goudreau, Daniel N; DeRosa, Maria C

2016-07-01

Aptamers are single-stranded oligonucleotides with the ability to bind tightly and selectively to a target analyte. High-affinity and specific aptamers for a variety of mycotoxins have been reported over the past decade. Increasingly, these molecular recognition elements are finding applications in biosensors and assays for the detection of mycotoxins in a variety of complex matrixes. This review article highlights the mycotoxin aptamers that are available for mycotoxin detection and the array of biosensing platforms into which they have been incorporated. Key advantages that aptamers have over analogous technology, and areas in which these advantages may be applied for the benefit of practical mycotoxin detection, are also discussed.

Detection of concealed explosives at stand-off distances using wide band swept millimetre waves

NASA Astrophysics Data System (ADS)

Andrews, David A.; Rezgui, Nacer D.; Smith, Sarah E.; Bowring, Nicholas; Southgate, Matthew; Baker, John G.

2008-10-01

Millimetre waves in the range 20 to 110 GHz have been used to detect the presence and thickness of dielectric materials, such as explosives, by measuring the frequency response of the return signal. Interference between the reflected signals from the front and back surfaces of the dielectric provides a characteristic frequency variation in the return signal, which may be processed to yield its optical depth [Bowring et al, Meas. Sci. Technol. 19, 024004 (2008)]. The depth resolution depends on the sweep bandwidth, which is typically 10 to 30 GHz. By using super-heterodyne detection the range of the object can also be determined, which enables a signal from a target, such as a suicide bomber to be extracted from background clutter. Using millimetre wave optics only a small area of the target is illuminated at a time, thus reducing interference from different parts of a human target. Results are presented for simulated explosive materials with water or human backing at stand-off distances. A method of data analysis that involves pattern recognition enables effective differentiation of target types.

An automated data exploitation system for airborne sensors

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2014-06-01

Advanced wide area persistent surveillance (WAPS) sensor systems on manned or unmanned airborne vehicles are essential for wide-area urban security monitoring in order to protect our people and our warfighter from terrorist attacks. Currently, human (imagery) analysts process huge data collections from full motion video (FMV) for data exploitation and analysis (real-time and forensic), providing slow and inaccurate results. An Automated Data Exploitation System (ADES) is urgently needed. In this paper, we present a recently developed ADES for airborne vehicles under heavy urban background clutter conditions. This system includes four processes: (1) fast image registration, stabilization, and mosaicking; (2) advanced non-linear morphological moving target detection; (3) robust multiple target (vehicles, dismounts, and human) tracking (up to 100 target tracks); and (4) moving or static target/object recognition (super-resolution). Test results with real FMV data indicate that our ADES can reliably detect, track, and recognize multiple vehicles under heavy urban background clutters. Furthermore, our example shows that ADES as a baseline platform can provide capability for vehicle abnormal behavior detection to help imagery analysts quickly trace down potential threats and crimes.

Assessing the performance of a motion tracking system based on optical joint transform correlation

NASA Astrophysics Data System (ADS)

Elbouz, M.; Alfalou, A.; Brosseau, C.; Ben Haj Yahia, N.; Alam, M. S.

2015-08-01

We present an optimized system specially designed for the tracking and recognition of moving subjects in a confined environment (such as an elderly remaining at home). In the first step of our study, we use a VanderLugt correlator (VLC) with an adapted pre-processing treatment of the input plane and a postprocessing of the correlation plane via a nonlinear function allowing us to make a robust decision. The second step is based on an optical joint transform correlation (JTC)-based system (NZ-NL-correlation JTC) for achieving improved detection and tracking of moving persons in a confined space. The proposed system has been found to have significantly superior discrimination and robustness capabilities allowing to detect an unknown target in an input scene and to determine the target's trajectory when this target is in motion. This system offers robust tracking performance of a moving target in several scenarios, such as rotational variation of input faces. Test results obtained using various real life video sequences show that the proposed system is particularly suitable for real-time detection and tracking of moving objects.

Lectin functionalized ZnO nanoarrays as a 3D nano-biointerface for bacterial detection.

PubMed

Zheng, Laibao; Wan, Yi; Qi, Peng; Sun, Yan; Zhang, Dun; Yu, Liangmin

2017-05-15

The detection of pathogenic bacteria is essential in various fields, such as food safety, water environmental analysis, or clinical diagnosis. Although rapid and selective techniques have been achieved based on the fast and specific binding of recognitions elements and target, the sensitive detection of bacterial pathogens was limited by their low targets-binding efficiency. The three-dimensional (3D) nano-biointerface, compared with the two-dimensional (2D) flat substrate, has a much higher binding capacity, which can offer more reactive sites to bind with bacterial targets, resulting in a great improvement of detection sensitivity. Herein, a lectin functionalized ZnO nanorod (ZnO-NR) array has been fabricated and employed as a 3D nano-biointerface for Escherichia coli (E. coli) capture and detection by multivalent binding of concanavalin A (ConA) with polysaccharides on the cellular surface of E. coli. The 3D lectin functionalized ZnO-NR array-based assay shows reasonable detection limit and efficiently expanded linear range (1.0×10 3 to 1.0×10 7 cfumL -1 ) for pathogen detection. The platform has a potential for further applications and provides an excellent sensitivity approach for detection of pathogenic bacteria. Copyright © 2017 Elsevier B.V. All rights reserved.

Universal, colorimetric microRNA detection strategy based on target-catalyzed toehold-mediated strand displacement reaction

NASA Astrophysics Data System (ADS)

Park, Yeonkyung; Lee, Chang Yeol; Kang, Shinyoung; Kim, Hansol; Park, Ki Soo; Park, Hyun Gyu

2018-02-01

In this work, we developed a novel, label-free, and enzyme-free strategy for the colorimetric detection of microRNA (miRNA), which relies on a target-catalyzed toehold-mediated strand displacement (TMSD) reaction. The system employs a detection probe that specifically binds to the target miRNA and sequentially releases a catalyst strand (CS) intended to trigger the subsequent TMSD reaction. Thus, the presence of target miRNA releases the CS that mediates the formation of an active G-quadruplex DNAzyme which is initially caged and inactivated by a blocker strand. In addition, a fuel strand that is supplemented for the recycling of the CS promotes another TMSD reaction, consequently generating a large number of active G-quadruplex DNAzymes. As a result, a distinct colorimetric signal is produced by the ABTS oxidation promoted by the peroxidase mimicking activity of the released G-quadruplex DNAzymes. Based on this novel strategy, we successfully detected miR-141, a promising biomarker for human prostate cancer, with high selectivity. The diagnostic capability of this system was also demonstrated by reliably determining target miR-141 in human serum, showing its great potential towards real clinical applications. Importantly, the proposed approach is composed of separate target recognition and signal transduction modules. Thus, it could be extended to analyze different target miRNAs by simply redesigning the detection probe while keeping the same signal transduction module as a universal signal amplification unit, which was successfully demonstrated by analyzing another target miRNA, let-7d.

Universal, colorimetric microRNA detection strategy based on target-catalyzed toehold-mediated strand displacement reaction.

PubMed

Park, Yeonkyung; Lee, Chang Yeol; Kang, Shinyoung; Kim, Hansol; Park, Ki Soo; Park, Hyun Gyu

2018-02-23

In this work, we developed a novel, label-free, and enzyme-free strategy for the colorimetric detection of microRNA (miRNA), which relies on a target-catalyzed toehold-mediated strand displacement (TMSD) reaction. The system employs a detection probe that specifically binds to the target miRNA and sequentially releases a catalyst strand (CS) intended to trigger the subsequent TMSD reaction. Thus, the presence of target miRNA releases the CS that mediates the formation of an active G-quadruplex DNAzyme which is initially caged and inactivated by a blocker strand. In addition, a fuel strand that is supplemented for the recycling of the CS promotes another TMSD reaction, consequently generating a large number of active G-quadruplex DNAzymes. As a result, a distinct colorimetric signal is produced by the ABTS oxidation promoted by the peroxidase mimicking activity of the released G-quadruplex DNAzymes. Based on this novel strategy, we successfully detected miR-141, a promising biomarker for human prostate cancer, with high selectivity. The diagnostic capability of this system was also demonstrated by reliably determining target miR-141 in human serum, showing its great potential towards real clinical applications. Importantly, the proposed approach is composed of separate target recognition and signal transduction modules. Thus, it could be extended to analyze different target miRNAs by simply redesigning the detection probe while keeping the same signal transduction module as a universal signal amplification unit, which was successfully demonstrated by analyzing another target miRNA, let-7d.

Advances in Doppler recognition for ground moving target indication

NASA Astrophysics Data System (ADS)

Kealey, Paul G.; Jahangir, Mohammed

2006-05-01

Ground Moving Target Indication (GMTI) radar provides a day/night, all-weather, wide-area surveillance capability to detect moving vehicles and personnel. Current GMTI radar sensors are limited to only detecting and tracking targets. The exploitation of GMTI data would be greatly enhanced by a capability to recognize accurately the detections as significant classes of target. Doppler classification exploits the differential internal motion of targets, e.g. due to the tracks, limbs and rotors. Recently, the QinetiQ Bayesian Doppler classifier has been extended to include a helicopter class in addition to wheeled, tracked and personnel classes. This paper presents the performance for these four classes using a traditional low-resolution GMTI surveillance waveform with an experimental radar system. We have determined the utility of an "unknown output decision" for enhancing the accuracy of the declared target classes. A confidence method has been derived, using a threshold of the difference in certainties, to assign uncertain classifications into an "unknown class". The trade-off between fraction of targets declared and accuracy of the classifier has been measured. To determine the operating envelope of a Doppler classification algorithm requires a detailed understanding of the Signal-to-Noise Ratio (SNR) performance of the algorithm. In this study the SNR dependence of the QinetiQ classifier has been determined.

Autonomous replication of nucleic acids by polymerization/nicking enzyme/DNAzyme cascades for the amplified detection of DNA and the aptamer-cocaine complex.

PubMed

Wang, Fuan; Freage, Lina; Orbach, Ron; Willner, Itamar

2013-09-03

The progressive development of amplified DNA sensors and aptasensors using replication/nicking enzymes/DNAzyme machineries is described. The sensing platforms are based on the tailoring of a DNA template on which the recognition of the target DNA or the formation of the aptamer-substrate complex trigger on the autonomous isothermal replication/nicking processes and the displacement of a Mg(2+)-dependent DNAzyme that catalyzes the generation of a fluorophore-labeled nucleic acid acting as readout signal for the analyses. Three different DNA sensing configurations are described, where in the ultimate configuration the target sequence is incorporated into a nucleic acid blocker structure associated with the sensing template. The target-triggered isothermal autonomous replication/nicking process on the modified template results in the formation of the Mg(2+)-dependent DNAzyme tethered to a free strand consisting of the target sequence. This activates additional template units for the nucleic acid self-replication process, resulting in the ultrasensitive detection of the target DNA (detection limit 1 aM). Similarly, amplified aptamer-based sensing platforms for cocaine are developed along these concepts. The modification of the cocaine-detection template by the addition of a nucleic acid sequence that enables the autonomous secondary coupled activation of a polymerization/nicking machinery and DNAzyme generation path leads to an improved analysis of cocaine (detection limit 10 nM).

Correlation based efficient face recognition and color change detection

NASA Astrophysics Data System (ADS)

Elbouz, M.; Alfalou, A.; Brosseau, C.; Alam, M. S.; Qasmi, S.

2013-01-01

Identifying the human face via correlation is a topic attracting widespread interest. At the heart of this technique lies the comparison of an unknown target image to a known reference database of images. However, the color information in the target image remains notoriously difficult to interpret. In this paper, we report a new technique which: (i) is robust against illumination change, (ii) offers discrimination ability to detect color change between faces having similar shape, and (iii) is specifically designed to detect red colored stains (i.e. facial bleeding). We adopt the Vanderlugt correlator (VLC) architecture with a segmented phase filter and we decompose the color target image using normalized red, green, and blue (RGB), and hue, saturation, and value (HSV) scales. We propose a new strategy to effectively utilize color information in signatures for further increasing the discrimination ability. The proposed algorithm has been found to be very efficient for discriminating face subjects with different skin colors, and those having color stains in different areas of the facial image.

ROBIN: a platform for evaluating automatic target recognition algorithms: I. Overview of the project and presentation of the SAGEM DS competition

NASA Astrophysics Data System (ADS)

Duclos, D.; Lonnoy, J.; Guillerm, Q.; Jurie, F.; Herbin, S.; D'Angelo, E.

2008-04-01

The last five years have seen a renewal of Automatic Target Recognition applications, mainly because of the latest advances in machine learning techniques. In this context, large collections of image datasets are essential for training algorithms as well as for their evaluation. Indeed, the recent proliferation of recognition algorithms, generally applied to slightly different problems, make their comparisons through clean evaluation campaigns necessary. The ROBIN project tries to fulfil these two needs by putting unclassified datasets, ground truths, competitions and metrics for the evaluation of ATR algorithms at the disposition of the scientific community. The scope of this project includes single and multi-class generic target detection and generic target recognition, in military and security contexts. From our knowledge, it is the first time that a database of this importance (several hundred thousands of visible and infrared hand annotated images) has been publicly released. Funded by the French Ministry of Defence (DGA) and by the French Ministry of Research, ROBIN is one of the ten Techno-vision projects. Techno-vision is a large and ambitious government initiative for building evaluation means for computer vision technologies, for various application contexts. ROBIN's consortium includes major companies and research centres involved in Computer Vision R&D in the field of defence: Bertin Technologies, CNES, ECA, DGA, EADS, INRIA, ONERA, MBDA, SAGEM, THALES. This paper, which first gives an overview of the whole project, is focused on one of ROBIN's key competitions, the SAGEM Defence Security database. This dataset contains more than eight hundred ground and aerial infrared images of six different vehicles in cluttered scenes including distracters. Two different sets of data are available for each target. The first set includes different views of each vehicle at close range in a "simple" background, and can be used to train algorithms. The second set contains many views of the same vehicle in different contexts and situations simulating operational scenarios.

Sticky-flares for in situ monitoring of human telomerase RNA in living cells.

PubMed

Wu, Qilong; Liu, Zhengjie; Su, Lei; Han, Guangmei; Liu, Renyong; Zhao, Jun; Zhao, Tingting; Jiang, Changlong; Zhang, Zhongping

2018-05-17

Human telomerase RNA (hTR), a template of telomerase for telomeric repeat synthesis, was used to reflect the telomerase activity and act as a potential target of antitumor therapy. Here, we report a novel DNA-conjugated AuNP probe termed sticky-flares for the in situ detection of intracellular human telomerase RNA. The sticky-flares probe is capable of entering living cells directly without any auxiliary and recognizing the binding domain of human telomerase RNA. On recognition, the fluorophore-modified recognition flares can specifically bind to the target, separate from the sticky-flares and act as a fluorescent reporter to quantify and dynamically profile human telomerase RNA in living cells. We envision that the sticky-flares probe would be a valuable platform to investigate the function and regulation of hTR in antitumor therapy and hTR-related drug invention.

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

PubMed Central

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

Echo scintillation Index affected by cat-eye target's caliber with Cassegrain lens

NASA Astrophysics Data System (ADS)

Shan, Cong-miao; Sun, Hua-yan; Zhao, Yan-zhong; Zheng, Yong-hui

2015-10-01

The optical aperture of cat-eye target has the aperture averaging effect to the active detecting laser of active laser detection system, which can be used to identify optical targets. The echo scintillation characteristics of the transmission-type lens target have been studied in previous work. Discussing the differences of the echo scintillation characteristics between the transmission-type lens target and Cassegrain lens target can be helpful to targets classified. In this paper, the echo scintillation characteristics of Cat-eye target's caliber with Cassegrain lens has been discussed . By using the flashing theory of spherical wave in the weak atmospheric turbulence, the annular aperture filter function and the Kolmogorov power spectrum, the analytic expression of the scintillation index of the cat-eye target echo of the horizontal path two-way transmission was given when the light is normal incidence. Then the impact of turbulence inner and outer scale to the echo scintillation index and the analytic expression of the echo scintillation index at the receiving aperture were presented using the modified Hill spectrum and the modified Von Karman spectrum. Echo scintillation index shows the tendency of decreasing with the target aperture increases and different ratios of the inner and outer aperture diameter show the different echo scintillation index curves. This conclusion has a certain significance for target recognition in the active laser detection system that can largely determine the target type by largely determining the scope of the cat-eye target which depending on echo scintillation index.

Flightspeed Integral Image Analysis Toolkit

NASA Technical Reports Server (NTRS)

Thompson, David R.

2009-01-01

The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles

Evidence for the contribution of a threshold retrieval process to semantic memory.

PubMed

Kempnich, Maria; Urquhart, Josephine A; O'Connor, Akira R; Moulin, Chris J A

2017-10-01

It is widely held that episodic retrieval can recruit two processes: a threshold context retrieval process (recollection) and a continuous signal strength process (familiarity). Conversely the processes recruited during semantic retrieval are less well specified. We developed a semantic task analogous to single-item episodic recognition to interrogate semantic recognition receiver-operating characteristics (ROCs) for a marker of a threshold retrieval process. We fitted observed ROC points to three signal detection models: two models typically used in episodic recognition (unequal variance and dual-process signal detection models) and a novel dual-process recollect-to-reject (DP-RR) signal detection model that allows a threshold recollection process to aid both target identification and lure rejection. Given the nature of most semantic questions, we anticipated the DP-RR model would best fit the semantic task data. Experiment 1 (506 participants) provided evidence for a threshold retrieval process in semantic memory, with overall best fits to the DP-RR model. Experiment 2 (316 participants) found within-subjects estimates of episodic and semantic threshold retrieval to be uncorrelated. Our findings add weight to the proposal that semantic and episodic memory are served by similar dual-process retrieval systems, though the relationship between the two threshold processes needs to be more fully elucidated.

Proximity hybridization-regulated electrogenerated chemiluminescence bioassay of α-fetoprotein via target-induced quenching mechanism.

PubMed

Gao, Hongfang; Wang, Xiaofei; Li, Man; Qi, Honglan; Gao, Qiang; Zhang, Chengxiao

2017-12-15

A proximity hybridization-regulated electrogenerated chemiluminescence (PLA-ECL) bioassay was developed for the detection of α-fetoprotein (AFP) on basis of the sensitization of gold nanoparticles (AuNPs) and target-induced quenching mechanism. Ru(bpy) 3 2+ was used as ECL signal while ferrocene (Fc) was used as ECL quencher. Ru(bpy) 3 2+ was electrostatically adsorbed into the AuNPs/Nafion film prepared by casting the mixture of Nafion and AuNPs onto the surface of glassy carbon electrode (GCE) to form an ECL platform (Ru(bpy) 3 2+ /AuNPs/Nafion/GCE), which displayed strong ECL emissions. A recognition platform was fabricated by self-assembling a capture DNA via thiol-gold bond on the surface of Ru(bpy) 3 2+ /AuNPs/Nafion/GCE. After sandwich immunoassay and proximity hybridization assay among capture DNA, AFP, a pair of antibody-oligonucleotide conjugates and a signal probe (DNA-Fc), Fc in DNA-Fc was brought close to the surface of electrode in conjunction with target induced ECL quenching. The ECL intensity decreased with the increasing concentration of the AFP and AFP was monitored with a linear range of 0.05-50ng/mL along with a detection limit of 0.04ng/mL. The ECL bioassay is successfully applied to the detection of AFP in serum samples with one-step recognition, short operating time and good accuracy. This method displays great potential for point-of-care testing and commercial application. Copyright © 2017 Elsevier B.V. All rights reserved.

Interactive object recognition assistance: an approach to recognition starting from target objects

NASA Astrophysics Data System (ADS)

Geisler, Juergen; Littfass, Michael

1999-07-01

Recognition of target objects in remotely sensed imagery required detailed knowledge about the target object domain as well as about mapping properties of the sensing system. The art of object recognition is to combine both worlds appropriately and to provide models of target appearance with respect to sensor characteristics. Common approaches to support interactive object recognition are either driven from the sensor point of view and address the problem of displaying images in a manner adequate to the sensing system. Or they focus on target objects and provide exhaustive encyclopedic information about this domain. Our paper discusses an approach to assist interactive object recognition based on knowledge about target objects and taking into account the significance of object features with respect to characteristics of the sensed imagery, e.g. spatial and spectral resolution. An `interactive recognition assistant' takes the image analyst through the interpretation process by indicating step-by-step the respectively most significant features of objects in an actual set of candidates. The significance of object features is expressed by pregenerated trees of significance, and by the dynamic computation of decision relevance for every feature at each step of the recognition process. In the context of this approach we discuss the question of modeling and storing the multisensorial/multispectral appearances of target objects and object classes as well as the problem of an adequate dynamic human-machine-interface that takes into account various mental models of human image interpretation.

Identities of P2 and P3 Residues of H-2Kb-Bound Peptides Determine Mouse Ly49C Recognition

PubMed Central

Marquez, Elsa A.; Kane, Kevin P.

2015-01-01

Ly49 receptors can be peptide selective in their recognition of MHC-I-peptide complexes, affording them a level of discrimination beyond detecting the presence or absence of specific MHC-I allele products. Despite this ability, little is understood regarding the properties that enable some peptides, when bound to MHC-I molecules, to support Ly49 recognition, but not others. Using RMA-S target cells expressing MHC-I molecules loaded with individual peptides and effector cells expressing the ectodomain of the inhibitory Ly49C receptor, we found that two adjacent amino acid residues, P2 and P3, both buried in the peptide binding groove of H-2Kb, determine mouse Ly49C specificity. If both are aliphatic residues, this is supportive. Whereas, small amino acids at P2 and aromatic amino acids at the P3 auxiliary anchor residue are detrimental to Ly49C recognition. These results resemble those with a rat Ly49 where the identity of a peptide anchor residue determines recognition, suggesting that dependence on specific peptide residues buried in the MHC-I peptide-binding groove may be fundamental to Ly49 peptide selectivity and recognition. PMID:26147851

Design Strategies for Aptamer-Based Biosensors

PubMed Central

Han, Kun; Liang, Zhiqiang; Zhou, Nandi

2010-01-01

Aptamers have been widely used as recognition elements for biosensor construction, especially in the detection of proteins or small molecule targets, and regarded as promising alternatives for antibodies in bioassay areas. In this review, we present an overview of reported design strategies for the fabrication of biosensors and classify them into four basic modes: target-induced structure switching mode, sandwich or sandwich-like mode, target-induced dissociation/displacement mode and competitive replacement mode. In view of the unprecedented advantages brought about by aptamers and smart design strategies, aptamer-based biosensors are expected to be one of the most promising devices in bioassay related applications. PMID:22399891

Doppler-Only Synthetic Aperture Radar

DTIC Science & Technology

2006-12-01

5 B. TARGET RECOGNITION TECHNIQUES .................................................6 1. Cooperative Targets...6 3. Techniques ............................................................................................6 C. TARGET RECOGNITION...3. Implementation of High Range Resolution Techniques .................12 F. TWO-DIMENSIONAL IMAGING

A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection.

PubMed

Wang, Fei; He, Yanbin; Pan, Jiahui; Xie, Qiuyou; Yu, Ronghao; Zhang, Rui; Li, Yuanqing

2015-06-30

Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC.

A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection

PubMed Central

Wang, Fei; He, Yanbin; Pan, Jiahui; Xie, Qiuyou; Yu, Ronghao; Zhang, Rui; Li, Yuanqing

2015-01-01

Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC. PMID:26123281

Spiny Nanorod and Upconversion Nanoparticle Satellite Assemblies for Ultrasensitive Detection of Messenger RNA in Living Cells.

PubMed

Gao, Rui; Hao, Changlong; Xu, Liguang; Xu, Chuanlai; Kuang, Hua

2018-04-17

Quantitation and in situ monitoring of target mRNA (mRNA) in living cells remains a significant challenge for the chemical and biomedical communities. To quantitatively detect mRNA expression levels in living cells, we have developed DNA-driven gold nanorod coated platinum-upconversion nanoparticle satellite assemblies (termed Au NR@Pt-UCNP satellites) for intracellular thymidine kinase 1 (TK1) mRNA analysis. The nanostructures were capable of recognizing target mRNA in a sequence-specific manner as luminescence of UCNPs was effectively quenched by Au NR@Pt within the assemblies. Following recognition, UCNPs detached from Au NR@Pt, resulting in luminescence restoration to achieve effective in situ imaging and quantifiable detection of target mRNA. The upconversional luminescence intensity of confocal images showed a good linear relationship with intracellular TK1 mRNA ranging from 1.17 to 65.21 fmol/10 μg RNA and a limit of detection (LOD) of 0.67 fmol/10 μg RNA. We believe that our present assay can be broadly applied for detection of endogenous biomolecules at the cellular and tissue levels and restoration of tissue homeostasis in vivo.

The use of multispectral sensing techniques to detect ponderosa pines trees under stress from insects or diseases

NASA Technical Reports Server (NTRS)

Heller, R. C.; Weber, F. P.; Zealear, K. A.

1970-01-01

The detection of stress induced by bark beetles in conifers is reviewed in two sections: (1) the analysis of very small scale aerial photographs taken by NASA's RB-57F aircraft on August 10, 1969, and (2) the analysis of multispectral imagery obtained by the optical-mechanical line scanner. Underexposure of all films taken from the RB-57 aircraft and inadequate flight coverage prevented drawing definitive conclusions regarding optimum scales and film combinations to detect the discolored infestations. Preprocessing of the scanner signals by both analog and digital computers improved the accuracy of target recognition. Selection and ranking of the best channels for signature recognition was the greatest contribution of digital processing. Improvements were made in separating hardwoods from conifers and old-kill pine trees from recent discolored trees and from healthy trees, but accuracy of detecting the green infested trees is still not acceptable on either the SPARC or thermal-contouring processor. From six years of experience in processing line scan data it is clear that the greatest gain in previsual detection of stress will occur when registered multispectral data from a single aperture or common instantaneous field of view scanner system can be collected and processed.

Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

NASA Astrophysics Data System (ADS)

Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

2018-04-01

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

Compact hybrid optoelectrical unit for image processing and recognition

NASA Astrophysics Data System (ADS)

Cheng, Gang; Jin, Guofan; Wu, Minxian; Liu, Haisong; He, Qingsheng; Yuan, ShiFu

1998-07-01

In this paper a compact opto-electric unit (CHOEU) for digital image processing and recognition is proposed. The central part of CHOEU is an incoherent optical correlator, which is realized with a SHARP QA-1200 8.4 inch active matrix TFT liquid crystal display panel which is used as two real-time spatial light modulators for both the input image and reference template. CHOEU can do two main processing works. One is digital filtering; the other is object matching. Using CHOEU an edge-detection operator is realized to extract the edges from the input images. Then the reprocessed images are sent into the object recognition unit for identifying the important targets. A novel template- matching method is proposed for gray-tome image recognition. A positive and negative cycle-encoding method is introduced to realize the absolute difference measurement pixel- matching on a correlator structure simply. The system has god fault-tolerance ability for rotation distortion, Gaussian noise disturbance or information losing. The experiments are given at the end of this paper.

Interplay of signal recognition particle and trigger factor at L23 near the nascent chain exit site on the Escherichia coli ribosome

PubMed Central

Ullers, Ronald S.; Houben, Edith N.G.; Raine, Amanda; ten Hagen-Jongman, Corinne M.; Ehrenberg, Måns; Brunner, Joseph; Oudega, Bauke; Harms, Nellie; Luirink, Joen

2003-01-01

As newly synthesized polypeptides emerge from the ribosome, they interact with chaperones and targeting factors that assist in folding and targeting to the proper location in the cell. In Escherichia coli, the chaperone trigger factor (TF) binds to nascent polypeptides early in biosynthesis facilitated by its affinity for the ribosomal proteins L23 and L29 that are situated around the nascent chain exit site on the ribosome. The targeting factor signal recognition particle (SRP) interacts specifically with the signal anchor (SA) sequence in nascent inner membrane proteins (IMPs). Here, we have used photocross-linking to map interactions of the SA sequence in a short, in vitro–synthesized, nascent IMP. Both TF and SRP were found to interact with the SA with partially overlapping binding specificity. In addition, extensive contacts with L23 and L29 were detected. Both purified TF and SRP could be cross-linked to L23 on nontranslating ribosomes with a competitive advantage for SRP. The results suggest a role for L23 in the targeting of IMPs as an attachment site for TF and SRP that is close to the emerging nascent chain. PMID:12756233

Cascaded automatic target recognition (Cascaded ATR)

NASA Astrophysics Data System (ADS)

Walls, Bradley

2010-04-01

The global war on terror has plunged US and coalition forces into a battle space requiring the continuous adaptation of tactics and technologies to cope with an elusive enemy. As a result, technologies that enhance the intelligence, surveillance, and reconnaissance (ISR) mission making the warfighter more effective are experiencing increased interest. In this paper we show how a new generation of smart cameras built around foveated sensing makes possible a powerful ISR technique termed Cascaded ATR. Foveated sensing is an innovative optical concept in which a single aperture captures two distinct fields of view. In Cascaded ATR, foveated sensing is used to provide a coarse resolution, persistent surveillance, wide field of view (WFOV) detector to accomplish detection level perception. At the same time, within the foveated sensor, these detection locations are passed as a cue to a steerable, high fidelity, narrow field of view (NFOV) detector to perform recognition level perception. Two new ISR mission scenarios, utilizing Cascaded ATR, are proposed.

Control system of hexacopter using color histogram footprint and convolutional neural network

NASA Astrophysics Data System (ADS)

Ruliputra, R. N.; Darma, S.

2017-07-01

The development of unmanned aerial vehicles (UAV) has been growing rapidly in recent years. The use of logic thinking which is implemented into the program algorithms is needed to make a smart system. By using visual input from a camera, UAV is able to fly autonomously by detecting a target. However, some weaknesses arose as usage in the outdoor environment might change the target's color intensity. Color histogram footprint overcomes the problem because it divides color intensity into separate bins that make the detection tolerant to the slight change of color intensity. Template matching compare its detection result with a template of the reference image to determine the target position and use it to position the vehicle in the middle of the target with visual feedback control based on Proportional-Integral-Derivative (PID) controller. Color histogram footprint method localizes the target by calculating the back projection of its histogram. It has an average success rate of 77 % from a distance of 1 meter. It can position itself in the middle of the target by using visual feedback control with an average positioning time of 73 seconds. After the hexacopter is in the middle of the target, Convolutional Neural Networks (CNN) classifies a number contained in the target image to determine a task depending on the classified number, either landing, yawing, or return to launch. The recognition result shows an optimum success rate of 99.2 %.

Combining spiral and target wave detection to analyze excitable media dynamics

NASA Astrophysics Data System (ADS)

Geberth, Daniel; Hütt, Marc-Thorsten

2010-01-01

Excitable media dynamics is the lossless active transmission of waves of excitation over a field of coupled elements, such as electrical excitation in heart tissue or nerve fibers, cAMP signaling in the slime mold Dictyostelium discoideum or waves of chemical activity in the Belousov-Zhabotinsky reaction. All these systems follow essentially the same generic dynamics, including undamped wave transmission and the self-organized emergence of circular target and self-sustaining spiral waves. We combine spiral recognition, using the established phase singularity technique, and a novel three-dimensional fitting algorithm for noise-resistant target wave recognition to extract all important events responsible for the layout of the asymptotic large-scale pattern. Space-time plots of these combined events reveal signatures of events leading to spiral formation, illuminating the microscopic mechanisms at work. This strategy can be applied to arbitrary excitable media data from either models or experiments, giving insight into for example the microscopic causes for formation of pathological spiral waves in heart tissue, which could lead to novel techniques for diagnosis, risk evaluation and treatment.

Neural Network Target Identification System for False Alarm Reduction

NASA Technical Reports Server (NTRS)

Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

Pattern-Recognition Algorithm for Locking Laser Frequency

NASA Technical Reports Server (NTRS)

Karayan, Vahag; Klipstein, William; Enzer, Daphna; Yates, Philip; Thompson, Robert; Wells, George

2006-01-01

A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

A universal entropy-driven mechanism for thioredoxin–target recognition

PubMed Central

Palde, Prakash B.; Carroll, Kate S.

2015-01-01

Cysteine residues in cytosolic proteins are maintained in their reduced state, but can undergo oxidation owing to posttranslational modification during redox signaling or under conditions of oxidative stress. In large part, the reduction of oxidized protein cysteines is mediated by a small 12-kDa thiol oxidoreductase, thioredoxin (Trx). Trx provides reducing equivalents for central metabolic enzymes and is implicated in redox regulation of a wide number of target proteins, including transcription factors. Despite its importance in cellular redox homeostasis, the precise mechanism by which Trx recognizes target proteins, especially in the absence of any apparent signature binding sequence or motif, remains unknown. Knowledge of the forces associated with the molecular recognition that governs Trx–protein interactions is fundamental to our understanding of target specificity. To gain insight into Trx–target recognition, we have thermodynamically characterized the noncovalent interactions between Trx and target proteins before S-S reduction using isothermal titration calorimetry (ITC). Our findings indicate that Trx recognizes the oxidized form of its target proteins with exquisite selectivity, compared with their reduced counterparts. Furthermore, we show that recognition is dependent on the conformational restriction inherent to oxidized targets. Significantly, the thermodynamic signatures for multiple Trx targets reveal favorable entropic contributions as the major recognition force dictating these protein–protein interactions. Taken together, our data afford significant new insight into the molecular forces responsible for Trx–target recognition and should aid the design of new strategies for thiol oxidoreductase inhibition. PMID:26080424

Invited: Advances Toward Practical Detection of Trace Chemical Hazards with Solid State Microarray Devices

NASA Astrophysics Data System (ADS)

Raman, Barani; Meier, Douglas; Shenoy, Rupa; Benkstein, Kurt; Semancik, Steve

2011-09-01

We describe progress on an array-based microsensor approach employed for detecting trace levels of toxic industrial chemicals (TICs) in air-based backgrounds with varied levels of humidity, and with occasional introduction of aggressive interferents. Our MEMS microhotplate arrays are populated with multiple chemiresistive sensing materials, and all elements are programmed to go through extensive temperature cycling over repetitive cycles with lengths of approximately 20 s. Under such operation, analytically-rich data streams are produced containing the required information for target recognition.

Hall effect biosensors with ultraclean graphene film for improved sensitivity of label-free DNA detection.

PubMed

Loan, Phan Thi Kim; Wu, Dongqin; Ye, Chen; Li, Xiaoqing; Tra, Vu Thanh; Wei, Qiuping; Fu, Li; Yu, Aimin; Li, Lain-Jong; Lin, Cheng-Te

2018-01-15

The quality of graphene strongly affects the performance of graphene-based biosensors which are highly demanded for the sensitive and selective detection of biomolecules, such as DNA. This work reported a novel transfer process for preparing a residue-free graphene film using a thin gold supporting layer. A Hall effect device made of this gold-transferred graphene was demonstrated to significantly enhance the sensitivity (≈ 5 times) for hybridization detection, with a linear detection range of 1pM to 100nM for DNA target. Our findings provide an efficient method to boost the sensitivity of graphene-based biosensors for DNA recognition. Copyright © 2017 Elsevier B.V. All rights reserved.

A model system for pathogen detection using a two-component bacteriophage/bioluminescent signal amplification assay

NASA Astrophysics Data System (ADS)

Bright, Nathan G.; Carroll, Richard J.; Applegate, Bruce M.

2004-03-01

Microbial contamination has become a mounting concern the last decade due to an increased emphasis of minimally processed food products specifically produce, and the recognition of foodborne pathogens such as Campylobacter jejuni, Escherichia coli O157:H7, and Listeria monocytogenes. This research investigates a detection approach utilizing bacteriophage pathogen specificity coupled with a bacterial bioluminescent bioreporter utilizing the quorum sensing molecule from Vibrio fischeri, N-(3-oxohexanoyl)-homoserine lactone (3-oxo-C6-HSL). The 3-oxo-C6-HSL molecules diffuse out of the target cell after infection and induce bioluminescence from a population of 3-oxo-C6-HSL bioreporters (ROLux). E. coli phage M13, a well-characterized bacteriophage, offers a model system testing the use of bacteriophage for pathogen detection through cell-to-cell communication via a LuxR/3-oxo-C6-HSL system. Simulated temperate phage assays tested functionality of the ROLux reporter and production of 3-oxo-C6-HSL by various test strains. These assays showed detection limits of 102cfu after 24 hours in a varietry of detection formats. Assays incorporating the bacteriophage M13-luxI with the ROLux reporter and a known population of target cells were subsequently developed and have shown consistent detection limits of 105cfu target organisms. Measurable light response from high concentrations of target cells was almost immediate, suggesting an enrichment step to further improve detection limits and reduce assay time.

Ultrafast scene detection and recognition with limited visual information

PubMed Central

Hagmann, Carl Erick; Potter, Mary C.

2016-01-01

Humans can detect target color pictures of scenes depicting concepts like picnic or harbor in sequences of six or twelve pictures presented as briefly as 13 ms, even when the target is named after the sequence (Potter, Wyble, Hagmann, & McCourt, 2014). Such rapid detection suggests that feedforward processing alone enabled detection without recurrent cortical feedback. There is debate about whether coarse, global, low spatial frequencies (LSFs) provide predictive information to high cortical levels through the rapid magnocellular (M) projection of the visual path, enabling top-down prediction of possible object identities. To test the “Fast M” hypothesis, we compared detection of a named target across five stimulus conditions: unaltered color, blurred color, grayscale, thresholded monochrome, and LSF pictures. The pictures were presented for 13–80 ms in six-picture rapid serial visual presentation (RSVP) sequences. Blurred, monochrome, and LSF pictures were detected less accurately than normal color or grayscale pictures. When the target was named before the sequence, all picture types except LSF resulted in above-chance detection at all durations. Crucially, when the name was given only after the sequence, performance dropped and the monochrome and LSF pictures (but not the blurred pictures) were at or near chance. Thus, without advance information, monochrome and LSF pictures were rarely understood. The results offer only limited support for the Fast M hypothesis, suggesting instead that feedforward processing is able to activate conceptual representations without complementary reentrant processing. PMID:28255263

Model-based recognition of 3D articulated target using ladar range data.

PubMed

Lv, Dan; Sun, Jian-Feng; Li, Qi; Wang, Qi

2015-06-10

Ladar is suitable for 3D target recognition because ladar range images can provide rich 3D geometric surface information of targets. In this paper, we propose a part-based 3D model matching technique to recognize articulated ground military vehicles in ladar range images. The key of this approach is to solve the decomposition and pose estimation of articulated parts of targets. The articulated components were decomposed into isolate parts based on 3D geometric properties of targets, such as surface point normals, data histogram distribution, and data distance relationships. The corresponding poses of these separate parts were estimated through the linear characteristics of barrels. According to these pose parameters, all parts of the target were roughly aligned to 3D point cloud models in a library and fine matching was finally performed to accomplish 3D articulated target recognition. The recognition performance was evaluated with 1728 ladar range images of eight different articulated military vehicles with various part types and orientations. Experimental results demonstrated that the proposed approach achieved a high recognition rate.

Outcome Analysis Tool for Army Refractive Surgery Program

DTIC Science & Technology

2005-03-01

analysis function produces reports on the following information: " Evaluation of the safety of PRK and LASIK for maintenance of optimal visual...performance and ocular integrity. " Evaluation of the efficacy of PRK and LASIK by assessing the improvement in uncorrected vision for target detection...discrimination and recognition. "* Evaluation of the efficacy of PRK and LASIK by evaluating the stability of the refractive error over time

Risk-Based Aviation Security: Diffusion and Acceptance

DTIC Science & Technology

2012-03-01

Association ATR Automated Target Recognition BDO Behavior Detection Officer BIB Budget-In-Brief CBP Customs and Border Protection CDRH Center...Radiological Health ( CDRH ) (Cerra, 2006), the National Institute for Standards and Technology (NIST) (TSA, n.d. g), and the Johns Hopkins University Applied...safety related to AIT may have come from the Food and Drug Administration’s (FDA) Center for Devices and Radiological Health ( CDRH ), the National

Improved facial affect recognition in schizophrenia following an emotion intervention, but not training attention-to-facial-features or treatment-as-usual.

PubMed

Tsotsi, Stella; Kosmidis, Mary H; Bozikas, Vasilis P

2017-08-01

In schizophrenia, impaired facial affect recognition (FAR) has been associated with patients' overall social functioning. Interventions targeting attention or FAR per se have invariably yielded improved FAR performance in these patients. Here, we compared the effects of two interventions, one targeting FAR and one targeting attention-to-facial-features, with treatment-as-usual on patients' FAR performance. Thirty-nine outpatients with schizophrenia were randomly assigned to one of three groups: FAR intervention (training to recognize emotional information, conveyed by changes in facial features), attention-to-facial-features intervention (training to detect changes in facial features), and treatment-as-usual. Also, 24 healthy controls, matched for age and education, were assigned to one of the two interventions. Two FAR measurements, baseline and post-intervention, were conducted using an original experimental procedure with alternative sets of stimuli. We found improved FAR performance following the intervention targeting FAR in comparison to the other patient groups, which in fact was comparable to the pre-intervention performance of healthy controls in the corresponding intervention group. This improvement was more pronounced in recognizing fear. Our findings suggest that compared to interventions targeting attention, and treatment-as-usual, training programs targeting FAR can be more effective in improving FAR in patients with schizophrenia, particularly assisting them in perceiving threat-related information more accurately. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

A cascade autocatalytic strand displacement amplification and hybridization chain reaction event for label-free and ultrasensitive electrochemical nucleic acid biosensing.

PubMed

Chen, Zhiqiang; Liu, Ying; Xin, Chen; Zhao, Jikuan; Liu, Shufeng

2018-08-15

Herein, an autocatalytic strand displacement amplification (ASDA) strategy was proposed for the first time, which was further ingeniously coupled with hybridization chain reaction (HCR) event for the isothermal, label-free and multiple amplification toward nucleic acid detection. During the ASDA module, the target recognition opens the immobilized hairpin probe (IP) and initiates the annealing of the auxiliary DNA strand (AS) with the opened IP for the successive polymerization and nicking reaction in the presence of DNA polymerase and nicking endonuclease. This induces the target recycling and generation of a large amount of intermediate DNA sequences, which can be used as target analogy to execute the autocatalytic strand displacement amplification. Simultaneously, the introduced AS strand can propagate the HCR between two hairpins (H1 and H2) to form a linear DNA concatamer with cytosine (C)-rich loop region, which can facilitate the in-situ synthesis of silver nanoclusters (AgNCs) as electrochemical tags for further amplification toward target responses. With current cascade ASDA and HCR strategy, the detection of target DNA could be achieved with a low detection limit of about 0.16 fM and a good selectivity. The developed biosensor also exhibits the distinct advantages of flexibility and simplicity in probe design and biosensor fabrication, and label-free electrochemical detection, thus opens a promising avenue for the detection of nucleic acid with low abundance in bioanalysis and clinical biomedicine. Copyright © 2018 Elsevier B.V. All rights reserved.

Directed area search using socio-biological vision algorithms and cognitive Bayesian reasoning

NASA Astrophysics Data System (ADS)

Medasani, S.; Owechko, Y.; Allen, D.; Lu, T. C.; Khosla, D.

2010-04-01

Volitional search systems that assist the analyst by searching for specific targets or objects such as vehicles, factories, airports, etc in wide area overhead imagery need to overcome multiple problems present in current manual and automatic approaches. These problems include finding targets hidden in terabytes of information, relatively few pixels on targets, long intervals between interesting regions, time consuming analysis requiring many analysts, no a priori representative examples or templates of interest, detecting multiple classes of objects, and the need for very high detection rates and very low false alarm rates. This paper describes a conceptual analyst-centric framework that utilizes existing technology modules to search and locate occurrences of targets of interest (e.g., buildings, mobile targets of military significance, factories, nuclear plants, etc.), from video imagery of large areas. Our framework takes simple queries from the analyst and finds the queried targets with relatively minimum interaction from the analyst. It uses a hybrid approach that combines biologically inspired bottom up attention, socio-biologically inspired object recognition for volitionally recognizing targets, and hierarchical Bayesian networks for modeling and representing the domain knowledge. This approach has the benefits of high accuracy, low false alarm rate and can handle both low-level visual information and high-level domain knowledge in a single framework. Such a system would be of immense help for search and rescue efforts, intelligence gathering, change detection systems, and other surveillance systems.

Infrared detection, recognition and identification of handheld objects

NASA Astrophysics Data System (ADS)

Adomeit, Uwe

2012-10-01

A main criterion for comparison and selection of thermal imagers for military applications is their nominal range performance. This nominal range performance is calculated for a defined task and standardized target and environmental conditions. The only standardization available to date is STANAG 4347. The target defined there is based on a main battle tank in front view. Because of modified military requirements, this target is no longer up-to-date. Today, different topics of interest are of interest, especially differentiation between friend and foe and identification of humans. There is no direct way to differentiate between friend and foe in asymmetric scenarios, but one clue can be that someone is carrying a weapon. This clue can be transformed in the observer tasks detection: a person is carrying or is not carrying an object, recognition: the object is a long / medium / short range weapon or civil equipment and identification: the object can be named (e. g. AK-47, M-4, G36, RPG7, Axe, Shovel etc.). These tasks can be assessed experimentally and from the results of such an assessment, a standard target for handheld objects may be derived. For a first assessment, a human carrying 13 different handheld objects in front of his chest was recorded at four different ranges with an IR-dual-band camera. From the recorded data, a perception experiment was prepared. It was conducted with 17 observers in a 13-alternative forced choice, unlimited observation time arrangement. The results of the test together with Minimum Temperature Difference Perceived measurements of the camera and temperature difference and critical dimension derived from the recorded imagery allowed defining a first standard target according to the above tasks. This standard target consist of 2.5 / 3.5 / 5 DRI line pairs on target, 0.24 m critical size and 1 K temperature difference. The values are preliminary and have to be refined in the future. Necessary are different aspect angles, different carriage and movement.

Ground target recognition using rectangle estimation.

PubMed

Grönwall, Christina; Gustafsson, Fredrik; Millnert, Mille

2006-11-01

We propose a ground target recognition method based on 3-D laser radar data. The method handles general 3-D scattered data. It is based on the fact that man-made objects of complex shape can be decomposed to a set of rectangles. The ground target recognition method consists of four steps; 3-D size and orientation estimation, target segmentation into parts of approximately rectangular shape, identification of segments that represent the target's functional/main parts, and target matching with CAD models. The core in this approach is rectangle estimation. The performance of the rectangle estimation method is evaluated statistically using Monte Carlo simulations. A case study on tank recognition is shown, where 3-D data from four fundamentally different types of laser radar systems are used. Although the approach is tested on rather few examples, we believe that the approach is promising.

Palindromic Molecule Beacon-Based Cascade Amplification for Colorimetric Detection of Cancer Genes.

PubMed

Shen, Zhi-Fa; Li, Feng; Jiang, Yi-Fan; Chen, Chang; Xu, Huo; Li, Cong-Cong; Yang, Zhe; Wu, Zai-Sheng

2018-03-06

A highly sensitive and selective colorimetric assay based on a multifunctional molecular beacon with palindromic tail (PMB) was proposed for the detection of target p53 gene. The PMB probe can serve as recognition element, primer, and polymerization template and contains a nicking site and a C-rich region complementary to a DNAzyme. In the presence of target DNA, the hairpin of PMB is opened, and the released palindromic tails intermolecularly hybridize with each other, triggering the autonomous polymerization/nicking/displacement cycles. Although only one type of probe is involved, the system can execute triple and continuous polymerization strand displacement amplifications, generating large amounts of G-quadruplex fragments. These G-rich fragments can bind to hemin and form the DNAzymes that possess the catalytic activity similar to horseradish peroxidase, catalyzing the oxidation of ABTS by H 2 O 2 and producing the colorimetric signal. Utilizing the newly proposed sensing system, target DNA can be detected down to 10 pM with a linear response range from 10 pM to 200 nM, and mutant target DNAs are able to be distinguished even by the naked eye. The desirable detection sensitivity, high specificity, and operation convenience without any separation step and chemical modification demonstrate that the palindromic molecular beacon holds the potential for detecting and monitoring a variety of nucleic acid-related biomarkers.

A graphene-based biosensing platform based on the release of DNA probes and rolling circle amplification.

PubMed

Liu, Meng; Song, Jinping; Shuang, Shaomin; Dong, Chuan; Brennan, John D; Li, Yingfu

2014-06-24

We report a versatile biosensing platform capable of achieving ultrasensitive detection of both small-molecule and macromolecular targets. The system features three components: reduced graphene oxide for its ability to adsorb single-stranded DNA molecules nonspecifically, DNA aptamers for their ability to bind reduced graphene oxide but undergo target-induced conformational changes that facilitate their release from the reduced graphene oxide surface, and rolling circle amplification (RCA) for its ability to amplify a primer-template recognition event into repetitive sequence units that can be easily detected. The key to the design is the tagging of a short primer to an aptamer sequence, which results in a small DNA probe that allows for both effective probe adsorption onto the reduced graphene oxide surface to mask the primer domain in the absence of the target, as well as efficient probe release in the presence of the target to make the primer available for template binding and RCA. We also made an observation that the circular template, which on its own does not cause a detectable level of probe release from the reduced graphene oxide, augments target-induced probe release. The synergistic release of DNA probes is interpreted to be a contributing factor for the high detection sensitivity. The broad utility of the platform is illustrated though engineering three different sensors that are capable of achieving ultrasensitive detection of a protein target, a DNA sequence and a small-molecule analyte. We envision that the approach described herein will find useful applications in the biological, medical, and environmental fields.

Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System

NASA Astrophysics Data System (ADS)

Wang, Hongcui; Kawahara, Tatsuya

CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.

The own-age face recognition bias is task dependent.

PubMed

Proietti, Valentina; Macchi Cassia, Viola; Mondloch, Catherine J

2015-08-01

The own-age bias (OAB) in face recognition (more accurate recognition of own-age than other-age faces) is robust among young adults but not older adults. We investigated the OAB under two different task conditions. In Experiment 1 young and older adults (who reported more recent experience with own than other-age faces) completed a match-to-sample task with young and older adult faces; only young adults showed an OAB. In Experiment 2 young and older adults completed an identity detection task in which we manipulated the identity strength of target and distracter identities by morphing each face with an average face in 20% steps. Accuracy increased with identity strength and facial age influenced older adults' (but not younger adults') strategy, but there was no evidence of an OAB. Collectively, these results suggest that the OAB depends on task demands and may be absent when searching for one identity. © 2014 The British Psychological Society.

On-chip learning of hyper-spectral data for real time target recognition

NASA Technical Reports Server (NTRS)

Duong, T. A.; Daud, T.; Thakoor, A.

2000-01-01

As the focus of our present paper, we have used the cascade error projection (CEP) learning algorithm (shown to be hardware-implementable) with on-chip learning (OCL) scheme to obtain three orders of magnitude speed-up in target recognition compared to software-based learning schemes. Thus, it is shown, real time learning as well as data processing for target recognition can be achieved.

Colorimetric Aptasensor Based on Enzyme for the Detection of Vibrio parahemolyticus.

PubMed

Wu, Shijia; Wang, Yinqiu; Duan, Nuo; Ma, Haile; Wang, Zhouping

2015-09-09

A simple colorimetric aptasensor system has been developed to detect Vibrio parahemolyticus. Magnetic nanoparticles (MNPs) are synthesized and conjugated with specific aptamers against target and used as capture probes. In addition, this method employs gold nanoparticles (AuNPs) as carriers of horseradish peroxidase (HRP) and aptamers, which served as signal probes. In the presence of target, a "sandwich-type" complex of AuNPs-HRP-aptamer-target-aptamer-MNPs is formed through specific recognition of aptamers and corresponding target. As a result, HRP molecules confined at the surface of the "sandwich" complexes catalyze the enzyme substrate, 3,3',5,5'-tetramethylbenzidine (TMB) and H2O2 and generate an optical signal. Under optimal conditions, the signals are linearly dependent on V. parahemolyticus concentrations from 10 to 10(6) colony-forming units (cfu)/mL in a logarithmic plot, with a limit of detection of 10 cfu/mL. Owing to AuNPs, a large amount of HRP could be loaded, resulting in an amplified signal, and the sensitivity would be improved. This strategy has the potential of being extended to the construction of simple monitor systems for a variety of biomolecules related to food safety.

Evaluation of tooth-click triggering and speech recognition in assistive technology for computer access.

PubMed

Simpson, Tyler; Gauthier, Michel; Prochazka, Arthur

2010-02-01

Computer access can play an important role in employment and leisure activities following spinal cord injury. The authors' prior work has shown that a tooth-click detecting device, when paired with an optical head mouse, may be used by people with tetraplegia for controlling cursor movement and mouse button clicks. To compare the efficacy of tooth clicks to speech recognition and that of an optical head mouse to a gyrometer head mouse for cursor and mouse button control of a computer. Six able-bodied and 3 tetraplegic subjects used the devices listed above to produce cursor movements and mouse clicks in response to a series of prompts displayed on a computer. The time taken to move to and click on each target was recorded. The use of tooth clicks in combination with either an optical head mouse or a gyrometer head mouse can provide hands-free cursor movement and mouse button control at a speed of up to 22% of that of a standard mouse. Tooth clicks were significantly faster at generating mouse button clicks than speech recognition when paired with either type of head mouse device. Tooth-click detection performed better than speech recognition when paired with both the optical head mouse and the gyrometer head mouse. Such a system may improve computer access for people with tetraplegia.

Multispectral image fusion based on fractal features

NASA Astrophysics Data System (ADS)

Tian, Jie; Chen, Jie; Zhang, Chunhua

2004-01-01

Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the composition of source pyramid images. So this fusion scheme is a multi-resolution analysis. The wavelet decomposition of image can be actually considered as special pyramid decomposition. According to wavelet decomposition theories, the approximation of image (formula available in paper) at resolution 2j+1 equal to its orthogonal projection in space , that is, where Ajf is the low-frequency approximation of image f(x, y) at resolution 2j and , , represent the vertical, horizontal and diagonal wavelet coefficients respectively at resolution 2j. These coefficients describe the high-frequency information of image at direction of vertical, horizontal and diagonal respectively. Ajf, , and are independent and can be considered as images. In this paper J is set to be 1, so the source image is decomposed to produce the son-images Af, D1f, D2f and D3f. To solve the problem of detecting artifacts, the concepts of vertical fractal dimension FD1, horizontal fractal dimension FD2 and diagonal fractal dimension FD3 are proposed in this paper. The vertical fractal dimension FD1 corresponds to the vertical wavelet coefficients image after the wavelet decomposition of source image, the horizontal fractal dimension FD2 corresponds to the horizontal wavelet coefficients and the diagonal fractal dimension FD3 the diagonal one. These definitions enrich the illustration of source images. Therefore they are helpful to classify the targets. Then the detection of artifacts in the decomposed images is a problem of pattern recognition in 4-D space. The combination of FD0, FD1, FD2 and FD3 make a vector of (FD0, FD1, FD2, FD3), which can be considered as a united feature vector of the studied image. All the parts of the images are classified in the 4-D pattern space created by the vector of (FD0, FD1, FD2, FD3) so that the area that contains man-made objects could be detected. This detection can be considered as a coarse recognition, and then the significant areas in each son-images are signed so that they can be dealt with special rules. There has been various fusion rules developed with each one aiming at a special problem. These rules have different performance, so it is very important to select an appropriate rule during the design of an image fusion system. Recent research denotes that the rule should be adjustable so that it is always suitable to extrude the features of targets and to preserve the pixels of useful information. In this paper, owing to the consideration that fractal dimension is one of the main features to distinguish man-made targets from natural objects, the fusion rule was defined that if the studied region of image contains man-made target, the pixels of the source image whose fractal dimension is minimal are saved to be the pixels of the fused image, otherwise, a weighted average operator is adopted to avoid loss of information. The main idea of this rule is to store the pixels with low fractal dimensions, so it can be named Minimal Fractal dimensions (MFD) fusion rule. This fractal-based algorithm is compared with a common weighted average fusion algorithm. An objective assessment is taken to the two fusion results. The criteria of Entropy, Cross-Entropy, Peak Signal-to-Noise Ratio (PSNR) and Standard Gray Scale Difference are defined in this paper. Reversely to the idea of constructing an ideal image as the assessing reference, the source images are selected to be the reference in this paper. It can be deemed that this assessment is to calculate how much the image quality has been enhanced and the quantity of information has been increased when the fused image is compared with the source images. The experimental results imply that the fractal-based multi-spectral fusion algorithm can effectively preserve the information of man-made objects with a high contrast. It is proved that this algorithm could well preserve features of military targets because that battlefield targets are most man-made objects and in common their images differ from fractal models obviously. Furthermore, the fractal features are not sensitive to the imaging conditions and the movement of targets, so this fractal-based algorithm may be very practical.

Advancing Porous Silicon Biosensor Technology for Use in Clinical Diagnostics

NASA Astrophysics Data System (ADS)

Bonanno, Lisa Marie

Inexpensive and robust analytical techniques for detecting molecular recognition events are in great demand in healthcare, food safety, and environmental monitoring. Despite vast research in this area, challanges remain to develop practical biomolecular platforms that, meet the rigorous demands of real-world applications. This includes maintaining low-cost devices that are sensitive and specific in complex test specimens, are stable after storage, have short assay time, and possess minimal complexity of instrumentation for readout. Nanostructured porous silicon (PSi) material has been identified as an ideal candidate towards achieving these goals and the past decade has seen diverse proof-of-principle studies developing optical-based sensing techniques. In Part 1 of this thesis, the impact of surface chemistry and PSi morphology on detection sensitivity of target molecules is investigated. Initial proof-of-concept that PSi devices facilitate detection of protein in whole blood is demonstrated. This work highlights the importance of material stability and blocking chemistry for sensor use in real world biological samples. In addition, the intrinisic filtering capability of the 3-D PSi morphology is shown as an advantage in complex solutions, such as whole blood. Ultimately, this initial work identified a need to improve detection sensitivity of the PSI biosensor technique to facilitate clinical diagnostic use over relevant target concentration ranges. The second part of this thesis, builds upon sensitivity challenges that are highlighted in the first part of the thesis and development of a surface-bound competitive inhibition immunoassay facilitated improved detection sensitivity of small molecular weight targets (opiates) over a relevant clinical concentration range. In addition, optimization of assay protocol addressed issues of maintaining stability of sensors after storage. Performance of the developed assay (specificity and sensitivity) was then validated in a blind clinical study that screened real patient urine samples (n=70) for opiates in collaboration with Strong Memorial Hospital Clinical Toxicology Laboratory. PSI sensor results showed improved clinical specificity over current commercial opiate immunoassay techniques and therefore, identified potential for a reduction in false-negative and false-positive screening results. Here, we demonstrate for the first time, successful clinical capability of a PSi sensor to detect opiates as a model target in real-world patient samples. The final part of this thesis explores novel sensor designs to leverage the tunable optical properties of PSi photonic devices and facilitate colorimetric readout of molecular recognition events by the unaided eye. Such a design is ideal for uncomplicated diagnostic screening at point-of-care as no instrumentation is needed for result readout. The photonic PSi transducers were integrated with target analyte-responsive hydrogels (TRAP-gels) that upon exposure to a target solution would swell and dissolute, inducing material property changes that were optically detected by the incorporated PSi transducer. This strategy extends target detection throughout the 3-ll internal volume of the PSi, improving upon current techniques that limit detection to the surface area (2-ll) of PSi. Work to acheive this approach involved design of TRAP-gel networks, polymer synthesis and characterization techniques, and optical characterization of the hybrid hydrogel-PSi material sensor. Successful implementation of a hybrid sensor design was exhibited for a. model chemical target (reducing agent), in which visual colorimetric change from red to green was observed for above-threshold exposure to the chemical target. In addition, initial proof-of-concept of an opiate responsive TRAP-gel is also demonstrated where cross-links are formed between antibody-antigen interactions and exposure to opiates induces bulk gel dissolution.

Convolutional neural network using generated data for SAR ATR with limited samples

NASA Astrophysics Data System (ADS)

Cong, Longjian; Gao, Lei; Zhang, Hui; Sun, Peng

2018-03-01

Being able to adapt all weather at all times, it has been a hot research topic that using Synthetic Aperture Radar(SAR) for remote sensing. Despite all the well-known advantages of SAR, it is hard to extract features because of its unique imaging methodology, and this challenge attracts the research interest of traditional Automatic Target Recognition(ATR) methods. With the development of deep learning technologies, convolutional neural networks(CNNs) give us another way out to detect and recognize targets, when a huge number of samples are available, but this premise is often not hold, when it comes to monitoring a specific type of ships. In this paper, we propose a method to enhance the performance of Faster R-CNN with limited samples to detect and recognize ships in SAR images.

Emotionally conditioning the target-speech voice enhances recognition of the target speech under "cocktail-party" listening conditions.

PubMed

Lu, Lingxi; Bao, Xiaohan; Chen, Jing; Qu, Tianshu; Wu, Xihong; Li, Liang

2018-05-01

Under a noisy "cocktail-party" listening condition with multiple people talking, listeners can use various perceptual/cognitive unmasking cues to improve recognition of the target speech against informational speech-on-speech masking. One potential unmasking cue is the emotion expressed in a speech voice, by means of certain acoustical features. However, it was unclear whether emotionally conditioning a target-speech voice that has none of the typical acoustical features of emotions (i.e., an emotionally neutral voice) can be used by listeners for enhancing target-speech recognition under speech-on-speech masking conditions. In this study we examined the recognition of target speech against a two-talker speech masker both before and after the emotionally neutral target voice was paired with a loud female screaming sound that has a marked negative emotional valence. The results showed that recognition of the target speech (especially the first keyword in a target sentence) was significantly improved by emotionally conditioning the target speaker's voice. Moreover, the emotional unmasking effect was independent of the unmasking effect of the perceived spatial separation between the target speech and the masker. Also, (skin conductance) electrodermal responses became stronger after emotional learning when the target speech and masker were perceptually co-located, suggesting an increase of listening efforts when the target speech was informationally masked. These results indicate that emotionally conditioning the target speaker's voice does not change the acoustical parameters of the target-speech stimuli, but the emotionally conditioned vocal features can be used as cues for unmasking target speech.

Target recognition of ladar range images using even-order Zernike moments.

PubMed

Liu, Zheng-Jun; Li, Qi; Xia, Zhi-Wei; Wang, Qi

2012-11-01

Ladar range images have attracted considerable attention in automatic target recognition fields. In this paper, Zernike moments (ZMs) are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of target recognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks (BPNNs) are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis. The experimental results demonstrate that combining the even-order ZMs with serial-parallel BPNNs can significantly improve the recognition rate for small samples.

Sensor fusion approaches for EMI and GPR-based subsurface threat identification

NASA Astrophysics Data System (ADS)

Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

2011-06-01

Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buried objects that threaten the lives of Soldiers and civilians. However, while neither GPR nor EMI sensing alone can provide optimal detection across all target types, the two approaches are highly complementary. As a result, many landmine systems seek to make use of both sensing modalities simultaneously and fuse the results from both sensors to improve detection performance for targets with widely varying metal content and GPR responses. Despite this, little work has focused on large-scale comparisons of different approaches to sensor fusion and machine learning for combining data from these highly orthogonal phenomenologies. In this work we explore a wide array of pattern recognition techniques for algorithm development and sensor fusion. Results with the ARA Nemesis landmine detection system suggest that nonlinear and non-parametric classification algorithms provide significant performance benefits for single-sensor algorithm development, and that fusion of multiple algorithms can be performed satisfactorily using basic parametric approaches, such as logistic discriminant classification, for the targets under consideration in our data sets.

Container-code recognition system based on computer vision and deep neural networks

NASA Astrophysics Data System (ADS)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Sensitive test for sea mine identification based on polarization-aided image processing.

PubMed

Leonard, I; Alfalou, A; Brosseau, C

2013-12-02

Techniques are widely sought to detect and identify sea mines. This issue is characterized by complicated mine shapes and underwater light propagation dependencies. In a preliminary study we use a preprocessing step for denoising underwater images before applying the algorithm for mine detection. Once a mine is detected, the protocol for identifying it is activated. Among many correlation filters, we have focused our attention on the asymmetric segmented phase-only filter for quantifying the recognition rate because it allows us to significantly increase the number of reference images in the fabrication of this filter. Yet they are not entirely satisfactory in terms of recognition rate and the obtained images revealed to be of low quality. In this report, we propose a way to improve upon this preliminary study by using a single wavelength polarimetric camera in order to denoise the images. This permits us to enhance images and improve depth visibility. We present illustrative results using in situ polarization imaging of a target through a milk-water mixture and demonstrate that our challenging objective of increasing the detection rate and decreasing the false alarm rate has been achieved.

Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images

NASA Astrophysics Data System (ADS)

Yao, Shoukui; Qin, Xiaojuan

2018-02-01

Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.

Constraints in distortion-invariant target recognition system simulation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Razzaque, Md A.

2000-11-01

Automatic target recognition (ATR) is a mature but active research area. In an earlier paper, we proposed a novel ATR approach for recognition of targets varying in fine details, rotation, and translation using a Learning Vector Quantization (LVQ) Neural Network (NN). The proposed approach performed segmentation of multiple objects and the identification of the objects using LVQNN. In this current paper, we extend the previous approach for recognition of targets varying in rotation, translation, scale, and combination of all three distortions. We obtain the analytical results of the system level design to show that the approach performs well with some constraints. The first constraint determines the size of the input images and input filters. The second constraint shows the limits on amount of rotation, translation, and scale of input objects. We present the simulation verification of the constraints using DARPA's Moving and Stationary Target Recognition (MSTAR) images with different depression and pose angles. The simulation results using MSTAR images verify the analytical constraints of the system level design.

Passive Polarimetric Information Processing for Target Classification

NASA Astrophysics Data System (ADS)

Sadjadi, Firooz; Sadjadi, Farzad

Polarimetric sensing is an area of active research in a variety of applications. In particular, the use of polarization diversity has been shown to improve performance in automatic target detection and recognition. Within the diverse scope of polarimetric sensing, the field of passive polarimetric sensing is of particular interest. This chapter presents several new methods for gathering in formation using such passive techniques. One method extracts three-dimensional (3D) information and surface properties using one or more sensors. Another method extracts scene-specific algebraic expressions that remain unchanged under polariza tion transformations (such as along the transmission path to the sensor).

Remote sensing based on hyperspectral data analysis

NASA Astrophysics Data System (ADS)

Sharifahmadian, Ershad

In remote sensing, accurate identification of far objects, especially concealed objects is difficult. In this study, to improve object detection from a distance, the hyperspecral imaging and wideband technology are employed with the emphasis on wideband radar. As the wideband data includes a broad range of frequencies, it can reveal information about both the surface of the object and its content. Two main contributions are made in this study: 1) Developing concept of return loss for target detection: Unlike typical radar detection methods which uses radar cross section to detect an object, it is possible to enhance the process of detection and identification of concealed targets using the wideband radar based on the electromagnetic characteristics --conductivity, permeability, permittivity, and return loss-- of materials. During the identification process, collected wideband data is evaluated with information from wideband signature library which has already been built. In fact, several classes (e.g. metal, wood, etc.) and subclasses (ex. metals with high conductivity) have been defined based on their electromagnetic characteristics. Materials in a scene are then classified based on these classes. As an example, materials with high electrical conductivity can be conveniently detected. In fact, increasing relative conductivity leads to a reduction in the return loss. Therefore, metals with high conductivity (ex. copper) shows stronger radar reflections compared with metals with low conductivity (ex. stainless steel). Thus, it is possible to appropriately discriminate copper from stainless steel. 2) Target recognition techniques: To detect and identify targets, several techniques have been proposed, in particular the Multi-Spectral Wideband Radar Image (MSWRI) which is able to localize and identify concealed targets. The MSWRI is based on the theory of robust capon beamformer. During identification process, information from wideband signature library is utilized. The WB signature library includes such parameters as conductivity, permeability, permittivity, and return loss at different frequencies for possible materials related to a target. In the MSWRI approach, identification procedure is performed by calculating the RLs at different selected frequencies. Based on similarity of the calculated RLs and RL from WB signature library, targets are detected and identified. Based on the simulation and experimental results, it is concluded that the MSWRI technique is a promising approach for standoff target detection.

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

NASA Astrophysics Data System (ADS)

Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

2017-05-01

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high-definition video exploitation.

Specific and Modular Binding Code for Cytosine Recognition in Pumilio/FBF (PUF) RNA-binding Domains

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, Shuyun; Wang, Yang; Cassidy-Amstutz, Caleb

2011-10-28

Pumilio/fem-3 mRNA-binding factor (PUF) proteins possess a recognition code for bases A, U, and G, allowing designed RNA sequence specificity of their modular Pumilio (PUM) repeats. However, recognition side chains in a PUM repeat for cytosine are unknown. Here we report identification of a cytosine-recognition code by screening random amino acid combinations at conserved RNA recognition positions using a yeast three-hybrid system. This C-recognition code is specific and modular as specificity can be transferred to different positions in the RNA recognition sequence. A crystal structure of a modified PUF domain reveals specific contacts between an arginine side chain and themore » cytosine base. We applied the C-recognition code to design PUF domains that recognize targets with multiple cytosines and to generate engineered splicing factors that modulate alternative splicing. Finally, we identified a divergent yeast PUF protein, Nop9p, that may recognize natural target RNAs with cytosine. This work deepens our understanding of natural PUF protein target recognition and expands the ability to engineer PUF domains to recognize any RNA sequence.« less

Using eye movements as an index of implicit face recognition in autism spectrum disorder.

PubMed

Hedley, Darren; Young, Robyn; Brewer, Neil

2012-10-01

Individuals with an autism spectrum disorder (ASD) typically show impairment on face recognition tasks. Performance has usually been assessed using overt, explicit recognition tasks. Here, a complementary method involving eye tracking was used to examine implicit face recognition in participants with ASD and in an intelligence quotient-matched non-ASD control group. Differences in eye movement indices between target and foil faces were used as an indicator of implicit face recognition. Explicit face recognition was assessed using old-new discrimination and reaction time measures. Stimuli were faces of studied (target) or unfamiliar (foil) persons. Target images at test were either identical to the images presented at study or altered by changing the lighting, pose, or by masking with visual noise. Participants with ASD performed worse than controls on the explicit recognition task. Eye movement-based measures, however, indicated that implicit recognition may not be affected to the same degree as explicit recognition. Autism Res 2012, 5: 363-379. © 2012 International Society for Autism Research, Wiley Periodicals, Inc. © 2012 International Society for Autism Research, Wiley Periodicals, Inc.

An electromagnetic induction method for underground target detection and characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bartel, L.C.; Cress, D.H.

1997-01-01

An improved capability for subsurface structure detection is needed to support military and nonproliferation requirements for inspection and for surveillance of activities of threatening nations. As part of the DOE/NN-20 program to apply geophysical methods to detect and characterize underground facilities, Sandia National Laboratories (SNL) initiated an electromagnetic induction (EMI) project to evaluate low frequency electromagnetic (EM) techniques for subsurface structure detection. Low frequency, in this case, extended from kilohertz to hundreds of kilohertz. An EMI survey procedure had already been developed for borehole imaging of coal seams and had successfully been applied in a surface mode to detect amore » drug smuggling tunnel. The SNL project has focused on building upon the success of that procedure and applying it to surface and low altitude airborne platforms. Part of SNL`s work has focused on improving that technology through improved hardware and data processing. The improved hardware development has been performed utilizing Laboratory Directed Research and Development (LDRD) funding. In addition, SNL`s effort focused on: (1) improvements in modeling of the basic geophysics of the illuminating electromagnetic field and its coupling to the underground target (partially funded using LDRD funds) and (2) development of techniques for phase-based and multi-frequency processing and spatial processing to support subsurface target detection and characterization. The products of this project are: (1) an evaluation of an improved EM gradiometer, (2) an improved gradiometer concept for possible future development, (3) an improved modeling capability, (4) demonstration of an EM wave migration method for target recognition, and a demonstration that the technology is capable of detecting targets to depths exceeding 25 meters.« less

Ultrasensitive and Multiple Disease-Related MicroRNA Detection Based on Tetrahedral DNA Nanostructures and Duplex-Specific Nuclease-Assisted Signal Amplification.

PubMed

Xu, Fang; Dong, Haifeng; Cao, Yu; Lu, Huiting; Meng, Xiangdan; Dai, Wenhao; Zhang, Xueji; Al-Ghanim, Khalid Abdullah; Mahboob, Shahid

2016-12-14

A highly sensitive and multiple microRNA (miRNA) detection method by combining three-dimensional (3D) DNA tetrahedron-structured probes (TSPs) to increase the probe reactivity and accessibility with duplex-specific nuclease (DSN) for signal amplification for sensitive miRNA detection was proposed. Briefly, 3D DNA TSPs labeled with different fluorescent dyes for specific target miRNA recognition were modified on a gold nanoparticle (GNP) surface to increase the reactivity and accessibility. Upon hybridization with a specific target, the TSPs immobilized on the GNP surface hybridized with the corresponding target miRNA to form DNA-RNA heteroduplexes, and the DSN can recognize the formed DNA-RNA heteroduplexes to hydrolyze the DNA in the heteroduplexes to produce a specific fluorescent signal corresponding to a specific miRNA, while the released target miRNA strands can initiate another cycle, resulting in a significant signal amplification for sensitive miRNA detection. Different targets can produce different fluorescent signals, leading to the development of a sensitive detection for multiple miRNAs in a homogeneous solution. Under optimized conditions, the proposed assay can simultaneously detect three different miRNAs in a homogeneous solution with a logarithmic linear range spanning 5 magnitudes (10 -12 -10 -16 ) and achieving a limit of detection down to attomolar concentrations. Meanwhile, the proposed miRNA assay exhibited the capability of discriminating single bases (three bases mismatched miRNAs) and showed good eligibility in the analysis of miRNAs extracted from cell lysates and miRNAs in cell incubation media, which indicates its potential use in biomedical research and clinical analysis.

Assessment of Spectral and Temporal Resolution in Cochlear Implant Users Using Psychoacoustic Discrimination and Speech Cue Categorization.

PubMed

Winn, Matthew B; Won, Jong Ho; Moon, Il Joon

This study was conducted to measure auditory perception by cochlear implant users in the spectral and temporal domains, using tests of either categorization (using speech-based cues) or discrimination (using conventional psychoacoustic tests). The authors hypothesized that traditional nonlinguistic tests assessing spectral and temporal auditory resolution would correspond to speech-based measures assessing specific aspects of phonetic categorization assumed to depend on spectral and temporal auditory resolution. The authors further hypothesized that speech-based categorization performance would ultimately be a superior predictor of speech recognition performance, because of the fundamental nature of speech recognition as categorization. Nineteen cochlear implant listeners and 10 listeners with normal hearing participated in a suite of tasks that included spectral ripple discrimination, temporal modulation detection, and syllable categorization, which was split into a spectral cue-based task (targeting the /ba/-/da/ contrast) and a timing cue-based task (targeting the /b/-/p/ and /d/-/t/ contrasts). Speech sounds were manipulated to contain specific spectral or temporal modulations (formant transitions or voice onset time, respectively) that could be categorized. Categorization responses were quantified using logistic regression to assess perceptual sensitivity to acoustic phonetic cues. Word recognition testing was also conducted for cochlear implant listeners. Cochlear implant users were generally less successful at utilizing both spectral and temporal cues for categorization compared with listeners with normal hearing. For the cochlear implant listener group, spectral ripple discrimination was significantly correlated with the categorization of formant transitions; both were correlated with better word recognition. Temporal modulation detection using 100- and 10-Hz-modulated noise was not correlated either with the cochlear implant subjects' categorization of voice onset time or with word recognition. Word recognition was correlated more closely with categorization of the controlled speech cues than with performance on the psychophysical discrimination tasks. When evaluating people with cochlear implants, controlled speech-based stimuli are feasible to use in tests of auditory cue categorization, to complement traditional measures of auditory discrimination. Stimuli based on specific speech cues correspond to counterpart nonlinguistic measures of discrimination, but potentially show better correspondence with speech perception more generally. The ubiquity of the spectral (formant transition) and temporal (voice onset time) stimulus dimensions across languages highlights the potential to use this testing approach even in cases where English is not the native language.

Fractal analysis of seafloor textures for target detection in synthetic aperture sonar imagery

NASA Astrophysics Data System (ADS)

Nabelek, T.; Keller, J.; Galusha, A.; Zare, A.

2018-04-01

Fractal analysis of an image is a mathematical approach to generate surface related features from an image or image tile that can be applied to image segmentation and to object recognition. In undersea target countermeasures, the targets of interest can appear as anomalies in a variety of contexts, visually different textures on the seafloor. In this paper, we evaluate the use of fractal dimension as a primary feature and related characteristics as secondary features to be extracted from synthetic aperture sonar (SAS) imagery for the purpose of target detection. We develop three separate methods for computing fractal dimension. Tiles with targets are compared to others from the same background textures without targets. The different fractal dimension feature methods are tested with respect to how well they can be used to detect targets vs. false alarms within the same contexts. These features are evaluated for utility using a set of image tiles extracted from a SAS data set generated by the U.S. Navy in conjunction with the Office of Naval Research. We find that all three methods perform well in the classification task, with a fractional Brownian motion model performing the best among the individual methods. We also find that the secondary features are just as useful, if not more so, in classifying false alarms vs. targets. The best classification accuracy overall, in our experimentation, is found when the features from all three methods are combined into a single feature vector.

[Multi-Target Recognition of Internal and External Defects of Potato by Semi-Transmission Hyperspectral Imaging and Manifold Learning Algorithm].

PubMed

Huang, Tao; Li, Xiao-yu; Jin, Rui; Ku, Jing; Xu, Sen-miao; Xu, Meng-ling; Wu, Zhen-zhong; Kong, De-guo

2015-04-01

The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.

Action Recognition in a Crowded Environment

PubMed Central

Nieuwenhuis, Judith; Bülthoff, Isabelle; Barraclough, Nick; de la Rosa, Stephan

2017-01-01

So far, action recognition has been mainly examined with small point-light human stimuli presented alone within a narrow central area of the observer’s visual field. Yet, we need to recognize the actions of life-size humans viewed alone or surrounded by bystanders, whether they are seen in central or peripheral vision. Here, we examined the mechanisms in central vision and far periphery (40° eccentricity) involved in the recognition of the actions of a life-size actor (target) and their sensitivity to the presence of a crowd surrounding the target. In Experiment 1, we used an action adaptation paradigm to probe whether static or idly moving crowds might interfere with the recognition of a target’s action (hug or clap). We found that this type of crowds whose movements were dissimilar to the target action hardly affected action recognition in central and peripheral vision. In Experiment 2, we examined whether crowd actions that were more similar to the target actions affected action recognition. Indeed, the presence of that crowd diminished adaptation aftereffects in central vision as wells as in the periphery. We replicated Experiment 2 using a recognition task instead of an adaptation paradigm. With this task, we found evidence of decreased action recognition accuracy, but this was significant in peripheral vision only. Our results suggest that the presence of a crowd carrying out actions similar to that of the target affects its recognition. We outline how these results can be understood in terms of high-level crowding effects that operate on action-sensitive perceptual channels. PMID:29308177

Integrated Color Coding and Monochrome Multi-Spectral Fusion

DTIC Science & Technology

1999-01-01

Suppl. pg s1002. [Katz 1987 ] Katz et al, "Application of Spectral Filtering to Missile Detection Using Staring Sensors at MWIR Wavelengths...34, Proceedings of the IRIS Conf. on Targets, Backgrounds, and Discrimination, Feb. 1987 [Morrone 1989], Morrone M.C. and D.C.,”Discrimination of spatial phase in...April) 1990, Orlando, FL. [Subramaniam 1997] Subramaniam and Biederman “Effect of Contrast Reversal on Object Recognition (ARVO 1997) Investigative

Application of automatic threshold in dynamic target recognition with low contrast

NASA Astrophysics Data System (ADS)

Miao, Hua; Guo, Xiaoming; Chen, Yu

2014-11-01

Hybrid photoelectric joint transform correlator can realize automatic real-time recognition with high precision through the combination of optical devices and electronic devices. When recognizing targets with low contrast using photoelectric joint transform correlator, because of the difference of attitude, brightness and grayscale between target and template, only four to five frames of dynamic targets can be recognized without any processing. CCD camera is used to capture the dynamic target images and the capturing speed of CCD is 25 frames per second. Automatic threshold has many advantages like fast processing speed, effectively shielding noise interference, enhancing diffraction energy of useful information and better reserving outline of target and template, so this method plays a very important role in target recognition with optical correlation method. However, the automatic obtained threshold by program can not achieve the best recognition results for dynamic targets. The reason is that outline information is broken to some extent. Optimal threshold is obtained by manual intervention in most cases. Aiming at the characteristics of dynamic targets, the processing program of improved automatic threshold is finished by multiplying OTSU threshold of target and template by scale coefficient of the processed image, and combining with mathematical morphology. The optimal threshold can be achieved automatically by improved automatic threshold processing for dynamic low contrast target images. The recognition rate of dynamic targets is improved through decreased background noise effect and increased correlation information. A series of dynamic tank images with the speed about 70 km/h are adapted as target images. The 1st frame of this series of tanks can correlate only with the 3rd frame without any processing. Through OTSU threshold, the 80th frame can be recognized. By automatic threshold processing of the joint images, this number can be increased to 89 frames. Experimental results show that the improved automatic threshold processing has special application value for the recognition of dynamic target with low contrast.

Information-based approach to performance estimation and requirements allocation in multisensor fusion for target recognition

NASA Astrophysics Data System (ADS)

Harney, Robert C.

1997-03-01

A novel methodology offering the potential for resolving two of the significant problems of implementing multisensor target recognition systems, i.e., the rational selection of a specific sensor suite and optimal allocation of requirements among sensors, is presented. Based on a sequence of conjectures (and their supporting arguments) concerning the relationship of extractable information content to recognition performance of a sensor system, a set of heuristics (essentially a reformulation of Johnson's criteria applicable to all sensor and data types) is developed. An approach to quantifying the information content of sensor data is described. Coupling this approach with the widely accepted Johnson's criteria for target recognition capabilities results in a quantitative method for comparing the target recognition ability of diverse sensors (imagers, nonimagers, active, passive, electromagnetic, acoustic, etc.). Extension to describing the performance of multiple sensors is straightforward. The application of the technique to sensor selection and requirements allocation is discussed.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

PubMed Central

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-01-01

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. PMID:29695113

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

PubMed

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-04-24

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

Automatic Surveying For Hazard Prevention On Glacier De GiÉtro, Switzerland

NASA Astrophysics Data System (ADS)

Bauder, A.; Funk, M.; Bösch, H.

Breaking off of large ice masses from the steep tongue of Glacier de Giétro may endanger a nearby reservoir. Such a falling ice mass could cause an oversplash over the dam at timeof a nearly filled lake. For this reason the glacier has been monitored intensively since the 1960's. An automatic theodolite was installed three years ago. It allows continuous displacement measurements of several targets on the glacier in order to detect short-term acceleration events. The installation includes a telemetric data transmission, which provides for immediate recognition of hazardous situations and early alarming. The obtained data were analysed in terms of precision and performance of the applied method. A high temporal resolution was gained. The comparison with traditional ob- servations shows clearly the potential of modern instruments to improve monitoring schems. We summarize the main results of this study and discuss the applicability of a modern motorized theodolite with target tracking and recognition ability for moni- toring purposes.

Helicase-Dependent Isothermal Amplification of DNA and RNA by Using Self-Avoiding Molecular Recognition Systems.

PubMed

Yang, Zunyi; McLendon, Chris; Hutter, Daniel; Bradley, Kevin M; Hoshika, Shuichi; Frye, Carole B; Benner, Steven A

2015-06-15

Assays that detect DNA or RNA (xNA) are highly sensitive, as small amounts of xNA can be amplified by PCR. Unfortunately, PCR is inconvenient in low-resource environments, and requires equipment and power that might not be available in these environments. Isothermal procedures, which avoid thermal cycling, are often confounded by primer dimers, off-target priming, and other artifacts. Here, we show how a "self avoiding molecular recognition system" (SAMRS) eliminates these artifacts and gives clean amplicons in a helicase-dependent isothermal amplification (SAMRS-HDA). We also show that incorporating SAMRS into the 3'-ends of primers facilitates the design and screening of primers for HDA assays. Finally, we show that SAMRS-HDA can be twofold multiplexed, difficult to achieve with HDA using standard primers. Thus, SAMRS-HDA is a more versatile approach than standard HDA, with a broader applicability for xNA-targeted diagnostics and research. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A CMOS Front-End With Integrated Magnetoresistive Sensors for Biomolecular Recognition Detection Applications.

PubMed

Costa, Tiago; Cardoso, Filipe A; Germano, Jose; Freitas, Paulo P; Piedade, Moises S

2017-10-01

The development of giant magnetoresistive (GMR) sensors has demonstrated significant advantages in nanomedicine, particularly for ultrasensitive point-of-care diagnostics. To this end, the detection system is required to be compact, portable, and low power consuming at the same time that a maximum signal to noise ratio is maintained. This paper reports a CMOS front-end with integrated magnetoresistive sensors for biomolecular recognition detection applications. Based on the characterization of the GMR sensor's signal and noise, CMOS building blocks (i.e., current source, multiplexers, and preamplifier) were designed targeting a negligible noise when compared with the GMR sensor's noise and a low power consumption. The CMOS front-end was fabricated using AMS [Formula: see text] technology and the magnetoresistive sensors were post-fabricated on top of the CMOS chip with high yield ( [Formula: see text]). Due to its low circuit noise (16 [Formula: see text]) and overall equivalent magnetic noise ([Formula: see text]), the full system was able to detect 250 nm magnetic nanoparticles with a circuit imposed signal-to-noise ratio degradation of only -1.4 dB. Furthermore, the low power consumption (6.5 mW) and small dimensions ([Formula: see text] ) of the presented solution guarantees the portability of the detection system allowing its usage at the point-of-care.

Target recognition of log-polar ladar range images using moment invariants

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jie; Yu, Haoyong

2017-01-01

The ladar range image has received considerable attentions in the automatic target recognition field. However, previous research does not cover target recognition using log-polar ladar range images. Therefore, we construct a target recognition system based on log-polar ladar range images in this paper. In this system combined moment invariants and backpropagation neural network are selected as shape descriptor and shape classifier, respectively. In order to fully analyze the effect of log-polar sampling pattern on recognition result, several comparative experiments based on simulated and real range images are carried out. Eventually, several important conclusions are drawn: (i) if combined moments are computed directly by log-polar range images, translation, rotation and scaling invariant properties of combined moments will be invalid (ii) when object is located in the center of field of view, recognition rate of log-polar range images is less sensitive to the changing of field of view (iii) as object position changes from center to edge of field of view, recognition performance of log-polar range images will decline dramatically (iv) log-polar range images has a better noise robustness than Cartesian range images. Finally, we give a suggestion that it is better to divide field of view into recognition area and searching area in the real application.

Adaptive optics to enhance target recognition

NASA Astrophysics Data System (ADS)

McAulay, Alastair D.

2012-06-01

Target recognition can be enhanced by reducing image degradation due to atmospheric turbulence. This is accomplished by an adaptive optic system. We discuss the forms of degradation when a target is viewed through the atmosphere1: scintillation from ground targets on a hot day in visible or infrared light; beam spreading and wavering around in time; atmospheric turbulence caused by motion of the target or by weather. In the case of targets we can use a beacon laser that reflects back from the target into a wavefront detector to measure the effects of turbulence on propagation to and from the target before imaging.1 A deformable mirror then corrects the wavefront shape of the transmitted, reflected or scattered data for enhanced imaging. Further, recognition of targets is enhanced by performing accurate distance measurements to localized parts of the target using lidar. Distance is obtained by sending a short pulse to the target and measuring the time for the pulse to return. There is inadequate time to scan the complete field of view so that the beam must be steered to regions of interest such as extremities of the image during image recognition. Distance is particularly valuable to recognize fine features in range along the target or when segmentation is required to separate a target from background or from other targets. We discuss the issues involved.

A fluorescent glycosyl-imprinted polymer for pH and temperature regulated sensing of target glycopeptide antibiotic.

PubMed

Chen, Kuncai; He, Rong; Luo, Xiaoyan; Qin, Pengzhe; Tan, Lei; Tang, Youwen; Yang, Zhicong

2017-08-15

This paper demonstrates a new strategy for developing a fluorescent glycosyl-imprinted polymer for pH and temperature regulated sensing of target glycopeptide antibiotic. The technique provides amino modified Mn-doped ZnS QDs as fluorescent supports, 4-vinylphenylbronic acid as a covalent monomer, N-isopropyl acrylamide as a thermo-responsive monomer in combination with acrylamide as a non-covalent monomer, and glycosyl moiety of a glycopeptide antibiotic as a template to produce fluorescent molecularly imprinted polymer (FMIP) in aqueous solution. The FMIP can alter its functional moieties and structure with pH and temperature stimulation. This allows recognition of target molecules through control of pH and temperature. The fluorescence intensity of the FMIP was enhanced gradually as the concentration of telavancin increased, and showed selective recognition toward the target glycopeptide antibiotic preferentially among other antibiotics. Using the FMIP as a sensing material, good linear correlations were obtained over the concentration range of 3.0-300.0μg/L and with a low limit of detection of 1.0μg/L. The analysis results of telavancin in real samples were consistent with that obtained by liquid chromatography tandem mass spectrometry. Copyright © 2017 Elsevier B.V. All rights reserved.

Electrophysiological evidence that top-down knowledge controls working memory processing for subsequent visual search.

PubMed

Kawashima, Tomoya; Matsumoto, Eriko

2016-03-23

Items in working memory guide visual attention toward a memory-matching object. Recent studies have shown that when searching for an object this attentional guidance can be modulated by knowing the probability that the target will match an item in working memory. Here, we recorded the P3 and contralateral delay activity to investigate how top-down knowledge controls the processing of working memory items. Participants performed memory task (recognition only) and memory-or-search task (recognition or visual search) in which they were asked to maintain two colored oriented bars in working memory. For visual search, we manipulated the probability that target had the same color as memorized items (0, 50, or 100%). Participants knew the probabilities before the task. Target detection in 100% match condition was faster than that in 50% match condition, indicating that participants used their knowledge of the probabilities. We found that the P3 amplitude in 100% condition was larger than in other conditions and that contralateral delay activity amplitude did not vary across conditions. These results suggest that more attention was allocated to the memory items when observers knew in advance that their color would likely match a target. This led to better search performance despite using qualitatively equal working memory representations.

Perceptual fluency and affect without recognition.

PubMed

Anand, P; Sternthal, B

1991-05-01

A dichotic listening task was used to investigate the affect-without-recognition phenomenon. Subjects performed a distractor task by responding to the information presented in one ear while ignoring the target information presented in the other ear. The subjects' recognition of and affect toward the target information as well as toward foils was measured. The results offer evidence for the affect-without-recognition phenomenon. Furthermore, the data suggest that the subjects' affect toward the stimuli depended primarily on the extent to which the stimuli were perceived as familiar (i.e., subjective familiarity), and this perception was influenced by the ear in which the distractor or the target information was presented. These data are interpreted in terms of current models of recognition memory and hemispheric lateralization.

Monkey’s short-term auditory memory nearly abolished by combined removal of the rostral superior temporal gyrus and rhinal cortices

PubMed Central

Fritz, Jonathan B.; Malloy, Megan; Mishkin, Mortimer; Saunders, Richard C.

2016-01-01

While monkeys easily acquire the rules for performing visual and tactile delayed matching-to-sample, a method for testing recognition memory, they have extraordinary difficulty acquiring a similar rule in audition. Another striking difference between the modalities is that whereas bilateral ablation of the rhinal cortex (RhC) leads to profound impairment in visual and tactile recognition, the same lesion has no detectable effect on auditory recognition memory (Fritz et al., 2005). In our previous study, a mild impairment in auditory memory was obtained following bilateral ablation of the entire medial temporal lobe (MTL), including the RhC, and an equally mild effect was observed after bilateral ablation of the auditory cortical areas in the rostral superior temporal gyrus (rSTG). In order to test the hypothesis that each of these mild impairments was due to partial disconnection of acoustic input to a common target (e.g., the ventromedial prefrontal cortex), in the current study we examined the effects of a more complete auditory disconnection of this common target by combining the removals of both the rSTG and the MTL. We found that the combined lesion led to forgetting thresholds (performance at 75% accuracy) that fell precipitously from the normal retention duration of ~30–40 seconds to a duration of ~1–2 seconds, thus nearly abolishing auditory recognition memory, and leaving behind only a residual echoic memory. PMID:26707975

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah

2017-02-01

Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

Scale invariant SURF detector and automatic clustering segmentation for infrared small targets detection

NASA Astrophysics Data System (ADS)

Zhang, Haiying; Bai, Jiaojiao; Li, Zhengjie; Liu, Yan; Liu, Kunhong

2017-06-01

The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture. Many researchers focus on mining more discriminative information of targets in temporal-spatial. However, such information may not be available with the change of imaging environments, and the targets size and intensity keep changing in different imaging distance. So in this paper, we propose a novel research scheme using density-based clustering and backtracking strategy. In this scheme, the speeded up robust feature (SURF) detector is applied to capture candidate targets in single frame at first. And then, these points are mapped into one frame, so that target traces form a local aggregation pattern. In order to isolate the targets from noises, a newly proposed density-based clustering algorithm, fast search and find of density peak (FSFDP for short), is employed to cluster targets by the spatial intensive distribution. Two important factors of the algorithm, percent and γ , are exploited fully to determine the clustering scale automatically, so as to extract the trace with highest clutter suppression ratio. And at the final step, a backtracking algorithm is designed to detect and discriminate target trace as well as to eliminate clutter. The consistence and continuity of the short-time target trajectory in temporal-spatial is incorporated into the bounding function to speed up the pruning. Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR). Furthermore, it avoids constructing the candidate target trajectory searching space, so its time complexity is limited to a polynomial level. The extensive experimental results show that it has superior performance in probability of detection (Pd) and false alarm suppressing rate aiming at variety of complex backgrounds.

Pharmacologic suppression of target cell recognition by engineered T cells expressing chimeric T-cell receptors.

PubMed

Alvarez-Vallina, L; Yañez, R; Blanco, B; Gil, M; Russell, S J

2000-04-01

Adoptive therapy with autologous T cells expressing chimeric T-cell receptors (chTCRs) is of potential interest for the treatment of malignancy. To limit possible T-cell-mediated damage to normal tissues that weakly express the targeted tumor antigen (Ag), we have tested a strategy for the suppression of target cell recognition by engineered T cells. Jurkat T cells were transduced with an anti-hapten chTCR tinder the control of a tetracycline-suppressible promoter and were shown to respond to Ag-positive (hapten-coated) but not to Ag-negative target cells. The engineered T cells were then reacted with hapten-coated target cells at different effector to target cell ratios before and after exposure to tetracycline. When the engineered T cells were treated with tetracycline, expression of the chTCR was greatly decreased and recognition of the hapten-coated target cells was completely suppressed. Tetracycline-mediated suppression of target cell recognition by engineered T cells may be a useful strategy to limit the toxicity of the approach to cancer gene therapy.

Ligation-rolling circle amplification combined with γ-cyclodextrin mediated stemless molecular beacon for sensitive and specific genotyping of single-nucleotide polymorphism.

PubMed

Zou, Zhen; Qing, Zhihe; He, Xiaoxiao; Wang, Kemin; He, Dinggeng; Shi, Hui; Yang, Xue; Qing, Taiping; Yang, Xiaoxiao

2014-07-01

A novel approach for highly sensitive and selective genotyping of single-nucleotide polymorphism (SNP) has been developed based on ligation-rolling circle amplification (L-RCA) and stemless molecular beacon. In this approach, two tailored DNA probes were involved. The stemless molecular beacon, formed through the inclusion interactions of γ-cyclodextrin (γ-CD) and bis-pyrene labeled DNA fragment, was served as signal probe. In the absence of mutant target, the two pyrene molecules were bound in the γ-CD cavity to form an excimer and showed a strong fluorescence at 475 nm. It was here named γ-CD-P-MB. The padlock DNA probe was designed as recognition probe. Upon the recognition of a point mutation DNA targets, the padlock probe was ligated to generate a circular template. An RCA amplification was then initiated using the circular template in the presence of Phi29 polymerase and dNTPs. The L-RCA products, containing repetitive sequence units, subsequently hybridized with the γ-CD-P-MB. This made pyrene molecules away from γ-CD cavity and caused a decrease of excimer fluorescence. As a proof-of-concept, SNP typing of β-thalassemia gene at position -28 was investigated using this approach. The detection limit of mutated target was determined to be 40 fM. In addition, DNA ligase offered high fidelity in distinguishing the mismatched bases at the ligation site, resulting in positive detection of mutant target even when the ratio of the wildtype to the mutant is 999:1. Given these attractive characteristics, the developed approach might provide a great genotyping platform for pathogenic diagnosis and genetic analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

How attentional boost interacts with reward: the effect of dopaminergic medications in Parkinson's disease.

PubMed

Kéri, Szabolcs; Nagy, Helga; Levy-Gigi, Einat; Kelemen, Oguz

2013-12-01

There is widespread evidence that dopamine is implicated in the regulation of reward and salience. However, it is less known how these processes interact with attention and recognition memory. To explore this question, we used the attentional boost test in patients with Parkinson's disease (PD) before and after the administration of dopaminergic medications. Participants performed a visual letter detection task (remembering rewarded target letters and ignoring distractor letters) while also viewing a series of photos of natural and urban scenes in the background of the letters. The aim of the game was to retrieve the target letter after each trial and to win as much virtual money as possible. The recognition of background scenes was not rewarded. We enrolled 26 drug-naïve, newly diagnosed patients with PD and 25 healthy controls who were evaluated at baseline and follow-up. Patients with PD received dopamine agonists (pramipexole, ropinirole, rotigotine) during the 12-week follow-up period. At baseline, we found intact attentional boost in patients with PD: they were able to recognize target-associated scenes similarly to controls. At follow-up, patients with PD outperformed controls for both target- and distractor-associated scenes, but not when scenes were presented without letters. The alerting, orienting and executive components of attention were intact in PD. Enhanced attentional boost was replicated in a smaller group of patients with PD (n = 15) receiving l-3,4-dihydroxyphenylalanine (L-DOPA). These results suggest that dopaminergic medications facilitate attentional boost for background information regardless of whether the central task (letter detection) is rewarded or not. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Component-based target recognition inspired by human vision

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Agyepong, Kwabena

2009-05-01

In contrast with machine vision, human can recognize an object from complex background with great flexibility. For example, given the task of finding and circling all cars (no further information) in a picture, you may build a virtual image in mind from the task (or target) description before looking at the picture. Specifically, the virtual car image may be composed of the key components such as driver cabin and wheels. In this paper, we propose a component-based target recognition method by simulating the human recognition process. The component templates (equivalent to the virtual image in mind) of the target (car) are manually decomposed from the target feature image. Meanwhile, the edges of the testing image can be extracted by using a difference of Gaussian (DOG) model that simulates the spatiotemporal response in visual process. A phase correlation matching algorithm is then applied to match the templates with the testing edge image. If all key component templates are matched with the examining object, then this object is recognized as the target. Besides the recognition accuracy, we will also investigate if this method works with part targets (half cars). In our experiments, several natural pictures taken on streets were used to test the proposed method. The preliminary results show that the component-based recognition method is very promising.

Formation of target-specific binding sites in enzymes: solid-phase molecular imprinting of HRP

NASA Astrophysics Data System (ADS)

Czulak, J.; Guerreiro, A.; Metran, K.; Canfarotta, F.; Goddard, A.; Cowan, R. H.; Trochimczuk, A. W.; Piletsky, S.

2016-05-01

Here we introduce a new concept for synthesising molecularly imprinted nanoparticles by using proteins as macro-functional monomers. For a proof-of-concept, a model enzyme (HRP) was cross-linked using glutaraldehyde in the presence of glass beads (solid-phase) bearing immobilized templates such as vancomycin and ampicillin. The cross-linking process links together proteins and protein chains, which in the presence of templates leads to the formation of permanent target-specific recognition sites without adverse effects on the enzymatic activity. Unlike complex protein engineering approaches commonly employed to generate affinity proteins, the method proposed can be used to produce protein-based ligands in a short time period using native protein molecules. These affinity materials are potentially useful tools especially for assays since they combine the catalytic properties of enzymes (for signaling) and molecular recognition properties of antibodies. We demonstrate this concept in an ELISA-format assay where HRP imprinted with vancomycin and ampicillin replaced traditional enzyme-antibody conjugates for selective detection of templates at micromolar concentrations. This approach can potentially provide a fast alternative to raising antibodies for targets that do not require high assay sensitivities; it can also find uses as a biochemical research tool, as a possible replacement for immunoperoxidase-conjugates.Here we introduce a new concept for synthesising molecularly imprinted nanoparticles by using proteins as macro-functional monomers. For a proof-of-concept, a model enzyme (HRP) was cross-linked using glutaraldehyde in the presence of glass beads (solid-phase) bearing immobilized templates such as vancomycin and ampicillin. The cross-linking process links together proteins and protein chains, which in the presence of templates leads to the formation of permanent target-specific recognition sites without adverse effects on the enzymatic activity. Unlike complex protein engineering approaches commonly employed to generate affinity proteins, the method proposed can be used to produce protein-based ligands in a short time period using native protein molecules. These affinity materials are potentially useful tools especially for assays since they combine the catalytic properties of enzymes (for signaling) and molecular recognition properties of antibodies. We demonstrate this concept in an ELISA-format assay where HRP imprinted with vancomycin and ampicillin replaced traditional enzyme-antibody conjugates for selective detection of templates at micromolar concentrations. This approach can potentially provide a fast alternative to raising antibodies for targets that do not require high assay sensitivities; it can also find uses as a biochemical research tool, as a possible replacement for immunoperoxidase-conjugates. Electronic supplementary information (ESI) available: Additional circular dichroism data and nanoparticle tracking analysis trace. See DOI: 10.1039/c6nr02009g

Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments

NASA Astrophysics Data System (ADS)

Tehsin, Sara; Rehman, Saad; Riaz, Farhan; Saeed, Omer; Hassan, Ali; Khan, Muazzam; Alam, Muhammad S.

2017-05-01

A fully invariant system helps in resolving difficulties in object detection when camera or object orientation and position are unknown. In this paper, the proposed correlation filter based mechanism provides the capability to suppress noise, clutter and occlusion. Minimum Average Correlation Energy (MACE) filter yields sharp correlation peaks while considering the controlled correlation peak value. Difference of Gaussian (DOG) Wavelet has been added at the preprocessing stage in proposed filter design that facilitates target detection in orientation variant cluttered environment. Logarithmic transformation is combined with a DOG composite minimum average correlation energy filter (WMACE), capable of producing sharp correlation peaks despite any kind of geometric distortion of target object. The proposed filter has shown improved performance over some of the other variant correlation filters which are discussed in the result section.

Mispronunciation Detection for Language Learning and Speech Recognition Adaptation

ERIC Educational Resources Information Center

Ge, Zhenhao

2013-01-01

The areas of "mispronunciation detection" (or "accent detection" more specifically) within the speech recognition community are receiving increased attention now. Two application areas, namely language learning and speech recognition adaptation, are largely driving this research interest and are the focal points of this work.…

Sensitive SERS detection of DNA methyltransferase by target triggering primer generation-based multiple signal amplification strategy.

PubMed

Li, Ying; Yu, Chuanfeng; Han, Huixia; Zhao, Caisheng; Zhang, Xiaoru

2016-07-15

A novel and sensitive surface-enhanced Raman scattering (SERS) method is proposed for the assay of DNA methyltransferase (MTase) activity and evaluation of inhibitors by developing a target triggering primer generation-based multiple signal amplification strategy. By using of a duplex substrate for Dam MTase, two hairpin templates and a Raman probe, multiple signal amplification mode is achieved. Once recognized by Dam MTase, the duplex substrate can be cleaved by Dpn I endonuclease and two primers are released for triggering the multiple signal amplification reaction. Consequently, a wide dynamic range and remarkably high sensitivity are obtained under isothermal conditions. The detection limit is 2.57×10(-4)UmL(-1). This assay exhibits an excellent selectivity and is successfully applied in the screening of inhibitors for Dam MTase. In addition, this novel sensing system is potentially universal as the recognition element can be conveniently designed for other target analytes by changing the substrate of DNA MTase. Copyright © 2016 Elsevier B.V. All rights reserved.

[Recognition of visual objects under forward masking. Effects of cathegorial similarity of test and masking stimuli].

PubMed

Gerasimenko, N Iu; Slavutskaia, A V; Kalinin, S A; Kulikov, M A; Mikhaĭlova, E S

2013-01-01

In 38 healthy subjects accuracy and response time were examined during recognition of two categories of images--animals andnonliving objects--under forward masking. We revealed new data that masking effects depended of categorical similarity of target and masking stimuli. The recognition accuracy was the lowest and the response time was the most slow, when the target and masking stimuli belongs to the same category, that was combined with high dispersion of response times. The revealed effects were more clear in the task of animal recognition in comparison with the recognition of nonliving objects. We supposed that the revealed effects connected with interference between cortical representations of the target and masking stimuli and discussed our results in context of cortical interference and negative priming.

A novel rotational invariants target recognition method for rotating motion blurred images

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Gong, Meiling; Dong, Mingwei; Zeng, Yiliang; Zhang, Yuzhen

2017-11-01

The imaging of the image sensor is blurred due to the rotational motion of the carrier and reducing the target recognition rate greatly. Although the traditional mode that restores the image first and then identifies the target can improve the recognition rate, it takes a long time to recognize. In order to solve this problem, a rotating fuzzy invariants extracted model was constructed that recognizes target directly. The model includes three metric layers. The object description capability of metric algorithms that contain gray value statistical algorithm, improved round projection transformation algorithm and rotation-convolution moment invariants in the three metric layers ranges from low to high, and the metric layer with the lowest description ability among them is as the input which can eliminate non pixel points of target region from degenerate image gradually. Experimental results show that the proposed model can improve the correct target recognition rate of blurred image and optimum allocation between the computational complexity and function of region.

A Label-Free, Redox Biosensor for Detection of Disease Biomarkers

NASA Astrophysics Data System (ADS)

Archibald, Michelle M.; Rizal, Binod; Connolly, Timothy; Burns, Michael J.; Naughton, Michael J.; Chiles, Thomas C.

2014-03-01

Technologies to detect early stage cancer would provide significant benefit to cancer disease patients. Clinical measurement of biomarkers offers the promise of a noninvasive and cost effective screening for early stage detection. We have developed a novel 3-dimensional ``nanocavity'' array for the detection of human cancer biomarkers in serum and other fluids. This all-electronic diagnostic sensor is based on a nanoscale coaxial array architecture that we have modified to enable molecular-level detection and identification. Each individual sensor in the array is a vertically-oriented coaxial capacitor, whose dielectric impedance is measurably changed when target molecules enter the coax annulus. We are designing a nanocoaxial biosensor based on electronic response to antibody recognition of a specific disease biomarker (e . g . CA-125 for early-stage ovarian cancer) on biofunctionalized metal surfaces within the nanocoax structure, thereby providing an all-electronic, ambient temperature, rapid-response, label-free redox biosensor. Our results demonstrate the feasibility of using this nanocoaxial array as an ultrasensitive device to detect a wide range of target proteins, including disease biomarkers. Supported by NIH (National Cancer Institute and the National Institute of Allergy and Infectious Diseases).

A novel visual saliency detection method for infrared video sequences

NASA Astrophysics Data System (ADS)

Wang, Xin; Zhang, Yuzhen; Ning, Chen

2017-12-01

Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the ;important; content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera.

PubMed

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-10-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

PubMed Central

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-01-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods. PMID:28974031

Visualization of hyperspectral imagery

NASA Astrophysics Data System (ADS)

Hogervorst, Maarten A.; Bijl, Piet; Toet, Alexander

2007-04-01

We developed four new techniques to visualize hyper spectral image data for man-in-the-loop target detection. The methods respectively: (1) display the subsequent bands as a movie ("movie"), (2) map the data onto three channels and display these as a colour image ("colour"), (3) display the correlation between the pixel signatures and a known target signature ("match") and (4) display the output of a standard anomaly detector ("anomaly"). The movie technique requires no assumptions about the target signature and involves no information loss. The colour technique produces a single image that can be displayed in real-time. A disadvantage of this technique is loss of information. A display of the match between a target signature and pixels and can be interpreted easily and fast, but this technique relies on precise knowledge of the target signature. The anomaly detector signifies pixels with signatures that deviate from the (local) background. We performed a target detection experiment with human observers to determine their relative performance with the four techniques,. The results show that the "match" presentation yields the best performance, followed by "movie" and "anomaly", while performance with the "colour" presentation was the poorest. Each scheme has its advantages and disadvantages and is more or less suited for real-time and post-hoc processing. The rationale is that the final interpretation is best done by a human observer. In contrast to automatic target recognition systems, the interpretation of hyper spectral imagery by the human visual system is robust to noise and image transformations and requires a minimal number of assumptions (about signature of target and background, target shape etc.) When more knowledge about target and background is available this may be used to help the observer interpreting the data (aided target detection).

Under what conditions is recognition spared relative to recall after selective hippocampal damage in humans?

PubMed

Holdstock, J S; Mayes, A R; Roberts, N; Cezayirli, E; Isaac, C L; O'Reilly, R C; Norman, K A

2002-01-01

The claim that recognition memory is spared relative to recall after focal hippocampal damage has been disputed in the literature. We examined this claim by investigating object and object-location recall and recognition memory in a patient, YR, who has adult-onset selective hippocampal damage. Our aim was to identify the conditions under which recognition was spared relative to recall in this patient. She showed unimpaired forced-choice object recognition but clearly impaired recall, even when her control subjects found the object recognition task to be numerically harder than the object recall task. However, on two other recognition tests, YR's performance was not relatively spared. First, she was clearly impaired at an equivalently difficult yes/no object recognition task, but only when targets and foils were very similar. Second, YR was clearly impaired at forced-choice recognition of object-location associations. This impairment was also unrelated to difficulty because this task was no more difficult than the forced-choice object recognition task for control subjects. The clear impairment of yes/no, but not of forced-choice, object recognition after focal hippocampal damage, when targets and foils are very similar, is predicted by the neural network-based Complementary Learning Systems model of recognition. This model postulates that recognition is mediated by hippocampally dependent recollection and cortically dependent familiarity; thus hippocampal damage should not impair item familiarity. The model postulates that familiarity is ineffective when very similar targets and foils are shown one at a time and subjects have to identify which items are old (yes/no recognition). In contrast, familiarity is effective in discriminating which of similar targets and foils, seen together, is old (forced-choice recognition). Independent evidence from the remember/know procedure also indicates that YR's familiarity is normal. The Complementary Learning Systems model can also accommodate the clear impairment of forced-choice object-location recognition memory if it incorporates the view that the most complete convergence of spatial and object information, represented in different cortical regions, occurs in the hippocampus.

Molecular Recognition of Human Liver Cancer Cells Using DNA Aptamers Generated via Cell-SELEX.

PubMed

Xu, Jiehua; Teng, I-Ting; Zhang, Liqin; Delgado, Stefanie; Champanhac, Carole; Cansiz, Sena; Wu, Cuichen; Shan, Hong; Tan, Weihong

2015-01-01

Most clinical cases of liver cancer cannot be diagnosed until they have evolved to an advanced stage, thus resulting in high mortality. It is well recognized that the implementation of early detection methods and the development of targeted therapies for liver cancer are essential to reducing the high mortality rates associated with this disease. To achieve these goals, molecular probes capable of recognizing liver cancer cell-specific targets are needed. Here we describe a panel of aptamers able to distinguish hepatocarcinoma from normal liver cells. The aptamers, which were selected by cell-based SELEX (Systematic Evolution of Ligands by Exponential Enrichment), have Kd values in the range of 64-349 nM toward the target human hepatoma cell HepG2, and also recognize ovarian cancer cells and lung adenocarcinoma. The proteinase treatment experiment indicated that all aptamers could recognize target HepG2 cells through surface proteins. This outcome suggested that these aptamers could be used as potential probes for further research in cancer studies, such as developing early detection assays, targeted therapies, and imaging agents, as well as for the investigation of common membrane proteins in these distinguishable cancers.

Putative pyramidal neurons and interneurons in the monkey parietal cortex make different contributions to the performance of a visual grouping task.

PubMed

Yokoi, Isao; Komatsu, Hidehiko

2010-09-01

Visual grouping of discrete elements is an important function for object recognition. We recently conducted an experiment to study neural correlates of visual grouping. We recorded neuronal activities while monkeys performed a grouping detection task in which they discriminated visual patterns composed of discrete dots arranged in a cross and detected targets in which dots with the same contrast were aligned horizontally or vertically. We found that some neurons in the lateral bank of the intraparietal sulcus exhibit activity related to visual grouping. In the present study, we analyzed how different types of neurons contribute to visual grouping. We classified the recorded neurons as putative pyramidal neurons or putative interneurons, depending on the duration of their action potentials. We found that putative pyramidal neurons exhibited selectivity for the orientation of the target, and this selectivity was enhanced by attention to a particular target orientation. By contrast, putative interneurons responded more strongly to the target stimuli than to the nontargets, regardless of the orientation of the target. These results suggest that different classes of parietal neurons contribute differently to the grouping of discrete elements.

On the applicability of brain reading for predictive human-machine interfaces in robotics.

PubMed

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

PubMed Central

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

An aptamer-based bio-barcode assay with isothermal recombinase polymerase amplification for cytochrome-c detection and anti-cancer drug screening.

PubMed

Loo, Jacky F C; Lau, P M; Ho, H P; Kong, S K

2013-10-15

Based on a recently reported ultra-sensitive bio-barcode (BBC) assay, we have developed an aptamer-based bio-barcode (ABC) alternative to detect a cell death marker cytochrome-c (Cyto-c) and its subsequent application to screen anti-cancer drugs. Aptamer is a short single-stranded DNA selected from a synthetic DNA library by virtue of its high binding affinity and specificity to its target based on its unique 3D structure from the nucleotide sequence after folding. In the BBC assay, an antigen (Ag) in analytes is captured by a micro-magnetic particle (MMP) coated with capturing antibodies (Abs). Gold nanoparticles (NPs) with another recognition Ab against the same target and hundreds of identical DNA molecules of known sequence are subsequently added to allow the formation of sandwich structures ([MMP-Ab1]-Ag-[Ab2-NP-DNA]). After isolating the sandwiches by a magnetic field, the DNAs hybridized to their complementary DNAs covalently bound on the NPs are released from the sandwiches after heating. Acting as an Ag identification tag, these bio-barcode DNAs with known DNA sequence are then amplified by polymerase chain reaction (PCR) and detected by fluorescence. In our ABC assay, we employed a Cyto-c-specific aptamer to substitute both the recognition Ab and barcode DNAs on the NPs in the BBC assay; and a novel isothermal recombinase polymerase amplification for the time-consuming PCR. The detection limit of our ABC assay for the Cyto-c was found to be 10 ng/mL and this new assay can be completed within 3h. Several potential anti-cancer drugs have been tested in vitro for their efficacy to kill liver cancer with or without multi-drug resistance. © 2013 Elsevier B.V. All rights reserved.

Good initialization model with constrained body structure for scene text recognition

NASA Astrophysics Data System (ADS)

Zhu, Anna; Wang, Guoyou; Dong, Yangbo

2016-09-01

Scene text recognition has gained significant attention in the computer vision community. Character detection and recognition are the promise of text recognition and affect the overall performance to a large extent. We proposed a good initialization model for scene character recognition from cropped text regions. We use constrained character's body structures with deformable part-based models to detect and recognize characters in various backgrounds. The character's body structures are achieved by an unsupervised discriminative clustering approach followed by a statistical model and a self-build minimum spanning tree model. Our method utilizes part appearance and location information, and combines character detection and recognition in cropped text region together. The evaluation results on the benchmark datasets demonstrate that our proposed scheme outperforms the state-of-the-art methods both on scene character recognition and word recognition aspects.

Ultrasensitive electrochemical detection of nucleic acids by template enhanced hybridization followed with rolling circle amplification.

PubMed

Ji, Hanxu; Yan, Feng; Lei, Jianping; Ju, Huangxian

2012-08-21

An ultrasensitive protocol for electrochemical detection of DNA is designed with quantum dots (QDs) as a signal tag by combining the template enhanced hybridization process (TEHP) and rolling circle amplification (RCA). Upon the recognition of the molecular beacon (MB) to target DNA, the MB hybridizes with assistants and target DNA to form a ternary ''Y-junction''. The target DNA can be dissociated from the structure under the reaction of nicking endonuclease to initiate the next hybridization process. The template enhanced MB fragments further act as the primers of the RCA reaction to produce thousands of repeated oligonucleotide sequences, which can bind with oligonucleotide functionalized QDs. The attached signal tags can be easily read out by square-wave voltammetry after dissolving with acid. Because of the cascade signal amplification and the specific TEHP and RCA reaction, this newly designed protocol provides an ultrasensitive electrochemical detection of DNA down to the attomolar level (11 aM) with a linear range of 6 orders of magnitude (from 1 × 10(-17) to 1 × 10(-11) M) and can discriminate mismatched DNA from perfect matched target DNA with high selectivity. The high sensitivity and specificity make this method a great potential for early diagnosis in gene-related diseases.

Distinguishing highly confident accurate and inaccurate memory: insights about relevant and irrelevant influences on memory confidence

PubMed Central

Chua, Elizabeth F.; Hannula, Deborah E.; Ranganath, Charan

2012-01-01

It is generally believed that accuracy and confidence in one’s memory are related, but there are many instances when they diverge. Accordingly, it is important to disentangle the factors which contribute to memory accuracy and confidence, especially those factors that contribute to confidence, but not accuracy. We used eye movements to separately measure fluent cue processing, the target recognition experience, and relative evidence assessment on recognition confidence and accuracy. Eye movements were monitored during a face-scene associative recognition task, in which participants first saw a scene cue, followed by a forced-choice recognition test for the associated face, with confidence ratings. Eye movement indices of the target recognition experience were largely indicative of accuracy, and showed a relationship to confidence for accurate decisions. In contrast, eye movements during the scene cue raised the possibility that more fluent cue processing was related to higher confidence for both accurate and inaccurate recognition decisions. In a second experiment, we manipulated cue familiarity, and therefore cue fluency. Participants showed higher confidence for cue-target associations for when the cue was more familiar, especially for incorrect responses. These results suggest that over-reliance on cue familiarity and under-reliance on the target recognition experience may lead to erroneous confidence. PMID:22171810

Distinguishing highly confident accurate and inaccurate memory: insights about relevant and irrelevant influences on memory confidence.

PubMed

Chua, Elizabeth F; Hannula, Deborah E; Ranganath, Charan

2012-01-01

It is generally believed that accuracy and confidence in one's memory are related, but there are many instances when they diverge. Accordingly it is important to disentangle the factors that contribute to memory accuracy and confidence, especially those factors that contribute to confidence, but not accuracy. We used eye movements to separately measure fluent cue processing, the target recognition experience, and relative evidence assessment on recognition confidence and accuracy. Eye movements were monitored during a face-scene associative recognition task, in which participants first saw a scene cue, followed by a forced-choice recognition test for the associated face, with confidence ratings. Eye movement indices of the target recognition experience were largely indicative of accuracy, and showed a relationship to confidence for accurate decisions. In contrast, eye movements during the scene cue raised the possibility that more fluent cue processing was related to higher confidence for both accurate and inaccurate recognition decisions. In a second experiment we manipulated cue familiarity, and therefore cue fluency. Participants showed higher confidence for cue-target associations for when the cue was more familiar, especially for incorrect responses. These results suggest that over-reliance on cue familiarity and under-reliance on the target recognition experience may lead to erroneous confidence.

Experimental evaluation of penetration capabilities of a Geiger-mode APD array laser radar system

NASA Astrophysics Data System (ADS)

Jonsson, Per; Tulldahl, Michael; Hedborg, Julia; Henriksson, Markus; Sjöqvist, Lars

2017-10-01

Laser radar 3D imaging has the potential to improve target recognition in many scenarios. One case that is challenging for most optical sensors is to recognize targets hidden in vegetation or behind camouflage. The range resolution of timeof- flight 3D sensors allows segmentation of obscuration and target if the surfaces are separated far enough so that they can be resolved as two distances. Systems based on time-correlated single-photon counting (TCSPC) have the potential to resolve surfaces closer to each other compared to laser radar systems based on proportional mode detection technologies and is therefore especially interesting. Photon counting detection is commonly performed with Geigermode Avalanche Photodiodes (GmAPD) that have the disadvantage that they can only detect one photon per laser pulse per pixel. A strong return from an obscuring object may saturate the detector and thus limit the possibility to detect the hidden target even if photons from the target reach the detector. The operational range where good foliage penetration is observed is therefore relatively narrow for GmAPD systems. In this paper we investigate the penetration capability through semi-transparent surfaces for a laser radar with a 128×32 pixel GmAPD array and a 1542 nm wavelength laser operating at a pulse repetition frequency of 90 kHz. In the evaluation a screen was placed behind different canvases with varying transmissions and the detected signals from the surfaces for different laser intensities were measured. The maximum return from the second surface occurs when the total detection probability is around 0.65-0.75 per pulse. At higher laser excitation power the signal from the second surface decreases. To optimize the foliage penetration capability it is thus necessary to adaptively control the laser power to keep the returned signal within this region. In addition to the experimental results, simulations to study the influence of the pulse energy on penetration through foliage in a scene with targets behind vegetation are presented. The optimum detection of targets occurs here at a slightly higher total photon count rate probability because a number of pixel have no obscuration in front the target in their field of view.

A novel nonenzymatic cascade amplification for ultrasensitive photoelectrochemical DNA sensing based on target driven to initiate cyclic assembly of hairpins.

PubMed

Wen, Guangming; Dong, Wenxia; Liu, Bin; Li, Zhongping; Fan, Lifang

2018-05-29

A novel cascade photoelectrochemical (PEC) signal amplification biosensing tactics was developed for DNA detection based on a target-driven DNA association to induce cyclic hairpin assembly. In the circulatory system there are two ssDNA (A and B) and two hairpins (C and D). The hybridization of these ssDNA led to the formation of an A-target-B structure. The close proximity of their toehold and branch-migration regions was able to induce the cyclic hairpin assembly. Afterwards, the assembly result further causes the separation of a double-stranded probe DNA (Q:F) to switch the PEC signal via toehold-mediated strand replacement. As such, the signal stranded DNA-CdS QDs (F) as the signal tag was released in the presence of the target DNA. The signal DNA-CdS QDs was then coated to F-doped tin oxide (FTO) electrode leading to the "signal-on" PEC signal. The designed biosensing strategy showed a low detection limit of 21.3 pM for target DNA and a broad linear range from 50 pM to 100 nM. This signal amplification PEC sensing method exhibited a potential application to detect protein molecules, RNA or metal ions via changing the sequence of A and B recognition. Copyright © 2018 Elsevier B.V. All rights reserved.

3D range-gated super-resolution imaging based on stereo matching for moving platforms and targets

NASA Astrophysics Data System (ADS)

Sun, Liang; Wang, Xinwei; Zhou, Yan

2017-11-01

3D range-gated superresolution imaging is a novel 3D reconstruction technique for target detection and recognition with good real-time performance. However, for moving targets or platforms such as airborne, shipborne, remote operated vehicle and autonomous vehicle, 3D reconstruction has a large error or failure. In order to overcome this drawback, we propose a method of stereo matching for 3D range-gated superresolution reconstruction algorithm. In experiment, the target is a doll of Mario with a height of 38cm at the location of 34m, and we obtain two successive frame images of the Mario. To confirm our method is effective, we transform the original images with translation, rotation, scale and perspective, respectively. The experimental result shows that our method has a good result of 3D reconstruction for moving targets or platforms.

Molecular Imprinting of Macromolecules for Sensor Applications

PubMed Central

Saylan, Yeşeren; Yilmaz, Fatma; Özgür, Erdoğan; Derazshamshir, Ali; Yavuz, Handan; Denizli, Adil

2017-01-01

Molecular recognition has an important role in numerous living systems. One of the most important molecular recognition methods is molecular imprinting, which allows host compounds to recognize and detect several molecules rapidly, sensitively and selectively. Compared to natural systems, molecular imprinting methods have some important features such as low cost, robustness, high recognition ability and long term durability which allows molecularly imprinted polymers to be used in various biotechnological applications, such as chromatography, drug delivery, nanotechnology, and sensor technology. Sensors are important tools because of their ability to figure out a potentially large number of analytical difficulties in various areas with different macromolecular targets. Proteins, enzymes, nucleic acids, antibodies, viruses and cells are defined as macromolecules that have wide range of functions are very important. Thus, macromolecules detection has gained great attention in concerning the improvement in most of the studies. The applications of macromolecule imprinted sensors will have a spacious exploration according to the low cost, high specificity and stability. In this review, macromolecules for molecularly imprinted sensor applications are structured according to the definition of molecular imprinting methods, developments in macromolecular imprinting methods, macromolecular imprinted sensors, and conclusions and future perspectives. This chapter follows the latter strategies and focuses on the applications of macromolecular imprinted sensors. This allows discussion on how sensor strategy is brought to solve the macromolecules imprinting. PMID:28422082

Fluorescent 2D WS2 Nanosheets Bearing Chemical Affinity Elements for the Recognition of Glycated Hemoglobin.

PubMed

Yang, Jin-Kyoung; Lee, Hye-Rim; Hwang, In-Jun; Kim, Hye-In; Yim, DaBin; Kim, Jong-Ho

2018-05-14

It is required to exfoliate and functionalize 2D transition metal dichalcogenides (TMDs) in an aqueous solution for biological and medical applications. Herein, the approach for the simultaneous exfoliation and functionalization of 2D WS 2 nanosheets using boronic acid-modified poly(vinyl alcohol) (B-PVA) in an aqueous solution is reported, and the B-PVA-functionalized WS 2 nanosheets (B-PVA-WS 2 ) are exploited as a fluorescent biosensor for the detection of glycated hemoglobin, HbA1c. The synthetic B-PVA polymer facilitates the exfoliation and functionalization of WS 2 nanosheets from the bulk counterpart in the aqueous solution via a pulsed sonication process, resulting in fluorescent B-PVA-WS 2 nanohybrids with a specific recognition of HbA1c. The fluorescence of the B-PVA-WS 2 is quenched in the presence of HbA1c, whereas PVA-functionalized WS 2 (PVA-WS 2 ), not bearing boronic acid as a recognition moiety, shows no fluorescence changes upon the addition of the target. The B-PVA-WS 2 is able to selectively detect HbA1c at the concentration as low as 3.3 × 10 -8 m based on its specific fluorescence quenching. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Molecular Imprinting of Macromolecules for Sensor Applications.

PubMed

Saylan, Yeşeren; Yilmaz, Fatma; Özgür, Erdoğan; Derazshamshir, Ali; Yavuz, Handan; Denizli, Adil

2017-04-19

Molecular recognition has an important role in numerous living systems. One of the most important molecular recognition methods is molecular imprinting, which allows host compounds to recognize and detect several molecules rapidly, sensitively and selectively. Compared to natural systems, molecular imprinting methods have some important features such as low cost, robustness, high recognition ability and long term durability which allows molecularly imprinted polymers to be used in various biotechnological applications, such as chromatography, drug delivery, nanotechnology, and sensor technology. Sensors are important tools because of their ability to figure out a potentially large number of analytical difficulties in various areas with different macromolecular targets. Proteins, enzymes, nucleic acids, antibodies, viruses and cells are defined as macromolecules that have wide range of functions are very important. Thus, macromolecules detection has gained great attention in concerning the improvement in most of the studies. The applications of macromolecule imprinted sensors will have a spacious exploration according to the low cost, high specificity and stability. In this review, macromolecules for molecularly imprinted sensor applications are structured according to the definition of molecular imprinting methods, developments in macromolecular imprinting methods, macromolecular imprinted sensors, and conclusions and future perspectives. This chapter follows the latter strategies and focuses on the applications of macromolecular imprinted sensors. This allows discussion on how sensor strategy is brought to solve the macromolecules imprinting.

A microfluidic chip containing a molecularly imprinted polymer and a DNA aptamer for voltammetric determination of carbofuran.

PubMed

Li, Shuhuai; Li, Jianping; Luo, Jinhui; Xu, Zhi; Ma, Xionghui

2018-05-11

An electrochemical microfluidic chip is described for the determination of the insecticide carbofuran. It is making use of a molecularly imprinted film (MIP) and a DNA aptamer as dual recognition units. The analyte (carbofuran) is transported to the MIP and captured at the identification site in the channel. Then, carbofuran is eluted with carbinol-acetic acid and transported to the DNA aptamer on the testing position of the chip. It is captured again, this time by the aptamer, and detected by differential pulse voltammetry (DPV). The dual recognition (by aptamer and MIP) results in outstanding selectivity. Additionally, graphene oxide-supported gold nanoparticles (GO-AuNPs) were used to improve the sensitivity of electrochemical detector. DPV response is linear in the 0.2 to 50 nM carbofuran concentration range at a potential of -1.2 V, with a 67 pM detection limit. The method has attractive features such as its potential for high throughput, high degree of automation, and high integration. Conceivably, the method may be extended to other analytes for which appropriate MIPs and aptamers are available. Graphical abstract Schematic of an electrochemical microfluidic chip for carbofuran detection based on a molecularly imprinted film (MIP) and a DNA aptamer as dual recognition units. In the chip, targets were recognized by MIP and aptamer, respectively. It shows promising potential for the design of electrochemical devices with high throughput, high automation, and high integration.

Detection of multiple airborne targets from multisensor data

NASA Astrophysics Data System (ADS)

Foltz, Mark A.; Srivastava, Anuj; Miller, Michael I.; Grenander, Ulf

1995-08-01

Previously we presented a jump-diffusion based random sampling algorithm for generating conditional mean estimates of scene representations for the tracking and recongition of maneuvering airborne targets. These representations include target positions and orientations along their trajectories and the target type associated with each trajectory. Taking a Bayesian approach, a posterior measure is defined on the parameter space by combining sensor models with a sophisticated prior based on nonlinear airplane dynamics. The jump-diffusion algorithm constructs a Markov process which visits the elements of the parameter space with frequencies proportional to the posterior probability. It consititutes both the infinitesimal, local search via a sample path continuous diffusion transform and the larger, global steps through discrete jump moves. The jump moves involve the addition and deletion of elements from the scene configuration or changes in the target type assoviated with each target trajectory. One such move results in target detection by the addition of a track seed to the inference set. This provides initial track data for the tracking/recognition algorithm to estimate linear graph structures representing tracks using the other jump moves and the diffusion process, as described in our earlier work. Target detection ideally involves a continuous research over a continuum of the observation space. In this work we conclude that for practical implemenations the search space must be discretized with lattice granularity comparable to sensor resolution, and discuss how fast Fourier transforms are utilized for efficient calcuation of sufficient statistics given our array models. Some results are also presented from our implementation on a networked system including a massively parallel machine architecture and a silicon graphics onyx workstation.

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network.

PubMed

Islam, Kh Tohidul; Raj, Ram Gopal

2017-04-13

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are 'traffic light ahead' or 'pedestrian crossing' indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

PubMed Central

Islam, Kh Tohidul; Raj, Ram Gopal

2017-01-01

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications. PMID:28406471

Recognising the forest, but not the trees: an effect of colour on scene perception and recognition.

PubMed

Nijboer, Tanja C W; Kanai, Ryota; de Haan, Edward H F; van der Smagt, Maarten J

2008-09-01

Colour has been shown to facilitate the recognition of scene images, but only when these images contain natural scenes, for which colour is 'diagnostic'. Here we investigate whether colour can also facilitate memory for scene images, and whether this would hold for natural scenes in particular. In the first experiment participants first studied a set of colour and greyscale natural and man-made scene images. Next, the same images were presented, randomly mixed with a different set. Participants were asked to indicate whether they had seen the images during the study phase. Surprisingly, performance was better for greyscale than for coloured images, and this difference is due to the higher false alarm rate for both natural and man-made coloured scenes. We hypothesized that this increase in false alarm rate was due to a shift from scrutinizing details of the image to recognition of the gist of the (coloured) image. A second experiment, utilizing images without a nameable gist, confirmed this hypothesis as participants now performed equally on greyscale and coloured images. In the final experiment we specifically targeted the more detail-based perception and recognition for greyscale images versus the more gist-based perception and recognition for coloured images with a change detection paradigm. The results show that changes to images are detected faster when image-pairs were presented in greyscale than in colour. This counterintuitive result held for both natural and man-made scenes (but not for scenes without nameable gist) and thus corroborates the shift from more detailed processing of images in greyscale to more gist-based processing of coloured images.

Behavioral dissociation between emotional and non-emotional facial expressions in congenital prosopagnosia

PubMed Central

Daini, Roberta; Comparetti, Chiara M.; Ricciardelli, Paola

2014-01-01

Neuropsychological and neuroimaging studies have shown that facial recognition and emotional expressions are dissociable. However, it is unknown if a single system supports the processing of emotional and non-emotional facial expressions. We aimed to understand if individuals with impairment in face recognition from birth (congenital prosopagnosia, CP) can use non-emotional facial expressions to recognize a face as an already seen one, and thus, process this facial dimension independently from features (which are impaired in CP), and basic emotional expressions. To this end, we carried out a behavioral study in which we compared the performance of 6 CP individuals to that of typical development individuals, using upright and inverted faces. Four avatar faces with a neutral expression were presented in the initial phase. The target faces presented in the recognition phase, in which a recognition task was requested (2AFC paradigm), could be identical (neutral) to those of the initial phase or present biologically plausible changes to features, non-emotional expressions, or emotional expressions. After this task, a second task was performed, in which the participants had to detect whether or not the recognized face exactly matched the study face or showed any difference. The results confirmed the CPs' impairment in the configural processing of the invariant aspects of the face, but also showed a spared configural processing of non-emotional facial expression (task 1). Interestingly and unlike the non-emotional expressions, the configural processing of emotional expressions was compromised in CPs and did not improve their change detection ability (task 2). These new results have theoretical implications for face perception models since they suggest that, at least in CPs, non-emotional expressions are processed configurally, can be dissociated from other facial dimensions, and may serve as a compensatory strategy to achieve face recognition. PMID:25520643

Behavioral dissociation between emotional and non-emotional facial expressions in congenital prosopagnosia.

PubMed

Daini, Roberta; Comparetti, Chiara M; Ricciardelli, Paola

2014-01-01

Neuropsychological and neuroimaging studies have shown that facial recognition and emotional expressions are dissociable. However, it is unknown if a single system supports the processing of emotional and non-emotional facial expressions. We aimed to understand if individuals with impairment in face recognition from birth (congenital prosopagnosia, CP) can use non-emotional facial expressions to recognize a face as an already seen one, and thus, process this facial dimension independently from features (which are impaired in CP), and basic emotional expressions. To this end, we carried out a behavioral study in which we compared the performance of 6 CP individuals to that of typical development individuals, using upright and inverted faces. Four avatar faces with a neutral expression were presented in the initial phase. The target faces presented in the recognition phase, in which a recognition task was requested (2AFC paradigm), could be identical (neutral) to those of the initial phase or present biologically plausible changes to features, non-emotional expressions, or emotional expressions. After this task, a second task was performed, in which the participants had to detect whether or not the recognized face exactly matched the study face or showed any difference. The results confirmed the CPs' impairment in the configural processing of the invariant aspects of the face, but also showed a spared configural processing of non-emotional facial expression (task 1). Interestingly and unlike the non-emotional expressions, the configural processing of emotional expressions was compromised in CPs and did not improve their change detection ability (task 2). These new results have theoretical implications for face perception models since they suggest that, at least in CPs, non-emotional expressions are processed configurally, can be dissociated from other facial dimensions, and may serve as a compensatory strategy to achieve face recognition.

Detection of Mycoplasma pneumoniae by real-time PCR.

PubMed

Winchell, Jonas M; Mitchell, Stephanie L

2013-01-01

Mycoplasma pneumoniae is a significant cause of respiratory disease, accounting for approximately 20% of cases of community-acquired pneumonia. Although several diagnostic methods exist to detect M. pneumoniae in respiratory specimens, real-time PCR has emerged as a significant improvement for the rapid diagnosis of this pathogen. The method described herein details the procedure for the detection of M. pneumoniae by real-time PCR (qPCR). The qPCR assay described can be performed with three targets specific for M. pneumoniae (Mp181, Mp3, and Mp7) and one marker for the detection of the RNaseP gene found in human nucleic acid as an internal control reaction. Recent studies have demonstrated the ability of this procedure to reliably identify this agent and facilitate the timely recognition of an outbreak.

Correlation Time of Ocean Ambient Noise Intensity in San Diego Bay and Target Recognition in Acoustic Daylight Images

NASA Astrophysics Data System (ADS)

Wadsworth, Adam J.

A method for passively detecting and imaging underwater targets using ambient noise as the sole source of illumination (named acoustic daylight) was successfully implemented in the form of the Acoustic Daylight Ocean Noise Imaging System (ADONIS). In a series of imaging experiments conducted in San Diego Bay, where the dominant source of high-frequency ambient noise is snapping shrimp, a large quantity of ambient noise intensity data was collected with the ADONIS (Epifanio, 1997). In a subset of the experimental data sets, fluctuations of time-averaged ambient noise intensity exhibited a diurnal pattern consistent with the increase in frequency of shrimp snapping near dawn and dusk. The same subset of experimental data is revisited here and the correlation time is estimated and analysed for sequences of ambient noise data several minutes in length, with the aim of detecting possible periodicities or other trends in the fluctuation of the shrimp-dominated ambient noise field. Using videos formed from sequences of acoustic daylight images along with other experimental information, candidate segments of static-configuration ADONIS raw ambient noise data were isolated. For each segment, the normalized intensity auto-correlation closely resembled the delta function, the auto-correlation of white noise. No intensity fluctuation patterns at timescales smaller than a few minutes were discernible, suggesting that the shrimp do not communicate, synchronise, or exhibit any periodicities in their snapping. Also presented here is a ADONIS-specific target recognition algorithm based on principal component analysis, along with basic experimental results using a database of acoustic daylight images.

Examination of soldier target recognition with direct view optics

NASA Astrophysics Data System (ADS)

Long, Frederick H.; Larkin, Gabriella; Bisordi, Danielle; Dorsey, Shauna; Marianucci, Damien; Goss, Lashawnta; Bastawros, Michael; Misiuda, Paul; Rodgers, Glenn; Mazz, John P.

2017-10-01

Target recognition and identification is a problem of great military and scientific importance. To examine the correlation between target recognition and optical magnification, ten U.S. Army soldiers were tasked with identifying letters on targets at 800 and 1300 meters away. Letters were used since they are a standard method for measuring visual acuity. The letters were approximately 90 cm high, which is the size of a well-known rifle. Four direct view optics with angular magnifications of 1.5x, 4x, 6x, and 9x were used. The goal of this approach was to measure actual probabilities for correct target identification. Previous scientific literature suggests that target recognition can be modeled as a linear response problem in angular frequency space using the established values for the contrast sensitivity function for a healthy human eye and the experimentally measured modulation transfer function of the optic. At the 9x magnification, the soldiers could identify the letters with almost no errors (i.e., 97% probability of correct identification). At lower magnification, errors in letter identification were more frequent. The identification errors were not random but occurred most frequently with a few pairs of letters (e.g., O and Q), which is consistent with the literature for letter recognition. In addition, in the small subject sample of ten soldiers, there was considerable variation in the observer recognition capability at 1.5x and a range of 800 meters. This can be directly attributed to the variation in the observer visual acuity.

Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

NASA Astrophysics Data System (ADS)

Tian, Qing; Yang, Dan; Zhang, Yuan; Qu, Hongquan

2018-04-01

This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems

PubMed Central

Shinozaki, Takahiro

2018-01-01

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. PMID:29425248

Strand-specific Recognition of DNA Damages by XPD Provides Insights into Nucleotide Excision Repair Substrate Versatility*

PubMed Central

Buechner, Claudia N.; Heil, Korbinian; Michels, Gudrun; Carell, Thomas; Kisker, Caroline; Tessmer, Ingrid

2014-01-01

Recognition and removal of DNA damages is essential for cellular and organismal viability. Nucleotide excision repair (NER) is the sole mechanism in humans for the repair of carcinogenic UV irradiation-induced photoproducts in the DNA, such as cyclobutane pyrimidine dimers. The broad substrate versatility of NER further includes, among others, various bulky DNA adducts. It has been proposed that the 5′-3′ helicase XPD (xeroderma pigmentosum group D) protein plays a decisive role in damage verification. However, despite recent advances such as the identification of a DNA-binding channel and central pore in the protein, through which the DNA is threaded, as well as a dedicated lesion recognition pocket near the pore, the exact process of target site recognition and verification in eukaryotic NER still remained elusive. Our single molecule analysis by atomic force microscopy reveals for the first time that XPD utilizes different recognition strategies to verify structurally diverse lesions. Bulky fluorescein damage is preferentially detected on the translocated strand, whereas the opposite strand preference is observed for a cyclobutane pyrimidine dimer lesion. Both states, however, lead to similar conformational changes in the resulting specific complexes, indicating a merge to a “final” verification state, which may then trigger the recruitment of further NER proteins. PMID:24338567

Intelligent data processing of an ultrasonic sensor system for pattern recognition improvements

NASA Astrophysics Data System (ADS)

Na, Seung You; Park, Min-Sang; Hwang, Won-Gul; Kee, Chang-Doo

1999-05-01

Though conventional time-of-flight ultrasonic sensor systems are popular due to the advantages of low cost and simplicity, the usage of the sensors is rather narrowly restricted within object detection and distance readings. There is a strong need to enlarge the amount of environmental information for mobile applications to provide intelligent autonomy. Wide sectors of such neighboring object recognition problems can be satisfactorily handled with coarse vision data such as sonar maps instead of accurate laser or optic measurements. For the usage of object pattern recognition, ultrasonic senors have inherent shortcomings of poor directionality and specularity which result in low spatial resolution and indistinctiveness of object patterns. To resolve these problems an array of increased number of sensor elements has been used for large objects. In this paper we propose a method of sensor array system with improved recognition capability using electronic circuits accompanying the sensor array and neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. Relying upon the known sensor characteristics, a set of different return signals from neighboring senors is manipulated to provide an enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

Object and event recognition for stroke rehabilitation

NASA Astrophysics Data System (ADS)

Ghali, Ahmed; Cunningham, Andrew S.; Pridmore, Tony P.

2003-06-01

Stroke is a major cause of disability and health care expenditure around the world. Existing stroke rehabilitation methods can be effective but are costly and need to be improved. Even modest improvements in the effectiveness of rehabilitation techniques could produce large benefits in terms of quality of life. The work reported here is part of an ongoing effort to integrate virtual reality and machine vision technologies to produce innovative stroke rehabilitation methods. We describe a combined object recognition and event detection system that provides real time feedback to stroke patients performing everyday kitchen tasks necessary for independent living, e.g. making a cup of coffee. The image plane position of each object, including the patient"s hand, is monitored using histogram-based recognition methods. The relative positions of hand and objects are then reported to a task monitor that compares the patient"s actions against a model of the target task. A prototype system has been constructed and is currently undergoing technical and clinical evaluation.

Effects of orthographic consistency on eye movement behavior: German and English children and adults process the same words differently.

PubMed

Rau, Anne K; Moll, Kristina; Snowling, Margaret J; Landerl, Karin

2015-02-01

The current study investigated the time course of cross-linguistic differences in word recognition. We recorded eye movements of German and English children and adults while reading closely matched sentences, each including a target word manipulated for length and frequency. Results showed differential word recognition processes for both developing and skilled readers. Children of the two orthographies did not differ in terms of total word processing time, but this equal outcome was achieved quite differently. Whereas German children relied on small-unit processing early in word recognition, English children applied small-unit decoding only upon rereading-possibly when experiencing difficulties in integrating an unfamiliar word into the sentence context. Rather unexpectedly, cross-linguistic differences were also found in adults in that English adults showed longer processing times than German adults for nonwords. Thus, although orthographic consistency does play a major role in reading development, cross-linguistic differences are detectable even in skilled adult readers. Copyright © 2014 Elsevier Inc. All rights reserved.

Collocation and Pattern Recognition Effects on System Failure Remediation

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Press, Hayes N.

2007-01-01

Previous research found that operators prefer to have status, alerts, and controls located on the same screen. Unfortunately, that research was done with displays that were not designed specifically for collocation. In this experiment, twelve subjects evaluated two displays specifically designed for collocating system information against a baseline that consisted of dial status displays, a separate alert area, and a controls panel. These displays differed in the amount of collocation, pattern matching, and parameter movement compared to display size. During the data runs, subjects kept a randomly moving target centered on a display using a left-handed joystick and they scanned system displays to find a problem in order to correct it using the provided checklist. Results indicate that large parameter movement aided detection and then pattern recognition is needed for diagnosis but the collocated displays centralized all the information subjects needed, which reduced workload. Therefore, the collocated display with large parameter movement may be an acceptable display after familiarization because of the possible pattern recognition developed with training and its use.

Single-Molecule View of Small RNA-Guided Target Search and Recognition.

PubMed

Globyte, Viktorija; Kim, Sung Hyun; Joo, Chirlmin

2018-05-20

Most everyday processes in life involve a necessity for an entity to locate its target. On a cellular level, many proteins have to find their target to perform their function. From gene-expression regulation to DNA repair to host defense, numerous nucleic acid-interacting proteins use distinct target search mechanisms. Several proteins achieve that with the help of short RNA strands known as guides. This review focuses on single-molecule advances studying the target search and recognition mechanism of Argonaute and CRISPR (clustered regularly interspaced short palindromic repeats) systems. We discuss different steps involved in search and recognition, from the initial complex prearrangement into the target-search competent state to the final proofreading steps. We focus on target search mechanisms that range from weak interactions, to one- and three-dimensional diffusion, to conformational proofreading. We compare the mechanisms of Argonaute and CRISPR with a well-studied target search system, RecA.

Terahertz spectroscopy for the isothermal detection of bacterial DNA by magnetic bead-based rolling circle amplification.

PubMed

Yang, Xiang; Yang, Ke; Zhao, Xiang; Lin, Zhongquan; Liu, Zhiyong; Luo, Sha; Zhang, Yang; Wang, Yunxia; Fu, Weiling

2017-12-04

The demand for rapid and sensitive bacterial detection is continuously increasing due to the significant requirements of various applications. In this study, a terahertz (THz) biosensor based on rolling circle amplification (RCA) was developed for the isothermal detection of bacterial DNA. The synthetic bacterium-specific sequence of 16S rDNA hybridized with a padlock probe (PLP) that contains a sequence fully complementary to the target sequence at the 5' and 3' ends. The linear PLP was circularized by ligation to form a circular PLP upon recognition of the target sequence; then the capture probe (CP) immobilized on magnetic beads (MBs) acted as a primer to initialize RCA. As DNA molecules are much less absorptive than water molecules in the THz range, the RCA products on the surface of the MBs cause a significant decrease in THz absorption, which can be sensitively probed by THz spectroscopy. Our results showed that 0.12 fmol of synthetic bacterial DNA and 0.05 ng μL -1 of genomic DNA could be effectively detected using this assay. In addition, the specificity of this strategy was demonstrated by its low signal response to interfering bacteria. The proposed strategy not only represents a new method for the isothermal detection of the target bacterial DNA but also provides a general methodology for sensitive and specific DNA biosensing using THz spectroscopy.

Exonuclease III-assisted graphene oxide amplified fluorescence anisotropy strategy for ricin detection.

PubMed

Xiao, Xue; Tao, Jing; Zhang, Hong Zhi; Huang, Cheng Zhi; Zhen, Shu Jun

2016-11-15

Graphene oxide (GO) is an excellent fluorescence anisotropy (FA) amplifier. However, in the conventional GO amplified FA strategy, one target can only induce the FA change of one fluorophore on probe, which limits the detection sensitivity. Herein, we developed an exonuclease III (Exo III) aided GO amplified FA strategy by using aptamer as an recognition element and ricin B-chain as a proof-of-concept target. The aptamer was hybridized with a blocker sequence and linked onto the surface of magnetic beads (MBs). Upon the addition of ricin B-chain, blocker was released from the surface of MBs and hybridized with the dye-modified probe DNA on the surface of GO through the toehold-mediated strand exchange reaction. The formed blocker-probe DNA duplex triggered the Exo III-assisted cyclic signal amplification by repeating the hybridization and digestion of probe DNA, liberating the fluorophore with several nucleotides (low FA value). Thus, ricin B-chain could be sensitively detected by the significantly decreased FA. The linear range was from 1.0μg/mL to 13.3μg/mL and the limit of detection (LOD) was 400ng/mL. This method improved the sensitivity of FA assay and it could be generalized to any kind of target detection based on the use of an appropriate aptamer. Copyright © 2016 Elsevier B.V. All rights reserved.

Differences in antigen presentation to MHC class I-and class II- restricted influenza virus-specific cytolytic T lymphocyte clones

PubMed Central

1986-01-01

We have examined requirements for antigen presentation to a panel of MHC class I-and class II-restricted, influenza virus-specific CTL clones by controlling the form of virus presented on the target cell surface. Both H-2K/D- and I region-restricted CTL recognize target cells exposed to infectious virus, but only the I region-restricted clones efficiently lysed histocompatible target cells pulsed with inactivated virus preparations. The isolated influenza hemagglutinin (HA) polypeptide also could sensitize target cells for recognition by class II-restricted, HA-specific CTL, but not by class I-restricted, HA- specific CTL. Inhibition of nascent viral protein synthesis abrogated the ability of target cells to present viral antigen relevant for class I-restricted CTL recognition. Significantly, presentation for class II- restricted recognition was unaffected in target cells exposed to preparations of either inactivated or infectious virus. This differential sensitivity suggested that these H-2I region-restricted CTL recognized viral polypeptides derived from the exogenously introduced virions, rather than viral polypeptides newly synthesized in the infected cell. In support of this contention, treatment of the target cells with the lysosomotropic agent chloroquine abolished recognition of infected target cells by class II-restricted CTL without diminishing class I-restricted recognition of infected target cells. Furthermore, when the influenza HA gene was introduced into target cells without exogenous HA polypeptide, the target cells that expressed the newly synthesized protein product of the HA gene were recognized only by H-2K/D-restricted CTL. These observations suggest that important differences may exist in requirements for antigen presentation between H-2K/D and H-2I region-restricted CTL. These differences may reflect the nature of the antigenic epitopes recognized by these two CTL subsets. PMID:3485173

Whole-face procedures for recovering facial images from memory.

PubMed

Frowd, Charlie D; Skelton, Faye; Hepton, Gemma; Holden, Laura; Minahil, Simra; Pitchford, Melanie; McIntyre, Alex; Brown, Charity; Hancock, Peter J B

2013-06-01

Research has indicated that traditional methods for accessing facial memories usually yield unidentifiable images. Recent research, however, has made important improvements in this area to the witness interview, method used for constructing the face and recognition of finished composites. Here, we investigated whether three of these improvements would produce even-more recognisable images when used in conjunction with each other. The techniques are holistic in nature: they involve processes which operate on an entire face. Forty participants first inspected an unfamiliar target face. Nominally 24h later, they were interviewed using a standard type of cognitive interview (CI) to recall the appearance of the target, or an enhanced 'holistic' interview where the CI was followed by procedures for focussing on the target's character. Participants then constructed a composite using EvoFIT, a recognition-type system that requires repeatedly selecting items from face arrays, with 'breeding', to 'evolve' a composite. They either saw faces in these arrays with blurred external features, or an enhanced method where these faces were presented with masked external features. Then, further participants attempted to name the composites, first by looking at the face front-on, the normal method, and then for a second time by looking at the face side-on, which research demonstrates facilitates recognition. All techniques improved correct naming on their own, but together promoted highly-recognisable composites with mean naming at 74% correct. The implication is that these techniques, if used together by practitioners, should substantially increase the detection of suspects using this forensic method of person identification. Copyright © 2013 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.

Flexible Piezoelectric Sensor-Based Gait Recognition.

PubMed

Cha, Youngsu; Kim, Hojoon; Kim, Doik

2018-02-05

Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

Solid-phase single molecule biosensing using dual-color colocalization of fluorescent quantum dot nanoprobes

NASA Astrophysics Data System (ADS)

Liu, Jianbo; Yang, Xiaohai; Wang, Kemin; Wang, Qing; Liu, Wei; Wang, Dong

2013-10-01

The development of solid-phase surface-based single molecule imaging technology has attracted significant interest during the past decades. Here we demonstrate a sandwich hybridization method for highly sensitive detection of a single thrombin protein at a solid-phase surface based on the use of dual-color colocalization of fluorescent quantum dot (QD) nanoprobes. Green QD560-modified thrombin binding aptamer I (QD560-TBA I) were deposited on a positive poly(l-lysine) assembled layer, followed by bovine serum albumin blocking. It allowed the thrombin protein to mediate the binding of the easily detectable red QD650-modified thrombin binding aptamer II (QD650-TBA II) to the QD560-TBA I substrate. Thus, the presence of the target thrombin can be determined based on fluorescent colocalization measurements of the nanoassemblies, without target amplification or probe separation. The detection limit of this assay reached 0.8 pM. This fluorescent colocalization assay has enabled single molecule recognition in a separation-free detection format, and can serve as a sensitive biosensing platform that greatly suppresses the nonspecific adsorption false-positive signal. This method can be extended to other areas such as multiplexed immunoassay, single cell analysis, and real time biomolecule interaction studies.The development of solid-phase surface-based single molecule imaging technology has attracted significant interest during the past decades. Here we demonstrate a sandwich hybridization method for highly sensitive detection of a single thrombin protein at a solid-phase surface based on the use of dual-color colocalization of fluorescent quantum dot (QD) nanoprobes. Green QD560-modified thrombin binding aptamer I (QD560-TBA I) were deposited on a positive poly(l-lysine) assembled layer, followed by bovine serum albumin blocking. It allowed the thrombin protein to mediate the binding of the easily detectable red QD650-modified thrombin binding aptamer II (QD650-TBA II) to the QD560-TBA I substrate. Thus, the presence of the target thrombin can be determined based on fluorescent colocalization measurements of the nanoassemblies, without target amplification or probe separation. The detection limit of this assay reached 0.8 pM. This fluorescent colocalization assay has enabled single molecule recognition in a separation-free detection format, and can serve as a sensitive biosensing platform that greatly suppresses the nonspecific adsorption false-positive signal. This method can be extended to other areas such as multiplexed immunoassay, single cell analysis, and real time biomolecule interaction studies. Electronic supplementary information (ESI) available: Absorbance and fluorescence spectra of quantum dot nanoprobes, electrophoresis analysis, and experimental setup for fluorescence imaging with dual channels. See DOI: 10.1039/c3nr03291d

Scene Analysis: Non-Linear Spatial Filtering for Automatic Target Detection.

DTIC Science & Technology

1982-12-01

In this thesis, a method for two-dimensional pattern recognition was developed and tested. The method included a global search scheme for candidate...test global switch TYPEO Creating negative video file only.W 11=0 12=256 13=512 14=768 GO 70 2 1 TYPE" Creating negative and horizontally flipped video...purpose was to develop a base of image processing software for the AFIT Digital Signal Processing Laboratory NOVA- ECLIPSE minicomputer system, for

Monkey׳s short-term auditory memory nearly abolished by combined removal of the rostral superior temporal gyrus and rhinal cortices.

PubMed

Fritz, Jonathan B; Malloy, Megan; Mishkin, Mortimer; Saunders, Richard C

2016-06-01

While monkeys easily acquire the rules for performing visual and tactile delayed matching-to-sample, a method for testing recognition memory, they have extraordinary difficulty acquiring a similar rule in audition. Another striking difference between the modalities is that whereas bilateral ablation of the rhinal cortex (RhC) leads to profound impairment in visual and tactile recognition, the same lesion has no detectable effect on auditory recognition memory (Fritz et al., 2005). In our previous study, a mild impairment in auditory memory was obtained following bilateral ablation of the entire medial temporal lobe (MTL), including the RhC, and an equally mild effect was observed after bilateral ablation of the auditory cortical areas in the rostral superior temporal gyrus (rSTG). In order to test the hypothesis that each of these mild impairments was due to partial disconnection of acoustic input to a common target (e.g., the ventromedial prefrontal cortex), in the current study we examined the effects of a more complete auditory disconnection of this common target by combining the removals of both the rSTG and the MTL. We found that the combined lesion led to forgetting thresholds (performance at 75% accuracy) that fell precipitously from the normal retention duration of ~30 to 40s to a duration of ~1 to 2s, thus nearly abolishing auditory recognition memory, and leaving behind only a residual echoic memory. This article is part of a Special Issue entitled SI: Auditory working memory. Published by Elsevier B.V.

Detection of buried objects by fusing dual-band infrared images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-11-01

We have conducted experiments to demonstrate the enhanced detectability of buried land mines using sensor fusion techniques. Multiple sensors, including visible imagery, infrared imagery, and ground penetrating radar (GPR), have been used to acquire data on a number of buried mines and mine surrogates. Because the visible wavelength and GPR data are currently incomplete. This paper focuses on the fusion of two-band infrared images. We use feature-level fusion and supervised learning with the probabilistic neural network (PNN) to evaluate detection performance. The novelty of the work lies in the application of advanced target recognition algorithms, the fusion of dual-band infraredmore » images and evaluation of the techniques using two real data sets.« less

Digital video steganalysis exploiting collusion sensitivity

NASA Astrophysics Data System (ADS)

Budhia, Udit; Kundur, Deepa

2004-09-01

In this paper we present an effective steganalyis technique for digital video sequences based on the collusion attack. Steganalysis is the process of detecting with a high probability and low complexity the presence of covert data in multimedia. Existing algorithms for steganalysis target detecting covert information in still images. When applied directly to video sequences these approaches are suboptimal. In this paper, we present a method that overcomes this limitation by using redundant information present in the temporal domain to detect covert messages in the form of Gaussian watermarks. Our gains are achieved by exploiting the collusion attack that has recently been studied in the field of digital video watermarking, and more sophisticated pattern recognition tools. Applications of our scheme include cybersecurity and cyberforensics.

Array Biosensor for Toxin Detection: Continued Advances

PubMed Central

Taitt, Chris Rowe; Shriver-Lake, Lisa C.; Ngundi, Miriam M.; Ligler, Frances S.

2008-01-01

The following review focuses on progress made in the last five years with the NRL Array Biosensor, a portable instrument for rapid and simultaneous detection of multiple targets. Since 2003, the Array Biosensor has been automated and miniaturized for operation at the point-of-use. The Array Biosensor has also been used to demonstrate (1) quantitative immunoassays against an expanded number of toxins and toxin indicators in food and clinical fluids, and (2) the efficacy of semi-selective molecules as alternative recognition moieties. Blind trials, with unknown samples in a variety of matrices, have demonstrated the versatility, sensitivity, and reliability of the automated system. PMID:27873991

Electrochemical detection of sequence-specific DNA based on formation of G-quadruplex-hemin through continuous hybridization chain reaction.

PubMed

Sun, Xiaofan; Chen, Haohan; Wang, Shuling; Zhang, Yiping; Tian, Yaping; Zhou, Nandi

2018-08-27

A high-sensitive detection of sequence-specific DNA was established based on the formation of G-quadruplex-hemin complex through continuous hybridization chain reaction (HCR). Taking HIV DNA sequence as an example, a capture probe complementary to part of HIV DNA was firstly self-assembled onto the surface of Au electrode. Then a specially designed assistant probe with both terminals complementary to the target DNA and a G-quadruplex-forming sequence in the center was introduced into the detection solution. In the presence of both the target DNA and the assistant probe, the target DNA can be captured on the electrode surface and then a continuous HCR can be conducted due to the mutual recognition of the target DNA and the assistant probe, leading to the formation of a large number of G-quadruplex on the electrode surface. With the help of hemin, a pronounced electrochemical signal can be observed in differential pulse voltammetry (DPV), due to the formation of G-quadruplex-hemin complex. The peak current is linearly related with the logarithm of the concentration of the target DNA in the range from 10 fM to 10 pM. The electrochemical sensor has high selectivity to clearly discriminate single-base mismatched and three-base mismatched sequences from the original HIV DNA sequence. Moreover, the established DNA sensor was challenged by detection of HIV DNA in human serum samples, which showed the low detection limit of 6.3 fM. Thus it has great application prospect in the field of clinical diagnosis and environmental monitoring. Copyright © 2018 Elsevier B.V. All rights reserved.

A sensitive electrochemical aptasensor for multiplex antibiotics detection based on high-capacity magnetic hollow porous nanotracers coupling exonuclease-assisted cascade target recycling.

PubMed

Yan, Zhongdan; Gan, Ning; Li, Tianhua; Cao, Yuting; Chen, Yinji

2016-04-15

A multiplex electrochemical aptasensor was developed for simultaneous detection of two antibiotics such as chloramphenicol (CAP) and oxytetracycline (OTC), and high-capacity magnetic hollow porous nanotracers coupling exonuclease-assisted target recycling was used to improve sensitivity. The cascade amplification process consists of the exonuclease-assisted target recycling amplification and metal ions encoded magnetic hollow porous nanoparticles (MHPs) to produce voltammetry signals. Upon the specific recognition of aptamers to targets (CAP and OTC), exonuclease I (Exo I) selectively digested the aptamers which were bound with CAP and OTC, then the released CAP and OTC participated new cycling to produce more single DNA, which can act as trigger strands to hybrid with nanotracers to generate further signal amplification. MHPs were used as carriers to load more amounts of metal ions and coupling with Exo I assisted cascade target recycling can amplify the signal for about 12 folds compared with silica based nanotracers. Owing to the dual signal amplification, the linear range between signals and the concentrations of CAP and OTC were obtained in the range of 0.0005-50 ng mL(-1). The detection limits of CAP and OTC were 0.15 and 0.10 ng mL(-1) (S/N=3) which is more than 2 orders lower than commercial enzyme-linked immunosorbent immunoassay (ELISA) method, respectively. The proposed method was successfully applied to simultaneously detection of CAP and OTC in milk samples. Besides, this aptasensor can be applied to other antibiotics detection by changing the corresponding aptamer. The whole scheme is facile, selective and sensitive enough for antibiotics screening in food safety. Copyright © 2015 Elsevier B.V. All rights reserved.

The utility of multiple synthesized views in the recognition of unfamiliar faces.

PubMed

Jones, Scott P; Dwyer, Dominic M; Lewis, Michael B

2017-05-01

The ability to recognize an unfamiliar individual on the basis of prior exposure to a photograph is notoriously poor and prone to errors, but recognition accuracy is improved when multiple photographs are available. In applied situations, when only limited real images are available (e.g., from a mugshot or CCTV image), the generation of new images might provide a technological prosthesis for otherwise fallible human recognition. We report two experiments examining the effects of providing computer-generated additional views of a target face. In Experiment 1, provision of computer-generated views supported better target face recognition than exposure to the target image alone and equivalent performance to that for exposure of multiple photograph views. Experiment 2 replicated the advantage of providing generated views, but also indicated an advantage for multiple viewings of the single target photograph. These results strengthen the claim that identifying a target face can be improved by providing multiple synthesized views based on a single target image. In addition, our results suggest that the degree of advantage provided by synthesized views may be affected by the quality of synthesized material.

Modularly assembled designer TAL effector nucleases for targeted gene knockout and gene replacement in eukaryotes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, T; Huang, S; Zhao, XF

Recent studies indicate that the DNA recognition domain of transcription activator-like (TAL) effectors can be combined with the nuclease domain of FokI restriction enzyme to produce TAL effector nucleases (TALENs) that, in pairs, bind adjacent DNA target sites and produce double-strand breaks between the target sequences, stimulating non-homologous end-joining and homologous recombination. Here, we exploit the four prevalent TAL repeats and their DNA recognition cipher to develop a 'modular assembly' method for rapid production of designer TALENs (dTALENs) that recognize unique DNA sequence up to 23 bases in any gene. We have used this approach to engineer 10 dTALENs tomore » target specific loci in native yeast chromosomal genes. All dTALENs produced high rates of site-specific gene disruptions and created strains with expected mutant phenotypes. Moreover, dTALENs stimulated high rates (up to 34%) of gene replacement by homologous recombination. Finally, dTALENs caused no detectable cytotoxicity and minimal levels of undesired genetic mutations in the treated yeast strains. These studies expand the realm of verified TALEN activity from cultured human cells to an intact eukaryotic organism and suggest that low-cost, highly dependable dTALENs can assume a significant role for gene modifications of value in human and animal health, agriculture and industry.« less

Signature analysis of ballistic missile warhead with micro-nutation in terahertz band

NASA Astrophysics Data System (ADS)

Li, Ming; Jiang, Yue-song

2013-08-01

In recent years, the micro-Doppler effect has been proposed as a new technique for signature analysis and extraction of radar targets. The ballistic missile is known as a typical radar target and has been paid many attentions for the complexities of its motions in current researches. The trajectory of a ballistic missile can be generally divided into three stages: boost phase, midcourse phase and terminal phase. The midcourse phase is the most important phase for radar target recognition and interception. In this stage, the warhead forms a typical micro-motion called micro-nutation which consists of three basic micro-motions: spinning, coning and wiggle. This paper addresses the issue of signature analysis of ballistic missile warhead in terahertz band via discussing the micro-Doppler effect. We establish a simplified model (cone-shaped) for the missile warhead followed by the micro-motion models including of spinning, coning and wiggle. Based on the basic formulas of these typical micro-motions, we first derive the theoretical formula of micro-nutation which is the main micro-motion of the missile warhead. Then, we calculate the micro-Doppler frequency in both X band and terahertz band via these micro-Doppler formulas. The simulations are given to show the superiority of our proposed method for the recognition and detection of radar micro targets in terahertz band.

A distributed automatic target recognition system using multiple low resolution sensors

NASA Astrophysics Data System (ADS)

Yue, Zhanfeng; Lakshmi Narasimha, Pramod; Topiwala, Pankaj

2008-04-01

In this paper, we propose a multi-agent system which uses swarming techniques to perform high accuracy Automatic Target Recognition (ATR) in a distributed manner. The proposed system can co-operatively share the information from low-resolution images of different looks and use this information to perform high accuracy ATR. An advanced, multiple-agent Unmanned Aerial Vehicle (UAV) systems-based approach is proposed which integrates the processing capabilities, combines detection reporting with live video exchange, and swarm behavior modalities that dramatically surpass individual sensor system performance levels. We employ real-time block-based motion analysis and compensation scheme for efficient estimation and correction of camera jitter, global motion of the camera/scene and the effects of atmospheric turbulence. Our optimized Partition Weighted Sum (PWS) approach requires only bitshifts and additions, yet achieves a stunning 16X pixel resolution enhancement, which is moreover parallizable. We develop advanced, adaptive particle-filtering based algorithms to robustly track multiple mobile targets by adaptively changing the appearance model of the selected targets. The collaborative ATR system utilizes the homographies between the sensors induced by the ground plane to overlap the local observation with the received images from other UAVs. The motion of the UAVs distorts estimated homography frame to frame. A robust dynamic homography estimation algorithm is proposed to address this, by using the homography decomposition and the ground plane surface estimation.

Expression of the B subunit of Escherichia coli heat-labile enterotoxin as a fusion protein in transgenic tomato.

PubMed

Walmsley, A M; Alvarez, M L; Jin, Y; Kirk, D D; Lee, S M; Pinkhasov, J; Rigano, M M; Arntzen, C J; Mason, H S

2003-06-01

Epitopes often require co-delivery with an adjuvant or targeting protein to enable recognition by the immune system. This paper reports the ability of transgenic tomato plants to express a fusion protein consisting of the B subunit of the Escherichia coli heat-labile enterotoxin (LTB) and an immunocontraceptive epitope. The fusion protein was found to assemble into pentamers, as evidenced by its ability to bind to gangliosides, and had an average expression level of 37.8 microg g(-1) in freeze-dried transgenic tissues. Processing of selected transgenic fruit resulted in a 16-fold increase in concentration of the antigen with minimal loss in detectable antigen. The species-specific nature of this epitope was shown by the inability of antibodies raised against non-target species to detect the LTB fusion protein. The immunocontraceptive ability of this vaccine will be tested in future pilot mice studies.

Amine-functionalized lanthanide-doped zirconia nanoparticles: optical spectroscopy, time-resolved fluorescence resonance energy transfer biodetection, and targeted imaging.

PubMed

Liu, Yongsheng; Zhou, Shanyong; Tu, Datao; Chen, Zhuo; Huang, Mingdong; Zhu, Haomiao; Ma, En; Chen, Xueyuan

2012-09-12

Ultrasmall inorganic oxide nanoparticles doped with trivalent lanthanide ions (Ln(3+)), a new and huge family of luminescent bioprobes, remain nearly untouched. Currently it is a challenge to synthesize biocompatible ultrasmall oxide bioprobes. Herein, we report a new inorganic oxide bioprobe based on sub-5 nm amine-functionalized tetragonal ZrO(2)-Ln(3+) nanoparticles synthesized via a facile solvothermal method and ligand exchange. By utilizing the long-lived luminescence of Ln(3+), we demonstrate its application as a sensitive time-resolved fluorescence resonance energy transfer (FRET) bioprobe to detect avidin with a record-low detection limit of 3.0 nM. The oxide nanoparticles also exhibit specific recognition of cancer cells overexpressed with urokinase plasminogen activator receptor (uPAR, an important marker of tumor biology and metastasis) and thus may have great potentials in targeted bioimaging.

Subverting Toll-Like Receptor Signaling by Bacterial Pathogens

PubMed Central

McGuire, Victoria A.; Arthur, J. Simon C.

2015-01-01

Pathogenic bacteria are detected by pattern-recognition receptors (PRRs) expressed on innate immune cells, which activate intracellular signal transduction pathways to elicit an immune response. Toll-like receptors are, perhaps, the most studied of the PRRs and can activate the mitogen-activated protein kinase (MAPK) and Nuclear Factor-κB (NF-κB) pathways. These pathways are critical for mounting an effective immune response. In order to evade detection and promote virulence, many pathogens subvert the host immune response by targeting components of these signal transduction pathways. This mini-review highlights the diverse mechanisms that bacterial pathogens have evolved to manipulate the innate immune response, with a particular focus on those that target MAPK and NF-κB signaling pathways. Understanding the elaborate strategies that pathogens employ to subvert the immune response not only highlights the importance of these proteins in mounting effective immune responses, but may also identify novel approaches for treatment or prevention of infection. PMID:26648936

Analyte-Triggered DNA-Probe Release from a Triplex Molecular Beacon for Nanopore Sensing.

PubMed

Guo, Bingyuan; Sheng, Yingying; Zhou, Ke; Liu, Quansheng; Liu, Lei; Wu, Hai-Chen

2018-03-26

A new nanopore sensing strategy based on triplex molecular beacon was developed for the detection of specific DNA or multivalent proteins. The sensor is composed of a triplex-forming molecular beacon and a stem-forming DNA component that is modified with a host-guest complex. Upon target DNA hybridizing with the molecular beacon loop or multivalent proteins binding to the recognition elements on the stem, the DNA probe is released and produces highly characteristic current signals when translocated through α-hemolysin. The frequency of current signatures can be used to quantify the concentrations of the target molecules. This sensing approach provides a simple, quick, and modular tool for the detection of specific macromolecules with high sensitivity and excellent selectivity. It may find useful applications in point-of-care diagnostics with a portable nanopore kit in the future. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Sheng, Yehua; Zhang, Ka; Ye, Chun; Liang, Cheng; Li, Jian

2008-04-01

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

Variability in the impairment of recognition memory in patients with frontal lobe lesions.

PubMed

Bastin, Christine; Van der Linden, Martial; Lekeu, Françoise; Andrés, Pilar; Salmon, Eric

2006-10-01

Fourteen patients with frontal lobe lesions and 14 normal subjects were tested on a recognition memory task that required discriminating between target words, new words that are synonyms of the targets and unrelated distractors. A deficit was found in 12 of the patients. Moreover, three different patterns of recognition impairment were identified: (I) poor memory for targets, (II) normal hits but increased false recognitions for both types of distractors, (III) normal hit rates, but increased false recognitions for synonyms only. Differences in terms of location of the damage and behavioral characteristics between these subgroups were examined. An encoding deficit was proposed to explain the performance of patients in subgroup I. The behavioral patterns of the patients in subgroups II and III could be interpreted as deficient post-retrieval verification processes and an inability to recollect item-specific information, respectively.

The nonverbal expression of pride: evidence for cross-cultural recognition.

PubMed

Tracy, Jessica L; Robins, Richard W

2008-03-01

The present research tests whether recognition for the nonverbal expression of pride generalizes across cultures. Study 1 provided the first evidence for cross-cultural recognition of pride, demonstrating that the expression generalizes across Italy and the United States. Study 2 found that the pride expression generalizes beyond Western cultures; individuals from a preliterate, highly isolated tribe in Burkina Faso, West Africa, reliably recognized pride, regardless of whether it was displayed by African or American targets. These Burkinabe participants were unlikely to have learned the pride expression through cross-cultural transmission, so their recognition suggests that pride may be a human universal. Studies 3 and 4 used drawn figures to systematically manipulate the ethnicity and gender of targets showing the expression, and demonstrated that pride recognition generalizes across male and female targets of African, Asian, and Caucasian descent. Discussion focuses on the implications of the findings for the universality of the pride expression.

Text Detection, Tracking and Recognition in Video: A Comprehensive Survey.

PubMed

Yin, Xu-Cheng; Zuo, Ze-Yu; Tian, Shu; Liu, Cheng-Lin

2016-04-14

Intelligent analysis of video data is currently in wide demand because video is a major source of sensory data in our lives. Text is a prominent and direct source of information in video, while recent surveys of text detection and recognition in imagery [1], [2] focus mainly on text extraction from scene images. Here, this paper presents a comprehensive survey of text detection, tracking and recognition in video with three major contributions. First, a generic framework is proposed for video text extraction that uniformly describes detection, tracking, recognition, and their relations and interactions. Second, within this framework, a variety of methods, systems and evaluation protocols of video text extraction are summarized, compared, and analyzed. Existing text tracking techniques, tracking based detection and recognition techniques are specifically highlighted. Third, related applications, prominent challenges, and future directions for video text extraction (especially from scene videos and web videos) are also thoroughly discussed.

In situ single cell detection via microfluidic magnetic bead assay

PubMed Central

KC, Pawan; Zhang, Ge; Zhe, Jiang

2017-01-01

We present a single cell detection device based on magnetic bead assay and micro Coulter counters. This device consists of two successive micro Coulter counters, coupled with a high gradient magnetic field generated by an external magnet. The device can identify single cells in terms of the transit time difference of the cell through the two micro Coulter counters. Target cells are conjugated with magnetic beads via specific antibody and antigen binding. A target cell traveling through the two Coulter counters interacts with the magnetic field, and have a longer transit time at the 1st counter than that at the 2nd counter. In comparison, a non-target cell has no interaction with the magnetic field, and hence has nearly the same transit times through the two counters. Each cell passing through the two counters generates two consecutive voltage pulses one after the other; the pulse widths and magnitudes indicating the cell’s transit times through the counters and the cell’s size respectively. Thus, by measuring the pulse widths (transit times) of each cell through the two counters, each single target cell can be differentiated from non-target cells even if they have similar sizes. We experimentally proved that the target human umbilical vein endothelial cells (HUVECs) and non-target rat adipose-derived stem cells (rASCs) have significant different transit time distribution, from which we can determine the recognition regions for both cell groups quantitatively. We further demonstrated that within a mixed cell population of rASCs and HUVECs, HUVECs can be detected in situ and the measured HUVECs ratios agree well with the pre-set ratios. With the simple device structure and easy sample preparation, this method is expected to enable single cell detection in a continuous flow and can be applied to facilitate general cell detection applications such as stem cell identification and enumeration. PMID:28222140

In situ single cell detection via microfluidic magnetic bead assay.

PubMed

Liu, Fan; Kc, Pawan; Zhang, Ge; Zhe, Jiang

2017-01-01

We present a single cell detection device based on magnetic bead assay and micro Coulter counters. This device consists of two successive micro Coulter counters, coupled with a high gradient magnetic field generated by an external magnet. The device can identify single cells in terms of the transit time difference of the cell through the two micro Coulter counters. Target cells are conjugated with magnetic beads via specific antibody and antigen binding. A target cell traveling through the two Coulter counters interacts with the magnetic field, and have a longer transit time at the 1st counter than that at the 2nd counter. In comparison, a non-target cell has no interaction with the magnetic field, and hence has nearly the same transit times through the two counters. Each cell passing through the two counters generates two consecutive voltage pulses one after the other; the pulse widths and magnitudes indicating the cell's transit times through the counters and the cell's size respectively. Thus, by measuring the pulse widths (transit times) of each cell through the two counters, each single target cell can be differentiated from non-target cells even if they have similar sizes. We experimentally proved that the target human umbilical vein endothelial cells (HUVECs) and non-target rat adipose-derived stem cells (rASCs) have significant different transit time distribution, from which we can determine the recognition regions for both cell groups quantitatively. We further demonstrated that within a mixed cell population of rASCs and HUVECs, HUVECs can be detected in situ and the measured HUVECs ratios agree well with the pre-set ratios. With the simple device structure and easy sample preparation, this method is expected to enable single cell detection in a continuous flow and can be applied to facilitate general cell detection applications such as stem cell identification and enumeration.

Dual-process theory and signal-detection theory of recognition memory.

PubMed

Wixted, John T

2007-01-01

Two influential models of recognition memory, the unequal-variance signal-detection model and a dual-process threshold/detection model, accurately describe the receiver operating characteristic, but only the latter model can provide estimates of recollection and familiarity. Such estimates often accord with those provided by the remember-know procedure, and both methods are now widely used in the neuroscience literature to identify the brain correlates of recollection and familiarity. However, in recent years, a substantial literature has accumulated directly contrasting the signal-detection model against the threshold/detection model, and that literature is almost unanimous in its endorsement of signal-detection theory. A dual-process version of signal-detection theory implies that individual recognition decisions are not process pure, and it suggests new ways to investigate the brain correlates of recognition memory. ((c) 2007 APA, all rights reserved).

The relationship between change detection and recognition of centrally attended objects in motion pictures.

PubMed

Angelone, Bonnie L; Levin, Daniel T; Simons, Daniel J

2003-01-01

Observers typically detect changes to central objects more readily than changes to marginal objects, but they sometimes miss changes to central, attended objects as well. However, even if observers do not report such changes, they may be able to recognize the changed object. In three experiments we explored change detection and recognition memory for several types of changes to central objects in motion pictures. Observers who failed to detect a change still performed at above chance levels on a recognition task in almost all conditions. In addition, observers who detected the change were no more accurate in their recognition than those who did not detect the change. Despite large differences in the detectability of changes across conditions, those observers who missed the change did not vary in their ability to recognize the changing object.

Motion dazzle and camouflage as distinct anti-predator defenses.

PubMed

Stevens, Martin; Searle, W Tom L; Seymour, Jenny E; Marshall, Kate L A; Ruxton, Graeme D

2011-11-25

Camouflage patterns that hinder detection and/or recognition by antagonists are widely studied in both human and animal contexts. Patterns of contrasting stripes that purportedly degrade an observer's ability to judge the speed and direction of moving prey ('motion dazzle') are, however, rarely investigated. This is despite motion dazzle having been fundamental to the appearance of warships in both world wars and often postulated as the selective agent leading to repeated patterns on many animals (such as zebra and many fish, snake, and invertebrate species). Such patterns often appear conspicuous, suggesting that protection while moving by motion dazzle might impair camouflage when stationary. However, the relationship between motion dazzle and camouflage is unclear because disruptive camouflage relies on high-contrast markings. In this study, we used a computer game with human subjects detecting and capturing either moving or stationary targets with different patterns, in order to provide the first empirical exploration of the interaction of these two protective coloration mechanisms. Moving targets with stripes were caught significantly less often and missed more often than targets with camouflage patterns. However, when stationary, targets with camouflage markings were captured less often and caused more false detections than those with striped patterns, which were readily detected. Our study provides the clearest evidence to date that some patterns inhibit the capture of moving targets, but that camouflage and motion dazzle are not complementary strategies. Therefore, the specific coloration that evolves in animals will depend on how the life history and ontogeny of each species influence the trade-off between the costs and benefits of motion dazzle and camouflage.

Intact suppression of increased false recognition in schizophrenia.

PubMed

Weiss, Anthony P; Dodson, Chad S; Goff, Donald C; Schacter, Daniel L; Heckers, Stephan

2002-09-01

Recognition memory is impaired in patients with schizophrenia, as they rely largely on item familiarity, rather than conscious recollection, to make mnemonic decisions. False recognition of novel items (foils) is increased in schizophrenia and may relate to this deficit in conscious recollection. By studying pictures of the target word during encoding, healthy adults can suppress false recognition. This study examined the effect of pictorial encoding on subsequent recognition of repeated foils in patients with schizophrenia. The study included 40 patients with schizophrenia and 32 healthy comparison subjects. After incidental encoding of 60 words or pictures, subjects were tested for recognition of target items intermixed with 60 new foils. These new foils were subsequently repeated following either a two- or 24-word delay. Subjects were instructed to label these repeated foils as new and not to mistake them for old target words. Schizophrenic patients showed greater overall false recognition of repeated foils. The rate of false recognition of repeated foils was lower after picture encoding than after word encoding. Despite higher levels of false recognition of repeated new items, patients and comparison subjects demonstrated a similar degree of false recognition suppression after picture, as compared to word, encoding. Patients with schizophrenia displayed greater false recognition of repeated foils than comparison subjects, suggesting both a decrement of item- (or source-) specific recollection and a consequent reliance on familiarity in schizophrenia. Despite these deficits, presenting pictorial information at encoding allowed schizophrenic subjects to suppress false recognition to a similar degree as the comparison group, implying the intact use of a high-level cognitive strategy in this population.

Attomolar detection of proteins via cascade strand-displacement amplification and polystyrene nanoparticle enhancement in fluorescence polarization aptasensors.

PubMed

Huang, Yong; Liu, Xiaoqian; Huang, Huakui; Qin, Jian; Zhang, Liangliang; Zhao, Shulin; Chen, Zhen-Feng; Liang, Hong

2015-08-18

Extremely sensitive and accurate measurements of protein markers for early detection and monitoring of diseases pose a formidable challenge. Herein, we develop a new type of amplified fluorescence polarization (FP) aptasensor based on allostery-triggered cascade strand-displacement amplification (CSDA) and polystyrene nanoparticle (PS NP) enhancement for ultrasensitive detection of proteins. The assay system consists of a fluorescent dye-labeled aptamer hairpin probe and a PS NP-modified DNA duplex (assistant DNA/trigger DNA duplex) probe with a single-stranded part and DNA polymerase. Two probes coexist stably in the absence of target, and the dye exhibits relatively low FP background. Upon recognition and binding with a target protein, the stem of the aptamer hairpin probe is opened, after which the opened hairpin probe hybridizes with the single-stranded part in the PS NP-modified DNA duplex probe and triggers the CSDA reaction through the polymerase-catalyzed recycling of both target protein and trigger DNA. Throughout this CSDA process, numerous massive dyes are assembled onto PS NPs, which results in a substantial FP increase that provides a readout signal for the amplified sensing process. Our newly proposed amplified FP aptasensor enables the quantitative measurement of proteins with the detection limit in attomolar range, which is about 6 orders of magnitude lower than that of traditional homogeneous aptasensors. Moreover, this sensing method also exhibits high specificity for target proteins and can be performed in homogeneous solutions. In addition, the suitability of this method for the quantification of target protein in biological samples has also been shown. Considering these distinct advantages, the proposed sensing method can be expected to provide an ultrasensitive platform for the analysis of various types of target molecules.

Aptamer Recognition of Multiplexed Small-Molecule-Functionalized Substrates.

PubMed

Nakatsuka, Nako; Cao, Huan H; Deshayes, Stephanie; Melkonian, Arin Lucy; Kasko, Andrea M; Weiss, Paul S; Andrews, Anne M

2018-05-31

Aptamers are chemically synthesized oligonucleotides or peptides with molecular recognition capabilities. We investigated recognition of substrate-tethered small-molecule targets, using neurotransmitters as examples, and fluorescently labeled DNA aptamers. Substrate regions patterned via microfluidic channels with dopamine or L-tryptophan were selectively recognized by previously identified dopamine or L-tryptophan aptamers, respectively. The on-substrate dissociation constant determined for the dopamine aptamer was comparable to, though slightly greater than the previously determined solution dissociation constant. Using pre-functionalized neurotransmitter-conjugated oligo(ethylene glycol) alkanethiols and microfluidics patterning, we produced multiplexed substrates to capture and to sort aptamers. Substrates patterned with L-DOPA, L-DOPS, and L-5-HTP enabled comparison of the selectivity of the dopamine aptamer for different targets via simultaneous determination of in situ binding constants. Thus, beyond our previous demonstrations of recognition by protein binding partners (i.e., antibodies and G-protein-coupled receptors), strategically optimized small-molecule-functionalized substrates show selective recognition of nucleic acid binding partners. These substrates are useful for side-by-side target comparisons, and future identification and characterization of novel aptamers targeting neurotransmitters or other important small-molecules.

Domain Regeneration for Cross-Database Micro-Expression Recognition

NASA Astrophysics Data System (ADS)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

Object recognition of real targets using modelled SAR images

NASA Astrophysics Data System (ADS)

Zherdev, D. A.

2017-12-01

In this work the problem of recognition is studied using SAR images. The algorithm of recognition is based on the computation of conjugation indices with vectors of class. The support subspaces for each class are constructed by exception of the most and the less correlated vectors in a class. In the study we examine the ability of a significant feature vector size reduce that leads to recognition time decrease. The images of targets form the feature vectors that are transformed using pre-trained convolutional neural network (CNN).

Effect of colour pop-out on the recognition of letters in crowding conditions.

PubMed

Põder, Endel

2007-11-01

The crowding effect of adjacent objects on the recognition of a target can be reduced when target and flankers differ in some feature, that is irrelevant to the recognition task. In this study, the mechanisms of this effect were explored using targets and flankers of the same and different colours. It was found that facilitation nearly equal to that of differently coloured targets and flankers can be observed with a differently coloured background blob in the location of the target. The different-colour effect does not require advance knowledge of the target and flanker colours, but the effect increases in the course of three trials with constant mapping of colours. The results are consistent with the notion of exogenous attention that facilitates the processing at the most salient locations in the visual field.

Detection of atherosclerotic lesions and intimal macrophages using CD36-targeted nanovesicles.

PubMed

Nie, Shufang; Zhang, Jia; Martinez-Zaguilan, Raul; Sennoune, Souad; Hossen, Md Nazir; Lichtenstein, Alice H; Cao, Jun; Meyerrose, Gary E; Paone, Ralph; Soontrapa, Suthipong; Fan, Zhaoyang; Wang, Shu

2015-12-28

Current approaches to the diagnosis and therapy of atherosclerosis cannot target lesion-determinant cells in the artery wall. Intimal macrophage infiltration promotes atherosclerotic lesion development by facilitating the accumulation of oxidized low-density lipoproteins (oxLDL) and increasing inflammatory responses. The presence of these cells is positively associated with lesion progression, severity and destabilization. Hence, they are an important diagnostic and therapeutic target. The objective of this study was to noninvasively assess the distribution and accumulation of intimal macrophages using CD36-targeted nanovesicles. Soy phosphatidylcholine was used to synthesize liposome-like nanovesicles. 1-(Palmitoyl)-2-(5-keto-6-octene-dioyl) phosphatidylcholine was incorporated on their surface to target the CD36 receptor. All in vitro data demonstrate that these targeted nanovesicles had a high binding affinity for the oxLDL binding site of the CD36 receptor and participated in CD36-mediated recognition and uptake of nanovesicles by macrophages. Intravenous administration into LDL receptor null mice of targeted compared to non-targeted nanovesicles resulted in higher uptake in aortic lesions. The nanovesicles co-localized with macrophages and their CD36 receptors in aortic lesions. This molecular target approach may facilitate the in vivo noninvasive imaging of atherosclerotic lesions in terms of intimal macrophage accumulation and distribution and disclose lesion features related to inflammation and possibly vulnerability thereby facilitate early lesion detection and targeted delivery of therapeutic compounds to intimal macrophages. Copyright © 2015 Elsevier B.V. All rights reserved.

Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yu, Yongtao; Li, Jonathan; Wen, Chenglu; Guan, Haiyan; Luo, Huan; Wang, Cheng

2016-03-01

This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MLS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

A graphene oxide-based strand displacement amplification platform for ricin detection using aptamer as recognition element.

PubMed

Li, Chun Hong; Xiao, Xue; Tao, Jing; Wang, Dong Mei; Huang, Cheng Zhi; Zhen, Shu Jun

2017-05-15

The toxic plant protein ricin is a potential agent for criminal or bioterrorist attacks due to the wide availability and relative ease of preparation. Herein, we developed a novel strategy for the detection of ricin B-chain (RTB) based on isothermal strand-displacement polymerase reaction (ISDPR) by using aptamer as a recognition element and graphene oxide (GO) as a low background platform. In this method, ricin-binding aptamer (RBA) hybridized with a short blocker firstly, and then was immobilized on the surface of streptavidin-coated magnetic beads (MBs). The addition of RTB could release the blocker, which could hybridize with the dye-modified hairpin probe and trigger the ISDPR, resulting in high fluorescence intensity. In the absence of RTB, however, the fluorescence of the dye could be quenched strongly by GO, resulting in the extremely low background signal. Thus, RTB could be sensitively detected by the significantly increased fluorescence signal. The linear range of the current analytical system was from 0.75μg/mL to 100μg/mL and the limit of detection (3σ) was 0.6μg/mL. This method has been successfully utilized for the detection of both the RTB and the entire ricin toxin in real samples, and it could be generalized to any kind of target detection based on an appropriate aptamer. Copyright © 2016 Elsevier B.V. All rights reserved.

Directed evolution of FLS2 towards novel flagellin peptide recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Helft, Laura; Thompson, Mikayla; Bent, Andrew F.

Microbe-associated molecular patterns (MAMPs) are molecules, or domains within molecules, that are conserved across microbial taxa and can be recognized by a plant or animal immune system. Although MAMP receptors have evolved to recognize conserved epitopes, the MAMPs in some microbial species or strains have diverged sufficiently to render them unrecognizable by some host immune systems. In this study, we carried out in vitro evolution of the Arabidopsis thaliana flagellin receptor FLAGELLIN-SENSING 2 (FLS2) to isolate derivatives that recognize one or more flagellin peptides from bacteria for which the wildtype Arabidopsis FLS2 confers little or no response. A targeted approachmore » generated amino acid variation at FLS2 residues in a region previously implicated in flagellin recognition. The primary screen tested for elevated response to the canonical flagellin peptide from Pseudomonas aeruginosa, flg22. From this pool, we then identified five alleles of FLS2 that confer modest (quantitatively partial) recognition of an Erwinia amylovora flagellin peptide. Use of this Erwinia-based flagellin peptide to stimulate Arabidopsis plants expressing the resulting FLS2 alleles did not lead to a detectable reduction of virulent P. syringae pv. tomato growth. However, combination of two identified mutations into a single allele further increased FLS2-mediated responses to the E. amylovora flagellin peptide. Furthermore, these studies demonstrate the potential to raise the sensitivity of MAMP receptors toward particular targets.« less

Directed evolution of FLS2 towards novel flagellin peptide recognition

DOE PAGES

Helft, Laura; Thompson, Mikayla; Bent, Andrew F.

2016-06-06

Microbe-associated molecular patterns (MAMPs) are molecules, or domains within molecules, that are conserved across microbial taxa and can be recognized by a plant or animal immune system. Although MAMP receptors have evolved to recognize conserved epitopes, the MAMPs in some microbial species or strains have diverged sufficiently to render them unrecognizable by some host immune systems. In this study, we carried out in vitro evolution of the Arabidopsis thaliana flagellin receptor FLAGELLIN-SENSING 2 (FLS2) to isolate derivatives that recognize one or more flagellin peptides from bacteria for which the wildtype Arabidopsis FLS2 confers little or no response. A targeted approachmore » generated amino acid variation at FLS2 residues in a region previously implicated in flagellin recognition. The primary screen tested for elevated response to the canonical flagellin peptide from Pseudomonas aeruginosa, flg22. From this pool, we then identified five alleles of FLS2 that confer modest (quantitatively partial) recognition of an Erwinia amylovora flagellin peptide. Use of this Erwinia-based flagellin peptide to stimulate Arabidopsis plants expressing the resulting FLS2 alleles did not lead to a detectable reduction of virulent P. syringae pv. tomato growth. However, combination of two identified mutations into a single allele further increased FLS2-mediated responses to the E. amylovora flagellin peptide. Furthermore, these studies demonstrate the potential to raise the sensitivity of MAMP receptors toward particular targets.« less

Cat-eye effect target recognition with single-pixel detectors

NASA Astrophysics Data System (ADS)

Jian, Weijian; Li, Li; Zhang, Xiaoyue

2015-12-01

A prototype of cat-eye effect target recognition with single-pixel detectors is proposed. Based on the framework of compressive sensing, it is possible to recognize cat-eye effect targets by projecting a series of known random patterns and measuring the backscattered light with three single-pixel detectors in different locations. The prototype only requires simpler, less expensive detectors and extends well beyond the visible spectrum. The simulations are accomplished to evaluate the feasibility of the proposed prototype. We compared our results to that obtained from conventional cat-eye effect target recognition methods using area array sensor. The experimental results show that this method is feasible and superior to the conventional method in dynamic and complicated backgrounds.

Increase in Speech Recognition Due to Linguistic Mismatch between Target and Masker Speech: Monolingual and Simultaneous Bilingual Performance

ERIC Educational Resources Information Center

Calandruccio, Lauren; Zhou, Haibo

2014-01-01

Purpose: To examine whether improved speech recognition during linguistically mismatched target-masker experiments is due to linguistic unfamiliarity of the masker speech or linguistic dissimilarity between the target and masker speech. Method: Monolingual English speakers (n = 20) and English-Greek simultaneous bilinguals (n = 20) listened to…

Chapter 17. Extension of endogenous primers as a tool to detect micro-RNA targets.

PubMed

Vatolin, Sergei; Weil, Robert J

2008-01-01

Mammalian cells express a large number of small, noncoding RNAs, including micro-RNAs (miRNAs), that can regulate both the level of a target mRNA and the protein produced by the target mRNA. Recognition of miRNA targets is a complicated process, as a single target mRNA may be regulated by several miRNAs. The potential for combinatorial miRNA-mediated regulation of miRNA targets complicates diagnostic and therapeutic applications of miRNAs. Despite significant progress in understanding the biology of miRNAs and advances in computational predictions of miRNA targets, methods that permit direct physical identification of miRNA-mRNA complexes in eukaryotic cells are still required. Several groups have utilized coimmunoprecipitation of RNA associated with a protein(s) that is part of the RNA silencing macromolecular complex. This chapter describes a detailed but straightforward strategy that identifies miRNA targets based on the assumption that small RNAs base paired with a complementary target mRNA can be used as a primer to synthesize cDNA that may be used for cloning, identification, and functional analysis.

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

NASA Astrophysics Data System (ADS)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

From face processing to face recognition: Comparing three different processing levels.

PubMed

Besson, G; Barragan-Jason, G; Thorpe, S J; Fabre-Thorpe, M; Puma, S; Ceccaldi, M; Barbeau, E J

2017-01-01

Verifying that a face is from a target person (e.g. finding someone in the crowd) is a critical ability of the human face processing system. Yet how fast this can be performed is unknown. The 'entry-level shift due to expertise' hypothesis suggests that - since humans are face experts - processing faces should be as fast - or even faster - at the individual than at superordinate levels. In contrast, the 'superordinate advantage' hypothesis suggests that faces are processed from coarse to fine, so that the opposite pattern should be observed. To clarify this debate, three different face processing levels were compared: (1) a superordinate face categorization level (i.e. detecting human faces among animal faces), (2) a face familiarity level (i.e. recognizing famous faces among unfamiliar ones) and (3) verifying that a face is from a target person, our condition of interest. The minimal speed at which faces can be categorized (∼260ms) or recognized as familiar (∼360ms) has largely been documented in previous studies, and thus provides boundaries to compare our condition of interest to. Twenty-seven participants were included. The recent Speed and Accuracy Boosting procedure paradigm (SAB) was used since it constrains participants to use their fastest strategy. Stimuli were presented either upright or inverted. Results revealed that verifying that a face is from a target person (minimal RT at ∼260ms) was remarkably fast but longer than the face categorization level (∼240ms) and was more sensitive to face inversion. In contrast, it was much faster than recognizing a face as familiar (∼380ms), a level severely affected by face inversion. Face recognition corresponding to finding a specific person in a crowd thus appears achievable in only a quarter of a second. In favor of the 'superordinate advantage' hypothesis or coarse-to-fine account of the face visual hierarchy, these results suggest a graded engagement of the face processing system across processing levels as reflected by the face inversion effects. Furthermore, they underline how verifying that a face is from a target person and detecting a face as familiar - both often referred to as "Face Recognition" - in fact differs. Copyright © 2016 Elsevier B.V. All rights reserved.

Lesion search and recognition by thymine DNA glycosylase revealed by single molecule imaging

PubMed Central

Buechner, Claudia N.; Maiti, Atanu; Drohat, Alexander C.; Tessmer, Ingrid

2015-01-01

The ability of DNA glycosylases to rapidly and efficiently detect lesions among a vast excess of nondamaged DNA bases is vitally important in base excision repair (BER). Here, we use single molecule imaging by atomic force microscopy (AFM) supported by a 2-aminopurine fluorescence base flipping assay to study damage search by human thymine DNA glycosylase (hTDG), which initiates BER of mutagenic and cytotoxic G:T and G:U mispairs in DNA. Our data reveal an equilibrium between two conformational states of hTDG–DNA complexes, assigned as search complex (SC) and interrogation complex (IC), both at target lesions and undamaged DNA sites. Notably, for both hTDG and a second glycosylase, hOGG1, which recognizes structurally different 8-oxoguanine lesions, the conformation of the DNA in the SC mirrors innate structural properties of their respective target sites. In the IC, the DNA is sharply bent, as seen in crystal structures of hTDG lesion recognition complexes, which likely supports the base flipping required for lesion identification. Our results support a potentially general concept of sculpting of glycosylases to their targets, allowing them to exploit the energetic cost of DNA bending for initial lesion sensing, coupled with continuous (extrahelical) base interrogation during lesion search by DNA glycosylases. PMID:25712093

Argonaute-based programmable RNase as a tool for cleavage of highly-structured RNA.

PubMed

Dayeh, Daniel M; Cantara, William A; Kitzrow, Jonathan P; Musier-Forsyth, Karin; Nakanishi, Kotaro

2018-06-12

The recent identification and development of RNA-guided enzymes for programmable cleavage of target nucleic acids offers exciting possibilities for both therapeutic and biotechnological applications. However, critical challenges such as expensive guide RNAs and inability to predict the efficiency of target recognition, especially for highly-structured RNAs, remain to be addressed. Here, we introduce a programmable RNA restriction enzyme, based on a budding yeast Argonaute (AGO), programmed with cost-effective 23-nucleotide (nt) single-stranded DNAs as guides. DNA guides offer the advantage that diverse sequences can be easily designed and purchased, enabling high-throughput screening to identify optimal recognition sites in the target RNA. Using this DNA-induced slicing complex (DISC) programmed with 11 different guide DNAs designed to span the sequence, sites of cleavage were identified in the 352-nt human immunodeficiency virus type 1 5'-untranslated region. This assay, coupled with primer extension and capillary electrophoresis, allows detection and relative quantification of all DISC-cleavage sites simultaneously in a single reaction. Comparison between DISC cleavage and RNase H cleavage reveals that DISC not only cleaves solvent-exposed sites, but also sites that become more accessible upon DISC binding. This study demonstrates the advantages of the DISC system for programmable cleavage of highly-structured, functional RNAs.

Formation of target-specific binding sites in enzymes: solid-phase molecular imprinting of HRP.

PubMed

Czulak, J; Guerreiro, A; Metran, K; Canfarotta, F; Goddard, A; Cowan, R H; Trochimczuk, A W; Piletsky, S

2016-06-07

Here we introduce a new concept for synthesising molecularly imprinted nanoparticles by using proteins as macro-functional monomers. For a proof-of-concept, a model enzyme (HRP) was cross-linked using glutaraldehyde in the presence of glass beads (solid-phase) bearing immobilized templates such as vancomycin and ampicillin. The cross-linking process links together proteins and protein chains, which in the presence of templates leads to the formation of permanent target-specific recognition sites without adverse effects on the enzymatic activity. Unlike complex protein engineering approaches commonly employed to generate affinity proteins, the method proposed can be used to produce protein-based ligands in a short time period using native protein molecules. These affinity materials are potentially useful tools especially for assays since they combine the catalytic properties of enzymes (for signaling) and molecular recognition properties of antibodies. We demonstrate this concept in an ELISA-format assay where HRP imprinted with vancomycin and ampicillin replaced traditional enzyme-antibody conjugates for selective detection of templates at micromolar concentrations. This approach can potentially provide a fast alternative to raising antibodies for targets that do not require high assay sensitivities; it can also find uses as a biochemical research tool, as a possible replacement for immunoperoxidase-conjugates.

Excimer-based peptide beacons: a convenient experimental approach for monitoring polypeptide-protein and polypeptide-oligonucleotide interactions.

PubMed

Oh, Kenneth J; Cash, Kevin J; Plaxco, Kevin W

2006-11-01

While protein-polypeptide and nucleic acid-polypeptide interactions are of significant experimental interest, quantitative methods for the characterization of such interactions are often cumbersome. Here we described a relatively simple means of optically monitoring such interactions using excimer-based peptide beacons (PBs). The design of PBs is based on the observation that, whereas short peptides are almost invariably unfolded and highly dynamic, they become rigid when complexed with macromolecular targets. Using this binding-induced folding to segregate two pyrene moieties and therefore inhibit excimer formation, we have produced PBs directed against both anti-HIV antibodies and the retroviral transactive response (TAR) RNA hairpin. For both polypeptides, target recognition is accompanied by a roughly 2-fold decrease in excimer emission, thus allowing the detection of their respective targets at concentrations of a few nanomolar. Because excimer emission requires the formation of a tight, precisely oriented pyrene dimer, even relatively trivial binding-induced segregation reduces fluorescence significantly. This suggests that the PB approach will be suitable for monitoring a wide range of peptide-macromolecule recognition events. Moreover, the synthesis of excimer-based PBs utilizes commercially available modified pyrenes in a simple and well-established protocol, making the approach well suited for routine laboratory applications.

Anodal tDCS targeting the right orbitofrontal cortex enhances facial expression recognition

PubMed Central

Murphy, Jillian M.; Ridley, Nicole J.; Vercammen, Ans

2015-01-01

The orbitofrontal cortex (OFC) has been implicated in the capacity to accurately recognise facial expressions. The aim of the current study was to determine if anodal transcranial direct current stimulation (tDCS) targeting the right OFC in healthy adults would enhance facial expression recognition, compared with a sham condition. Across two counterbalanced sessions of tDCS (i.e. anodal and sham), 20 undergraduate participants (18 female) completed a facial expression labelling task comprising angry, disgusted, fearful, happy, sad and neutral expressions, and a control (social judgement) task comprising the same expressions. Responses on the labelling task were scored for accuracy, median reaction time and overall efficiency (i.e. combined accuracy and reaction time). Anodal tDCS targeting the right OFC enhanced facial expression recognition, reflected in greater efficiency and speed of recognition across emotions, relative to the sham condition. In contrast, there was no effect of tDCS to responses on the control task. This is the first study to demonstrate that anodal tDCS targeting the right OFC boosts facial expression recognition. This finding provides a solid foundation for future research to examine the efficacy of this technique as a means to treat facial expression recognition deficits, particularly in individuals with OFC damage or dysfunction. PMID:25971602

Individual differences in forced-choice recognition memory: partitioning contributions of recollection and familiarity.

PubMed

Migo, Ellen M; Quamme, Joel R; Holmes, Selina; Bendell, Andrew; Norman, Kenneth A; Mayes, Andrew R; Montaldi, Daniela

2014-01-01

In forced-choice recognition memory, two different testing formats are possible under conditions of high target-foil similarity: Each target can be presented alongside foils similar to itself (forced-choice corresponding; FCC), or alongside foils similar to other targets (forced-choice noncorresponding; FCNC). Recent behavioural and neuropsychological studies suggest that FCC performance can be supported by familiarity whereas FCNC performance is supported primarily by recollection. In this paper, we corroborate this finding from an individual differences perspective. A group of older adults were given a test of FCC and FCNC recognition for object pictures, as well as standardized tests of recall, recognition, and IQ. Recall measures were found to predict FCNC, but not FCC performance, consistent with a critical role for recollection in FCNC only. After the common influence of recall was removed, standardized tests of recognition predicted FCC, but not FCNC performance. This is consistent with a contribution of only familiarity in FCC. Simulations show that a two-process model, where familiarity and recollection make separate contributions to recognition, is 10 times more likely to give these results than a single-process model. This evidence highlights the importance of recognition memory test design when examining the involvement of recollection and familiarity.

Recognition of visual stimuli and memory for spatial context in schizophrenic patients and healthy volunteers.

PubMed

Brébion, Gildas; David, Anthony S; Pilowsky, Lyn S; Jones, Hugh

2004-11-01

Verbal and visual recognition tasks were administered to 40 patients with schizophrenia and 40 healthy comparison subjects. The verbal recognition task consisted of discriminating between 16 target words and 16 new words. The visual recognition task consisted of discriminating between 16 target pictures (8 black-and-white and 8 color) and 16 new pictures (8 black-and-white and 8 color). Visual recognition was followed by a spatial context discrimination task in which subjects were required to remember the spatial location of the target pictures at encoding. Results showed that recognition deficit in patients was similar for verbal and visual material. In both schizophrenic and healthy groups, men, but not women, obtained better recognition scores for the colored than for the black-and-white pictures. However, men and women similarly benefited from color to reduce spatial context discrimination errors. Patients showed a significant deficit in remembering the spatial location of the pictures, independently of accuracy in remembering the pictures themselves. These data suggest that patients are impaired in the amount of visual information that they can encode. With regards to the perceptual attributes of the stimuli, memory for spatial information appears to be affected, but not processing of color information.

Fully automatic segmentation of the femur from 3D-CT images using primitive shape recognition and statistical shape models.

PubMed

Ben Younes, Lassad; Nakajima, Yoshikazu; Saito, Toki

2014-03-01

Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM. Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting. The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm. The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.

Three-dimensional obstacle classification in laser range data

NASA Astrophysics Data System (ADS)

Armbruster, Walter; Bers, Karl-Heinz

1998-10-01

The threat of hostile surveillance and weapon systems require military aircraft to fly under extreme conditions such as low altitude, high speed, poor visibility and incomplete terrain information. The probability of collision with natural and man-made obstacles during such contour missions is high if detection capability is restricted to conventional vision aids. Forward-looking scanning laser rangefinders which are presently being flight tested and evaluated at German proving grounds, provide a possible solution, having a large field of view, high angular and range resolution, a high pulse repetition rate, and sufficient pulse energy to register returns from wires at over 500 m range (depends on the system) with a high hit-and-detect probability. Despite the efficiency of the sensor, acceptance of current obstacle warning systems by test pilots is not very high, mainly due to the systems' inadequacies in obstacle recognition and visualization. This has motivated the development and the testing of more advanced 3d-scene analysis algorithm at FGAN-FIM to replace the obstacle recognition component of current warning systems. The basic ideas are to increase the recognition probability and to reduce the false alarm rate for hard-to-extract obstacles such as wires, by using more readily recognizable objects such as terrain, poles, pylons, trees, etc. by implementing a hierarchical classification procedure to generate a parametric description of the terrain surface as well as the class, position, orientation, size and shape of all objects in the scene. The algorithms can be used for other applications such as terrain following, autonomous obstacle avoidance, and automatic target recognition.

A biomolecular recognition approach for the functionalization of cellulose with gold nanoparticles.

PubMed

Almeida, A; Rosa, A M M; Azevedo, A M; Prazeres, D M F

2017-09-01

Materials with new and improved functionalities can be obtained by modifying cellulose with gold nanoparticles (AuNPs) via the in situ reduction of a gold precursor or the deposition or covalent immobilization of pre-synthesized AuNPs. Here, we present an alternative biomolecular recognition approach to functionalize cellulose with biotin-AuNPs that relies on a complex of 2 recognition elements: a ZZ-CBM3 fusion that combines a carbohydrate-binding module (CBM) with the ZZ fragment of the staphylococcal protein A and an anti-biotin antibody. Paper and cellulose microparticles with AuNPs immobilized via the ZZ-CBM3:anti-biotin IgG supramolecular complex displayed an intense red color, whereas essentially no color was detected when AuNPs were deposited over the unmodified materials. Scanning electron microscopy analysis revealed a homogeneous distribution of AuNPs when immobilized via ZZ-CBM3:anti-biotin IgG complexes and aggregation of AuNPs when deposited over paper, suggesting that color differences are due to interparticle plasmon coupling effects. The approach could be used to functionalize paper substrates and cellulose nanocrystals with AuNPs. More important, however, is the fact that the occurrence of a biomolecular recognition event between the CBM-immobilized antibody and its specific, AuNP-conjugated antigen is signaled by red color. This opens up the way for the development of simple and straightforward paper/cellulose-based tests where detection of a target analyte can be made by direct use of color signaling. Copyright © 2017 John Wiley & Sons, Ltd.

Integrating visual learning within a model-based ATR system

NASA Astrophysics Data System (ADS)

Carlotto, Mark; Nebrich, Mark

2017-05-01

Automatic target recognition (ATR) systems, like human photo-interpreters, rely on a variety of visual information for detecting, classifying, and identifying manmade objects in aerial imagery. We describe the integration of a visual learning component into the Image Data Conditioner (IDC) for target/clutter and other visual classification tasks. The component is based on an implementation of a model of the visual cortex developed by Serre, Wolf, and Poggio. Visual learning in an ATR context requires the ability to recognize objects independent of location, scale, and rotation. Our method uses IDC to extract, rotate, and scale image chips at candidate target locations. A bootstrap learning method effectively extends the operation of the classifier beyond the training set and provides a measure of confidence. We show how the classifier can be used to learn other features that are difficult to compute from imagery such as target direction, and to assess the performance of the visual learning process itself.

Airborne ladar man-in-the-loop operations in tactical environments

NASA Astrophysics Data System (ADS)

Grobmyer, Joseph E., Jr.; Lum, Tommy; Morris, Robert E.; Hard, Sarah J.; Pratt, H. L.; Florence, Tom; Peddycoart, Ed

2004-09-01

The U.S. Army Research, Development and Engineering Command (RDECOM) is developing approaches and processes that will exploit the characteristics of current and future Laser Radar (LADAR) sensor systems for critical man-in-the-loop tactical processes. The importance of timely and accurate target detection, classification, identification, and engagement for future combat systems has been documented and is viewed as a critical enabling factor for FCS survivability and lethality. Recent work has demonstrated the feasibility of using low cost but relatively capable personal computer class systems to exploit the information available in Ladar sensor frames to present the war fighter or analyst with compelling and usable imagery for use in the target identification and engagement processes in near real time. The advantages of LADAR imagery are significant in environments presenting cover for targets and the associated difficulty for automated target recognition (ATR) technologies.

Vision-based object detection and recognition system for intelligent vehicles

NASA Astrophysics Data System (ADS)

Ran, Bin; Liu, Henry X.; Martono, Wilfung

1999-01-01

Recently, a proactive crash mitigation system is proposed to enhance the crash avoidance and survivability of the Intelligent Vehicles. Accurate object detection and recognition system is a prerequisite for a proactive crash mitigation system, as system component deployment algorithms rely on accurate hazard detection, recognition, and tracking information. In this paper, we present a vision-based approach to detect and recognize vehicles and traffic signs, obtain their information, and track multiple objects by using a sequence of color images taken from a moving vehicle. The entire system consist of two sub-systems, the vehicle detection and recognition sub-system and traffic sign detection and recognition sub-system. Both of the sub- systems consist of four models: object detection model, object recognition model, object information model, and object tracking model. In order to detect potential objects on the road, several features of the objects are investigated, which include symmetrical shape and aspect ratio of a vehicle and color and shape information of the signs. A two-layer neural network is trained to recognize different types of vehicles and a parameterized traffic sign model is established in the process of recognizing a sign. Tracking is accomplished by combining the analysis of single image frame with the analysis of consecutive image frames. The analysis of the single image frame is performed every ten full-size images. The information model will obtain the information related to the object, such as time to collision for the object vehicle and relative distance from the traffic sings. Experimental results demonstrated a robust and accurate system in real time object detection and recognition over thousands of image frames.

Biosensing of DNA oxidative damage: a model of using glucose meter for non-glucose biomarker detection.

PubMed

Zhu, Xuena; Sarwar, Mehenur; Yue, Qiaoli; Chen, Chunying; Li, Chen-Zhong

2017-01-01

Non-glucose biomarker-DNA oxidative damage biomarker 8-hydroxy-2'-deoxyguanosine (8-OHdG) has been successfully detected using a smartphone-enabled glucose meter. Through a series of immune reactions and enzymatic reactions on a solid lateral flow platform, 8-OHdG concentration has been converted to a relative amount of glucose, and therefore can be detected by conventional glucose meter directly. The device was able to detect 8-OHdG concentrations in phosphate buffer saline as low as 1.73 ng mL -1 with a dynamic range of 1-200 ng mL -1 . Considering the inherent advantages of the personal glucose meter, the demonstration of this device, therefore, should provide new opportunities for the monitoring of a wide range of biomarkers and various target analytes in connection with different molecular recognition events.

Detection of Suspicious Persons using Internet Camera

NASA Astrophysics Data System (ADS)

Terada, Kenji; Kamogashira, Daisuke

Recently, many brutal crimes have shocked us. Therefore, the importance of security and self-defense have increased more and more. It is necessary to develop an automatic method of detecting suspicious persons. In this paper, we propose a method of detecting suspicious persons using the internet camera. An image sequence is obtained by the internet camera. By using these images, the recognition of suspicious persons is carried out. Our method classifies the condition of the target person into 3 postures: walking, staying and sitting. The system employs the subspace method which uses three features: the value of movement, the number of looking around restlessly, and the rate of stopping and going. Some experimental results using a simple experimental system are also reported, which indicate effectiveness of the proposed method. In most scenes, the suspicious persons are able to be detected by the proposed method.

A multispectral automatic target recognition application for maritime surveillance, search, and rescue

NASA Astrophysics Data System (ADS)

Schoonmaker, Jon; Reed, Scott; Podobna, Yuliya; Vazquez, Jose; Boucher, Cynthia

2010-04-01

Due to increased security concerns, the commitment to monitor and maintain security in the maritime environment is increasingly a priority. A country's coast is the most vulnerable area for the incursion of illegal immigrants, terrorists and contraband. This work illustrates the ability of a low-cost, light-weight, multi-spectral, multi-channel imaging system to handle the environment and see under difficult marine conditions. The system and its implemented detecting and tracking technologies should be organic to the maritime homeland security community for search and rescue, fisheries, defense, and law enforcement. It is tailored for airborne and ship based platforms to detect, track and monitor suspected objects (such as semi-submerged targets like marine mammals, vessels in distress, and drug smugglers). In this system, automated detection and tracking technology is used to detect, classify and localize potential threats or objects of interest within the imagery provided by the multi-spectral system. These algorithms process the sensor data in real time, thereby providing immediate feedback when features of interest have been detected. A supervised detection system based on Haar features and Cascade Classifiers is presented and results are provided on real data. The system is shown to be extendable and reusable for a variety of different applications.

Volatile organic compounds discrimination based on dual mode detection

NASA Astrophysics Data System (ADS)

Yu, Yuanyuan; Wu, Enxiu; Chen, Yan; Feng, Zhihong; Zheng, Shijun; Zhang, Hao; Pang, Wei; Liu, Jing; Zhang, Daihua

2018-06-01

We report on a volatile organic compound (VOC) sensor that can provide concentration-independent signals toward target gases. The device is based on a dual-mode detection mechanism that can simultaneously record the mechanical (resonant frequency, f r) and electrical (current, I) responses of the same gas adsorption event. The two independent signals form a unique I–f r trace for each target VOC as the concentration varies. The mechanical response (frequency shift, Δf r) resulting from mass load on the device is directly related to the amount of surface adsorptions, while the electrical response (current variation, ΔI) is associated with charge transfer across the sensing interface and changes in carrier mobility. The two responses resulting from independent physical processes reflect intrinsic physical properties of each target gas. The ΔI–Δf r trace combined with the concentration dependent frequency (or current) signals can therefore be used to achieve target both recognition and quantification. The dual-mode device is designed and fabricated using standard complementary metal oxide semiconductor (CMOS) compatible processes. It exhibits consistent and stable performance in our tests with six different VOCs including ethanol, methanol, acetone, formaldehyde, benzene and hexane.

Volatile organic compounds discrimination based on dual mode detection.

PubMed

Yu, Yuanyuan; Wu, Enxiu; Chen, Yan; Feng, Zhihong; Zheng, Shijun; Zhang, Hao; Pang, Wei; Liu, Jing; Zhang, Daihua

2018-06-15

We report on a volatile organic compound (VOC) sensor that can provide concentration-independent signals toward target gases. The device is based on a dual-mode detection mechanism that can simultaneously record the mechanical (resonant frequency, f r ) and electrical (current, I) responses of the same gas adsorption event. The two independent signals form a unique I-f r trace for each target VOC as the concentration varies. The mechanical response (frequency shift, Δf r ) resulting from mass load on the device is directly related to the amount of surface adsorptions, while the electrical response (current variation, ΔI) is associated with charge transfer across the sensing interface and changes in carrier mobility. The two responses resulting from independent physical processes reflect intrinsic physical properties of each target gas. The ΔI-Δf r trace combined with the concentration dependent frequency (or current) signals can therefore be used to achieve target both recognition and quantification. The dual-mode device is designed and fabricated using standard complementary metal oxide semiconductor (CMOS) compatible processes. It exhibits consistent and stable performance in our tests with six different VOCs including ethanol, methanol, acetone, formaldehyde, benzene and hexane.

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Automated target recognition and tracking using an optical pattern recognition neural network

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Target Recognition Using Neural Networks for Model Deformation Measurements

NASA Technical Reports Server (NTRS)

Ross, Richard W.; Hibler, David L.

1999-01-01

Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. This paper describes a technique using a neural network and image processing technologies which increases the reliability of target recognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

PubMed

Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

Destabilization of the MutSα's protein-protein interface due to binding to the DNA adduct induced by anticancer agent carboplatin via molecular dynamics simulations.

PubMed

Negureanu, Lacramioara; Salsbury, Freddie R

2013-11-01

DNA mismatch repair (MMR) proteins maintain genetic integrity in all organisms by recognizing and repairing DNA errors. Such alteration of hereditary information can lead to various diseases, including cancer. Besides their role in DNA repair, MMR proteins detect and initiate cellular responses to certain type of DNA damage. Its response to the damaged DNA has made the human MMR pathway a useful target for anticancer agents such as carboplatin. This study indicates that strong, specific interactions at the interface of MutSα in response to the mismatched DNA recognition are replaced by weak, non-specific interactions in response to the damaged DNA recognition. Data suggest a severe impairment of the dimerization of MutSα in response to the damaged DNA recognition. While the core of MutSα is preserved in response to the damaged DNA recognition, the loss of contact surface and the rearrangement of contacts at the protein interface suggest a different packing in response to the damaged DNA recognition. Coupled in response to the mismatched DNA recognition, interaction energies, hydrogen bonds, salt bridges, and solvent accessible surface areas at the interface of MutSα and within the subunits are uncoupled or asynchronously coupled in response to the damaged DNA recognition. These pieces of evidence suggest that the loss of a synchronous mode of response in the MutSα's surveillance for DNA errors would possibly be one of the mechanism(s) of signaling the MMR-dependent programed cell death much wanted in anticancer therapies. The analysis was drawn from dynamics simulations.

Early effects of duloxetine on emotion recognition in healthy volunteers

PubMed Central

Bamford, Susan; Penton-Voak, Ian; Pinkney, Verity; Baldwin, David S; Munafò, Marcus R; Garner, Matthew

2015-01-01

The serotonin-noradrenaline reuptake inhibitor (SNRI) duloxetine is an effective treatment for major depression and generalised anxiety disorder. Neuropsychological models of antidepressant drug action suggest therapeutic effects might be mediated by the early correction of maladaptive biases in emotion processing, including the recognition of emotional expressions. Sub-chronic administration of duloxetine (for two weeks) produces adaptive changes in neural circuitry implicated in emotion processing; however, its effects on emotional expression recognition are unknown. Forty healthy participants were randomised to receive either 14 days of duloxetine (60 mg/day, titrated from 30 mg after three days) or matched placebo (with sham titration) in a double-blind, between-groups, repeated-measures design. On day 0 and day 14 participants completed a computerised emotional expression recognition task that measured sensitivity to the six primary emotions. Thirty-eight participants (19 per group) completed their course of tablets and were included in the analysis. Results provide evidence that duloxetine, compared to placebo, may reduce the accurate recognition of sadness. Drug effects were driven by changes in participants’ ability to correctly detect subtle expressions of sadness, with greater change observed in the placebo relative to the duloxetine group. These effects occurred in the absence of changes in mood. Our preliminary findings require replication, but complement recent evidence that sadness recognition is a therapeutic target in major depression, and a mechanism through which SNRIs could resolve negative biases in emotion processing to achieve therapeutic effects. PMID:25759400

Repetition priming across distinct contexts: effects of lexical status, word frequency, and retrieval test.

PubMed

Coane, Jennifer H; Balota, David A

2010-12-01

Repetition priming, the facilitation observed when a target is preceded by an identity prime, is a robust phenomenon that occurs across a variety of conditions. Oliphant (1983), however, failed to observe repetition priming for targets embedded in the instructions to an experiment in a subsequent lexical decision task. In the present experiments, we examined the roles of priming context (list or instructions), target lexicality, and target frequency in both lexical decision and episodic recognition performance. Initial encoding context did not modulate priming in lexical decision or recognition memory for low-frequency targets or nonwords, whereas context strongly modulated episodic recognition for high-frequency targets. The results indicate that priming across contexts is sensitive to the distinctiveness of the trace and the reliance on episodic retrieval mechanisms. These results also shed light on the influence of event boundaries, such that priming occurs across different events for relatively distinct (low-frequency) items.

Fly eye radar or micro-radar sensor technology

NASA Astrophysics Data System (ADS)

Molchanov, Pavlo; Asmolova, Olga

2014-05-01

To compensate for its eye's inability to point its eye at a target, the fly's eye consists of multiple angularly spaced sensors giving the fly the wide-area visual coverage it needs to detect and avoid the threats around him. Based on a similar concept a revolutionary new micro-radar sensor technology is proposed for detecting and tracking ground and/or airborne low profile low altitude targets in harsh urban environments. Distributed along a border or around a protected object (military facility and buildings, camp, stadium) small size, low power unattended radar sensors can be used for target detection and tracking, threat warning, pre-shot sniper protection and provides effective support for homeland security. In addition it can provide 3D recognition and targets classification due to its use of five orders more pulses than any scanning radar to each space point, by using few points of view, diversity signals and intelligent processing. The application of an array of directional antennas eliminates the need for a mechanical scanning antenna or phase processor. It radically decreases radar size and increases bearing accuracy several folds. The proposed micro-radar sensors can be easy connected to one or several operators by point-to-point invisible protected communication. The directional antennas have higher gain, can be multi-frequency and connected to a multi-functional network. Fly eye micro-radars are inexpensive, can be expendable and will reduce cost of defense.

Ultrasensitive electrochemical detection of DNA based on Zn²⁺ assistant DNA recycling followed with hybridization chain reaction dual amplification.

PubMed

Qian, Yong; Wang, Chunyan; Gao, Fenglei

2015-01-15

A new strategy to combine Zn(2+) assistant DNA recycling followed with hybridization chain reaction dual amplification was designed for highly sensitive electrochemical detection of target DNA. A gold electrode was used to immobilize molecular beacon (MB) as the recognition probe and perform the amplification procedure. In the presence of the target DNA, the hairpin probe 1 was opened, and the DNAzyme was liberated from the caged structure. The activated DNAzyme hybridized with the MB and catalyzed its cleavage in the presence of Zn(2+) cofactor and resulting in a free DNAzyme strand. Finally, each target-induced activated DNAzyme underwent many cycles triggering the cleavage of MB, thus forming numerous MB fragments. The MB fragments triggered the HCR and formed a long double-helix DNA structure. Because both H1 and H2 were labeled by biotin, a lot of SA-ALP was captured on the electrode surface, thus catalyzing a silver deposition process for electrochemical stripping analysis. This novel cascade signal amplification strategy can detect target DNA down to the attomolar level with a dynamic range spanning 6 orders of magnitude. This highly sensitive and specific assay has a great potential to become a promising DNA quantification method in biomedical research and clinical diagnosis. Copyright © 2014 Elsevier B.V. All rights reserved.

Landscape Phage: Evolution from Phage Display to Nanobiotechnology.

PubMed

Petrenko, Valery A

2018-06-07

The development of phage engineering technology has led to the construction of a novel type of phage display library-a collection of nanofiber materials with diverse molecular landscapes accommodated on the surface of phage particles. These new nanomaterials, called the "landscape phage", serve as a huge resource of diagnostic/detection probes and versatile construction materials for the preparation of phage-functionalized biosensors and phage-targeted nanomedicines. Landscape-phage-derived probes interact with biological threat agents and generate detectable signals as a part of robust and inexpensive molecular recognition interfaces introduced in mobile detection devices. The use of landscape-phage-based interfaces may greatly improve the sensitivity, selectivity, robustness, and longevity of these devices. In another area of bioengineering, landscape-phage technology has facilitated the development and testing of targeted nanomedicines. The development of high-throughput phage selection methods resulted in the discovery of a variety of cancer cell-associated phages and phage proteins demonstrating natural proficiency to self-assemble into various drug- and gene-targeting nanovehicles. The application of this new "phage-programmed-nanomedicines" concept led to the development of a number of cancer cell-targeting nanomedicine platforms, which demonstrated anticancer efficacy in both in vitro and in vivo experiments. This review was prepared to attract the attention of chemical scientists and bioengineers seeking to develop functionalized nanomaterials and use them in different areas of bioscience, medicine, and engineering.

Wavelet-based polarimetry analysis

NASA Astrophysics Data System (ADS)

Ezekiel, Soundararajan; Harrity, Kyle; Farag, Waleed; Alford, Mark; Ferris, David; Blasch, Erik

2014-06-01

Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.

Requirements for the Development of Bacillus Anthracis Spore Reference Materials Used to Test Detection Systems

PubMed Central

Almeida, Jamie L.; Wang, Lili; Morrow, Jayne B.; Cole, Kenneth D.

2006-01-01

Bacillus anthracis spores have been used as biological weapons and the possibility of their further use requires surveillance systems that can accurately and reliably detect their presence in the environment. These systems must collect samples from a variety of matrices, process the samples, and detect the spores. The processing of the sample may include removal of inhibitors, concentration of the target, and extraction of the target in a form suitable for detection. Suitable reference materials will allow the testing of each of these steps to determine the sensitivity and specificity of the detection systems. The development of uniform and well-characterized reference materials will allow the comparison of different devices and technologies as well as assure the continued performance of detection systems. This paper discusses the special requirements of reference materials for Bacillus anthracis spores that could be used for testing detection systems. The detection of Bacillus anthracis spores is based on recognition of specific characteristics (markers) on either the spore surface or in the nucleic acids (DNA). We have reviewed the specific markers and their relevance to characterization of reference materials. We have also included the approach for the characterization of candidate reference materials that we are developing at the NIST laboratories. Additional applications of spore reference materials would include testing sporicidal treatments, techniques for sampling the environment, and remediation of spore-contaminated environments. PMID:27274929

Structural basis for microRNA targeting

DOE PAGES

Schirle, Nicole T.; Sheu-Gruttadauria, Jessica; MacRae, Ian J.

2014-10-31

MicroRNAs (miRNAs) control expression of thousands of genes in plants and animals. miRNAs function by guiding Argonaute proteins to complementary sites in messenger RNAs (mRNAs) targeted for repression. In this paper, we determined crystal structures of human Argonaute-2 (Ago2) bound to a defined guide RNA with and without target RNAs representing miRNA recognition sites. These structures suggest a stepwise mechanism, in which Ago2 primarily exposes guide nucleotides (nt) 2 to 5 for initial target pairing. Pairing to nt 2 to 5 promotes conformational changes that expose nt 2 to 8 and 13 to 16 for further target recognition. Interactions withmore » the guide-target minor groove allow Ago2 to interrogate target RNAs in a sequence-independent manner, whereas an adenosine binding-pocket opposite guide nt 1 further facilitates target recognition. Spurious slicing of miRNA targets is avoided through an inhibitory coordination of one catalytic magnesium ion. Finally, these results explain the conserved nucleotide-pairing patterns in animal miRNA target sites first observed over two decades ago.« less

In vitro selection of single-stranded DNA molecular recognition elements against S. aureus alpha toxin and sensitive detection in human serum.

PubMed

Hong, Ka L; Battistella, Luisa; Salva, Alysia D; Williams, Ryan M; Sooter, Letha J

2015-01-27

Alpha toxin is one of the major virulence factors secreted by Staphylococcus aureus, a bacterium that is responsible for a wide variety of infections in both community and hospital settings. Due to the prevalence of S. aureus related infections and the emergence of methicillin-resistant S. aureus, rapid and accurate diagnosis of S. aureus infections is crucial in benefiting patient health outcomes. In this study, a rigorous Systematic Evolution of Ligands by Exponential Enrichment (SELEX) variant previously developed by our laboratory was utilized to select a single-stranded DNA molecular recognition element (MRE) targeting alpha toxin with high affinity and specificity. At the end of the 12-round selection, the selected MRE had an equilibrium dissociation constant (Kd) of 93.7 ± 7.0 nM. Additionally, a modified sandwich enzyme-linked immunosorbent assay (ELISA) was developed by using the selected ssDNA MRE as the toxin-capturing element and a sensitive detection of 200 nM alpha toxin in undiluted human serum samples was achieved.

An Indirect Homologous Competitive Enzyme-linked Immunosorbent Assay for the Detection of a Class of Glycosylated Dihydrochalcones

PubMed Central

Ranganathan, Anupama; Paradise, Grace A.; Hansen, Chad A.; McCoy, Mark R.; Gee, Shirley J.; Zhong, Ping; Chang, Dan; Hammock, Bruce D.

2013-01-01

Hesperetin dihydrochalcone 4′-glucoside, 1 and phloretin 4′-glucoside, 2 belong to a family of dihydrochalcone glycosides that exhibit flavorant properties. We have developed a competitive, indirect homologous ELISA for the detection of targets 1 and 2 in fermentation media. Immunogen and coating antigen were prepared by conjugating hapten, 4-(3-oxo-3-(2,6-dihydroxy-4-glucoside phenyl)propyl) benzoic acid to thyroglobulin and bovine serum albumin, respectively. Antibodies raised in rabbits M6122, M6123 and M6124 and the coating antigen were screened and characterized to determine their optimum concentrations. The optimized ELISA, developed with antibody M6122, gave IC50 values of 27.8 and 21.8 ng/mL for 1 and 2, respectively. Selectivity of the assay was assessed by measuring cross-reactivity of antibody M6122 to related congeners such as aglycones and the 2′-glycosides of hesperetin dihydrochalcone, 5 and phloretin, 6. Antibody M6122 showed very low recognition of 5 and virtually no recognition of the aglycones and 6. PMID:23767873

Facial recognition trial: biometric identification of non-compliant subjects using CCTV

NASA Astrophysics Data System (ADS)

Best, Tim

2007-10-01

LogicaCMG were provided with an opportunity to deploy a facial recognition system in a realistic scenario. 12 cameras were installed at an international airport covering all entrances to the immigration hall. The evaluation took place over several months with numerous adjustments to both the hardware (i.e. cameras, servers and capture cards) and software. The learning curve has been very steep but a stage has now been reached where both LogicaCMG and the client are confident that, subject to the right environmental conditions (lighting and camera location) an effective system can be defined with a high probability of successful detection of the target individual, with minimal false alarms. To the best of our knowledge, results with a >90% detection rate, of non-compliant subjects 'at range' has not been achieved anywhere else. This puts this location at the forefront of capability in this area. The results achieved demonstrate that, given optimised conditions, it is possible to achieve a long range biometric identification of a non compliant subject, with a high rate of success.

Proposed biomimetic molecular sensor array for astrobiology applications

NASA Astrophysics Data System (ADS)

Cullen, D. C.; Grant, W. D.; Piletsky, S.; Sims, M. R.

2001-08-01

A key objective of future astrobiology lander missions, e.g. to Mars and Europa, is the detection of biomarkers - molecules whose presence indicates the existence of either current or extinct life. To address limitations of current analytical methods for biomarker detection, we describe the methodology of a new project for demonstration of a robust molecular-recognition sensor array for astrobiology biomarkers. The sensor array will be realised by assembling components that have been demonstrated individually in previous or current research projects. The major components are (1) robust artificial molecular receptors comprised of molecular imprinted polymer (MIP) recognition systems and (2) a sensor array comprised of both optical and electrochemical sensor elements. These components will be integrated together using ink-jet printing technology coupled with in situ photo-polymerisation of MIPs. For demonstration, four model biomarkers are chosen as targets and represent various classes of potential biomarkers. Objectives of the proposed work include (1) demonstration of practical proof-of-concept, (2) identify areas for further development and (3) provide performance and design data for follow-up projects leading to astrobiology missions.

Novel readout method for molecular diagnostic assays based on optical measurements of magnetic nanobead dynamics.

PubMed

Donolato, Marco; Antunes, Paula; Bejhed, Rebecca S; Zardán Gómez de la Torre, Teresa; Østerberg, Frederik W; Strömberg, Mattias; Nilsson, Mats; Strømme, Maria; Svedlindh, Peter; Hansen, Mikkel F; Vavassori, Paolo

2015-02-03

We demonstrate detection of DNA coils formed from a Vibrio cholerae DNA target at picomolar concentrations using a novel optomagnetic approach exploiting the dynamic behavior and optical anisotropy of magnetic nanobead (MNB) assemblies. We establish that the complex second harmonic optical transmission spectra of MNB suspensions measured upon application of a weak uniaxial AC magnetic field correlate well with the rotation dynamics of the individual MNBs. Adding a target analyte to the solution leads to the formation of permanent MNB clusters, namely, to the suppression of the dynamic MNB behavior. We prove that the optical transmission spectra are highly sensitive to the formation of permanent MNB clusters and, thereby to the target analyte concentration. As a specific clinically relevant diagnostic case, we detect DNA coils formed via padlock probe recognition and isothermal rolling circle amplification and benchmark against a commercial equipment. The results demonstrate the fast optomagnetic readout of rolling circle products from bacterial DNA utilizing the dynamic properties of MNBs in a miniaturized and low-cost platform requiring only a transparent window in the chip.

Finger tips detection for two handed gesture recognition

NASA Astrophysics Data System (ADS)

Bhuyan, M. K.; Kar, Mithun Kumar; Neog, Debanga Raj

2011-10-01

In this paper, a novel algorithm is proposed for fingertips detection in view of two-handed static hand pose recognition. In our method, finger tips of both hands are detected after detecting hand regions by skin color-based segmentation. At first, the face is removed in the image by using Haar classifier and subsequently, the regions corresponding to the gesturing hands are isolated by a region labeling technique. Next, the key geometric features characterizing gesturing hands are extracted for two hands. Finally, for all possible/allowable finger movements, a probabilistic model is developed for pose recognition. Proposed method can be employed in a variety of applications like sign language recognition and human-robot-interactions etc.

Artificial neural networks for acoustic target recognition

NASA Astrophysics Data System (ADS)

Robertson, James A.; Mossing, John C.; Weber, Bruce A.

1995-04-01

Acoustic sensors can be used to detect, track and identify non-line-of-sight targets passively. Attempts to alter acoustic emissions often result in an undesirable performance degradation. This research project investigates the use of neural networks for differentiating between features extracted from the acoustic signatures of sources. Acoustic data were filtered and digitized using a commercially available analog-digital convertor. The digital data was transformed to the frequency domain for additional processing using the FFT. Narrowband peak detection algorithms were incorporated to select peaks above a user defined SNR. These peaks were then used to generate a set of robust features which relate specifically to target components in varying background conditions. The features were then used as input into a backpropagation neural network. A K-means unsupervised clustering algorithm was used to determine the natural clustering of the observations. Comparisons between a feature set consisting of the normalized amplitudes of the first 250 frequency bins of the power spectrum and a set of 11 harmonically related features were made. Initial results indicate that even though some different target types had a tendency to group in the same clusters, the neural network was able to differentiate the targets. Successful identification of acoustic sources under varying operational conditions with high confidence levels was achieved.

Recognition intent and visual word recognition.

PubMed

Wang, Man-Ying; Ching, Chi-Le

2009-03-01

This study adopted a change detection task to investigate whether and how recognition intent affects the construction of orthographic representation in visual word recognition. Chinese readers (Experiment 1-1) and nonreaders (Experiment 1-2) detected color changes in radical components of Chinese characters. Explicit recognition demand was imposed in Experiment 2 by an additional recognition task. When the recognition was implicit, a bias favoring the radical location informative of character identity was found in Chinese readers (Experiment 1-1), but not nonreaders (Experiment 1-2). With explicit recognition demands, the effect of radical location interacted with radical function and word frequency (Experiment 2). An estimate of identification performance under implicit recognition was derived in Experiment 3. These findings reflect the joint influence of recognition intent and orthographic regularity in shaping readers' orthographic representation. The implication for the role of visual attention in word recognition was also discussed.

The telltale face: possible mechanisms behind defector and cooperator recognition revealed by emotional facial expression metrics.

PubMed

Kovács-Bálint, Zsófia; Bereczkei, Tamás; Hernádi, István

2013-11-01

In this study, we investigated the role of facial cues in cooperator and defector recognition. First, a face image database was constructed from pairs of full face portraits of target subjects taken at the moment of decision-making in a prisoner's dilemma game (PDG) and in a preceding neutral task. Image pairs with no deficiencies (n = 67) were standardized for orientation and luminance. Then, confidence in defector and cooperator recognition was tested with image rating in a different group of lay judges (n = 62). Results indicate that (1) defectors were better recognized (58% vs. 47%), (2) they looked different from cooperators (p < .01), (3) males but not females evaluated the images with a relative bias towards the cooperator category (p < .01), and (4) females were more confident in detecting defectors (p < .05). According to facial microexpression analysis, defection was strongly linked with depressed lower lips and less opened eyes. Significant correlation was found between the intensity of micromimics and the rating of images in the cooperator-defector dimension. In summary, facial expressions can be considered as reliable indicators of momentary social dispositions in the PDG. Females may exhibit an evolutionary-based overestimation bias to detecting social visual cues of the defector face. © 2012 The British Psychological Society.

Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at participating nodes. Therefore, the feature-extraction method based on the Haar DWT is presented that employs a maximum-entropy measure to determine significant wavelet coefficients. Features are formed by calculating the energy of coefficients grouped around the competing clusters. A DWT-based feature extraction algorithm used for vehicle classification in WSNs can be enhanced by an added rule for selecting the optimal number of resolution levels to improve the correct classification rate and reduce energy consumption expended in local algorithm computations. Published field trial data for vehicular ground targets, measured with multiple sensor types, are used to evaluate the wavelet-assisted algorithms. Extracted features are used in established target recognition routines, e.g., the Bayesian minimum-error-rate classifier, to compare the effects on the classification performance of the wavelet compression. Simulations of feature sets and recognition routines at different resolution levels in target scenarios indicate the impact on classification rates, while formulas are provided to estimate reduction in resource use due to distributed compression.

Identifying and detecting facial expressions of emotion in peripheral vision.

PubMed

Smith, Fraser W; Rossit, Stephanie

2018-01-01

Facial expressions of emotion are signals of high biological value. Whilst recognition of facial expressions has been much studied in central vision, the ability to perceive these signals in peripheral vision has only seen limited research to date, despite the potential adaptive advantages of such perception. In the present experiment, we investigate facial expression recognition and detection performance for each of the basic emotions (plus neutral) at up to 30 degrees of eccentricity. We demonstrate, as expected, a decrease in recognition and detection performance with increasing eccentricity, with happiness and surprised being the best recognized expressions in peripheral vision. In detection however, while happiness and surprised are still well detected, fear is also a well detected expression. We show that fear is a better detected than recognized expression. Our results demonstrate that task constraints shape the perception of expression in peripheral vision and provide novel evidence that detection and recognition rely on partially separate underlying mechanisms, with the latter more dependent on the higher spatial frequency content of the face stimulus.

Identifying and detecting facial expressions of emotion in peripheral vision

PubMed Central

Rossit, Stephanie

2018-01-01

Facial expressions of emotion are signals of high biological value. Whilst recognition of facial expressions has been much studied in central vision, the ability to perceive these signals in peripheral vision has only seen limited research to date, despite the potential adaptive advantages of such perception. In the present experiment, we investigate facial expression recognition and detection performance for each of the basic emotions (plus neutral) at up to 30 degrees of eccentricity. We demonstrate, as expected, a decrease in recognition and detection performance with increasing eccentricity, with happiness and surprised being the best recognized expressions in peripheral vision. In detection however, while happiness and surprised are still well detected, fear is also a well detected expression. We show that fear is a better detected than recognized expression. Our results demonstrate that task constraints shape the perception of expression in peripheral vision and provide novel evidence that detection and recognition rely on partially separate underlying mechanisms, with the latter more dependent on the higher spatial frequency content of the face stimulus. PMID:29847562

Automatic polymerase chain reaction product detection system for food safety monitoring using zinc finger protein fused to luciferase.

PubMed

Yoshida, Wataru; Kezuka, Aki; Murakami, Yoshiyuki; Lee, Jinhee; Abe, Koichi; Motoki, Hiroaki; Matsuo, Takafumi; Shimura, Nobuaki; Noda, Mamoru; Igimi, Shizunobu; Ikebukuro, Kazunori

2013-11-01

An automatic polymerase chain reaction (PCR) product detection system for food safety monitoring using zinc finger (ZF) protein fused to luciferase was developed. ZF protein fused to luciferase specifically binds to target double stranded DNA sequence and has luciferase enzymatic activity. Therefore, PCR products that comprise ZF protein recognition sequence can be detected by measuring the luciferase activity of the fusion protein. We previously reported that PCR products from Legionella pneumophila and Escherichia coli (E. coli) O157 genomic DNA were detected by Zif268, a natural ZF protein, fused to luciferase. In this study, Zif268-luciferase was applied to detect the presence of Salmonella and coliforms. Moreover, an artificial zinc finger protein (B2) fused to luciferase was constructed for a Norovirus detection system. In the luciferase activity detection assay, several bound/free separation process is required. Therefore, an analyzer that automatically performed the bound/free separation process was developed to detect PCR products using the ZF-luciferase fusion protein. By means of the automatic analyzer with ZF-luciferase fusion protein, target pathogenic genomes were specifically detected in the presence of other pathogenic genomes. Moreover, we succeeded in the detection of 10 copies of E. coli BL21 without extraction of genomic DNA by the automatic analyzer and E. coli was detected with a logarithmic dependency in the range of 1.0×10 to 1.0×10(6) copies. Copyright © 2013 Elsevier B.V. All rights reserved.

Background Characterization Techniques For Pattern Recognition Applications

NASA Astrophysics Data System (ADS)

Noah, Meg A.; Noah, Paul V.; Schroeder, John W.; Kessler, Bernard V.; Chernick, Julian A.

1989-08-01

The Department of Defense has a requirement to investigate technologies for the detection of air and ground vehicles in a clutter environment. The use of autonomous systems using infrared, visible, and millimeter wave detectors has the potential to meet DOD's needs. In general, however, the hard-ware technology (large detector arrays with high sensitivity) has outpaced the development of processing techniques and software. In a complex background scene the "problem" is as much one of clutter rejection as it is target detection. The work described in this paper has investigated a new, and innovative, methodology for background clutter characterization, target detection and target identification. The approach uses multivariate statistical analysis to evaluate a set of image metrics applied to infrared cloud imagery and terrain clutter scenes. The techniques are applied to two distinct problems: the characterization of atmospheric water vapor cloud scenes for the Navy's Infrared Search and Track (IRST) applications to support the Infrared Modeling Measurement and Analysis Program (IRAMMP); and the detection of ground vehicles for the Army's Autonomous Homing Munitions (AHM) problems. This work was sponsored under two separate Small Business Innovative Research (SBIR) programs by the Naval Surface Warfare Center (NSWC), White Oak MD, and the Army Material Systems Analysis Activity at Aberdeen Proving Ground MD. The software described in this paper will be available from the respective contract technical representatives.

Building a Three-Dimensional Nano-Bio Interface for Aptasensing: An Analytical Methodology Based on Steric Hindrance Initiated Signal Amplification Effect.

PubMed

Du, Xiaojiao; Jiang, Ding; Hao, Nan; Qian, Jing; Dai, Liming; Zhou, Lei; Hu, Jianping; Wang, Kun

2016-10-04

The development of novel detection methodologies in electrochemiluminescence (ECL) aptasensor fields with simplicity and ultrasensitivity is essential for constructing biosensing architectures. Herein, a facile, specific, and sensitive methodology was developed unprecedentedly for quantitative detection of microcystin-LR (MC-LR) based on three-dimensional boron and nitrogen codoped graphene hydrogels (BN-GHs) assisted steric hindrance amplifying effect between the aptamer and target analytes. The recognition reaction was monitored by quartz crystal microbalance (QCM) to validate the possible steric hindrance effect. First, the BN-GHs were synthesized via self-assembled hydrothermal method and then applied as the Ru(bpy) 3 2+ immobilization platform for further loading the biomolecule aptamers due to their nanoporous structure and large specific surface area. Interestingly, we discovered for the first time that, without the aid of conventional double-stranded DNA configuration, such three-dimensional nanomaterials can directly amplify the steric hindrance effect between the aptamer and target analytes to a detectable level, and this facile methodology could be for an exquisite assay. With the MC-LR as a model, this novel ECL biosensor showed a high sensitivity and a wide linear range. This strategy supplies a simple and versatile platform for specific and sensitive determination of a wide range of aptamer-related targets, implying that three-dimensional nanomaterials would play a crucial role in engineering and developing novel detection methodologies for ECL aptasensing fields.

Establishment of a universal and rational gene detection strategy through three-way junction-based remote transduction.

PubMed

Tang, Yidan; Lu, Baiyang; Zhu, Zhentong; Li, Bingling

2018-01-21

The polymerase chain reaction and many isothermal amplifications are able to achieve super gene amplification. Unfortunately, most commonly-used transduction methods, such as dye staining and Taqman-like probing, still suffer from shortcomings including false signals or difficult probe design, or are incompatible with multi-analysis. Here a universal and rational gene detection strategy has been established by translating isothermal amplicons to enzyme-free strand displacement circuits via three-way junction-based remote transduction. An assistant transduction probe was imported to form a partial hybrid with the target single-stranded nucleic acid. After systematic optimization the hybrid could serve as an associative trigger to activate a downstream circuit detector via a strand displacement reaction across the three-way junction. By doing so, the detection selectivity can be double-guaranteed through both amplicon-transducer recognition and the amplicon-circuit reaction. A well-optimized circuit can be immediately applied to a new target detection through simply displacing only 10-12 nt on only one component, according to the target. More importantly, this property for the first time enables multi-analysis and logic-analysis in a single reaction, sharing a single fluorescence reporter. In an applicable model, trace amounts of Cronobacter and Enterobacteria genes have been clearly distinguished from samples with no bacteria or one bacterium, with ultra-high sensitivity and selectivity.

Extended target recognition in cognitive radar networks.

PubMed

Wei, Yimin; Meng, Huadong; Liu, Yimin; Wang, Xiqin

2010-01-01

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

DNA-mediated strand displacement facilitates sensitive electronic detection of antibodies in human serums.

PubMed

Dou, Baoting; Yang, Jianmei; Shi, Kai; Yuan, Ruo; Xiang, Yun

2016-09-15

We describe here the development of a sensitive and convenient electronic sensor for the detection of antibodies in human serums. The sensor is constructed by self-assembly formation of a mixed monolayer containing the small molecule epitope conjugated double stranded DNA probes on gold electrode. The target antibody binds the epitope on the dsDNA probe and lowers the melting temperature of the duplex, which facilitates the displacement of the antibody-linked strand of the duplex probe by an invading methylene blue-tagged single stranded DNA (MB-ssDNA) through the strand displacement reaction and leads to the capture of many MB-ssDNA on the sensor surface. Subsequent electrochemical oxidation of the methylene blue labels results in amplified current response for sensitive monitoring of the antibodies. The antibody assay conditions are optimized and the sensor exhibits a linear range between 1.0 and 25.0nM with a detection limit of 0.67nM for the target antibody. The sensor is also selective and can be employed to detect the target antibodies in human serum samples. With the advantages of using small molecule epitope as the antibody recognition element over traditional antigen, the versatile manipulability of the DNA probes and the unique properties of the electrochemical transduction technique, the developed sensor thus hold great potential for simple and sensitive detection of different antibodies and other proteins in real samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Bioinspired Pollen-Like Hierarchical Surface for Efficient Recognition of Target Cancer Cells.

PubMed

Wang, Wenshuo; Yang, Gao; Cui, Haijun; Meng, Jingxin; Wang, Shutao; Jiang, Lei

2017-08-01

The efficient recognition and isolation of rare cancer cells holds great promise for cancer diagnosis and prognosis. In nature, pollens exploit spiky structures to realize recognition and adhesion to stigma. Herein, a bioinspired pollen-like hierarchical surface is developed by replicating the assembly of pollen grains, and efficient and specific recognition to target cancer cells is achieved. The pollen-like surface is fabricated by combining filtering-assisted assembly and soft lithography-based replication of pollen grains of wild chrysanthemum. After modification with a capture agent specific to cancer cells, the pollen-like surface enables the capture of target cancer cells with high efficiency and specificity. In addition, the pollen-like surface not only assures high viability of captured cells but also performs well in cell mixture system and at low cell density. This study represents a good example of constructing cell recognition biointerfaces inspired by pollen-stigma adhesion. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Effect of aging on microRNAs and regulation of pathogen recognition receptors

PubMed Central

Olivieri, Fabiola; Procopio, Antonio Dormenico

2014-01-01

Immunosenescence is the multifactorial age-associated immune deteriorization that leads to increased susceptibility to infections and decreased responses to vaccines. Recent studies have shown a fundamental role for microRNAs (miRNAs) in regulating immune responses, and nearly all the miRNAs involved in immune regulation show modulation during aging. Aging-associated miRNAs are largely negative regulators of the immune innate response and target central nodes of aging-associated networks, in particular, NF-κB, the downstream effector of TLR signals that leads to induction of proinflammatory responses. Multiple miRNAs have been reported to share similar regulatory activity. Here we review miRNA regulation of human innate immune recognition in aging, including both activation and resolution of inflammation, critical issues in detection, and areas of active investigation into our understanding of immunosenescence. PMID:24769423

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Eyes on crowding: crowding is preserved when responding by eye and similarly affects identity and position accuracy.

PubMed

Yildirim, Funda; Meyer, Vincent; Cornelissen, Frans W

2015-02-16

Peripheral vision guides recognition and selection of targets for eye movements. Crowding—a decline in recognition performance that occurs when a potential target is surrounded by other, similar, objects—influences peripheral object recognition. A recent model study suggests that crowding may be due to increased uncertainty about both the identity and the location of peripheral target objects, but very few studies have assessed these properties in tandem. Eye tracking can integrally provide information on both the perceived identity and the position of a target and therefore could become an important approach in crowding studies. However, recent reports suggest that around the moment of saccade preparation crowding may be significantly modified. If these effects were to generalize to regular crowding tasks, it would complicate the interpretation of results obtained with eye tracking and the comparison to results obtained using manual responses. For this reason, we first assessed whether the manner by which participants responded—manually or by eye—affected their performance. We found that neither recognition performance nor response time was affected by the response type. Hence, we conclude that crowding magnitude was preserved when observers responded by eye. In our main experiment, observers made eye movements to the location of a tilted Gabor target while we varied flanker tilt to manipulate target-flanker similarity. The results indicate that this similarly affected the accuracy of peripheral recognition and saccadic target localization. Our results inform about the importance of both location and identity uncertainty in crowding. © 2015 ARVO.

ROBIN: a platform for evaluating automatic target recognition algorithms: II. Protocols used for evaluating algorithms and results obtained on the SAGEM DS database

NASA Astrophysics Data System (ADS)

Duclos, D.; Lonnoy, J.; Guillerm, Q.; Jurie, F.; Herbin, S.; D'Angelo, E.

2008-04-01

Over the five past years, the computer vision community has explored many different avenues of research for Automatic Target Recognition. Noticeable advances have been made and we are now in the situation where large-scale evaluations of ATR technologies have to be carried out, to determine what the limitations of the recently proposed methods are and to determine the best directions for future works. ROBIN, which is a project funded by the French Ministry of Defence and by the French Ministry of Research, has the ambition of being a new reference for benchmarking ATR algorithms in operational contexts. This project, headed by major companies and research centers involved in Computer Vision R&D in the field of Defense (Bertin Technologies, CNES, ECA, DGA, EADS, INRIA, ONERA, MBDA, SAGEM, THALES) recently released a large dataset of several thousands of hand-annotated infrared and RGB images of different targets in different situations. Setting up an evaluation campaign requires us to define, accurately and carefully, sets of data (both for training ATR algorithms and for their evaluation), tasks to be evaluated, and finally protocols and metrics for the evaluation. ROBIN offers interesting contributions to each one of these three points. This paper first describes, justifies and defines the set of functions used in the ROBIN competitions and relevant for evaluating ATR algorithms (Detection, Localization, Recognition and Identification). It also defines the metrics and the protocol used for evaluating these functions. In the second part of the paper, the results obtained by several state-of-the-art algorithms on the SAGEM DS database (a subpart of ROBIN) are presented and discussed

Subject independent facial expression recognition with robust face detection using a convolutional neural network.

PubMed

Matsugu, Masakazu; Mori, Katsuhiko; Mitari, Yusuke; Kaneda, Yuji

2003-01-01

Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning

PubMed Central

Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). PMID:28264011

A hierarchical, automated target recognition algorithm for a parallel analog processor

NASA Technical Reports Server (NTRS)

Woodward, Gail; Padgett, Curtis

1997-01-01

A hierarchical approach is described for an automated target recognition (ATR) system, VIGILANTE, that uses a massively parallel, analog processor (3DANN). The 3DANN processor is capable of performing 64 concurrent inner products of size 1x4096 every 250 nanoseconds.

Poly-adenine-mediated fluorescent spherical nucleic acid probes for live-cell imaging of endogenous tumor-related mRNA.

PubMed

Zhu, Dan; Zhao, Dongxia; Huang, Jiaxuan; Zhu, Yu; Chao, Jie; Su, Shao; Li, Jiang; Wang, Lihua; Shi, Jiye; Zuo, Xiaolei; Weng, Lixing; Li, Qian; Wang, Lianhui

2018-05-16

Identification of tumor-related mRNA in living cells hold great promise for early cancer diagnosis and pathological research. Herein, we present poly-adenine (polyA)-mediated fluorescent spherical nucleic acid (FSNA) probes for intracellular mRNA detection with regulable sensitivities by programmably adjusting the loading density of DNA on gold nano-interface. Gold nanoparticles (AuNPs) functionalized with polyA-tailed recognition sequences were hybridized to fluorescent "reporter" strands to fabricate fluorescence-quenched FSNA probes. While exposed to target gene, the "reporter" strands were released from FSNA through strand displacement and fluorescence was recovered. With polyA20 tail as the attaching block, the detection limit of FSNA probes was calculated to be 0.31 nM, which is ~55 fold lower than that of thiolated probes without surface density regulation. Quantitative intracellular mRNA detection and imaging could be achieved with polyA-mediated FSNA probes within 2 hours, indicating their application potential in rapid and sensitive intracellular target imaging. Copyright © 2018. Published by Elsevier Inc.

Structural basis for the recognition of guide RNA and target DNA heteroduplex by Argonaute

PubMed Central

Miyoshi, Tomohiro; Ito, Kosuke; Murakami, Ryo; Uchiumi, Toshio

2016-01-01

Argonaute proteins are key players in the gene silencing mechanisms mediated by small nucleic acids in all domains of life from bacteria to eukaryotes. However, little is known about the Argonaute protein that recognizes guide RNA/target DNA. Here, we determine the 2 Å crystal structure of Rhodobacter sphaeroides Argonaute (RsAgo) in a complex with 18-nucleotide guide RNA and its complementary target DNA. The heteroduplex maintains Watson–Crick base-pairing even in the 3′-region of the guide RNA between the N-terminal and PIWI domains, suggesting a recognition mode by RsAgo for stable interaction with the target strand. In addition, the MID/PIWI interface of RsAgo has a system that specifically recognizes the 5′ base-U of the guide RNA, and the duplex-recognition loop of the PAZ domain is important for the DNA silencing activity. Furthermore, we show that Argonaute discriminates the nucleic acid type (RNA/DNA) by recognition of the duplex structure of the seed region. PMID:27325485

Structural basis for the recognition of guide RNA and target DNA heteroduplex by Argonaute.

PubMed

Miyoshi, Tomohiro; Ito, Kosuke; Murakami, Ryo; Uchiumi, Toshio

2016-06-21

Argonaute proteins are key players in the gene silencing mechanisms mediated by small nucleic acids in all domains of life from bacteria to eukaryotes. However, little is known about the Argonaute protein that recognizes guide RNA/target DNA. Here, we determine the 2 Å crystal structure of Rhodobacter sphaeroides Argonaute (RsAgo) in a complex with 18-nucleotide guide RNA and its complementary target DNA. The heteroduplex maintains Watson-Crick base-pairing even in the 3'-region of the guide RNA between the N-terminal and PIWI domains, suggesting a recognition mode by RsAgo for stable interaction with the target strand. In addition, the MID/PIWI interface of RsAgo has a system that specifically recognizes the 5' base-U of the guide RNA, and the duplex-recognition loop of the PAZ domain is important for the DNA silencing activity. Furthermore, we show that Argonaute discriminates the nucleic acid type (RNA/DNA) by recognition of the duplex structure of the seed region.

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-01-01

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

Single-Cell Droplet Microfluidic Screening for Antibodies Specifically Binding to Target Cells.

PubMed

Shembekar, Nachiket; Hu, Hongxing; Eustace, David; Merten, Christoph A

2018-02-20

Monoclonal antibodies are a main player in modern drug discovery. Many antibody screening formats exist, each with specific advantages and limitations. Nonetheless, it remains challenging to screen antibodies for the binding of cell-surface receptors (the most important class of all drug targets) or for the binding to target cells rather than purified proteins. Here, we present a high-throughput droplet microfluidics approach employing dual-color normalized fluorescence readout to detect antibody binding. This enables us to obtain quantitative data on target cell recognition, using as little as 33 fg of IgG per assay. Starting with an excess of hybridoma cells releasing unspecific antibodies, individual clones secreting specific binders (of target cells co-encapsulated into droplets) could be enriched 220-fold after sorting 80,000 clones in a single experiment. This opens the way for therapeutic antibody discovery, especially since the single-cell approach is in principle also applicable to primary human plasma cells. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Model based approach to UXO imaging using the time domain electromagnetic method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lavely, E.M.

1999-04-01

Time domain electromagnetic (TDEM) sensors have emerged as a field-worthy technology for UXO detection in a variety of geological and environmental settings. This success has been achieved with commercial equipment that was not optimized for UXO detection and discrimination. The TDEM response displays a rich spatial and temporal behavior which is not currently utilized. Therefore, in this paper the author describes a research program for enhancing the effectiveness of the TDEM method for UXO detection and imaging. Fundamental research is required in at least three major areas: (a) model based imaging capability i.e. the forward and inverse problem, (b) detectormore » modeling and instrument design, and (c) target recognition and discrimination algorithms. These research problems are coupled and demand a unified treatment. For example: (1) the inverse solution depends on solution of the forward problem and knowledge of the instrument response; (2) instrument design with improved diagnostic power requires forward and inverse modeling capability; and (3) improved target recognition algorithms (such as neural nets) must be trained with data collected from the new instrument and with synthetic data computed using the forward model. Further, the design of the appropriate input and output layers of the net will be informed by the results of the forward and inverse modeling. A more fully developed model of the TDEM response would enable the joint inversion of data collected from multiple sensors (e.g., TDEM sensors and magnetometers). Finally, the author suggests that a complementary approach to joint inversions is the statistical recombination of data using principal component analysis. The decomposition into principal components is useful since the first principal component contains those features that are most strongly correlated from image to image.« less

Structural basis for the high specificity of a Trypanosoma congolense immunoassay targeting glycosomal aldolase.

PubMed

Pinto, Joar; Odongo, Steven; Lee, Felicity; Gaspariunaite, Vaiva; Muyldermans, Serge; Magez, Stefan; Sterckx, Yann G-J

2017-09-01

Animal African trypanosomosis (AAT) is a neglected tropical disease which imposes a heavy burden on the livestock industry in Sub-Saharan Africa. Its causative agents are Trypanosoma parasites, with T. congolense and T. vivax being responsible for the majority of the cases. Recently, we identified a Nanobody (Nb474) that was employed to develop a homologous sandwich ELISA targeting T. congolense fructose-1,6-bisphosphate aldolase (TcoALD). Despite the high sequence identity between trypanosomatid aldolases, the Nb474-based immunoassay is highly specific for T. congolense detection. The results presented in this paper yield insights into the molecular principles underlying the assay's high specificity. The structure of the Nb474-TcoALD complex was determined via X-ray crystallography. Together with analytical gel filtration, the structure reveals that a single TcoALD tetramer contains four binding sites for Nb474. Through a comparison with the crystal structures of two other trypanosomatid aldolases, TcoALD residues Ala77 and Leu106 were identified as hot spots for specificity. Via ELISA and surface plasmon resonance (SPR), we demonstrate that mutation of these residues does not abolish TcoALD recognition by Nb474, but does lead to a lack of detection in the Nb474-based homologous sandwich immunoassay. The results show that the high specificity of the Nb474-based immunoassay is not determined by the initial recognition event between Nb474 and TcoALD, but rather by its homologous sandwich design. This (i) provides insights into the optimal set-up of the assay, (ii) may be of great significance for field applications as it could explain the potential detection escape of certain T. congolense strains, and (iii) may be of general interest to those developing similar assays.

Towards Autonomous Modular UAV Missions: The Detection, Geo-Location and Landing Paradigm

PubMed Central

Kyristsis, Sarantis; Antonopoulos, Angelos; Chanialakis, Theofilos; Stefanakis, Emmanouel; Linardos, Christos; Tripolitsiotis, Achilles; Partsinevelos, Panagiotis

2016-01-01

Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following and finally landing on a high speed moving platform. The underlying methodology includes AprilTag target identification through Graphics Processing Unit (GPU) parallelized processing, image processing and several optimized locations and approach algorithms employing gimbal movement, Global Navigation Satellite System (GNSS) readings and UAV navigation. For the experimentation, a commercial and a custom made quad-copter prototype were used, portraying a high and a low-computational embedded platform alternative. Among the successful targeting and follow procedures, it is shown that the landing approach can be successfully performed even under high platform speeds. PMID:27827883

Towards Autonomous Modular UAV Missions: The Detection, Geo-Location and Landing Paradigm.

PubMed

Kyristsis, Sarantis; Antonopoulos, Angelos; Chanialakis, Theofilos; Stefanakis, Emmanouel; Linardos, Christos; Tripolitsiotis, Achilles; Partsinevelos, Panagiotis

2016-11-03

Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following and finally landing on a high speed moving platform. The underlying methodology includes AprilTag target identification through Graphics Processing Unit (GPU) parallelized processing, image processing and several optimized locations and approach algorithms employing gimbal movement, Global Navigation Satellite System (GNSS) readings and UAV navigation. For the experimentation, a commercial and a custom made quad-copter prototype were used, portraying a high and a low-computational embedded platform alternative. Among the successful targeting and follow procedures, it is shown that the landing approach can be successfully performed even under high platform speeds.

Listeners Experience Linguistic Masking Release in Noise-Vocoded Speech-in-Speech Recognition.

PubMed

Viswanathan, Navin; Kokkinakis, Kostas; Williams, Brittany T

2018-02-15

The purpose of this study was to evaluate whether listeners with normal hearing perceiving noise-vocoded speech-in-speech demonstrate better intelligibility of target speech when the background speech was mismatched in language (linguistic release from masking [LRM]) and/or location (spatial release from masking [SRM]) relative to the target. We also assessed whether the spectral resolution of the noise-vocoded stimuli affected the presence of LRM and SRM under these conditions. In Experiment 1, a mixed factorial design was used to simultaneously manipulate the masker language (within-subject, English vs. Dutch), the simulated masker location (within-subject, right, center, left), and the spectral resolution (between-subjects, 6 vs. 12 channels) of noise-vocoded target-masker combinations presented at +25 dB signal-to-noise ratio (SNR). In Experiment 2, the study was repeated using a spectral resolution of 12 channels at +15 dB SNR. In both experiments, listeners' intelligibility of noise-vocoded targets was better when the background masker was Dutch, demonstrating reliable LRM in all conditions. The pattern of results in Experiment 1 was not reliably different across the 6- and 12-channel noise-vocoded speech. Finally, a reliable spatial benefit (SRM) was detected only in the more challenging SNR condition (Experiment 2). The current study is the first to report a clear LRM benefit in noise-vocoded speech-in-speech recognition. Our results indicate that this benefit is available even under spectrally degraded conditions and that it may augment the benefit due to spatial separation of target speech and competing backgrounds.

Research on quantitative relationship between NIIRS and the probabilities of discrimination

NASA Astrophysics Data System (ADS)

Bai, Honggang

2011-08-01

There are a large number of electro-optical (EO) and infrared (IR) sensors used on military platforms including ground vehicle, low altitude air vehicle, high altitude air vehicle, and satellite systems. Ground vehicle and low-altitude air vehicle (rotary and fixed-wing aircraft) sensors typically use the probabilities of discrimination (detection, recognition, and identification) as design requirements and system performance indicators. High-altitude air vehicles and satellite sensors have traditionally used the National Imagery Interpretation Rating Scale (NIIRS) performance measures for guidance in design and measures of system performance. Recently, there has a large effort to make strategic sensor information available to tactical forces or make the information of targets acquisition can be used by strategic systems. In this paper, the two techniques about the probabilities of discrimination and NIIRS for sensor design are presented separately. For the typical infrared remote sensor design parameters, the function of the probability of recognition and NIIRS scale as the distance R is given to Standard NATO Target and M1Abrams two different size targets based on the algorithm of predicting the field performance and NIIRS. For Standard NATO Target, M1Abrams, F-15, and B-52 four different size targets, the conversion from NIIRS to the probabilities of discrimination are derived and calculated, and the similarities and differences between NIIRS and the probabilities of discrimination are analyzed based on the result of calculation. Comparisons with preliminary calculation results show that the conversion between NIIRS and the probabilities of discrimination is probable although more validation experiments are needed.

A fusion approach for coarse-to-fine target recognition

NASA Astrophysics Data System (ADS)

Folkesson, Martin; Grönwall, Christina; Jungert, Erland

2006-04-01

A fusion approach in a query based information system is presented. The system is designed for querying multimedia data bases, and here applied to target recognition using heterogeneous data sources. The recognition process is coarse-to-fine, with an initial attribute estimation step and a following matching step. Several sensor types and algorithms are involved in each of these two steps. An independence of the matching results, on the origin of the estimation results, is observed. It allows for distribution of data between algorithms in an intermediate fusion step, without risk of data incest. This increases the overall chance of recognising the target. An implementation of the system is described.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Feature long axis size and local luminance contrast determine ship target acquisition performance: strong evidence for the TOD case

NASA Astrophysics Data System (ADS)

Bijl, Piet; Toet, Alexander; Kooi, Frank L.

2016-10-01

Visual images of a civilian target ship on a sea background were produced using a CAD model. The total set consisted of 264 images and included 3 different color schemes, 2 ship viewing aspects, 5 sun illumination conditions, 2 sea reflection values, 2 ship positions with respect to the horizon and 3 values of atmospheric contrast reduction. In a perception experiment, the images were presented on a display in a long darkened corridor. Observers were asked to indicate the range at which they were able to detect the ship and classify the following 5 ship elements: accommodation, funnel, hull, mast, and hat above the bridge. This resulted in a total of 1584 Target Acquisition (TA) range estimates for two observers. Next, the ship contour, ship elements and corresponding TA ranges were analyzed applying several feature size and contrast measures. Most data coincide on a contrast versus angular size plot using (1) the long axis as characteristic ship/ship feature size and (2) local Weber contrast as characteristic ship/ship feature contrast. Finally, the data were compared with a variety of visual performance functions assumed to be representative for Target Acquisition: the TOD (Triangle Orientation Discrimination), MRC (Minimum Resolvable Contrast), CTF (Contrast Threshold Function), TTP (Targeting Task Performance) metric and circular disc detection data for the unaided eye (Blackwell). The results provide strong evidence for the TOD case: both position and slope of the TOD curve match the ship detection and classification data without any free parameter. In contrast, the MRC and CTF are too steep, the TTP and disc detection curves are too shallow and all these curves need an overall scaling factor in order to coincide with the ship and ship feature recognition data.

Water quality monitoring using an automated portable fiber optic biosensor: RAPTOR

NASA Astrophysics Data System (ADS)

Anderson, George P.; Rowe-Taitt, Chris A.

2001-03-01

The RAPTOR is a portable, automated biosensor capable of performing rapid, ten-minute assays on a sample for four target analytes simultaneously. Samples are analyzed using a fluorescent sandwich immunoassay on the surface of short polystyrene optical probes with capture antibody adsorbed to the probe surface. Target analytes bound to the fiber by capture antibodies are detected with fluorescently labeled tracer antibodies, which are held in a separate reservoir. Since target recognition is a two-step process, selectivity is enhanced, and the optical probes can be reused up to forty times, or until a positive result is obtained. This greatly reduces the logistical burden for field operations. Numerous assays for toxins, such as SEB and ricin, and bacteria, such as Bacillus anthracis and Francisella tularensis, have been developed for the RAPTOR. An assay of particular interest for water quality monitoring and the screening of fruits and vegetables is detection of Giardia cysts. Giardia lamblia is a parasitic protozoan common in the developing world that causes severe intestinal infections. Thus, a simple field assay for screening water supplies would be highly useful. Such an assay has been developed using the RAPTOR. The detection limit for Giardia cysts was 5x104/ml for a 10-minute assay.

Individual differences in forced-choice recognition memory: Partitioning contributions of recollection and familiarity

PubMed Central

Migo, Ellen M.; Quamme, Joel R.; Holmes, Selina; Bendell, Andrew; Norman, Kenneth A.; Mayes, Andrew R.; Montaldi, Daniela

2014-01-01

In forced-choice recognition memory, two different testing formats are possible under conditions of high target-foil similarity: each target can be presented alongside foils similar to itself (forced-choice corresponding; FCC), or alongside foils similar to other targets (forced-choice non-corresponding; FCNC).Recent behavioural and neuropsychological studies suggest that FCC performance can be supported by familiarity whereas FCNC performance is supported primarily by recollection. In this paper, we corroborate this finding from an individual differences perspective. A group of older adults were given a test of FCC and FCNC recognition for object pictures, as well as standardised tests of recall, recognition and IQ. Recall measures were found to predict FCNC, but not FCC performance, consistent with a critical role for recollection in FCNC only. After the common influence of recall was removed, standardised tests of recognition predicted FCC, but not FCNC performance. This is consistent with a contribution of only familiarity in FCC. Simulations show that a two process model, where familiarity and recollection make separate contributions to recognition, is ten times more likely to give these results than a single-process model. This evidence highlights the importance of recognition memory test design when examining the involvement of recollection and familiarity. PMID:24796268