Lesion detectability in 2D-mammography and digital breast tomosynthesis using different targets and observers

NASA Astrophysics Data System (ADS)

Elangovan, Premkumar; Mackenzie, Alistair; Dance, David R.; Young, Kenneth C.; Wells, Kevin

2018-05-01

This work investigates the detection performance of specialist and non-specialist observers for different targets in 2D-mammography and digital breast tomosynthesis (DBT) using the OPTIMAM virtual clinical trials (VCT) Toolbox and a 4-alternative forced choice (4AFC) assessment paradigm. Using 2D-mammography and DBT images of virtual breast phantoms, we compare the detection limits of simple uniform spherical targets and irregular solid masses. Target diameters of 4 mm and 6 mm have been chosen to represent target sizes close to the minimum detectable size found in breast screening, across a range of controlled contrast levels. The images were viewed by a set of specialist observers (five medical physicists and six experienced clinical readers) and five non-specialists. Combined results from both observer groups indicate that DBT has a significantly lower detectable threshold contrast than 2D-mammography for small masses (4 mm: 2.1% [DBT] versus 6.9% [2D]; 6 mm: 0.7% [DBT] versus 3.9% [2D]) and spheres (4 mm: 2.9% [DBT] versus 5.3% [2D]; 6 mm: 0.3% [DBT] versus 2.2% [2D]) (p  <  0.0001). Both observer groups found spheres significantly easier to detect than irregular solid masses for both sizes and modalities (p  <  0.0001) (except 4 mm DBT). The detection performances of specialist and non-specialist observers were generally found to be comparable, where each group marginally outperformed the other in particular detection tasks. Within the specialist group, the clinical readers performed better than the medical physicists with irregular masses (p  <  0.0001). The results indicate that using spherical targets in such studies may produce over-optimistic detection thresholds compared to more complex masses, and that the superiority of DBT for detecting masses over 2D-mammography has been quantified. The results also suggest specialist observers may be supplemented by non-specialist observers (with training) in some types of 4AFC studies.

Direct detection of sub-GeV dark matter with semiconductor targets

DOE PAGES

Essig, Rouven; Fernández-Serra, Marivi; Mardon, Jeremy; ...

2016-05-09

Dark matter in the sub-GeV mass range is a theoretically motivated but largely unexplored paradigm. Such light masses are out of reach for conventional nuclear recoil direct detection experiments, but may be detected through the small ionization signals caused by dark matter-electron scattering. Semiconductors are well-studied and are particularly promising target materials because their O(1 eV) band gaps allow for ionization signals from dark matter particles as light as a few hundred keV. Current direct detection technologies are being adapted for dark matter-electron scattering. In this paper, we provide the theoretical calculations for dark matter-electron scattering rate in semiconductors, overcomingmore » several complications that stem from the many-body nature of the problem. We use density functional theory to numerically calculate the rates for dark matter-electron scattering in silicon and germanium, and estimate the sensitivity for upcoming experiments such as DAMIC and SuperCDMS. We find that the reach for these upcoming experiments has the potential to be orders of magnitude beyond current direct detection constraints and that sub-GeV dark matter has a sizable modulation signal. We also give the first direct detection limits on sub-GeV dark matter from its scattering off electrons in a semiconductor target (silicon) based on published results from DAMIC. We make available publicly our code, QEdark, with which we calculate our results. Our results can be used by experimental collaborations to calculate their own sensitivities based on their specific setup. In conclusion, the searches we propose will probe vast new regions of unexplored dark matter model and parameter space.« less

Novel Cognitive Paradigms for the Detection of Memory Impairment in Preclinical Alzheimer’s Disease

PubMed Central

Loewenstein, David A.; Curiel, Rosie E.; Duara, Ranjan; Buschke, Herman

2017-01-01

In spite of advances in neuroimaging and other brain biomarkers to assess preclinical Alzheimer’s disease (AD), cognitive assessment has relied on traditional memory paradigms developed well over six decades ago. This has led to a growing concern about their effectiveness in the early diagnosis of AD which is essential to develop preventive and early targeted interventions before the occurrence of multisystem brain degeneration. We describe the development of novel tests that are more cognitively challenging, minimize variability in learning strategies, enhance initial acquisition and retrieval using cues, and exploit vulnerabilities in persons with incipient AD such as the susceptibility to proactive semantic interference, and failure to recover from proactive semantic interference. The advantages of various novel memory assessment paradigms are examined as well as how they compare with traditional neuropsychological assessments of memory. Finally, future directions for the development of more effective assessment paradigms are suggested. PMID:29214859

Detecting targets hidden in random forests

NASA Astrophysics Data System (ADS)

Kouritzin, Michael A.; Luo, Dandan; Newton, Fraser; Wu, Biao

2009-05-01

Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted on a surveillance aircraft or unmanned aerial vehicle is used to capture the images of the forests with possible hidden targets, e.g., rocket launchers. We consider random forests of longitudinal and latitudinal correlations. Specifically, foliage coverage is encoded with a binary representation (i.e., foliage or no foliage), and is correlated in adjacent regions. We address the detection problem of camouflaged targets hidden in random forests by building memory into the observations. In particular, we propose an efficient algorithm to generate random forests, ground, and camouflage of hidden targets with two dimensional correlations. The observations are a sequence of snapshots consisting of foliage-obscured ground or target. Theoretically, detection is possible because there are subtle differences in the correlations of the ground and camouflage of the rocket launcher. However, these differences are well beyond human perception. To detect the presence of hidden targets automatically, we develop a Markov representation for these sequences and modify the classical filtering equations to allow the Markov chain observation. Particle filters are used to estimate the position of the targets in combination with a novel random weighting technique. Furthermore, we give positive proof-of-concept simulations.

Space moving target detection using time domain feature

NASA Astrophysics Data System (ADS)

Wang, Min; Chen, Jin-yong; Gao, Feng; Zhao, Jin-yu

2018-01-01

The traditional space target detection methods mainly use the spatial characteristics of the star map to detect the targets, which can not make full use of the time domain information. This paper presents a new space moving target detection method based on time domain features. We firstly construct the time spectral data of star map, then analyze the time domain features of the main objects (target, stars and the background) in star maps, finally detect the moving targets using single pulse feature of the time domain signal. The real star map target detection experimental results show that the proposed method can effectively detect the trajectory of moving targets in the star map sequence, and the detection probability achieves 99% when the false alarm rate is about 8×10-5, which outperforms those of compared algorithms.

Covariance descriptor fusion for target detection

NASA Astrophysics Data System (ADS)

Cukur, Huseyin; Binol, Hamidullah; Bal, Abdullah; Yavuz, Fatih

2016-05-01

Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

Whole-genome resequencing: changing the paradigms of SNP detection, molecular mapping and gene discovery

USDA-ARS?s Scientific Manuscript database

The next generation sequencing (NGS) technologies have opened a wealth of opportunities for plant breeding and genomics research, and changed the paradigms of marker detection, genotyping, and gene discovery. Abundant genomic resources have been generated using a whole genome resequencing (WGR) str...

Target discrimination strategies in optics detection

NASA Astrophysics Data System (ADS)

Sjöqvist, Lars; Allard, Lars; Henriksson, Markus; Jonsson, Per; Pettersson, Magnus

2013-10-01

Detection and localisation of optical assemblies used for weapon guidance or sniper rifle scopes has attracted interest for security and military applications. Typically a laser system is used to interrogate a scene of interest and the retro-reflected radiation is detected. Different system approaches for area coverage can be realised ranging from flood illumination to step-and-stare or continuous scanning schemes. Independently of the chosen approach target discrimination is a crucial issue, particularly if a complex scene such as in an urban environment and autonomous operation is considered. In this work target discrimination strategies in optics detection are discussed. Typical parameters affecting the reflected laser radiation from the target are the wavelength, polarisation properties, temporal effects and the range resolution. Knowledge about the target characteristics is important to predict the target discrimination capability. Two different systems were used to investigate polarisation properties and range resolution information from targets including e.g. road signs, optical reflexes, rifle sights and optical references. The experimental results and implications on target discrimination will be discussed. If autonomous operation is required target discrimination becomes critical in order to reduce the number of false alarms.

A method for detecting small targets based on cumulative weighted value of target properties

NASA Astrophysics Data System (ADS)

Jin, Xing; Sun, Gang; Wang, Wei-hua; Liu, Fang; Chen, Zeng-ping

2015-03-01

Laser detection based on the "cat's eye effect" has become the hot research project for its initiative compared to the passivity of sound detection and infrared detection. And the target detection is one of the core technologies in this system. The paper puts forward a method for detecting small targets based on cumulative weighted value of target properties using given data. Firstly, we make a frame difference to the images, then make image processing based on Morphology Principles. Secondly, we segment images, and screen the targets; then find some interesting locations. Finally, comparing to a quantity of frames, we locate the target. We did an exam to 394 true frames, the experimental result shows that the mathod can detect small targets efficiently.

Improving detection of low SNR targets using moment-based detection

NASA Astrophysics Data System (ADS)

Young, Shannon R.; Steward, Bryan J.; Hawks, Michael; Gross, Kevin C.

2016-05-01

Increases in the number of cameras deployed, frame rate, and detector array sizes have led to a dramatic increase in the volume of motion imagery data that is collected. Without a corresponding increase in analytical manpower, much of the data is not analyzed to full potential. This creates a need for fast, automated, and robust methods for detecting signals of interest. Current approaches fall into two categories: detect-before-track (DBT), which are fast but often poor at detecting dim targets, and track-before-detect (TBD) methods which can offer better performance but are typically much slower. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallelizable. It was found that intelligent selection of parameters can improve probability of detection by as much as 25% compared to earlier work with higherorder moments. The present method can reduce detection thresholds by 40% compared to the Reed-Xiaoli anomaly detector for low SNR targets (for a given probability of detection and false alarm).

Detection technique of targets for missile defense system

NASA Astrophysics Data System (ADS)

Guo, Hua-ling; Deng, Jia-hao; Cai, Ke-rong

2009-11-01

Ballistic missile defense system (BMDS) is a weapon system for intercepting enemy ballistic missiles. It includes ballistic-missile warning system, target discrimination system, anti-ballistic-missile guidance systems, and command-control communication system. Infrared imaging detection and laser imaging detection are widely used in BMDS for surveillance, target detection, target tracking, and target discrimination. Based on a comprehensive review of the application of target-detection techniques in the missile defense system, including infrared focal plane arrays (IRFPA), ground-based radar detection technology, 3-dimensional imaging laser radar with a photon counting avalanche photodiode (APD) arrays and microchip laser, this paper focuses on the infrared and laser imaging detection techniques in missile defense system, as well as the trends for their future development.

The Bamberg Trucking Game: A Paradigm for Assessing the Detection of Win–Win Solutions in a Potential Conflict Scenario

PubMed Central

Nalis, Dario; Schütz, Astrid; Pastukhov, Alexander

2018-01-01

In win–win solutions, all parties benefit more from the solution than they would if they each pursued their own individual goals. Such solutions are beneficial at individual and collective levels and thus represent optimal solutions. Win–win solutions are desirable but often difficult to find. To allow the study of individual differences and situational factors that help or hinder the detection of win–win solutions, we created a paradigm that fills a gap in the repertoire of psychological instruments used to assess collaboration, cooperation, negotiation, and prosocial behavior. The new paradigm differs from previous ones in two aspects: (a) In existing paradigms that focus on social motivation, possible strategies are evident, whereas we focused here on the question of whether people can detect the solution and thus disentangle ability from motivation, (b) Paradigms that focus on cooperation typically entail a risk associated with the partner’s defection, whereas cooperation in our paradigm is not associated with risk. We adjusted the Trucking Game—a method for assessing bargaining—to include a situation in which two parties can help each other achieve their respective goals and thus benefit over and above the pursuit of individual goals or compromising. We tested scenario-based and interaction-based versions with samples of 154 and 112 participants, respectively. Almost one third of the participants or dyads found the win–win solution. General mental abilities were not related to detecting the win–win solution in either version. The paradigm provides a way to extend research on cooperation and conflict and can thus be useful for research and training. PMID:29487558

Infrared dim target detection based on visual attention

NASA Astrophysics Data System (ADS)

Wang, Xin; Lv, Guofang; Xu, Lizhong

2012-11-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. Based on human visual attention mechanisms, an automatic detection algorithm for infrared dim target is presented. After analyzing the characteristics of infrared dim target images, the method firstly designs Difference of Gaussians (DoG) filters to compute the saliency map. Then the salient regions where the potential targets exist in are extracted by searching through the saliency map with a control mechanism of winner-take-all (WTA) competition and inhibition-of-return (IOR). At last, these regions are identified by the characteristics of the dim IR targets, so the true targets are detected, and the spurious objects are rejected. The experiments are performed for some real-life IR images, and the results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

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.

Multiple targets detection method in detection of UWB through-wall radar

NASA Astrophysics Data System (ADS)

Yang, Xiuwei; Yang, Chuanfa; Zhao, Xingwen; Tian, Xianzhong

2017-11-01

In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.

Changing the paradigm: the potential for targeted therapy in laryngeal squamous cell carcinoma

PubMed Central

Ludwig, Megan L.; Birkeland, Andrew C.; Hoesli, Rebecca; Swiecicki, Paul; Spector, Matthew E.; Brenner, J. Chad

2016-01-01

Laryngeal squamous cell carcinoma (LSCC) remains a highly morbid and fatal disease. Historically, it has been a model example for organ preservation and treatment stratification paradigms. Unfortunately, survival for LSCC has stagnated over the past few decades. As the era of next-generation sequencing and personalized treatment for cancer approaches, LSCC may be an ideal disease for consideration of further treatment stratification and personalization. Here, we will discuss the important history of LSCC as a model system for organ preservation, unique and potentially targetable genetic signatures of LSCC, and methods for bringing stratified, personalized treatment strategies to the 21st century. PMID:27144065

A comparison of directed search target detection versus in-scene target detection in Worldview-2 datasets

NASA Astrophysics Data System (ADS)

Grossman, S.

2015-05-01

Since the events of September 11, 2001, the intelligence focus has moved from large order-of-battle targets to small targets of opportunity. Additionally, the business community has discovered the use of remotely sensed data to anticipate demand and derive data on their competition. This requires the finer spectral and spatial fidelity now available to recognize those targets. This work hypothesizes that directed searches using calibrated data perform at least as well as inscene manually intensive target detection searches. It uses calibrated Worldview-2 multispectral images with NEF generated signatures and standard detection algorithms to compare bespoke directed search capabilities against ENVI™ in-scene search capabilities. Multiple execution runs are performed at increasing thresholds to generate detection rates. These rates are plotted and statistically analyzed. While individual head-to-head comparison results vary, 88% of the directed searches performed at least as well as in-scene searches with 50% clearly outperforming in-scene methods. The results strongly support the premise that directed searches perform at least as well as comparable in-scene searches.

Biased normalized cuts for target detection in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Xuewen; Dorado-Munoz, Leidy P.; Messinger, David W.; Cahill, Nathan D.

2016-05-01

The Biased Normalized Cuts (BNC) algorithm is a useful technique for detecting targets or objects in RGB imagery. In this paper, we propose modifying BNC for the purpose of target detection in hyperspectral imagery. As opposed to other target detection algorithms that typically encode target information prior to dimensionality reduction, our proposed algorithm encodes target information after dimensionality reduction, enabling a user to detect different targets in interactive mode. To assess the proposed BNC algorithm, we utilize hyperspectral imagery (HSI) from the SHARE 2012 data campaign, and we explore the relationship between the number and the position of expert-provided target labels and the precision/recall of the remaining targets in the scene.

Advancing Treatment of Pituitary Adenomas through Targeted Molecular Therapies: The Acromegaly and Cushing Disease Paradigms.

PubMed

Mooney, Michael A; Simon, Elias D; Little, Andrew S

2016-01-01

The current treatment of pituitary adenomas requires a balance of conservative management, surgical resection, and in select tumor types, molecular therapy. Acromegaly treatment is an evolving field where our understanding of molecular targets and drug therapies has improved treatment options for patients with excess growth hormone levels. We highlight the use of molecular therapies in this disease process and advances in this field, which may represent a paradigm shift for the future of pituitary adenoma treatment.

Voluntary saccadic eye movements in humans studied with a double-cue paradigm.

PubMed

Sheliga, B M; Brown, V J; Miles, F A

2002-07-01

In the classic double-step paradigm, subjects are required to make a saccade to a visual target that is briefly presented at one location and then shifted to a new location before the subject has responded. The saccades in this situation are "reflexive" in that they are made in response to the appearance of the target itself. In the present experiments we adapted the double-step paradigm to study "voluntary" saccades. For this, several identical targets were always visible and subjects were given a cue to indicate that they should make a saccade to one of them. This cue was then changed to indicate another of the targets before the subject had responded: double-cue (DC) paradigm. The saccadic eye movements in our DC paradigm had many features in common with those in the double-step paradigm and we show that apparent differences can be attributed to the spatio-temporal arrangements of the cues/targets rather than to any intrinsic differences in the programming of these two kinds of eye movements. For example, a feature of our DC paradigm that is not seen in the usual double-step paradigm is that the second cue could cause transient delays of the initial saccade, and these delays still occurred when the second cue was reflexive--provided that it was at the fovea (as in our DC paradigm) and not in the periphery (as in the usual double-step paradigm). Thus, the critical factor for the delay was the retinal (foveal) location of the second cue/target--not whether it was cognitive or reflexive--and we argue that the second cue/target is here acting as a distractor. We conclude that the DC paradigm can be used to study the programming of voluntary saccades in the same way that the double-step paradigm can be used to study reflexive saccades.

Spectral Target Detection using Schroedinger Eigenmaps

NASA Astrophysics Data System (ADS)

Dorado-Munoz, Leidy P.

Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific objects or materials, usually few or small, which are surrounded by other materials that clutter the scene and hide the relevant information. This target detection process has been boosted lately by the use of hyperspectral imagery (HSI) since its high spectral dimension provides more detailed spectral information that is desirable in data exploitation. Typical spectral target detectors rely on statistical or geometric models to characterize the spectral variability of the data. However, in many cases these parametric models do not fit well HSI data that impacts the detection performance. On the other hand, non-linear transformation methods, mainly based on manifold learning algorithms, have shown a potential use in HSI transformation, dimensionality reduction and classification. In target detection, non-linear transformation algorithms are used as preprocessing techniques that transform the data to a more suitable lower dimensional space, where the statistical or geometric detectors are applied. One of these non-linear manifold methods is the Schroedinger Eigenmaps (SE) algorithm that has been introduced as a technique for semi-supervised classification. The core tool of the SE algorithm is the Schroedinger operator that includes a potential term that encodes prior information about the materials present in a scene, and enables the embedding to be steered in some convenient directions in order to cluster similar pixels together. A completely novel target detection methodology based on SE algorithm is proposed for the first time in this thesis. The proposed methodology does not just include the transformation of the data to a lower dimensional space but also includes the definition of a detector that capitalizes on the theory behind SE. The fact that target pixels and

Texture metric that predicts target detection performance

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.

2015-12-01

Two texture metrics based on gray level co-occurrence error (GLCE) are used to predict probability of detection and mean search time. The two texture metrics are local clutter metrics and are based on the statistics of GLCE probability distributions. The degree of correlation between various clutter metrics and the target detection performance of the nine military vehicles in complex natural scenes found in the Search_2 dataset are presented. Comparison is also made between four other common clutter metrics found in the literature: root sum of squares, Doyle, statistical variance, and target structure similarity. The experimental results show that the GLCE energy metric is a better predictor of target detection performance when searching for targets in natural scenes than the other clutter metrics studied.

Marine Targets Detection in Pol-SAR Data

NASA Astrophysics Data System (ADS)

Chen, Peng; Yang, Jingsong

2016-08-01

In this poster, we present a new method of marine target detection in Pol-SAR data. One band SAR image, like HH, VV or VH, can be used to find marine target using a Contant False Alarm Ratio (CFAR) algorithm. But some false detection may happen, as the sidelobe of antenna, Azimuth ambiguity, strong speckle noise and so on in the single band SAR image. Pol-SAR image can get more information of targets. After decomposition and false color composite, the sidelobe of antenna and Azimuth ambiguity could be deleted. So, the method presented include three steps, decomposion, false color composite and supervised classification. The result of Radarsat-2 SAR image test indicates a good accuracy. The detection results are compared with Automatic Indentify Sistem (AIS) data, the accuracy of right detection is above 95% and false detection ratio is below 5%.

A fast automatic target detection method for detecting ships in infrared scenes

NASA Astrophysics Data System (ADS)

Özertem, Kemal Arda

2016-05-01

Automatic target detection in infrared scenes is a vital task for many application areas like defense, security and border surveillance. For anti-ship missiles, having a fast and robust ship detection algorithm is crucial for overall system performance. In this paper, a straight-forward yet effective ship detection method for infrared scenes is introduced. First, morphological grayscale reconstruction is applied to the input image, followed by an automatic thresholding onto the suppressed image. For the segmentation step, connected component analysis is employed to obtain target candidate regions. At this point, it can be realized that the detection is defenseless to outliers like small objects with relatively high intensity values or the clouds. To deal with this drawback, a post-processing stage is introduced. For the post-processing stage, two different methods are used. First, noisy detection results are rejected with respect to target size. Second, the waterline is detected by using Hough transform and the detection results that are located above the waterline with a small margin are rejected. After post-processing stage, there are still undesired holes remaining, which cause to detect one object as multi objects or not to detect an object as a whole. To improve the detection performance, another automatic thresholding is implemented only to target candidate regions. Finally, two detection results are fused and post-processing stage is repeated to obtain final detection result. The performance of overall methodology is tested with real world infrared test data.

Small target pre-detection with an attention mechanism

NASA Astrophysics Data System (ADS)

Wang, Yuehuan; Zhang, Tianxu; Wang, Guoyou

2002-04-01

We introduce the concept of predetection based on an attention mechanism to improve the efficiency of small-target detection by limiting the image region of detection. According to the characteristics of small-target detection, local contrast is taken as the only feature in predetection and a nonlinear sampling model is adopted to make the predetection adaptive to detect small targets with different area sizes. To simplify the predetection itself and decrease the false alarm probability, neighboring nodes in the sampling grid are used to generate a saliency map, and a short-term memory is adopted to accelerate the `pop-out' of targets. We discuss the fact that the proposed approach is simple enough in computational complexity. In addition, even in a cluttered background, attention can be led to targets in a satisfying few iterations, which ensures that the detection efficiency will not be decreased due to false alarms. Experimental results are presented to demonstrate the applicability of the approach.

Quantum Dots in Diagnostics and Detection: Principles and Paradigms

PubMed Central

Pisanic, T. R.; Zhang, Y.; Wang, T. H.

2014-01-01

Quantum dots are semiconductor nanocrystals that exhibit exceptional optical and electrical behaviors not found in their bulk counterparts. Following seminal work in the development of water-soluble quantum dots in the late 1990's, researchers have sought to develop interesting and novel ways of exploiting the extraordinary properties of quantum dots for biomedical applications. Since that time, over 10,000 articles have been published related to the use of quantum dots in biomedicine, many of which regard their use in detection and diagnostic bioassays. This review presents a didactic overview of fundamental physical phenomena associated with quantum dots and paradigm examples of how these phenomena can and have been readily exploited for manifold uses in nanobiotechnology with a specific focus on their implementation in in vitro diagnostic assays and biodetection. PMID:24770716

Detection and identification of human targets in radar data

NASA Astrophysics Data System (ADS)

Gürbüz, Sevgi Z.; Melvin, William L.; Williams, Douglas B.

2007-04-01

Radar offers unique advantages over other sensors, such as visual or seismic sensors, for human target detection. Many situations, especially military applications, prevent the placement of video cameras or implantment seismic sensors in the area being observed, because of security or other threats. However, radar can operate far away from potential targets, and functions during daytime as well as nighttime, in virtually all weather conditions. In this paper, we examine the problem of human target detection and identification using single-channel, airborne, synthetic aperture radar (SAR). Human targets are differentiated from other detected slow-moving targets by analyzing the spectrogram of each potential target. Human spectrograms are unique, and can be used not just to identify targets as human, but also to determine features about the human target being observed, such as size, gender, action, and speed. A 12-point human model, together with kinematic equations of motion for each body part, is used to calculate the expected target return and spectrogram. A MATLAB simulation environment is developed including ground clutter, human and non-human targets for the testing of spectrogram-based detection and identification algorithms. Simulations show that spectrograms have some ability to detect and identify human targets in low noise. An example gender discrimination system correctly detected 83.97% of males and 91.11% of females. The problems and limitations of spectrogram-based methods in high clutter environments are discussed. The SNR loss inherent to spectrogram-based methods is quantified. An alternate detection and identification method that will be used as a basis for future work is proposed.

Entanglement-enhanced Neyman-Pearson target detection using quantum illumination

NASA Astrophysics Data System (ADS)

Zhuang, Quntao; Zhang, Zheshen; Shapiro, Jeffrey H.

2017-08-01

Quantum illumination (QI) provides entanglement-based target detection---in an entanglement-breaking environment---whose performance is significantly better than that of optimum classical-illumination target detection. QI's performance advantage was established in a Bayesian setting with the target presumed equally likely to be absent or present and error probability employed as the performance metric. Radar theory, however, eschews that Bayesian approach, preferring the Neyman-Pearson performance criterion to avoid the difficulties of accurately assigning prior probabilities to target absence and presence and appropriate costs to false-alarm and miss errors. We have recently reported an architecture---based on sum-frequency generation (SFG) and feedforward (FF) processing---for minimum error-probability QI target detection with arbitrary prior probabilities for target absence and presence. In this paper, we use our results for FF-SFG reception to determine the receiver operating characteristic---detection probability versus false-alarm probability---for optimum QI target detection under the Neyman-Pearson criterion.

Multispectral infrared target detection: phenomenology and modeling

NASA Astrophysics Data System (ADS)

Cederquist, Jack N.; Rogne, Timothy J.; Schwartz, Craig R.

1993-10-01

Many targets of interest provide only very small signature differences from the clutter background. The ability to detect these small difference targets should be improved by using data which is diverse in space, time, wavelength or some other observable. Target materials often differ from background materials in the variation of their reflectance or emittance with wavelength. A multispectral sensor is therefore considered as a means to improve detection of small signal targets. If this sensor operates in the thermal infrared, it will not need solar illumination and will be useful at night as well as during the day. An understanding of the phenomenology of the spectral properties of materials and an ability to model and simulate target and clutter signatures is needed to understand potential target detection performance from multispectral infrared sensor data. Spectral variations in material emittance are due to vibrational energy transitions in molecular bonds. The spectral emittances of many materials of interest have been measured. Examples are vegetation, soil, construction and road materials, and paints. A multispectral infrared signature model has been developed which includes target and background temperature and emissivity, sky, sun, cloud and background irradiance, multiple reflection effects, path radiance, and atmospheric attenuation. This model can be used to predict multispectral infrared signatures for small signal targets.

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Real-time multisensor data fusion for target detection, classification, tracking, counting, and range estimates

NASA Astrophysics Data System (ADS)

Tsui, Eddy K.; Thomas, Russell L.

2004-09-01

As part of the Commanding General of Army Material Command's Research, Development & Engineering Command (RDECOM), the U.S. Army Research Development and Engineering Center (ARDEC), Picatinny funded a joint development effort with McQ Associates, Inc. to develop an Advanced Minefield Sensor (AMS) as a technology evaluation prototype for the Anti-Personnel Landmine Alternatives (APLA) Track III program. This effort laid the fundamental groundwork of smart sensors for detection and classification of targets, identification of combatant or noncombatant, target location and tracking at and between sensors, fusion of information across targets and sensors, and automatic situation awareness to the 1st responder. The efforts have culminated in developing a performance oriented architecture meeting the requirements of size, weight, and power (SWAP). The integrated digital signal processor (DSP) paradigm is capable of computing signals from sensor modalities to extract needed information within either a 360° or fixed field of view with acceptable false alarm rate. This paper discusses the challenges in the developments of such a sensor, focusing on achieving reasonable operating ranges, achieving low power, small size and low cost, and applications for extensions of this technology.

Manifold structure preservative for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Imani, Maryam

2018-05-01

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.

Detection of Moving Targets Using Soliton Resonance Effect

NASA Technical Reports Server (NTRS)

Kulikov, Igor K.; Zak, Michail

2013-01-01

The objective of this research was to develop a fundamentally new method for detecting hidden moving targets within noisy and cluttered data-streams using a novel "soliton resonance" effect in nonlinear dynamical systems. The technique uses an inhomogeneous Korteweg de Vries (KdV) equation containing moving-target information. Solution of the KdV equation will describe a soliton propagating with the same kinematic characteristics as the target. The approach uses the time-dependent data stream obtained with a sensor in form of the "forcing function," which is incorporated in an inhomogeneous KdV equation. When a hidden moving target (which in many ways resembles a soliton) encounters the natural "probe" soliton solution of the KdV equation, a strong resonance phenomenon results that makes the location and motion of the target apparent. Soliton resonance method will amplify the moving target signal, suppressing the noise. The method will be a very effective tool for locating and identifying diverse, highly dynamic targets with ill-defined characteristics in a noisy environment. The soliton resonance method for the detection of moving targets was developed in one and two dimensions. Computer simulations proved that the method could be used for detection of singe point-like targets moving with constant velocities and accelerations in 1D and along straight lines or curved trajectories in 2D. The method also allows estimation of the kinematic characteristics of moving targets, and reconstruction of target trajectories in 2D. The method could be very effective for target detection in the presence of clutter and for the case of target obscurations.

Dim target detection method based on salient graph fusion

NASA Astrophysics Data System (ADS)

Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

2018-02-01

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

Spatial Probability Dynamically Modulates Visual Target Detection in Chickens

PubMed Central

Sridharan, Devarajan; Ramamurthy, Deepa L.; Knudsen, Eric I.

2013-01-01

The natural world contains a rich and ever-changing landscape of sensory information. To survive, an organism must be able to flexibly and rapidly locate the most relevant sources of information at any time. Humans and non-human primates exploit regularities in the spatial distribution of relevant stimuli (targets) to improve detection at locations of high target probability. Is the ability to flexibly modify behavior based on visual experience unique to primates? Chickens (Gallus domesticus) were trained on a multiple alternative Go/NoGo task to detect a small, briefly-flashed dot (target) in each of the quadrants of the visual field. When targets were presented with equal probability (25%) in each quadrant, chickens exhibited a distinct advantage for detecting targets at lower, relative to upper, hemifield locations. Increasing the probability of presentation in the upper hemifield locations (to 80%) dramatically improved detection performance at these locations to be on par with lower hemifield performance. Finally, detection performance in the upper hemifield changed on a rapid timescale, improving with successive target detections, and declining with successive detections at the diagonally opposite location in the lower hemifield. These data indicate the action of a process that in chickens, as in primates, flexibly and dynamically modulates detection performance based on the spatial probabilities of sensory stimuli as well as on recent performance history. PMID:23734188

Heterogeneous CPU-GPU moving targets detection for UAV video

NASA Astrophysics Data System (ADS)

Li, Maowen; Tang, Linbo; Han, Yuqi; Yu, Chunlei; Zhang, Chao; Fu, Huiquan

2017-07-01

Moving targets detection is gaining popularity in civilian and military applications. On some monitoring platform of motion detection, some low-resolution stationary cameras are replaced by moving HD camera based on UAVs. The pixels of moving targets in the HD Video taken by UAV are always in a minority, and the background of the frame is usually moving because of the motion of UAVs. The high computational cost of the algorithm prevents running it at higher resolutions the pixels of frame. Hence, to solve the problem of moving targets detection based UAVs video, we propose a heterogeneous CPU-GPU moving target detection algorithm for UAV video. More specifically, we use background registration to eliminate the impact of the moving background and frame difference to detect small moving targets. In order to achieve the effect of real-time processing, we design the solution of heterogeneous CPU-GPU framework for our method. The experimental results show that our method can detect the main moving targets from the HD video taken by UAV, and the average process time is 52.16ms per frame which is fast enough to solve the problem.

Camouflage target detection via hyperspectral imaging plus information divergence measurement

NASA Astrophysics Data System (ADS)

Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Ji, Yiqun; Shen, Weimin

2016-01-01

Target detection is one of most important applications in remote sensing. Nowadays accurate camouflage target distinction is often resorted to spectral imaging technique due to its high-resolution spectral/spatial information acquisition ability as well as plenty of data processing methods. In this paper, hyper-spectral imaging technique together with spectral information divergence measure method is used to solve camouflage target detection problem. A self-developed visual-band hyper-spectral imaging device is adopted to collect data cubes of certain experimental scene before spectral information divergences are worked out so as to discriminate target camouflage and anomaly. Full-band information divergences are measured to evaluate target detection effect visually and quantitatively. Information divergence measurement is proved to be a low-cost and effective tool for target detection task and can be further developed to other target detection applications beyond spectral imaging technique.

Hyperspectral target detection using manifold learning and multiple target spectra

DOE PAGES

Ziemann, Amanda K.; Theiler, James; Messinger, David W.

2016-03-31

Imagery collected from satellites and airborne platforms provides an important tool for remotely analyzing the content of a scene. In particular, the ability to remotely detect a specific material within a scene is of critical importance in nonproliferation and other applications. The sensor systems that process hyperspectral images collect the high-dimensional spectral information necessary to perform these detection analyses. For a d-dimensional hyperspectral image, however, where d is the number of spectral bands, it is common for the data to inherently occupy an m-dimensional space with m << d. In the remote sensing community, this has led to recent interestmore » in the use of manifold learning, which seeks to characterize the embedded lower-dimensional, nonlinear manifold that the data discretely approximate. The research presented in this paper focuses on a graph theory and manifold learning approach to target detection, using an adaptive version of locally linear embedding that is biased to separate target pixels from background pixels. Finally, this approach incorporates multiple target signatures for a particular material, accounting for the spectral variability that is often present within a solid material of interest.« less

Subpixel target detection and enhancement in hyperspectral images

NASA Astrophysics Data System (ADS)

Tiwari, K. C.; Arora, M.; Singh, D.

2011-06-01

Hyperspectral data due to its higher information content afforded by higher spectral resolution is increasingly being used for various remote sensing applications including information extraction at subpixel level. There is however usually a lack of matching fine spatial resolution data particularly for target detection applications. Thus, there always exists a tradeoff between the spectral and spatial resolutions due to considerations of type of application, its cost and other associated analytical and computational complexities. Typically whenever an object, either manmade, natural or any ground cover class (called target, endmembers, components or class) gets spectrally resolved but not spatially, mixed pixels in the image result. Thus, numerous manmade and/or natural disparate substances may occur inside such mixed pixels giving rise to mixed pixel classification or subpixel target detection problems. Various spectral unmixing models such as Linear Mixture Modeling (LMM) are in vogue to recover components of a mixed pixel. Spectral unmixing outputs both the endmember spectrum and their corresponding abundance fractions inside the pixel. It, however, does not provide spatial distribution of these abundance fractions within a pixel. This limits the applicability of hyperspectral data for subpixel target detection. In this paper, a new inverse Euclidean distance based super-resolution mapping method has been presented that achieves subpixel target detection in hyperspectral images by adjusting spatial distribution of abundance fraction within a pixel. Results obtained at different resolutions indicate that super-resolution mapping may effectively aid subpixel target detection.

Texture orientation-based algorithm for detecting infrared maritime targets.

PubMed

Wang, Bin; Dong, Lili; Zhao, Ming; Wu, Houde; Xu, Wenhai

2015-05-20

Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.

Automatic target detection using binary template matching

NASA Astrophysics Data System (ADS)

Jun, Dong-San; Sun, Sun-Gu; Park, HyunWook

2005-03-01

This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

Initial study of Schroedinger eigenmaps for spectral target detection

NASA Astrophysics Data System (ADS)

Dorado-Munoz, Leidy P.; Messinger, David W.

2016-08-01

Spectral target detection refers to the process of searching for a specific material with a known spectrum over a large area containing materials with different spectral signatures. Traditional target detection methods in hyperspectral imagery (HSI) require assuming the data fit some statistical or geometric models and based on the model, to estimate parameters for defining a hypothesis test, where one class (i.e., target class) is chosen over the other classes (i.e., background class). Nonlinear manifold learning methods such as Laplacian eigenmaps (LE) have extensively shown their potential use in HSI processing, specifically in classification or segmentation. Recently, Schroedinger eigenmaps (SE), which is built upon LE, has been introduced as a semisupervised classification method. In SE, the former Laplacian operator is replaced by the Schroedinger operator. The Schroedinger operator includes by definition, a potential term V that steers the transformation in certain directions improving the separability between classes. In this regard, we propose a methodology for target detection that is not based on the traditional schemes and that does not need the estimation of statistical or geometric parameters. This method is based on SE, where the potential term V is taken into consideration to include the prior knowledge about the target class and use it to steer the transformation in directions where the target location in the new space is known and the separability between target and background is augmented. An initial study of how SE can be used in a target detection scheme for HSI is shown here. In-scene pixel and spectral signature detection approaches are presented. The HSI data used comprise various target panels for testing simultaneous detection of multiple objects with different complexities.

A new method of small target detection based on neural network

NASA Astrophysics Data System (ADS)

Hu, Jing; Hu, Yongli; Lu, Xinxin

2018-02-01

The detection and tracking of moving dim target in infrared image have been an research hotspot for many years. The target in each frame of images only occupies several pixels without any shape and structure information. Moreover, infrared small target is often submerged in complicated background with low signal-to-clutter ratio, making the detection very difficult. Different backgrounds exhibit different statistical properties, making it becomes extremely complex to detect the target. If the threshold segmentation is not reasonable, there may be more noise points in the final detection, which is unfavorable for the detection of the trajectory of the target. Single-frame target detection may not be able to obtain the desired target and cause high false alarm rate. We believe the combination of suspicious target detection spatially in each frame and temporal association for target tracking will increase reliability of tracking dim target. The detection of dim target is mainly divided into two parts, In the first part, we adopt bilateral filtering method in background suppression, after the threshold segmentation, the suspicious target in each frame are extracted, then we use LSTM(long short term memory) neural network to predict coordinates of target of the next frame. It is a brand-new method base on the movement characteristic of the target in sequence images which could respond to the changes in the relationship between past and future values of the values. Simulation results demonstrate proposed algorithm can effectively predict the trajectory of the moving small target and work efficiently and robustly with low false alarm.

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.

Infrared small target detection based on Danger Theory

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Yang, Xiao

2009-11-01

To solve the problem that traditional method can't detect the small objects whose local SNR is less than 2 in IR images, a Danger Theory-based model to detect infrared small target is presented in this paper. First, on the analog with immunology, the definition is given, in this paper, to such terms as dangerous signal, antigens, APC, antibodies. Besides, matching rule between antigen and antibody is improved. Prior to training the detection model and detecting the targets, the IR images are processed utilizing adaptive smooth filter to decrease the stochastic noise. Then at the training process, deleting rule, generating rule, crossover rule and the mutation rule are established after a large number of experiments in order to realize immediate convergence and obtain good antibodies. The Danger Theory-based model is built after the training process, and this model can detect the target whose local SNR is only 1.5.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-05-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-09-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Infrared small target detection with kernel Fukunaga Koontz transform

NASA Astrophysics Data System (ADS)

Liu, Rui-ming; Liu, Er-qi; Yang, Jie; Zhang, Tian-hao; Wang, Fang-lin

2007-09-01

The Fukunaga-Koontz transform (FKT) has been proposed for many years. It can be used to solve two-pattern classification problems successfully. However, there are few researchers who have definitely extended FKT to kernel FKT (KFKT). In this paper, we first complete this task. Then a method based on KFKT is developed to detect infrared small targets. KFKT is a supervised learning algorithm. How to construct training sets is very important. For automatically detecting targets, the synthetic target images and real background images are used to train KFKT. Because KFKT can represent the higher order statistical properties of images, we expect better detection performance of KFKT than that of FKT. The well-devised experiments verify that KFKT outperforms FKT in detecting infrared small targets.

Targeted Analyte Detection by Standard Addition Improves Detection Limits in MALDI Mass Spectrometry

PubMed Central

Eshghi, Shadi Toghi; Li, Xingde; Zhang, Hui

2014-01-01

Matrix-assisted laser desorption/ionization has proven an effective tool for fast and accurate determination of many molecules. However, the detector sensitivity and chemical noise compromise the detection of many invaluable low-abundance molecules from biological and clinical samples. To challenge this limitation, we developed a targeted analyte detection (TAD) technique. In TAD, the target analyte is selectively elevated by spiking a known amount of that analyte into the sample, thereby raising its concentration above the noise level, where we take advantage of the improved sensitivity to detect the presence of the endogenous analyte in the sample. We assessed TAD on three peptides in simple and complex background solutions with various exogenous analyte concentrations in two MALDI matrices. TAD successfully improved the limit of detection (LOD) of target analytes when the target peptides were added to the sample in a concentration close to optimum concentration. The optimum exogenous concentration was estimated through a quantitative method to be approximately equal to the original LOD for each target. Also, we showed that TAD could achieve LOD improvements on an average of 3-fold in a simple and 2-fold in a complex sample. TAD provides a straightforward assay to improve the LOD of generic target analytes without the need for costly hardware modifications. PMID:22877355

Unintended Embodiment of Concepts into Percepts: Sensory Activation Boosts Attention for Same-Modality Concepts in the Attentional Blink Paradigm

ERIC Educational Resources Information Center

Vermeulen, Nicolas; Mermillod, Martial; Godefroid, Jimmy; Corneille, Olivier

2009-01-01

This study shows that sensory priming facilitates reports of same-modality concepts in an attentional blink paradigm. Participants had to detect and report two target words (T1 and T2) presented for 53 ms each among a series of nonwords distractors at a frequency of up to 19 items per second. SOA between target words was set to 53 ms or 213 ms,…

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.

Performing target specific band reduction using artificial neural networks and assessment of its efficacy using various target detection algorithms

NASA Astrophysics Data System (ADS)

Yadav, Deepti; Arora, M. K.; Tiwari, K. C.; Ghosh, J. K.

2016-04-01

Hyperspectral imaging is a powerful tool in the field of remote sensing and has been used for many applications like mineral detection, detection of landmines, target detection etc. Major issues in target detection using HSI are spectral variability, noise, small size of the target, huge data dimensions, high computation cost, complex backgrounds etc. Many of the popular detection algorithms do not work for difficult targets like small, camouflaged etc. and may result in high false alarms. Thus, target/background discrimination is a key issue and therefore analyzing target's behaviour in realistic environments is crucial for the accurate interpretation of hyperspectral imagery. Use of standard libraries for studying target's spectral behaviour has limitation that targets are measured in different environmental conditions than application. This study uses the spectral data of the same target which is used during collection of the HSI image. This paper analyze spectrums of targets in a way that each target can be spectrally distinguished from a mixture of spectral data. Artificial neural network (ANN) has been used to identify the spectral range for reducing data and further its efficacy for improving target detection is verified. The results of ANN proposes discriminating band range for targets; these ranges were further used to perform target detection using four popular spectral matching target detection algorithm. Further, the results of algorithms were analyzed using ROC curves to evaluate the effectiveness of the ranges suggested by ANN over full spectrum for detection of desired targets. In addition, comparative assessment of algorithms is also performed using ROC.

Synthetic aperture radar target detection, feature extraction, and image formation techniques

NASA Technical Reports Server (NTRS)

Li, Jian

1994-01-01

This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

Manipulation Detection and Preference Alterations in a Choice Blindness Paradigm

PubMed Central

Taya, Fumihiko; Gupta, Swati; Farber, Ilya; Mullette-Gillman, O'Dhaniel A.

2014-01-01

Objectives It is commonly believed that individuals make choices based upon their preferences and have access to the reasons for their choices. Recent studies in several areas suggest that this is not always the case. In choice blindness paradigms, two-alternative forced-choice in which chosen-options are later replaced by the unselected option, individuals often fail to notice replacement of their chosen option, confabulate explanations for why they chose the unselected option, and even show increased preferences for the unselected-but-replaced options immediately after choice (seconds). Although choice blindness has been replicated across a variety of domains, there are numerous outstanding questions. Firstly, we sought to investigate how individual- or trial-factors modulated detection of the manipulations. Secondly, we examined the nature and temporal duration (minutes vs. days) of the preference alterations induced by these manipulations. Methods Participants performed a computerized choice blindness task, selecting the more attractive face between presented pairs of female faces, and providing a typewritten explanation for their choice on half of the trials. Chosen-face cue manipulations were produced on a subset of trials by presenting the unselected face during the choice explanation as if it had been selected. Following all choice trials, participants rated the attractiveness of each face individually, and rated the similarity of each face pair. After approximately two weeks, participants re-rated the attractiveness of each individual face online. Results Participants detected manipulations on only a small proportion of trials, with detections by fewer than half of participants. Detection rates increased with the number of prior detections, and detection rates subsequent to first detection were modulated by the choice certainty. We show clear short-term modulation of preferences in both manipulated and non-manipulated explanation trials compared to choice

Comparison of human and algorithmic target detection in passive infrared imagery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Hutchinson, Meredith

2003-09-01

We have designed an experiment that compares the performance of human observers and a scale-insensitive target detection algorithm that uses pixel level information for the detection of ground targets in passive infrared imagery. The test database contains targets near clutter whose detectability ranged from easy to very difficult. Results indicate that human observers detect more "easy-to-detect" targets, and with far fewer false alarms, than the algorithm. For "difficult-to-detect" targets, human and algorithm detection rates are considerably degraded, and algorithm false alarms excessive. Analysis of detections as a function of observer confidence shows that algorithm confidence attribution does not correspond to human attribution, and does not adequately correlate with correct detections. The best target detection score for any human observer was 84%, as compared to 55% for the algorithm for the same false alarm rate. At 81%, the maximum detection score for the algorithm, the same human observer had 6 false alarms per frame as compared to 29 for the algorithm. Detector ROC curves and observer-confidence analysis benchmarks the algorithm and provides insights into algorithm deficiencies and possible paths to improvement.

Kepler Planet Detection Metrics: Per-Target Detection Contours for Data Release 25

NASA Technical Reports Server (NTRS)

Burke, Christopher J.; Catanzarite, Joseph

2017-01-01

A necessary input to planet occurrence calculations is an accurate model for the pipeline completeness (Burke et al., 2015). This document describes the use of the Kepler planet occurrence rate products in order to calculate a per-target detection contour for the measured Data Release 25 (DR25) pipeline performance. A per-target detection contour measures for a given combination of orbital period, Porb, and planet radius, Rp, what fraction of transit signals are recoverable by the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017). The steps for calculating a detection contour follow the procedure outlined in Burke et al. (2015), but have been updated to provide improved accuracy enabled by the substantially larger database of transit injection and recovery tests that were performed on the final version (i.e., SOC 9.3) of the Kepler pipeline (Christiansen, 2017; Burke Catanzarite, 2017a). In the following sections, we describe the main inputs to the per-target detection contour and provide a worked example of the python software released with this document (Kepler Planet Occurrence Rate Tools KeplerPORTs)1 that illustrates the generation of a detection contour in practice. As background material for this document and its nomenclature, we recommend the reader be familiar with the previous method of calculating a detection contour (Section 2 of Burke et al.,2015), input parameters relevant for describing the data quantity and quality of Kepler targets (Burke Catanzarite, 2017b), and the extensive new transit injection and recovery tests of the Kepler pipeline (Christiansen et al., 2016; Burke Catanzarite, 2017a; Christiansen, 2017).

Robust Small Target Co-Detection from Airborne Infrared Image Sequences.

PubMed

Gao, Jingli; Wen, Chenglin; Liu, Meiqin

2017-09-29

In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference.

An Experiment Quantifying The Effect Of Clutter On Target Detection

NASA Astrophysics Data System (ADS)

Weathersby, Marshall R.; Schmieder, David E.

1985-01-01

Experiments were conducted to determine the influence of background clutter on target detection criteria. The experiment consisted of placing observers in front of displayed images on a TV monitor. Observer ability to detect military targets embedded in simulated natural and manmade background clutter was measured when there was unlimited viewing time. Results were described in terms of detection probability versus target resolution for various signal to clutter ratios (SCR). The experiments were preceded by a search for a meaningful clutter definition. The selected definition was a statistical measure computed by averaging the standard deviation of contiguous scene cells over the whole scene. The cell size was comparable to the target size. Observer test results confirmed the expectation that the resolution required for a given detection probability was a continuum function of the clutter level. At the lower SCRs the resolution required for a high probability of detection was near 6 lines pairs per target (LP/TGT), while at the higher SCRs it was found that a resolution of less than 0.25 LP/TGT would yield a high probability of detection. These results are expected to aid in target acquisition performance modeling and to lead to improved specifications for imaging automatic target screeners.

Small target detection using objectness and saliency

NASA Astrophysics Data System (ADS)

Zhang, Naiwen; Xiao, Yang; Fang, Zhiwen; Yang, Jian; Wang, Li; Li, Tao

2017-10-01

We are motived by the need for generic object detection algorithm which achieves high recall for small targets in complex scenes with acceptable computational efficiency. We propose a novel object detection algorithm, which has high localization quality with acceptable computational cost. Firstly, we obtain the objectness map as in BING[1] and use NMS to get the top N points. Then, k-means algorithm is used to cluster them into K classes according to their location. We set the center points of the K classes as seed points. For each seed point, an object potential region is extracted. Finally, a fast salient object detection algorithm[2] is applied to the object potential regions to highlight objectlike pixels, and a series of efficient post-processing operations are proposed to locate the targets. Our method runs at 5 FPS on 1000*1000 images, and significantly outperforms previous methods on small targets in cluttered background.

Paradigm Shifts in Ophthalmic Diagnostics*

PubMed Central

Sebag, J.; Sadun, Alfredo A.; Pierce, Eric A.

2016-01-01

Purpose Future advances in ophthalmology will see a paradigm shift in diagnostics from a focus on dysfunction and disease to better measures of psychophysical function and health. Practical methods to define genotypes will be increasingly important and non-invasive nanotechnologies are needed to detect molecular changes that predate histopathology. Methods This is not a review nor meant to be comprehensive. Specific topics have been selected to illustrate the principles of important paradigm shifts that will influence the future of ophthalmic diagnostics. It is our impression that future evaluation of vision will go beyond visual acuity to assess ocular health in terms of psychophysical function. The definition of disease will incorporate genotype into what has historically been a phenotype-centric discipline. Non-invasive nanotechnologies will enable a paradigm shift from disease detection on a cellular level to a sub-cellular molecular level. Results Vision can be evaluated beyond visual acuity by measuring contrast sensitivity, color vision, and macular function, as these provide better insights into the impact of aging and disease. Distortions can be quantified and the psychophysical basis of vision can be better evaluated than in the past by designing tests that assess particular macular cell function(s). Advances in our understanding of the genetic basis of eye diseases will enable better characterization of ocular health and disease. Non-invasive nanotechnologies can assess molecular changes in the lens, vitreous, and macula that predate visible pathology. Oxygen metabolism and circulatory physiology are measurable indices of ocular health that can detect variations of physiology and early disease. Conclusions This overview of paradigm shifts in ophthalmology suggests that the future will see significant improvements in ophthalmic diagnostics. The selected topics illustrate the principles of these paradigm shifts and should serve as a guide to further

Camouflage, detection and identification of moving targets

PubMed Central

Hall, Joanna R.; Cuthill, Innes C.; Baddeley, Roland; Shohet, Adam J.; Scott-Samuel, Nicholas E.

2013-01-01

Nearly all research on camouflage has investigated its effectiveness for concealing stationary objects. However, animals have to move, and patterns that only work when the subject is static will heavily constrain behaviour. We investigated the effects of different camouflages on the three stages of predation—detection, identification and capture—in a computer-based task with humans. An initial experiment tested seven camouflage strategies on static stimuli. In line with previous literature, background-matching and disruptive patterns were found to be most successful. Experiment 2 showed that if stimuli move, an isolated moving object on a stationary background cannot avoid detection or capture regardless of the type of camouflage. Experiment 3 used an identification task and showed that while camouflage is unable to slow detection or capture, camouflaged targets are harder to identify than uncamouflaged targets when similar background objects are present. The specific details of the camouflage patterns have little impact on this effect. If one has to move, camouflage cannot impede detection; but if one is surrounded by similar targets (e.g. other animals in a herd, or moving background distractors), then camouflage can slow identification. Despite previous assumptions, motion does not entirely ‘break’ camouflage. PMID:23486439

Camouflage, detection and identification of moving targets.

PubMed

Hall, Joanna R; Cuthill, Innes C; Baddeley, Roland; Shohet, Adam J; Scott-Samuel, Nicholas E

2013-05-07

Nearly all research on camouflage has investigated its effectiveness for concealing stationary objects. However, animals have to move, and patterns that only work when the subject is static will heavily constrain behaviour. We investigated the effects of different camouflages on the three stages of predation-detection, identification and capture-in a computer-based task with humans. An initial experiment tested seven camouflage strategies on static stimuli. In line with previous literature, background-matching and disruptive patterns were found to be most successful. Experiment 2 showed that if stimuli move, an isolated moving object on a stationary background cannot avoid detection or capture regardless of the type of camouflage. Experiment 3 used an identification task and showed that while camouflage is unable to slow detection or capture, camouflaged targets are harder to identify than uncamouflaged targets when similar background objects are present. The specific details of the camouflage patterns have little impact on this effect. If one has to move, camouflage cannot impede detection; but if one is surrounded by similar targets (e.g. other animals in a herd, or moving background distractors), then camouflage can slow identification. Despite previous assumptions, motion does not entirely 'break' camouflage.

Target attribute-based false alarm rejection in small infrared target detection

NASA Astrophysics Data System (ADS)

Kim, Sungho

2012-11-01

Infrared search and track is an important research area in military applications. Although there are a lot of works on small infrared target detection methods, we cannot apply them in real field due to high false alarm rate caused by clutters. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. Eight kinds of target features are extracted and analyzed statistically. Learning-based classifiers such as SVM and Adaboost are developed and compared with conventional classifiers for real infrared images. In addition, the generalization capability is also inspected for various infrared clutters.

Top-attack modeling and automatic target detection using synthetic FLIR scenery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Penn, Joseph A.

2004-09-01

A series of experiments have been performed to verify the utility of algorithmic tools for the modeling and analysis of cold-target signatures in synthetic, top-attack, FLIR video sequences. The tools include: MuSES/CREATION for the creation of synthetic imagery with targets, an ARL target detection algorithm to detect imbedded synthetic targets in scenes, and an ARL scoring algorithm, using Receiver-Operating-Characteristic (ROC) curve analysis, to evaluate detector performance. Cold-target detection variability was examined as a function of target emissivity, surrounding clutter type, and target placement in non-obscuring clutter locations. Detector metrics were also individually scored so as to characterize the effect of signature/clutter variations. Results show that using these tools, a detailed, physically meaningful, target detection analysis is possible and that scenario specific target detectors may be developed by selective choice and/or weighting of detector metrics. However, developing these tools into a reliable predictive capability will require the extension of these results to the modeling and analysis of a large number of data sets configured for a wide range of target and clutter conditions. Finally, these tools should also be useful for the comparison of competitive detection algorithms by providing well defined, and controllable target detection scenarios, as well as for the training and testing of expert human observers.

Assessment of target detection limits in hyperspectral data

NASA Astrophysics Data System (ADS)

Gross, W.; Boehler, J.; Schilling, H.; Middelmann, W.; Weyermann, J.; Wellig, P.; Oechslin, R.; Kneubuehler, M.

2015-10-01

Hyperspectral remote sensing data can be used for civil and military applications to detect and classify target objects that cannot be reliably separated using broadband sensors. The comparably low spatial resolution is compensated by the fact that small targets, even below image resolution, can still be classified. The goal of this paper is to determine the target size to spatial resolution ratio for successful classification of different target and background materials. Airborne hyperspectral data is used to simulate data with known mixture ratios and to estimate the detection threshold for given false alarm rates. The data was collected in July 2014 over Greding, Germany, using airborne aisaEAGLE and aisaHAWK hyperspectral sensors. On the ground, various target materials were placed on natural background. The targets were four quadratic molton patches with an edge length of 7 meters in the colors black, white, grey and green. Also, two different types of polyethylene (camouflage nets) with an edge length of approximately 5.5 meters were deployed. Synthetic data is generated from the original data using spectral mixtures. Target signatures are linearly combined with different background materials in specific ratios. The simulated mixtures are appended to the original data and the target areas are removed for evaluation. Commonly used classification algorithms, e.g. Matched Filtering, Adaptive Cosine Estimator are used to determine the detection limit. Fixed false alarm rates are employed to find and analyze certain regions where false alarms usually occur first. A combination of 18 targets and 12 backgrounds is analyzed for three VNIR and two SWIR data sets of the same area.

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.

Dim target trajectory-associated detection in bright earth limb background

NASA Astrophysics Data System (ADS)

Chen, Penghui; Xu, Xiaojian; He, Xiaoyu; Jiang, Yuesong

2015-09-01

The intensive emission of earth limb in the field of view of sensors contributes much to the observation images. Due to the low signal-to-noise ratio (SNR), it is a challenge to detect small targets in earth limb background, especially for the detection of point-like targets from a single frame. To improve the target detection, track before detection (TBD) based on the frame sequence is performed. In this paper, a new technique is proposed to determine the target associated trajectories, which jointly carries out background removing, maximum value projection (MVP) and Hough transform. The background of the bright earth limb in the observation images is removed according to the profile characteristics. For a moving target, the corresponding pixels in the MVP image are shifting approximately regularly in time sequence. And the target trajectory is determined by Hough transform according to the pixel characteristics of the target and the clutter and noise. Comparing with traditional frame-by-frame methods, determining associated trajectories from MVP reduces the computation load. Numerical simulations are presented to demonstrate the effectiveness of the approach proposed.

A target sample of adolescents and reward processing: same neural and behavioral correlates engaged in common paradigms?

PubMed

Nees, Frauke; Vollstädt-Klein, Sabine; Fauth-Bühler, Mira; Steiner, Sabina; Mann, Karl; Poustka, Luise; Banaschewski, Tobias; Büchel, Christian; Conrod, Patricia J; Garavan, Hugh; Heinz, Andreas; Ittermann, Bernd; Artiges, Eric; Paus, Tomas; Pausova, Zdenka; Rietschel, Marcella; Smolka, Michael N; Struve, Maren; Loth, Eva; Schumann, Gunter; Flor, Herta

2012-11-01

Adolescence is a transition period that is assumed to be characterized by increased sensitivity to reward. While there is growing research on reward processing in adolescents, investigations into the engagement of brain regions under different reward-related conditions in one sample of healthy adolescents, especially in a target age group, are missing. We aimed to identify brain regions preferentially activated in a reaction time task (monetary incentive delay (MID) task) and a simple guessing task (SGT) in a sample of 14-year-old adolescents (N = 54) using two commonly used reward paradigms. Functional magnetic resonance imaging was employed during the MID with big versus small versus no win conditions and the SGT with big versus small win and big versus small loss conditions. Analyses focused on changes in blood oxygen level-dependent contrasts during reward and punishment processing in anticipation and feedback phases. We found clear magnitude-sensitive response in reward-related brain regions such as the ventral striatum during anticipation in the MID task, but not in the SGT. This was also true for reaction times. The feedback phase showed clear reward-related, but magnitude-independent, response patterns, for example in the anterior cingulate cortex, in both tasks. Our findings highlight neural and behavioral response patterns engaged in two different reward paradigms in one sample of 14-year-old healthy adolescents and might be important for reference in future studies investigating reward and punishment processing in a target age group.

Open source tracking and analysis of adult Drosophila locomotion in Buridan's paradigm with and without visual targets.

PubMed

Colomb, Julien; Reiter, Lutz; Blaszkiewicz, Jedrzej; Wessnitzer, Jan; Brembs, Bjoern

2012-01-01

Insects have been among the most widely used model systems for studying the control of locomotion by nervous systems. In Drosophila, we implemented a simple test for locomotion: in Buridan's paradigm, flies walk back and forth between two inaccessible visual targets [1]. Until today, the lack of easily accessible tools for tracking the fly position and analyzing its trajectory has probably contributed to the slow acceptance of Buridan's paradigm. We present here a package of open source software designed to track a single animal walking in a homogenous environment (Buritrack) and to analyze its trajectory. The Centroid Trajectory Analysis (CeTrAn) software is coded in the open source statistics project R. It extracts eleven metrics and includes correlation analyses and a Principal Components Analysis (PCA). It was designed to be easily customized to personal requirements. In combination with inexpensive hardware, these tools can readily be used for teaching and research purposes. We demonstrate the capabilities of our package by measuring the locomotor behavior of adult Drosophila melanogaster (whose wings were clipped), either in the presence or in the absence of visual targets, and comparing the latter to different computer-generated data. The analysis of the trajectories confirms that flies are centrophobic and shows that inaccessible visual targets can alter the orientation of the flies without changing their overall patterns of activity. Using computer generated data, the analysis software was tested, and chance values for some metrics (as well as chance value for their correlation) were set. Our results prompt the hypothesis that fixation behavior is observed only if negative phototaxis can overcome the propensity of the flies to avoid the center of the platform. Together with our companion paper, we provide new tools to promote Open Science as well as the collection and analysis of digital behavioral data.

Wavelength band selection method for multispectral target detection.

PubMed

Karlholm, Jörgen; Renhorn, Ingmar

2002-11-10

A framework is proposed for the selection of wavelength bands for multispectral sensors by use of hyperspectral reference data. Using the results from the detection theory we derive a cost function that is minimized by a set of spectral bands optimal in terms of detection performance for discrimination between a class of small rare targets and clutter with known spectral distribution. The method may be used, e.g., in the design of multispectral infrared search and track and electro-optical missile warning sensors, where a low false-alarm rate and a high-detection probability for detection of small targets against a clutter background are of critical importance, but the required high frame rate prevents the use of hyperspectral sensors.

An Automated Directed Spectral Search Methodology for Small Target Detection

NASA Astrophysics Data System (ADS)

Grossman, Stanley I.

Much of the current efforts in remote sensing tackle macro-level problems such as determining the extent of wheat in a field, the general health of vegetation or the extent of mineral deposits in an area. However, for many of the remaining remote sensing challenges being studied currently, such as border protection, drug smuggling, treaty verification, and the war on terror, most targets are very small in nature - a vehicle or even a person. While in typical macro-level problems the objective vegetation is in the scene, for small target detection problems it is not usually known if the desired small target even exists in the scene, never mind finding it in abundance. The ability to find specific small targets, such as vehicles, typifies this problem. Complicating the analyst's life, the growing number of available sensors is generating mountains of imagery outstripping the analysts' ability to visually peruse them. This work presents the important factors influencing spectral exploitation using multispectral data and suggests a different approach to small target detection. The methodology of directed search is presented, including the use of scene-modeled spectral libraries, various search algorithms, and traditional statistical and ROC curve analysis. The work suggests a new metric to calibrate analysis labeled the analytic sweet spot as well as an estimation method for identifying the sweet spot threshold for an image. It also suggests a new visualization aid for highlighting the target in its entirety called nearest neighbor inflation (NNI). It brings these all together to propose that these additions to the target detection arena allow for the construction of a fully automated target detection scheme. This dissertation next details experiments to support the hypothesis that the optimum detection threshold is the analytic sweet spot and that the estimation method adequately predicts it. Experimental results and analysis are presented for the proposed directed

Eye Movements and Display Change Detection during Reading

ERIC Educational Resources Information Center

Slattery, Timothy J.; Angele, Bernhard; Rayner, Keith

2011-01-01

In the boundary change paradigm (Rayner, 1975), when a reader's eyes cross an invisible boundary location, a preview word is replaced by a target word. Readers are generally unaware of such changes due to saccadic suppression. However, some readers detect changes on a few trials and a small percentage of them detect many changes. Two experiments…

Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Bal, A.; Alam, M. S.; Aslan, M. S.

2006-05-01

Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.

Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah

2016-05-01

A novel hyperspectral target detection technique based on Fukunaga-Koontz transform (FKT) is presented. FKT offers significant properties for feature selection and ordering. However, it can only be used to solve multi-pattern classification problems. Target detection may be considered as a two-class classification problem, i.e., target versus background clutter. Nevertheless, background clutter typically contains different types of materials. That's why; target detection techniques are different than classification methods by way of modeling clutter. To avoid the modeling of the background clutter, we have improved one-class FKT (OC-FKT) for target detection. The statistical properties of target training samples are used to define tunnel-like boundary of the target class. Non-target samples are then created synthetically as to be outside of the boundary. Thus, only limited target samples become adequate for training of FKT. The hyperspectral image experiments confirm that the proposed OC-FKT technique provides an effective means for target detection.

Single molecule targeted sequencing for cancer gene mutation detection.

PubMed

Gao, Yan; Deng, Liwei; Yan, Qin; Gao, Yongqian; Wu, Zengding; Cai, Jinsen; Ji, Daorui; Li, Gailing; Wu, Ping; Jin, Huan; Zhao, Luyang; Liu, Song; Ge, Liangjin; Deem, Michael W; He, Jiankui

2016-05-19

With the rapid decline in cost of sequencing, it is now affordable to examine multiple genes in a single disease-targeted clinical test using next generation sequencing. Current targeted sequencing methods require a separate step of targeted capture enrichment during sample preparation before sequencing. Although there are fast sample preparation methods available in market, the library preparation process is still relatively complicated for physicians to use routinely. Here, we introduced an amplification-free Single Molecule Targeted Sequencing (SMTS) technology, which combined targeted capture and sequencing in one step. We demonstrated that this technology can detect low-frequency mutations using artificially synthesized DNA sample. SMTS has several potential advantages, including simple sample preparation thus no biases and errors are introduced by PCR reaction. SMTS has the potential to be an easy and quick sequencing technology for clinical diagnosis such as cancer gene mutation detection, infectious disease detection, inherited condition screening and noninvasive prenatal diagnosis.

Infrared small target detection based on directional zero-crossing measure

NASA Astrophysics Data System (ADS)

Zhang, Xiangyue; Ding, Qinghai; Luo, Haibo; Hui, Bin; Chang, Zheng; Zhang, Junchao

2017-12-01

Infrared small target detection under complex background and low signal-to-clutter ratio (SCR) condition is of great significance to the development on precision guidance and infrared surveillance. In order to detect targets precisely and extract targets from intricate clutters effectively, a detection method based on zero-crossing saliency (ZCS) map is proposed. The original map is first decomposed into different first-order directional derivative (FODD) maps by using FODD filters. Then the ZCS map is obtained by fusing all directional zero-crossing points. At last, an adaptive threshold is adopted to segment targets from the ZCS map. Experimental results on a series of images show that our method is effective and robust for detection under complex backgrounds. Moreover, compared with other five state-of-the-art methods, our method achieves better performance in terms of detection rate, SCR gain and background suppression factor.

Spatial decoupling of targets and flashing stimuli for visual brain-computer interfaces

NASA Astrophysics Data System (ADS)

Waytowich, Nicholas R.; Krusienski, Dean J.

2015-06-01

Objective. Recently, paradigms using code-modulated visual evoked potentials (c-VEPs) have proven to achieve among the highest information transfer rates for noninvasive brain-computer interfaces (BCIs). One issue with current c-VEP paradigms, and visual-evoked paradigms in general, is that they require direct foveal fixation of the flashing stimuli. These interfaces are often visually unpleasant and can be irritating and fatiguing to the user, thus adversely impacting practical performance. In this study, a novel c-VEP BCI paradigm is presented that attempts to perform spatial decoupling of the targets and flashing stimuli using two distinct concepts: spatial separation and boundary positioning. Approach. For the paradigm, the flashing stimuli form a ring that encompasses the intended non-flashing targets, which are spatially separated from the stimuli. The user fixates on the desired target, which is classified using the changes to the EEG induced by the flashing stimuli located in the non-foveal visual field. Additionally, a subset of targets is also positioned at or near the stimulus boundaries, which decouples targets from direct association with a single stimulus. This allows a greater number of target locations for a fixed number of flashing stimuli. Main results. Results from 11 subjects showed practical classification accuracies for the non-foveal condition, with comparable performance to the direct-foveal condition for longer observation lengths. Online results from 5 subjects confirmed the offline results with an average accuracy across subjects of 95.6% for a 4-target condition. The offline analysis also indicated that targets positioned at or near the boundaries of two stimuli could be classified with the same accuracy as traditional superimposed (non-boundary) targets. Significance. The implications of this research are that c-VEPs can be detected and accurately classified to achieve comparable BCI performance without requiring potentially irritating

Automated multiple target detection and tracking in UAV videos

NASA Astrophysics Data System (ADS)

Mao, Hongwei; Yang, Chenhui; Abousleman, Glen P.; Si, Jennie

2010-04-01

In this paper, a novel system is presented to detect and track multiple targets in Unmanned Air Vehicles (UAV) video sequences. Since the output of the system is based on target motion, we first segment foreground moving areas from the background in each video frame using background subtraction. To stabilize the video, a multi-point-descriptor-based image registration method is performed where a projective model is employed to describe the global transformation between frames. For each detected foreground blob, an object model is used to describe its appearance and motion information. Rather than immediately classifying the detected objects as targets, we track them for a certain period of time and only those with qualified motion patterns are labeled as targets. In the subsequent tracking process, a Kalman filter is assigned to each tracked target to dynamically estimate its position in each frame. Blobs detected at a later time are used as observations to update the state of the tracked targets to which they are associated. The proposed overlap-rate-based data association method considers the splitting and merging of the observations, and therefore is able to maintain tracks more consistently. Experimental results demonstrate that the system performs well on real-world UAV video sequences. Moreover, careful consideration given to each component in the system has made the proposed system feasible for real-time applications.

A critique of the molecular target-based drug discovery paradigm based on principles of metabolic control: advantages of pathway-based discovery.

PubMed

Hellerstein, Marc K

2008-01-01

Contemporary drug discovery and development (DDD) is dominated by a molecular target-based paradigm. Molecular targets that are potentially important in disease are physically characterized; chemical entities that interact with these targets are identified by ex vivo high-throughput screening assays, and optimized lead compounds enter testing as drugs. Contrary to highly publicized claims, the ascendance of this approach has in fact resulted in the lowest rate of new drug approvals in a generation. The primary explanation for low rates of new drugs is attrition, or the failure of candidates identified by molecular target-based methods to advance successfully through the DDD process. In this essay, I advance the thesis that this failure was predictable, based on modern principles of metabolic control that have emerged and been applied most forcefully in the field of metabolic engineering. These principles, such as the robustness of flux distributions, address connectivity relationships in complex metabolic networks and make it unlikely a priori that modulating most molecular targets will have predictable, beneficial functional outcomes. These same principles also suggest, however, that unexpected therapeutic actions will be common for agents that have any effect (i.e., that complexity can be exploited therapeutically). A potential operational solution (pathway-based DDD), based on observability rather than predictability, is described, focusing on emergent properties of key metabolic pathways in vivo. Recent examples of pathway-based DDD are described. In summary, the molecular target-based DDD paradigm is built on a naïve and misleading model of biologic control and is not heuristically adequate for advancing the mission of modern therapeutics. New approaches that take account of and are built on principles described by metabolic engineers are needed for the next generation of DDD.

Selective targeting of bioengineered platelets to prostate cancer vasculature: new paradigm for therapeutic modalities

PubMed Central

Montecinos, Viviana P; Morales, Claudio H; Fischer, Thomas H; Burns, Sarah; San Francisco, Ignacio F; Godoy, Alejandro S; Smith, Gary J

2015-01-01

Androgen deprivation therapy (ADT) provides palliation for most patients with advanced prostate cancer (CaP); however, greater than 80% subsequently fail ADT. ADT has been indicated to induce an acute but transient destabilization of the prostate vasculature in animal models and humans. Human re-hydrated lyophilized platelets (hRL-P) were investigated as a prototype for therapeutic agents designed to target selectively the tumour-associated vasculature in CaP. The ability of hRL-P to bind the perturbed endothelial cells was tested using thrombin- and ADP-activated human umbilical vein endothelial cells (HUVEC), as well as primary xenografts of human prostate tissue undergoing acute vascular involution in response to ADT. hRL-P adhered to activated HUVEC in a dose-responsive manner. Systemically administered hRL-P, and hRL-P loaded with super-paramagnetic iron oxide (SPIO) nanoparticles, selectively targeted the ADT-damaged human microvasculature in primary xenografts of human prostate tissue. This study demonstrated that hRL-P pre-loaded with chemo-therapeutics or nanoparticles could provide a new paradigm for therapeutic modalities to prevent the rebound/increase in prostate vasculature after ADT, inhibiting the transition to castration-recurrent growth. PMID:25736582

Development of a microcapillary column for detecting targeted messenger RNA molecules.

PubMed

Ohnishi, Michihiro

2006-03-24

A capillary column in a rapid-flow system has been developed for detecting targeted messenger RNA (mRNA) molecules. The column has a structure made of two beds-one bed of porous microbeads and one bed of microbeads with a polythymidine base sequence. The targeted eukaryotic mRNA molecules are detected by two-step hybridization (sandwich hybridization) composed of polyadenosine selection of mRNA molecules and formation of a probe-target (targeted mRNA) hybrid. The sandwich hybridization, which is accomplished within 1 h, was tested using synthetic polydeoxynucleotides. Ten picomoles of the targeted polydeoxynucleotide were detected.

Target molecules detection by waveguiding in a photonic silicon membrane

DOEpatents

Letant, Sonia E [Livermore, CA; Van Buuren, Anthony [Livermore, CA; Terminello, Louis [Danville, CA; Hart, Bradley R [Brentwood, CA

2006-12-26

Disclosed herein is a porous silicon filter capable of binding and detecting biological and chemical target molecules in liquid or gas samples. A photonic waveguiding silicon filter with chemical and/or biological anchors covalently attached to the pore walls bind target molecules. The system uses transmission curve engineering principles to allow measurements to be made in situ and in real time to detect the presence of various target molecules and calculate the concentration of bound target.

Target molecules detection by waveguiding in a photonic silicon membrane

DOEpatents

Letant, Sonia; Van Buuren, Anthony; Terminello, Louis

2004-08-31

Disclosed herein is a photonic silicon filter capable of binding and detecting biological and chemical target molecules in liquid or gas samples. A photonic waveguiding silicon filter with chemical and/or biological anchors covalently attached to the pore walls selectively bind target molecules. The system uses transmission curve engineering principles to allow measurements to be made in situ and in real time to detect the presence of various target molecules and determine the concentration of bound target.

Open and closed cortico-subcortical loops: A neuro-computational account of access to consciousness in the distractor-induced blindness paradigm.

PubMed

Ebner, Christian; Schroll, Henning; Winther, Gesche; Niedeggen, Michael; Hamker, Fred H

2015-09-01

How the brain decides which information to process 'consciously' has been debated over for decades without a simple explanation at hand. While most experiments manipulate the perceptual energy of presented stimuli, the distractor-induced blindness task is a prototypical paradigm to investigate gating of information into consciousness without or with only minor visual manipulation. In this paradigm, subjects are asked to report intervals of coherent dot motion in a rapid serial visual presentation (RSVP) stream, whenever these are preceded by a particular color stimulus in a different RSVP stream. If distractors (i.e., intervals of coherent dot motion prior to the color stimulus) are shown, subjects' abilities to perceive and report intervals of target dot motion decrease, particularly with short delays between intervals of target color and target motion. We propose a biologically plausible neuro-computational model of how the brain controls access to consciousness to explain how distractor-induced blindness originates from information processing in the cortex and basal ganglia. The model suggests that conscious perception requires reverberation of activity in cortico-subcortical loops and that basal-ganglia pathways can either allow or inhibit this reverberation. In the distractor-induced blindness paradigm, inadequate distractor-induced response tendencies are suppressed by the inhibitory 'hyperdirect' pathway of the basal ganglia. If a target follows such a distractor closely, temporal aftereffects of distractor suppression prevent target identification. The model reproduces experimental data on how delays between target color and target motion affect the probability of target detection. Copyright © 2015 Elsevier Inc. All rights reserved.

Target detection in insects: optical, neural and behavioral optimizations.

PubMed

Gonzalez-Bellido, Paloma T; Fabian, Samuel T; Nordström, Karin

2016-12-01

Motion vision provides important cues for many tasks. Flying insects, for example, may pursue small, fast moving targets for mating or feeding purposes, even when these are detected against self-generated optic flow. Since insects are small, with size-constrained eyes and brains, they have evolved to optimize their optical, neural and behavioral target visualization solutions. Indeed, even if evolutionarily distant insects display different pursuit strategies, target neuron physiology is strikingly similar. Furthermore, the coarse spatial resolution of the insect compound eye might actually be beneficial when it comes to detection of moving targets. In conclusion, tiny insects show higher than expected performance in target visualization tasks. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Paradigm shift in bacteriophage-mediated delivery of anticancer drugs: from targeted 'magic bullets' to self-navigated 'magic missiles'.

PubMed

Petrenko, Valery A; Gillespie, James W

2017-03-01

New phage-directed nanomedicines have emerged recently as a result of the in-depth study of the genetics and structure of filamentous phage and evolution of phage display and phage nanobiotechnology. This review focuses on the progress made in the development of the cancer-targeted nanomaterials and discusses the trends in using phage as a bioselectable molecular navigation system. Areas covered: The merging of phage display technologies with nanotechnology in recent years has proved promising in different areas of medicine and technology, such as medical diagnostics, molecular imaging, vaccine development and targeted drug/gene delivery, which is the focus of this review. The authors used data obtained from their research group and sourced using Science Citation Index (Web of Science) and NCBI PubMed search resources. Expert opinion: First attempts of adapting traditional concepts of direct targeting of tumor using phage-targeted nanomedicines has shown minimal improvements. With discovery and study of biological and technical barriers that prevent anti-tumor drug delivery, a paradigm shift from traditional drug targeting to nanomedicine navigation systems is required. The advanced bacteriophage-driven self-navigation systems are thought to overcome those barriers using more precise, localized phage selection methods, multi-targeting 'promiscuous' ligands and advanced multifunctional nanomedicine platforms.

Effects of age and eccentricity on visual target detection.

PubMed

Gruber, Nicole; Müri, René M; Mosimann, Urs P; Bieri, Rahel; Aeschimann, Andrea; Zito, Giuseppe A; Urwyler, Prabitha; Nyffeler, Thomas; Nef, Tobias

2013-01-01

The aim of this study was to examine the effects of aging and target eccentricity on a visual search task comprising 30 images of everyday life projected into a hemisphere, realizing a ±90° visual field. The task performed binocularly allowed participants to freely move their eyes to scan images for an appearing target or distractor stimulus (presented at 10°; 30°, and 50° eccentricity). The distractor stimulus required no response, while the target stimulus required acknowledgment by pressing the response button. One hundred and seventeen healthy subjects (mean age = 49.63 years, SD = 17.40 years, age range 20-78 years) were studied. The results show that target detection performance decreases with age as well as with increasing eccentricity, especially for older subjects. Reaction time also increases with age and eccentricity, but in contrast to target detection, there is no interaction between age and eccentricity. Eye movement analysis showed that younger subjects exhibited a passive search strategy while older subjects exhibited an active search strategy probably as a compensation for their reduced peripheral detection performance.

A saliency-based approach to detection of infrared target

NASA Astrophysics Data System (ADS)

Chen, Yanfei; Sang, Nong; Dan, Zhiping

2013-10-01

Automatic target detection in infrared images is a hot research field of national defense technology. We propose a new saliency-based infrared target detection model in this paper, which is based on the fact that human focus of attention is directed towards the relevant target to interpret the most promising information. For a given image, the convolution of the image log amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector in the frequency domain. At the same time, orientation and shape features extracted are combined into a saliency map in the spatial domain. Our proposed model decides salient targets based on a final saliency map, which is generated by integration of the saliency maps in the frequency and spatial domain. At last, the size of each salient target is obtained by maximizing entropy of the final saliency map. Experimental results show that the proposed model can highlight both small and large salient regions in infrared image, as well as inhibit repeated distractors in cluttered image. In addition, its detecting efficiency has improved significantly.

An infrared small target detection method based on multiscale local homogeneity measure

NASA Astrophysics Data System (ADS)

Nie, Jinyan; Qu, Shaocheng; Wei, Yantao; Zhang, Liming; Deng, Lizhen

2018-05-01

Infrared (IR) small target detection plays an important role in the field of image detection area owing to its intrinsic characteristics. This paper presents a multiscale local homogeneity measure (MLHM) for infrared small target detection, which can enhance the performance of IR small target detection system. Firstly, intra-patch homogeneity of the target itself and the inter-patch heterogeneity between target and the local background regions are integrated to enhance the significant of small target. Secondly, a multiscale measure based on local regions is proposed to obtain the most appropriate response. Finally, an adaptive threshold method is applied to small target segmentation. Experimental results on three different scenarios indicate that the MLHM has good performance under the interference of strong noise.

Incorporating signal-dependent noise for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Morman, Christopher J.; Meola, Joseph

2015-05-01

The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.

A computational imaging target specific detectivity metric

NASA Astrophysics Data System (ADS)

Preece, Bradley L.; Nehmetallah, George

2017-05-01

Due to the large quantity of low-cost, high-speed computational processing available today, computational imaging (CI) systems are expected to have a major role for next generation multifunctional cameras. The purpose of this work is to quantify the performance of theses CI systems in a standardized manner. Due to the diversity of CI system designs that are available today or proposed in the near future, significant challenges in modeling and calculating a standardized detection signal-to-noise ratio (SNR) to measure the performance of these systems. In this paper, we developed a path forward for a standardized detectivity metric for CI systems. The detectivity metric is designed to evaluate the performance of a CI system searching for a specific known target or signal of interest, and is defined as the optimal linear matched filter SNR, similar to the Hotelling SNR, calculated in computational space with special considerations for standardization. Therefore, the detectivity metric is designed to be flexible, in order to handle various types of CI systems and specific targets, while keeping the complexity and assumptions of the systems to a minimum.

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.

A new method for detecting small and dim targets in starry background

NASA Astrophysics Data System (ADS)

Yao, Rui; Zhang, Yanning; Jiang, Lei

2011-08-01

Small visible optical space targets detection is one of the key issues in the research of long-range early warning and space debris surveillance. The SNR(Signal to Noise Ratio) of the target is very low because of the self influence of image device. Random noise and background movement also increase the difficulty of target detection. In order to detect small visible optical space targets effectively and rapidly, we bring up a novel detecting method based on statistic theory. Firstly, we get a reasonable statistical model of visible optical space image. Secondly, we extract SIFT(Scale-Invariant Feature Transform) feature of the image frames, and calculate the transform relationship, then use the transform relationship to compensate whole visual field's movement. Thirdly, the influence of star was wiped off by using interframe difference method. We find segmentation threshold to differentiate candidate targets and noise by using OTSU method. Finally, we calculate statistical quantity to judge whether there is the target for every pixel position in the image. Theory analysis shows the relationship of false alarm probability and detection probability at different SNR. The experiment result shows that this method could detect target efficiently, even the target passing through stars.

Clever eye algorithm for target detection of remote sensing imagery

NASA Astrophysics Data System (ADS)

Geng, Xiurui; Ji, Luyan; Sun, Kang

2016-04-01

Target detection algorithms for hyperspectral remote sensing imagery, such as the two most commonly used remote sensing detection algorithms, the constrained energy minimization (CEM) and matched filter (MF), can usually be attributed to the inner product between a weight filter (or detector) and a pixel vector. CEM and MF have the same expression except that MF requires data centralization first. However, this difference leads to a difference in the target detection results. That is to say, the selection of the data origin could directly affect the performance of the detector. Therefore, does there exist another data origin other than the zero and mean-vector points for a better target detection performance? This is a very meaningful issue in the field of target detection, but it has not been paid enough attention yet. In this study, we propose a novel objective function by introducing the data origin as another variable, and the solution of the function is corresponding to the data origin with the minimal output energy. The process of finding the optimal solution can be vividly regarded as a clever eye automatically searching the best observing position and direction in the feature space, which corresponds to the largest separation between the target and background. Therefore, this new algorithm is referred to as the clever eye algorithm (CE). Based on the Sherman-Morrison formula and the gradient ascent method, CE could derive the optimal target detection result in terms of energy. Experiments with both synthetic and real hyperspectral data have verified the effectiveness of our method.

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.

Automatic small target detection in synthetic infrared images

NASA Astrophysics Data System (ADS)

Yardımcı, Ozan; Ulusoy, Ä.°lkay

2017-05-01

Automatic detection of targets from far distances is a very challenging problem. Background clutter and small target size are the main difficulties which should be solved while reaching a high detection performance as well as a low computational load. The pre-processing, detection and post-processing approaches are very effective on the final results. In this study, first of all, various methods in the literature were evaluated separately for each of these stages using the simulated test scenarios. Then, a full system of detection was constructed among available solutions which resulted in the best performance in terms of detection. However, although a precision rate as 100% was reached, the recall values stayed low around 25-45%. Finally, a post-processing method was proposed which increased the recall value while keeping the precision at 100%. The proposed post-processing method, which is based on local operations, increased the recall value to 65-95% in all test scenarios.

Penalty dynamic programming algorithm for dim targets detection in sensor systems.

PubMed

Huang, Dayu; Xue, Anke; Guo, Yunfei

2012-01-01

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems

PubMed Central

Huang, Dayu; Xue, Anke; Guo, Yunfei

2012-01-01

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations. PMID:22666074

Improved target detection by IR dual-band image fusion

NASA Astrophysics Data System (ADS)

Adomeit, U.; Ebert, R.

2009-09-01

Dual-band thermal imagers acquire information simultaneously in both the 8-12 μm (long-wave infrared, LWIR) and the 3-5 μm (mid-wave infrared, MWIR) spectral range. Compared to single-band thermal imagers they are expected to have several advantages in military applications. These advantages include the opportunity to use the best band for given atmospheric conditions (e. g. cold climate: LWIR, hot and humid climate: MWIR), the potential to better detect camouflaged targets and an improved discrimination between targets and decoys. Most of these advantages have not yet been verified and/or quantified. It is expected that image fusion allows better exploitation of the information content available with dual-band imagers especially with respect to detection of targets. We have developed a method for dual-band image fusion based on the apparent temperature differences in the two bands. This method showed promising results in laboratory tests. In order to evaluate its performance under operational conditions we conducted a field trial in an area with high thermal clutter. In such areas, targets are hardly to detect in single-band images because they vanish in the clutter structure. The image data collected in this field trial was used for a perception experiment. This perception experiment showed an enhanced target detection range and reduced false alarm rate for the fused images compared to the single-band images.

High-Speed Incoming Infrared Target Detection by Fusion of Spatial and Temporal Detectors

PubMed Central

Kim, Sungho

2015-01-01

This paper presents a method for detecting high-speed incoming targets by the fusion of spatial and temporal detectors to achieve a high detection rate for an active protection system (APS). The incoming targets have different image velocities according to the target-camera geometry. Therefore, single-target detector-based approaches, such as a 1D temporal filter, 2D spatial filter and 3D matched filter, cannot provide a high detection rate with moderate false alarms. The target speed variation was analyzed according to the incoming angle and target velocity. The speed of the distant target at the firing time is almost stationary and increases slowly. The speed varying targets are detected stably by fusing the spatial and temporal filters. The stationary target detector is activated by an almost zero temporal contrast filter (TCF) and identifies targets using a spatial filter called the modified mean subtraction filter (M-MSF). A small motion (sub-pixel velocity) target detector is activated by a small TCF value and finds targets using the same spatial filter. A large motion (pixel-velocity) target detector works when the TCF value is high. The final target detection is terminated by fusing the three detectors based on the threat priority. The experimental results of the various target sequences show that the proposed fusion-based target detector produces the highest detection rate with an acceptable false alarm rate. PMID:25815448

Acute Promyelocytic Leukemia: A Paradigm for Oncoprotein-Targeted Cure.

PubMed

de Thé, Hugues; Pandolfi, Pier Paolo; Chen, Zhu

2017-11-13

Recent clinical trials have demonstrated that the immense majority of acute promyelocytic leukemia (APL) patients can be definitively cured by the combination of two targeted therapies: retinoic acid (RA) and arsenic. Mouse models have provided unexpected insights into the mechanisms involved. Restoration of PML nuclear bodies upon RA- and/or arsenic-initiated PML/RARA degradation is essential, while RA-triggered transcriptional activation is dispensable for APL eradication. Mutations of the arsenic-binding site of PML/RARA, but also PML, have been detected in therapy-resistant patients, demonstrating the key role of PML in APL cure. PML nuclear bodies are druggable and could be harnessed in other conditions. Copyright © 2017 Elsevier Inc. All rights reserved.

A chest-shape target automatic detection method based on Deformable Part Models

NASA Astrophysics Data System (ADS)

Zhang, Mo; Jin, Weiqi; Li, Li

2016-10-01

Automatic weapon platform is one of the important research directions at domestic and overseas, it needs to accomplish fast searching for the object to be shot under complex background. Therefore, fast detection for given target is the foundation of further task. Considering that chest-shape target is common target of shoot practice, this paper treats chestshape target as the target and studies target automatic detection method based on Deformable Part Models. The algorithm computes Histograms of Oriented Gradient(HOG) features of the target and trains a model using Latent variable Support Vector Machine(SVM); In this model, target image is divided into several parts then we can obtain foot filter and part filters; Finally, the algorithm detects the target at the HOG features pyramid with method of sliding window. The running time of extracting HOG pyramid with lookup table can be shorten by 36%. The result indicates that this algorithm can detect the chest-shape target in natural environments indoors or outdoors. The true positive rate of detection reaches 76% with many hard samples, and the false positive rate approaches 0. Running on a PC (Intel(R)Core(TM) i5-4200H CPU) with C++ language, the detection time of images with the resolution of 640 × 480 is 2.093s. According to TI company run library about image pyramid and convolution for DM642 and other hardware, our detection algorithm is expected to be implemented on hardware platform, and it has application prospect in actual system.

Utility as a rationale for choosing observer performance assessment paradigms for detection tasks in medical imaging.

PubMed

Wunderlich, Adam; Abbey, Craig K

2013-11-01

Studies of lesion detectability are often carried out to evaluate medical imaging technology. For such studies, several approaches have been proposed to measure observer performance, such as the receiver operating characteristic (ROC), the localization ROC (LROC), the free-response ROC (FROC), the alternative free-response ROC (AFROC), and the exponentially transformed FROC (EFROC) paradigms. Therefore, an experimenter seeking to carry out such a study is confronted with an array of choices. Traditionally, arguments for different approaches have been made on the basis of practical considerations (statistical power, etc.) or the gross level of analysis (case-level or lesion-level). This article contends that a careful consideration of utility should form the rationale for matching the assessment paradigm to the clinical task of interest. In utility theory, task performance is commonly evaluated with total expected utility, which integrates the various event utilities against the probability of each event. To formalize the relationship between expected utility and the summary curve associated with each assessment paradigm, the concept of a "natural" utility structure is proposed. A natural utility structure is defined for a summary curve when the variables associated with the summary curve axes are sufficient for computing total expected utility, assuming that the disease prevalence is known. Natural utility structures for ROC, LROC, FROC, AFROC, and EFROC curves are introduced, clarifying how the utilities of correct and incorrect decisions are aggregated by summary curves. Further, conditions are given under which general utility structures for localization-based methodologies reduce to case-based assessment. Overall, the findings reveal how summary curves correspond to natural utility structures of diagnostic tasks, suggesting utility as a motivating principle for choosing an assessment paradigm.

Target Detection Using an AOTF Hyperspectral Imager

NASA Technical Reports Server (NTRS)

Cheng, L-J.; Mahoney, J.; Reyes, F.; Suiter, H.

1994-01-01

This paper reports results of a recent field experiment using a prototype system to evaluate the acousto-optic tunable filter polarimetric hyperspectral imaging technology for target detection applications.

Quantum Illumination-Based Target Detection and Discrimination

DTIC Science & Technology

2014-06-30

amplifier (EDFA) was combined with the signal to simulate a high-noise environment, with a noise photon number per mode NB in the range 40–300. The...Research Triangle Park, NC 27709-2211 quantum communication, target detection, entanglement , parametric downconversion, optical parametric amplifiers...laser system of the same average transmitted photon number, when the target return has random-amplitude behavior. Receiver operating characteristic

The single lineup paradigm: A new way to manipulate target presence in eyewitness identification experiments.

PubMed

Oriet, Chris; Fitzgerald, Ryan J

2018-02-01

The suspect in eyewitness lineups may be guilty or innocent. These possibilities are traditionally simulated in eyewitness identification studies using a dual-lineup paradigm: All witnesses observe the same perpetrator and then receive one of two lineups. In this paradigm, the suspect's guilt is manipulated by including the perpetrator in one lineup and an innocent suspect in the other. The lineup is then filled with people matched to either the suspect (resulting in different fillers in perpetrator-present and perpetrator-absent lineups) or to the perpetrator (resulting in the same fillers in each lineup). An inescapable feature of the dual-lineup paradigm is that the perpetrator-present and perpetrator-absent lineups differ not only in the suspect's guilt, but also in their composition. Here, we describe a single-lineup paradigm: Subjects observe one of two perpetrators and then all subjects receive the same lineup containing one of the perpetrators. This alternative paradigm allows manipulation of the suspect's guilt without changing the lineup's composition. In three experiments, we applied the single-lineup paradigm to explore suspect-filler similarity and consistently found that increasing similarity reduced perpetrator identifications but did little to prevent innocent suspect misidentifications. Conversely, when fillers were matched to the perpetrator using a dual-lineup paradigm, increasing similarity reduced identification of perpetrators and innocent suspects. This finding suggests that the effect of filler similarity may depend on the person to whom the fillers are matched. We suggest that the single-lineup paradigm is a more ecologically valid and better controlled approach to creating suspect-matched lineups in laboratory investigations of eyewitness memory than existing procedures. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Portable apparatus for separating sample and detecting target analytes

DOEpatents

Renzi, Ronald F.; Wally, Karl; Crocker, Robert W.; Stamps, James F.; Griffiths; Stewart K. ,; Fruetel, Julia A.; Horn, Brent A.; Shokair, Isaac R.; Yee, Daniel D.; VanderNoot, Victoria A.; Wiedenman, Boyd J.; West, Jason A. A.; Ferko, Scott M.

2008-11-18

Portable devices and methods for determining the presence of a target analyte using a portable device are provided. The portable device is preferably hand-held. A sample is injected to the portable device. A microfluidic separation is performed within the portable device and at least one separated component detected by a detection module within the portable device, in embodiments of the invention. A target analyte is identified, based on the separated component, and the presence of the target analyte is indicated on an output interface of the portable device, in accordance with embodiments of the invention.

Invariant target detection by a correlation radiometer

NASA Astrophysics Data System (ADS)

Murza, L. P.

1986-12-01

The paper is concerned with the problem of the optimal detection of a heat-emitting target by a two-channel radiometer with an unstable amplification circuit. An expression is obtained for an asymptotically sufficient detection statistic which is invariant to changes in the amplification coefficients of the channels. The algorithm proposed here can be implemented numerically using a relatively simple program.

Advancing Clinical Proteomics via Analysis Based on Biological Complexes: A Tale of Five Paradigms.

PubMed

Goh, Wilson Wen Bin; Wong, Limsoon

2016-09-02

Despite advances in proteomic technologies, idiosyncratic data issues, for example, incomplete coverage and inconsistency, resulting in large data holes, persist. Moreover, because of naïve reliance on statistical testing and its accompanying p values, differential protein signatures identified from such proteomics data have little diagnostic power. Thus, deploying conventional analytics on proteomics data is insufficient for identifying novel drug targets or precise yet sensitive biomarkers. Complex-based analysis is a new analytical approach that has potential to resolve these issues but requires formalization. We categorize complex-based analysis into five method classes or paradigms and propose an even-handed yet comprehensive evaluation rubric based on both simulated and real data. The first four paradigms are well represented in the literature. The fifth and newest paradigm, the network-paired (NP) paradigm, represented by a method called Extremely Small SubNET (ESSNET), dominates in precision-recall and reproducibility, maintains strong performance in small sample sizes, and sensitively detects low-abundance complexes. In contrast, the commonly used over-representation analysis (ORA) and direct-group (DG) test paradigms maintain good overall precision but have severe reproducibility issues. The other two paradigms considered here are the hit-rate and rank-based network analysis paradigms; both of these have good precision-recall and reproducibility, but they do not consider low-abundance complexes. Therefore, given its strong performance, NP/ESSNET may prove to be a useful approach for improving the analytical resolution of proteomics data. Additionally, given its stability, it may also be a powerful new approach toward functional enrichment tests, much like its ORA and DG counterparts.

Sensor Compromise Detection in Multiple-Target Tracking Systems

PubMed Central

Doucette, Emily A.; Curtis, Jess W.

2018-01-01

Tracking multiple targets using a single estimator is a problem that is commonly approached within a trusted framework. There are many weaknesses that an adversary can exploit if it gains control over the sensors. Because the number of targets that the estimator has to track is not known with anticipation, an adversary could cause a loss of information or a degradation in the tracking precision. Other concerns include the introduction of false targets, which would result in a waste of computational and material resources, depending on the application. In this work, we study the problem of detecting compromised or faulty sensors in a multiple-target tracker, starting with the single-sensor case and then considering the multiple-sensor scenario. We propose an algorithm to detect a variety of attacks in the multiple-sensor case, via the application of finite set statistics (FISST), one-class classifiers and hypothesis testing using nonparametric techniques. PMID:29466314

Glint-induced false alarm reduction in signature adaptive target detection

NASA Astrophysics Data System (ADS)

Crosby, Frank J.

2002-07-01

The signal adaptive target detection algorithm developed by Crosby and Riley uses target geometry to discern anomalies in local backgrounds. Detection is not restricted based on specific target signatures. The robustness of the algorithm is limited by an increased false alarm potential. The base algorithm is extended to eliminate one common source of false alarms in a littoral environment. This common source is glint reflected on the surface of water. The spectral and spatial transience of glint prevent straightforward characterization and complicate exclusion. However, the statistical basis of the detection algorithm and its inherent computations allow for glint discernment and the removal of its influence.

Ostracism Online: A social media ostracism paradigm.

PubMed

Wolf, Wouter; Levordashka, Ana; Ruff, Johanna R; Kraaijeveld, Steven; Lueckmann, Jan-Matthis; Williams, Kipling D

2015-06-01

We describe Ostracism Online, a novel, social media-based ostracism paradigm designed to (1) keep social interaction experimentally controlled, (2) provide researchers with the flexibility to manipulate the properties of the social situation to fit their research purposes, (3) be suitable for online data collection, (4) be convenient for studying subsequent within-group behavior, and (5) be ecologically valid. After collecting data online, we compared the Ostracism Online paradigm with the Cyberball paradigm (Williams & Jarvis Behavior Research Methods, 38, 174-180, 2006) on need-threat and mood questionnaire scores (van Beest & Williams Journal of Personality and Social Psychology 91, 918-928, 2006). We also examined whether ostracized targets of either paradigm would be more likely to conform to their group members than if they had been included. Using a Bayesian analysis of variance to examine the individual effects of the different paradigms and to compare these effects across paradigms, we found analogous effects on need-threat and mood. Perhaps because we examined conformity to the ostracizers (rather than neutral sources), neither paradigm showed effects of ostracism on conformity. We conclude that Ostracism Online is a cost-effective, easy to use, and ecologically valid research tool for studying the psychological and behavioral effects of ostracism.

A long-term target detection approach in infrared image sequence

NASA Astrophysics Data System (ADS)

Li, Hang; Zhang, Qi; Wang, Xin; Hu, Chao

2016-10-01

An automatic target detection method used in long term infrared (IR) image sequence from a moving platform is proposed. Firstly, based on POME(the principle of maximum entropy), target candidates are iteratively segmented. Then the real target is captured via two different selection approaches. At the beginning of image sequence, the genuine target with litter texture is discriminated from other candidates by using contrast-based confidence measure. On the other hand, when the target becomes larger, we apply online EM method to estimate and update the distributions of target's size and position based on the prior detection results, and then recognize the genuine one which satisfies both the constraints of size and position. Experimental results demonstrate that the presented method is accurate, robust and efficient.

Detection and classification of underwater targets by echolocating dolphins

NASA Astrophysics Data System (ADS)

Au, Whitlow

2003-10-01

Many experiments have been performed with echolocating dolphins to determine their target detection and discrimination capabilities. Target detection experiments have been performed in a naturally noisy environment, with masking noise and with both phantom echoes and masking noise, and in reverberation. The echo energy to rms noise spectral density for the Atlantic bottlenose dolphin (Tursiops truncatus) at the 75% correct response threshold is approximately 7.5 dB whereas for the beluga whale (Delphinapterus leucas) the threshold is approximately 1 dB. The dolphin's detection threshold in reverberation is approximately 2.5 dB vs 2 dB for the beluga. The difference in performance between species can probably be ascribed to differences in how both species perceived the task. The bottlenose dolphin may be performing a combination detection/discrimination task whereas the beluga may be performing a simple detection task. Echolocating dolphins also have the capability to make fine discriminate of target properties such as wall thickness difference of water-filled cylinders and material differences in metallic plates. The high resolution property of the animal's echolocation signals and the high dynamic range of its auditory system are important factors in their outstanding discrimination capabilities.

Target detection portal

DOEpatents

Linker, Kevin L.; Brusseau, Charles A.

2002-01-01

A portal apparatus for screening persons or objects for the presence of trace amounts of target substances such as explosives, narcotics, radioactive materials, and certain chemical materials. The portal apparatus can have a one-sided exhaust for an exhaust stream, an interior wall configuration with a concave-shape across a horizontal cross-section for each of two facing sides to result in improved airflow and reduced washout relative to a configuration with substantially flat parallel sides; air curtains to reduce washout; ionizing sprays to collect particles bound by static forces, as well as gas jet nozzles to dislodge particles bound by adhesion to the screened person or object. The portal apparatus can be included in a detection system with a preconcentrator and a detector.

The variable target model: a paradigm shift in the incremental haemodialysis prescription.

PubMed

Casino, Francesco Gaetano; Basile, Carlo

2017-01-01

The recent interest in incremental haemodialysis (HD) is hindered by the current prescription based on a fixed target model (FTM) for the total (dialytic + renal) equivalent continuous clearance (ECC). The latter is expressed either as standard Kt/V (stdKt/V), i.e. the pre-dialysis averaged concentration of urea-based ECC, or EKRc, i.e. the time averaged concentration-based ECC, corrected for volume (V) = 40 L. Accordingly, there are two different targets: stdKt/V = 2.3 volumes per week (v/wk) and EKRc = 13 mL/min/40 L. However, fixing the total ECC necessarily implies perfect equivalence of its components-the residual renal urea clearance (Kru) and dialysis clearance (Kd). This assumption is wrong because Kru has much greater clinical weight than Kd. Here we propose that the ECC target varies as an inverse function of Kru, from a maximum value in anuria to a minimum value at Kru levels not yet requiring dialysis. The aim of the present study was to compare the current FTM with the proposed variable target model (VTM). The double pool urea kinetic model was used to model dialysis sessions for 360 virtual patients and establish equations predicting the ECC as a function of Kd, Kru and the number of sessions per week. An end-dialysis urea distribution V of 35 L (corresponding to a body surface area of 1.73 m 2 ) was used, so that the current EKRc target of 13 mL/min/40 L could be recalculated at an EKRc 35 value of 12 mL/min/35 L equal to 12 mL/min/1.73 m 2 . The latter also coincides with the maximum value of the EKRc 35 variable target in anuria. The minimum target value of EKRc 35 was assumed to coincide with Kru corrected for V = 35 L (i.e. Krc 35 = 6 mL/min/1.73 m 2 ). The corresponding target for stdKt/V was assumed to vary from 2.3 v/wk at Krc 35 = 0 to 1.7 v/wk at Krc 35 = 6 mL/min/1.73 m 2 . On this basis, the variable target values can be obtained from the following linear equations: target EKRc 35 = 12 - Krc 35 ; target stdKt/V = 2.3 - 0.1 × Krc 35 . Two

Applying the log-normal distribution to target detection

NASA Astrophysics Data System (ADS)

Holst, Gerald C.

1992-09-01

Holst and Pickard experimentally determined that MRT responses tend to follow a log-normal distribution. The log normal distribution appeared reasonable because nearly all visual psychological data is plotted on a logarithmic scale. It has the additional advantage that it is bounded to positive values; an important consideration since probability of detection is often plotted in linear coordinates. Review of published data suggests that the log-normal distribution may have universal applicability. Specifically, the log-normal distribution obtained from MRT tests appears to fit the target transfer function and the probability of detection of rectangular targets.

Contrast, size, and orientation-invariant target detection in infrared imagery

NASA Astrophysics Data System (ADS)

Zhou, Yi-Tong; Crawshaw, Richard D.

1991-08-01

Automatic target detection in IR imagery is a very difficult task due to variations in target brightness, shape, size, and orientation. In this paper, the authors present a contrast, size, and orientation invariant algorithm based on Gabor functions for detecting targets from a single IR image frame. The algorithms consists of three steps. First, it locates potential targets by using low-resolution Gabor functions which resist noise and background clutter effects, then, it removes false targets and eliminates redundant target points based on a similarity measure. These two steps mimic human vision processing but are different from Zeevi's Foveating Vision System. Finally, it uses both low- and high-resolution Gabor functions to verify target existence. This algorithm has been successfully tested on several IR images that contain multiple examples of military vehicles with different size and brightness in various background scenes and orientations.

Molecular Markers and Targeted Therapeutics in Metastatic Tumors of the Spine: Changing the Treatment Paradigms.

PubMed

Goodwin, C Rory; Abu-Bonsrah, Nancy; Rhines, Laurence D; Verlaan, Jorrit-Jan; Bilsky, Mark H; Laufer, Ilya; Boriani, Stefano; Sciubba, Daniel M; Bettegowda, Chetan

2016-10-15

A review of the literature. The aim of this study was to discuss the evolution of molecular signatures and the history and development of targeted therapeutics in metastatic tumor types affecting the spinal column. Molecular characterization of metastatic spine tumors is expected to usher in a revolution in diagnostic and treatment paradigms. Molecular characterization will provide critical information that can be used for initial diagnosis, prognosticating the ideal treatment strategy, assessment of treatment efficacy, surveillance and monitoring recurrence, and predicting complications, clinical outcome, and overall survival in patients diagnosed with metastatic cancers to the spinal column. A review of the literature was performed focusing on illustrative examples of the role that molecular-based therapeutics have played in clinical outcomes for patients diagnosed with metastatic tumor types affecting the spinal column. The impact of molecular therapeutics including receptor tyrosine kinases and immune checkpoint inhibitors and the ability of molecular signatures to provide prognostic information are discussed in metastatic breast cancer, lung cancer, prostate cancer, melanoma, and renal cell cancer affecting the spinal column. For the providers who will ultimately counsel patients diagnosed with metastases to the spinal column, molecular advancements will radically alter the management/surgical paradigms utilized. Ultimately, the translation of these molecular advancements into routine clinical care will greatly improve the quality and quantity of life for patients diagnosed with spinal malignancies and provide better overall outcomes and counseling for treating physicians. N/A.

Directional detection of dark matter with two-dimensional targets

NASA Astrophysics Data System (ADS)

Hochberg, Yonit; Kahn, Yonatan; Lisanti, Mariangela; Tully, Christopher G.; Zurek, Kathryn M.

2017-09-01

We propose two-dimensional materials as targets for direct detection of dark matter. Using graphene as an example, we focus on the case where dark matter scattering deposits sufficient energy on a valence-band electron to eject it from the target. We show that the sensitivity of graphene to dark matter of MeV to GeV mass can be comparable, for similar exposure and background levels, to that of semiconductor targets such as silicon and germanium. Moreover, a two-dimensional target is an excellent directional detector, as the ejected electron retains information about the angular dependence of the incident dark matter particle. This proposal can be implemented by the PTOLEMY experiment, presenting for the first time an opportunity for directional detection of sub-GeV dark matter.

Directional detection of dark matter with two-dimensional targets

DOE PAGES

Hochberg, Yonit; Kahn, Yonatan; Lisanti, Mariangela; ...

2017-09-01

We propose two-dimensional materials as targets for direct detection of dark matter. Using graphene as an example, we focus on the case where dark matter scattering deposits sufficient energy on a valence-band electron to eject it from the target. Here, we show that the sensitivity of graphene to dark matter of MeV to GeV mass can be comparable, for similar exposure and background levels, to that of semiconductor targets such as silicon and germanium. Moreover, a two-dimensional target is an excellent directional detector, as the ejected electron retains information about the angular dependence of the incident dark matter particle. Ourmore » proposal can be implemented by the PTOLEMY experiment, presenting for the first time an opportunity for directional detection of sub-GeV dark matter.« less

Directional detection of dark matter with two-dimensional targets

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

Hochberg, Yonit; Kahn, Yonatan; Lisanti, Mariangela

We propose two-dimensional materials as targets for direct detection of dark matter. Using graphene as an example, we focus on the case where dark matter scattering deposits sufficient energy on a valence-band electron to eject it from the target. Here, we show that the sensitivity of graphene to dark matter of MeV to GeV mass can be comparable, for similar exposure and background levels, to that of semiconductor targets such as silicon and germanium. Moreover, a two-dimensional target is an excellent directional detector, as the ejected electron retains information about the angular dependence of the incident dark matter particle. Ourmore » proposal can be implemented by the PTOLEMY experiment, presenting for the first time an opportunity for directional detection of sub-GeV dark matter.« less

Detection of dual-band infrared small target based on joint dynamic sparse representation

NASA Astrophysics Data System (ADS)

Zhou, Jinwei; Li, Jicheng; Shi, Zhiguang; Lu, Xiaowei; Ren, Dongwei

2015-10-01

Infrared small target detection is a crucial and yet still is a difficult issue in aeronautic and astronautic applications. Sparse representation is an important mathematic tool and has been used extensively in image processing in recent years. Joint sparse representation is applied in dual-band infrared dim target detection in this paper. Firstly, according to the characters of dim targets in dual-band infrared images, 2-dimension Gaussian intensity model was used to construct target dictionary, then the dictionary was classified into different sub-classes according to different positions of Gaussian function's center point in image block; The fact that dual-band small targets detection can use the same dictionary and the sparsity doesn't lie in atom-level but in sub-class level was utilized, hence the detection of targets in dual-band infrared images was converted to be a joint dynamic sparse representation problem. And the dynamic active sets were used to describe the sparse constraint of coefficients. Two modified sparsity concentration index (SCI) criteria was proposed to evaluate whether targets exist in the images. In experiments, it shows that the proposed algorithm can achieve better detecting performance and dual-band detection is much more robust to noise compared with single-band detection. Moreover, the proposed method can be expanded to multi-spectrum small target detection.

Neural Dynamics Underlying Target Detection in the Human Brain

PubMed Central

Bansal, Arjun K.; Madhavan, Radhika; Agam, Yigal; Golby, Alexandra; Madsen, Joseph R.

2014-01-01

Sensory signals must be interpreted in the context of goals and tasks. To detect a target in an image, the brain compares input signals and goals to elicit the correct behavior. We examined how target detection modulates visual recognition signals by recording intracranial field potential responses from 776 electrodes in 10 epileptic human subjects. We observed reliable differences in the physiological responses to stimuli when a cued target was present versus absent. Goal-related modulation was particularly strong in the inferior temporal and fusiform gyri, two areas important for object recognition. Target modulation started after 250 ms post stimulus, considerably after the onset of visual recognition signals. While broadband signals exhibited increased or decreased power, gamma frequency power showed predominantly increases during target presence. These observations support models where task goals interact with sensory inputs via top-down signals that influence the highest echelons of visual processing after the onset of selective responses. PMID:24553944

The Face-to-Face Light Detection Paradigm: A New Methodology for Investigating Visuospatial Attention Across Different Face Regions in Live Face-to-Face Communication Settings.

PubMed

Thompson, Laura A; Malloy, Daniel M; Cone, John M; Hendrickson, David L

2010-01-01

We introduce a novel paradigm for studying the cognitive processes used by listeners within interactive settings. This paradigm places the talker and the listener in the same physical space, creating opportunities for investigations of attention and comprehension processes taking place during interactive discourse situations. An experiment was conducted to compare results from previous research using videotaped stimuli to those obtained within the live face-to-face task paradigm. A headworn apparatus is used to briefly display LEDs on the talker's face in four locations as the talker communicates with the participant. In addition to the primary task of comprehending speeches, participants make a secondary task light detection response. In the present experiment, the talker gave non-emotionally-expressive speeches that were used in past research with videotaped stimuli. Signal detection analysis was employed to determine which areas of the face received the greatest focus of attention. Results replicate previous findings using videotaped methods.

The Face-to-Face Light Detection Paradigm: A New Methodology for Investigating Visuospatial Attention Across Different Face Regions in Live Face-to-Face Communication Settings

PubMed Central

Thompson, Laura A.; Malloy, Daniel M.; Cone, John M.; Hendrickson, David L.

2009-01-01

We introduce a novel paradigm for studying the cognitive processes used by listeners within interactive settings. This paradigm places the talker and the listener in the same physical space, creating opportunities for investigations of attention and comprehension processes taking place during interactive discourse situations. An experiment was conducted to compare results from previous research using videotaped stimuli to those obtained within the live face-to-face task paradigm. A headworn apparatus is used to briefly display LEDs on the talker’s face in four locations as the talker communicates with the participant. In addition to the primary task of comprehending speeches, participants make a secondary task light detection response. In the present experiment, the talker gave non-emotionally-expressive speeches that were used in past research with videotaped stimuli. Signal detection analysis was employed to determine which areas of the face received the greatest focus of attention. Results replicate previous findings using videotaped methods. PMID:21113354

Sparsity based target detection for compressive spectral imagery

NASA Astrophysics Data System (ADS)

Boada, David Alberto; Arguello Fuentes, Henry

2016-09-01

Hyperspectral imagery provides significant information about the spectral characteristics of objects and materials present in a scene. It enables object and feature detection, classification, or identification based on the acquired spectral characteristics. However, it relies on sophisticated acquisition and data processing systems able to acquire, process, store, and transmit hundreds or thousands of image bands from a given area of interest which demands enormous computational resources in terms of storage, computationm, and I/O throughputs. Specialized optical architectures have been developed for the compressed acquisition of spectral images using a reduced set of coded measurements contrary to traditional architectures that need a complete set of measurements of the data cube for image acquisition, dealing with the storage and acquisition limitations. Despite this improvement, if any processing is desired, the image has to be reconstructed by an inverse algorithm in order to be processed, which is also an expensive task. In this paper, a sparsity-based algorithm for target detection in compressed spectral images is presented. Specifically, the target detection model adapts a sparsity-based target detector to work in a compressive domain, modifying the sparse representation basis in the compressive sensing problem by means of over-complete training dictionaries and a wavelet basis representation. Simulations show that the presented method can achieve even better detection results than the state of the art methods.

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

PubMed Central

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-01-01

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

PubMed

Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

2016-07-19

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

Scan statistics with local vote for target detection in distributed system

NASA Astrophysics Data System (ADS)

Luo, Junhai; Wu, Qi

2017-12-01

Target detection has occupied a pivotal position in distributed system. Scan statistics, as one of the most efficient detection methods, has been applied to a variety of anomaly detection problems and significantly improves the probability of detection. However, scan statistics cannot achieve the expected performance when the noise intensity is strong, or the signal emitted by the target is weak. The local vote algorithm can also achieve higher target detection rate. After the local vote, the counting rule is always adopted for decision fusion. The counting rule does not use the information about the contiguity of sensors but takes all sensors' data into consideration, which makes the result undesirable. In this paper, we propose a scan statistics with local vote (SSLV) method. This method combines scan statistics with local vote decision. Before scan statistics, each sensor executes local vote decision according to the data of its neighbors and its own. By combining the advantages of both, our method can obtain higher detection rate in low signal-to-noise ratio environment than the scan statistics. After the local vote decision, the distribution of sensors which have detected the target becomes more intensive. To make full use of local vote decision, we introduce a variable-step-parameter for the SSLV. It significantly shortens the scan period especially when the target is absent. Analysis and simulations are presented to demonstrate the performance of our method.

A long-term target detection approach in infrared image sequence

NASA Astrophysics Data System (ADS)

Li, Hang; Zhang, Qi; Li, Yuanyuan; Wang, Liqiang

2015-12-01

An automatic target detection method used in long term infrared (IR) image sequence from a moving platform is proposed. Firstly, based on non-linear histogram equalization, target candidates are coarse-to-fine segmented by using two self-adapt thresholds generated in the intensity space. Then the real target is captured via two different selection approaches. At the beginning of image sequence, the genuine target with litter texture is discriminated from other candidates by using contrast-based confidence measure. On the other hand, when the target becomes larger, we apply online EM method to iteratively estimate and update the distributions of target's size and position based on the prior detection results, and then recognize the genuine one which satisfies both the constraints of size and position. Experimental results demonstrate that the presented method is accurate, robust and efficient.

Multisensor fusion for the detection of mines and minelike targets

NASA Astrophysics Data System (ADS)

Hanshaw, Terilee

1995-06-01

The US Army's Communications and Electronics Command through the auspices of its Night Vision and Electronics Sensors Directorate (CECOM-NVESD) is actively applying multisensor techniques to the detection of mine targets. This multisensor research results from the 'detection activity' with its broad range of operational conditions and targets. Multisensor operation justifies significant attention by yielding high target detection and low false alarm statistics. Furthermore, recent advances in sensor and computing technologies make its practical application realistic and affordable. The mine detection field-of-endeavor has since its WWI baptismal investigated the known spectra for applicable mine observation phenomena. Countless sensors, algorithms, processors, networks, and other techniques have been investigated to determine candidacy for mine detection. CECOM-NVESD efforts have addressed a wide range of sensors spanning the spectrum from gravity field perturbations, magentic field disturbances, seismic sounding, electromagnetic fields, earth penetrating radar imagery, and infrared/visible/ultraviolet surface imaging technologies. Supplementary analysis has considered sensor candidate applicability by testing under field conditions (versus laboratory), in determination of fieldability. As these field conditions directly effect the probability of detection and false alarms, sensor employment and design must be considered. Consequently, as a given sensor's performance is influenced directly by the operational conditions, tradeoffs are necessary. At present, mass produced and fielded mine detection techniques are limited to those incorporating a single sensor/processor methodology such as, pulse induction and megnetometry, as found in hand held detectors. The most sensitive fielded systems can detect minute metal components in small mine targets but result in very high false alarm rates reducing velocity in operation environments. Furthermore, the actual speed of

Beyond the margins: real-time detection of cancer using targeted fluorophores

PubMed Central

Zhang, Ray R.; Schroeder, Alexandra B.; Grudzinski, Joseph J.; Rosenthal, Eben L.; Warram, Jason M.; Pinchuk, Anatoly N.; Eliceiri, Kevin W.; Kuo, John S.; Weichert, Jamey P.

2017-01-01

Over the past two decades, synergistic innovations in imaging technology have resulted in a revolution in which a range of biomedical applications are now benefiting from fluorescence imaging. Specifically, advances in fluorophore chemistry and imaging hardware, and the identification of targetable biomarkers have now positioned intraoperative fluorescence as a highly specific real-time detection modality for surgeons in oncology. In particular, the deeper tissue penetration and limited autofluorescence of near-infrared (NIR) fluorescence imaging improves the translational potential of this modality over visible-light fluorescence imaging. Rapid developments in fluorophores with improved characteristics, detection instrumentation, and targeting strategies led to the clinical testing in the early 2010s of the first targeted NIR fluorophores for intraoperative cancer detection. The foundations for the advances that underline this technology continue to be nurtured by the multidisciplinary collaboration of chemists, biologists, engineers, and clinicians. In this Review, we highlight the latest developments in NIR fluorophores, cancer-targeting strategies, and detection instrumentation for intraoperative cancer detection, and consider the unique challenges associated with their effective application in clinical settings. PMID:28094261

Peptide vaccines and peptidomimetics targeting HER and VEGF proteins may offer a potentially new paradigm in cancer immunotherapy

PubMed Central

Kaumaya, Pravin TP; Foy, Kevin Chu

2013-01-01

The ErbB family (HER-1, HER-2, HER-3 and HER-4) of receptor tyrosine kinases has been the focus of cancer immunotherapeutic strategies while antiangiogenic therapies have focused on VEGF and its receptors VEGFR-1 and VEGFR-2. Agents targeting receptor tyrosine kinases in oncology include therapeutic antibodies to receptor tyrosine kinase ligands or the receptors themselves, and small-molecule inhibitors. Many of the US FDA-approved therapies targeting HER-2 and VEGF exhibit unacceptable toxicities, and show problems of efficacy, development of resistance and unacceptable safety profiles that continue to hamper their clinical progress. The combination of dif ferent peptide vaccines and peptidomimetics targeting specific molecular pathways that are dysregulated in tumors may potentiate anticancer immune responses, bypass immune tolerance and circumvent resistance mechanisms. The focus of this review is to discuss efforts in our laboratory spanning two decades of rationally developing peptide vaccines and therapeutics for breast cancer. This review highlights the prospective benefit of a new, untapped category of therapies biologically targeted to EGF receptor (HER-1), HER-2 and VEGF with potential peptide ‘blockbusters‘ that could lay the foundation of a new paradigm in cancer immunotherapy by creating clinical breakthroughs for safe and efficacious cancer cures. PMID:22894670

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Adaptive waveform optimization design for target detection in cognitive radar

NASA Astrophysics Data System (ADS)

Zhang, Xiaowen; Wang, Kaizhi; Liu, Xingzhao

2017-01-01

The problem of adaptive waveform design for target detection in cognitive radar (CR) is investigated. This problem is analyzed in signal-dependent interference, as well as additive channel noise for extended target with unknown target impulse response (TIR). In order to estimate the TIR accurately, the Kalman filter is used in target tracking. In each Kalman filtering iteration, a flexible online waveform spectrum optimization design taking both detection and range resolution into account is modeled in Fourier domain. Unlike existing CR waveform, the proposed waveform can be simultaneously updated according to the environment information fed back by receiver and radar performance demands. Moreover, the influence of waveform spectral phase to radar performance is analyzed. Simulation results demonstrate that CR with the proposed waveform performs better than a traditional radar system with a fixed waveform and offers more flexibility and suitability. In addition, waveform spectral phase will not influence tracking, detection, and range resolution performance but will greatly influence waveform forming speed and peak-to-average power ratio.

UGS video target detection and discrimination

NASA Astrophysics Data System (ADS)

Roberts, G. Marlon; Fitzgerald, James; McCormack, Michael; Steadman, Robert; Vitale, Joseph D.

2007-04-01

This project focuses on developing electro-optic algorithms which rank images by their likelihood of containing vehicles and people. These algorithms have been applied to images obtained from Textron's Terrain Commander 2 (TC2) Unattended Ground Sensor system. The TC2 is a multi-sensor surveillance system used in military applications. It combines infrared, acoustic, seismic, magnetic, and electro-optic sensors to detect nearby targets. When targets are detected by the seismic and acoustic sensors, the system is triggered and images are taken in the visible and infrared spectrum. The original Terrain Commander system occasionally captured and transmitted an excessive number of images, sometimes triggered by undesirable targets such as swaying trees. This wasted communications bandwidth, increased power consumption, and resulted in a large amount of end-user time being spent evaluating unimportant images. The algorithms discussed here help alleviate these problems. These algorithms are currently optimized for infra-red images, which give the best visibility in a wide range of environments, but could be adapted to visible imagery as well. It is important that the algorithms be robust, with minimal dependency on user input. They should be effective when tracking varying numbers of targets of different sizes and orientations, despite the low resolutions of the images used. Most importantly, the algorithms must be appropriate for implementation on a low-power processor in real time. This would enable us to maintain frame rates of 2 Hz for effective surveillance operations. Throughout our project we have implemented several algorithms, and used an appropriate methodology to quantitatively compare their performance. They are discussed in this paper.

Multi-target Detection, Tracking, and Data Association on Road Networks Using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Barkley, Brett E.

A cooperative detection and tracking algorithm for multiple targets constrained to a road network is presented for fixed-wing Unmanned Air Vehicles (UAVs) with a finite field of view. Road networks of interest are formed into graphs with nodes that indicate the target likelihood ratio (before detection) and position probability (after detection). A Bayesian likelihood ratio tracker recursively assimilates target observations until the cumulative observations at a particular location pass a detection criterion. At this point, a target is considered detected and a position probability is generated for the target on the graph. Data association is subsequently used to route future measurements to update the likelihood ratio tracker (for undetected target) or to update a position probability (a previously detected target). Three strategies for motion planning of UAVs are proposed to balance searching for new targets with tracking known targets for a variety of scenarios. Performance was tested in Monte Carlo simulations for a variety of mission parameters, including tracking on road networks with varying complexity and using UAVs at various altitudes.

A New Preclinical Paradigm for Testing Anti-Aging Therapeutics.

PubMed

Ladiges, Warren; Snyder, Jessica M; Wilkinson, Erby; Imai, Denise M; Snider, Tim; Ge, Xuan; Ciol, Marcia; Pettan-Brewer, Christina; Pillai, Smitha P S; Morton, John; Quarles, Ellen; Rabinovitch, Peter; Niedernhofer, Laura; Liggitt, Denny

2017-06-01

Testing drugs for anti-aging effects has historically been conducted in mouse life-span studies, but are costly and time consuming, and more importantly, difficult to recapitulate in humans. In addition, life-span studies in mice are not well suited to testing drug combinations that target multiple factors involved in aging. Additional paradigms for testing therapeutics aimed at slowing aging are needed. A new paradigm, designated as the Geropathology Grading Platform (GGP), is based on a standardized set of guidelines developed to detect the presence or absence of low-impact histopathological lesions and to determine the level of severity of high-impact lesions in organs from aged mice. The GGP generates a numerical score for each age-related lesion in an organ, summed for total lesions, and averaged over multiple mice to obtain a composite lesion score (CLS). Preliminary studies show that the platform generates CLSs that increase with the age of mice in an organ-dependent manner. The CLSs are sensitive enough to detect changes elicited by interventions that extend mouse life span, and thus help validate the GGP as a novel tool to measure biological aging. While currently optimized for mice, the GGP could be adapted to any preclinical animal model. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Method of detecting luminescent target ions with modified magnetic microspheres

DOEpatents

Shkrob, Ilya A; Kaminski, Michael D

2014-05-13

This invention provides methods of using modified magnetic microspheres to extract target ions from a sample in order to detect their presence in a microfluidic environment. In one or more embodiments, the microspheres are modified with molecules on the surface that allow the target ions in the sample to form complexes with specific ligand molecules on the microsphere surface. In one or more embodiments, the microspheres are modified with molecules that sequester the target ions from the sample, but specific ligand molecules in solution subsequently re-extract the target ions from the microspheres into the solution, where the complexes form independent of the microsphere surface. Once the complexes form, they are exposed to an excitation wavelength light source suitable for exciting the target ion to emit a luminescent signal pattern. Detection of the luminescent signal pattern allows for determination of the presence of the target ions in the sample.

Remote sensing image ship target detection method based on visual attention model

NASA Astrophysics Data System (ADS)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

The influence of the earth radiation on space target detection system

NASA Astrophysics Data System (ADS)

Su, Xiaofeng; Chen, FanSheng; Cuikun, .; Liuyan, .

2017-05-01

In the view of space remote sensing such as satellite detection space debris detection etc. visible band is usually used in order to have the all-weather detection capability, long wavelength infrared (LWIR) detection is also an important supplement. However, in the tow wave band, the earth can be a very strong interference source, especially in the dim target detecting. When the target is close to the earth, especially the LEO target, the background radiation of the earth will also enter into the baffle, and became the stray light through reflection, the stray light can reduce the signal to clutter ratio (SCR) of the target and make it difficult to be detected. In the visible band, the solar albedo by the earth is the main clutter source while in the LWIR band the radiation of the earth is the main clutter source. So, in this paper, we establish the energy transformation from the earth background radiation to the detection system to assess the effects of the stray light. Firstly, we discretize the surface of the earth to different unit, and using MODTRAN to calculate the radiation of the discrete point in different light and climate conditions, then, we integral all the radiation which can reach the baffle in the same observation angles to get the energy distribution, finally, according the target energy and the non-uniformity of the detector, we can calculate the design requirement of the system stray light suppression, which provides the design basis for the optical system.

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%.

Targeted analyte detection by standard addition improves detection limits in matrix-assisted laser desorption/ionization mass spectrometry.

PubMed

Toghi Eshghi, Shadi; Li, Xingde; Zhang, Hui

2012-09-18

Matrix-assisted laser desorption/ionization (MALDI) has proven an effective tool for fast and accurate determination of many molecules. However, the detector sensitivity and chemical noise compromise the detection of many invaluable low-abundance molecules from biological and clinical samples. To challenge this limitation, we developed a targeted analyte detection (TAD) technique. In TAD, the target analyte is selectively elevated by spiking a known amount of that analyte into the sample, thereby raising its concentration above the noise level, where we take advantage of the improved sensitivity to detect the presence of the endogenous analyte in the sample. We assessed TAD on three peptides in simple and complex background solutions with various exogenous analyte concentrations in two MALDI matrices. TAD successfully improved the limit of detection (LOD) of target analytes when the target peptides were added to the sample in a concentration close to optimum concentration. The optimum exogenous concentration was estimated through a quantitative method to be approximately equal to the original LOD for each target. Also, we showed that TAD could achieve LOD improvements on an average of 3-fold in a simple and 2-fold in a complex sample. TAD provides a straightforward assay to improve the LOD of generic target analytes without the need for costly hardware modifications.

Attention to baseline: does orienting visuospatial attention really facilitate target detection?

PubMed

Albares, Marion; Criaud, Marion; Wardak, Claire; Nguyen, Song Chi Trung; Ben Hamed, Suliann; Boulinguez, Philippe

2011-08-01

Standard protocols testing the orientation of visuospatial attention usually present spatial cues before targets and compare valid-cue trials with invalid-cue trials. The valid/invalid contrast results in a relative behavioral or physiological difference that is generally interpreted as a benefit of attention orientation. However, growing evidence suggests that inhibitory control of response is closely involved in this kind of protocol that requires the subjects to withhold automatic responses to cues, probably biasing behavioral and physiological baselines. Here, we used two experiments to disentangle the inhibitory control of automatic responses from orienting of visuospatial attention in a saccadic reaction time task in humans, a variant of the classical cue-target detection task and a sustained visuospatial attentional task. Surprisingly, when referring to a simple target detection task in which there is no need to refrain from reacting to avoid inappropriate responses, we found no consistent evidence of facilitation of target detection at the attended location. Instead, we observed a cost at the unattended location. Departing from the classical view, our results suggest that reaction time measures of visuospatial attention probably relie on the attenuation of elementary processes involved in visual target detection and saccade initiation away from the attended location rather than on facilitation at the attended location. This highlights the need to use proper control conditions in experimental designs to disambiguate relative from absolute cueing benefits on target detection reaction times, both in psychophysical and neurophysiological studies.

Visual performance on detection tasks with double-targets of the same and different difficulty.

PubMed

Chan, Alan H S; Courtney, Alan J; Ma, C W

2002-10-20

This paper reports a study of measurement of horizontal visual sensitivity limits for 16 subjects in single-target and double-targets detection tasks. Two phases of tests were conducted in the double-targets task; targets of the same difficulty were tested in phase one while targets of different difficulty were tested in phase two. The range of sensitivity for the double-targets test was found to be smaller than that for single-target in both the same and different target difficulty cases. The presence of another target was found to affect performance to a marked degree. Interference effect of the difficult target on detection of the easy one was greater than that of the easy one on the detection of the difficult one. Performance decrement was noted when correct percentage detection was plotted against eccentricity of target in both the single-target and double-targets tests. Nevertheless, the non-significant correlation found between the performance for the two tasks demonstrated that it was impossible to predict quantitatively ability for detection of double targets from the data for single targets. This indicated probable problems in generalizing data for single target visual lobes to those for multiple targets. Also lobe area values obtained from measurements using a single-target task cannot be applied in a mathematical model for situations with multiple occurrences of targets.

Quantitative subpixel spectral detection of targets in multispectral images. [terrestrial and planetary surfaces

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Adams, John B.; Smith, Milton O.

1992-01-01

The conditions that affect the spectral detection of target materials at the subpixel scale are examined. Two levels of spectral mixture analysis for determining threshold detection limits of target materials in a spectral mixture are presented, the cases where the target is detected as: (1) a component of a spectral mixture (continuum threshold analysis) and (2) residuals (residual threshold analysis). The results of these two analyses are compared under various measurement conditions. The examples illustrate the general approach that can be used for evaluating the spectral detectability of terrestrial and planetary targets at the subpixel scale.

A particle filter for multi-target tracking in track before detect context

NASA Astrophysics Data System (ADS)

Amrouche, Naima; Khenchaf, Ali; Berkani, Daoud

2016-10-01

The track-before-detect (TBD) approach can be used to track a single target in a highly noisy radar scene. This is because it makes use of unthresholded observations and incorporates a binary target existence variable into its target state estimation process when implemented as a particle filter (PF). This paper proposes the recursive PF-TBD approach to detect multiple targets in low-signal-to noise ratios (SNR). The algorithm's successful performance is demonstrated using a simulated two target example.

Space moving target detection and tracking method in complex background

NASA Astrophysics Data System (ADS)

Lv, Ping-Yue; Sun, Sheng-Li; Lin, Chang-Qing; Liu, Gao-Rui

2018-06-01

The background of the space-borne detectors in real space-based environment is extremely complex and the signal-to-clutter ratio is very low (SCR ≈ 1), which increases the difficulty for detecting space moving targets. In order to solve this problem, an algorithm combining background suppression processing based on two-dimensional least mean square filter (TDLMS) and target enhancement based on neighborhood gray-scale difference (GSD) is proposed in this paper. The latter can filter out most of the residual background clutter processed by the former such as cloud edge. Through this procedure, both global and local SCR have obtained substantial improvement, indicating that the target has been greatly enhanced. After removing the detector's inherent clutter region through connected domain processing, the image only contains the target point and the isolated noise, in which the isolated noise could be filtered out effectively through multi-frame association. The proposed algorithm in this paper has been compared with some state-of-the-art algorithms for moving target detection and tracking tasks. The experimental results show that the performance of this algorithm is the best in terms of SCR gain, background suppression factor (BSF) and detection results.

Differential Sources for 2 Neural Signatures of Target Detection: An Electrocorticography Study.

PubMed

Kam, J W Y; Szczepanski, S M; Canolty, R T; Flinker, A; Auguste, K I; Crone, N E; Kirsch, H E; Kuperman, R A; Lin, J J; Parvizi, J; Knight, R T

2018-01-01

Electrophysiology and neuroimaging provide conflicting evidence for the neural contributions to target detection. Scalp electroencephalography (EEG) studies localize the P3b event-related potential component mainly to parietal cortex, whereas neuroimaging studies report activations in both frontal and parietal cortices. We addressed this discrepancy by examining the sources that generate the target-detection process using electrocorticography (ECoG). We recorded ECoG activity from cortex in 14 patients undergoing epilepsy monitoring, as they performed an auditory or visual target-detection task. We examined target-related responses in 2 domains: high frequency band (HFB) activity and the P3b. Across tasks, we observed a greater proportion of electrodes that showed target-specific HFB power relative to P3b over frontal cortex, but their proportions over parietal cortex were comparable. Notably, there was minimal overlap in the electrodes that showed target-specific HFB and P3b activity. These results revealed that the target-detection process is characterized by at least 2 different neural markers with distinct cortical distributions. Our findings suggest that separate neural mechanisms are driving the differential patterns of activity observed in scalp EEG and neuroimaging studies, with the P3b reflecting EEG findings and HFB activity reflecting neuroimaging findings, highlighting the notion that target detection is not a unitary phenomenon. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Research on Aircraft Target Detection Algorithm Based on Improved Radial Gradient Transformation

NASA Astrophysics Data System (ADS)

Zhao, Z. M.; Gao, X. M.; Jiang, D. N.; Zhang, Y. Q.

2018-04-01

Aiming at the problem that the target may have different orientation in the unmanned aerial vehicle (UAV) image, the target detection algorithm based on the rotation invariant feature is studied, and this paper proposes a method of RIFF (Rotation-Invariant Fast Features) based on look up table and polar coordinate acceleration to be used for aircraft target detection. The experiment shows that the detection performance of this method is basically equal to the RIFF, and the operation efficiency is greatly improved.

Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

NASA Astrophysics Data System (ADS)

Li, Miao; Lin, Zaiping; Long, Yunli; An, Wei; Zhou, Yiyu

2016-05-01

The high variability of target size makes small target detection in Infrared Search and Track (IRST) a challenging task. A joint detection and tracking method based on block-wise sparse decomposition is proposed to address this problem. For detection, the infrared image is divided into overlapped blocks, and each block is weighted on the local image complexity and target existence probabilities. Target-background decomposition is solved by block-wise inexact augmented Lagrange multipliers. For tracking, label multi-Bernoulli (LMB) tracker tracks multiple targets taking the result of single-frame detection as input, and provides corresponding target existence probabilities for detection. Unlike fixed-size methods, the proposed method can accommodate size-varying targets, due to no special assumption for the size and shape of small targets. Because of exact decomposition, classical target measurements are extended and additional direction information is provided to improve tracking performance. The experimental results show that the proposed method can effectively suppress background clutters, detect and track size-varying targets in infrared images.

Novel fMRI working memory paradigm accurately detects cognitive impairment in Multiple Sclerosis

PubMed Central

Nelson, Flavia; Akhtar, Mohammad A.; Zúñiga, Edward; Perez, Carlos A.; Hasan, Khader M.; Wilken, Jeffrey; Wolinsky, Jerry S.; Narayana, Ponnada A.; Steinberg, Joel L.

2016-01-01

Background Cognitive impairment (CI) cannot be diagnosed by MRI. Functional MRI (fMRI) paradigms such as the immediate/delayed memory task (I/DMT), detect varying degrees of working memory. Preliminary findings using I/DMT, showed differences in Blood Oxygenation Level Dependent (BOLD) activation between impaired (MSCI, n=12) and non-impaired (MSNI, n=9) MS patients. Objectives To confirm CI detection based on I/DMT’ BOLD activation in a larger cohort of MS patients. The role of T2 lesion volume (LV) and EDSS in magnitude of BOLD signal were also sought. Methods Fifty patients [EDSS mean (m) = 3.2, DD m =12 yr., age m =40yr.] underwent the Minimal Assessment of Cognitive Function in MS (MACFIMS) and the I/DMT. Working-memory activation (WMa) represents BOLD signal during DMT minus signal during IMT. CI was based on MACFIMS. Results 10 MSNI, 30 MSCI and 4 borderline patients were included in analyses. ANOVA showed MSNI had significantly greater WMa than MSCI, in the left (L) prefrontal cortex and L supplementary motor area (p = 0.032). Regression analysis showed significant inverse correlations between WMa and T2 LV/EDSS in similar areas (p = 0.005, 0.004 respectively). Conclusion I/DMT-based BOLD activation detects CI in MS, larger studies are needed to confirm these findings. PMID:27613119

Detection of Marine Radar Targets

NASA Astrophysics Data System (ADS)

Briggs, John N.

A radar must detect targets before it can display them. Yet manufacturers' data sheets rarely tell us what the products will detect at what range. Many of the bigger radars are Type Approved so we consult the relevant IMO performance standard A 477 (XII). Paraphrasing Section 3.1 of the draft forthcoming revision (NAV 41/6): under normal propagation conditions with the scanner at height of 15 m, in the absence of clutter, the radar is required to give clear indication of an object such as a navigational buoy having a radar cross section area (RCS) of 10 m2 at 2 n.m. and, as examples, coastlines whose ground rises to 60/6 m at ranges of 20/7 n.m., a ship of 5000 tons at any aspect at 7 n.m. and a small vessel 10 m long at 3 n.m.This helps, but suppose we must pick up a 5 m2 buoy at g km? What happens in clutter? Should we prefer S- or X-band? To answer such questions we use equations which define the performance of surveillance radars, but the textbooks and specialist papers containing them often generalize with aeronautical and defence topics, making life difficult for the nonspecialist.This paper attempts a concise and self-contained engineering account of all main factors affecting detection of passive and active targets on civil marine and vessel traffic service (VTS) radars. We develop a set of equations for X- and S-band (3 and 10 cm, centred on 9400 and 3000 MHz respectively), suited for spreadsheet calculation.Sufficient theory is sketched in to indicate where results should be valid. Some simplifications of conventional treatments have been identified.

CT colonography: investigation of the optimum reader paradigm by using computer-aided detection software.

PubMed

Taylor, Stuart A; Charman, Susan C; Lefere, Philippe; McFarland, Elizabeth G; Paulson, Erik K; Yee, Judy; Aslam, Rizwan; Barlow, John M; Gupta, Arun; Kim, David H; Miller, Chad M; Halligan, Steve

2008-02-01

To prospectively compare the diagnostic performance and time efficiency of both second and concurrent computer-aided detection (CAD) reading paradigms for retrospectively obtained computed tomographic (CT) colonography data sets by using consensus reading (three radiologists) of colonoscopic findings as a reference standard. Ethical permission, HIPAA compliance (for U.S. institutions), and patient consent were obtained from all institutions for use of CT colonography data sets in this study. Ten radiologists each read 25 CT colonography data sets (12 men, 13 women; mean age, 61 years) containing 69 polyps (28 were 1-5 mm, 41 were >or=6 mm) by using workstations integrated with CAD software. Reading was randomized to either "second read" CAD (applied only after initial unassisted assessment) or "concurrent read" CAD (applied at the start of assessment). Data sets were reread 6 weeks later by using the opposing paradigm. Polyp sensitivity and reading times were compared by using multilevel logistic and linear regression, respectively. Receiver operating characteristic (ROC) curves were generated. Compared with the unassisted read, odds of improved polyp (>or=6 mm) detection were 1.5 (95% confidence interval [CI]: 1.0, 2.2) and 1.3 (95% CI: 0.9, 1.9) by using CAD as second and concurrent reader, respectively. Detection odds by using CAD concurrently were 0.87 (95% CI: 0.59, 1.3) and 0.76 (95% CI: 0.57, 1.01) those of second read CAD, excluding and including polyps 1-5 mm, respectively. The concurrent read took 2.9 minutes (95% CI: -3.8, -1.9) less than did second read. The mean areas under the ROC curve (95% CI) for the unassisted read, second read CAD, and concurrent read CAD were 0.83 (95% CI: 0.78, 0.87), 0.86 (95% CI: 0.82, 0.90), and 0.88 (95% CI: 0.83, 0.92), respectively. CAD is more time efficient when used concurrently than when used as a second reader, with similar sensitivity for polyps 6 mm or larger. However, use of second read CAD maximizes sensitivity

Detection and tracking of a moving target using SAR images with the particle filter-based track-before-detect algorithm.

PubMed

Gao, Han; Li, Jingwen

2014-06-19

A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB.

Detection and Tracking of a Moving Target Using SAR Images with the Particle Filter-Based Track-Before-Detect Algorithm

PubMed Central

Gao, Han; Li, Jingwen

2014-01-01

A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB. PMID:24949640

Sea-Based Infrared Scene Interpretation by Background Type Classification and Coastal Region Detection for Small Target Detection

PubMed Central

Kim, Sungho

2015-01-01

Sea-based infrared search and track (IRST) is important for homeland security by detecting missiles and asymmetric boats. This paper proposes a novel scheme to interpret various infrared scenes by classifying the infrared background types and detecting the coastal regions in omni-directional images. The background type or region-selective small infrared target detector should be deployed to maximize the detection rate and to minimize the number of false alarms. A spatial filter-based small target detector is suitable for identifying stationary incoming targets in remote sea areas with sky only. Many false detections can occur if there is an image sector containing a coastal region, due to ground clutter and the difficulty in finding true targets using the same spatial filter-based detector. A temporal filter-based detector was used to handle these problems. Therefore, the scene type and coastal region information is critical to the success of IRST in real-world applications. In this paper, the infrared scene type was determined using the relationships between the sensor line-of-sight (LOS) and a horizontal line in an image. The proposed coastal region detector can be activated if the background type of the probing sector is determined to be a coastal region. Coastal regions can be detected by fusing the region map and curve map. The experimental results on real infrared images highlight the feasibility of the proposed sea-based scene interpretation. In addition, the effects of the proposed scheme were analyzed further by applying region-adaptive small target detection. PMID:26404308

A new paradigm of oral cancer detection using digital infrared thermal imaging

NASA Astrophysics Data System (ADS)

Chakraborty, M.; Mukhopadhyay, S.; Dasgupta, A.; Banerjee, S.; Mukhopadhyay, S.; Patsa, S.; Ray, J. G.; Chaudhuri, K.

2016-03-01

Histopathology is considered the gold standard for oral cancer detection. But a major fraction of patient pop- ulation is incapable of accessing such healthcare facilities due to poverty. Moreover, such analysis may report false negatives when test tissue is not collected from exact cancerous location. The proposed work introduces a pioneering computer aided paradigm of fast, non-invasive and non-ionizing modality for oral cancer detection us- ing Digital Infrared Thermal Imaging (DITI). Due to aberrant metabolic activities in carcinogenic facial regions, heat signatures of patients are different from that of normal subjects. The proposed work utilizes asymmetry of temperature distribution of facial regions as principle cue for cancer detection. Three views of a subject, viz. front, left and right are acquired using long infrared (7:5 - 13μm) camera for analysing distribution of temperature. We study asymmetry of facial temperature distribution between: a) left and right profile faces and b) left and right half of frontal face. Comparison of temperature distribution suggests that patients manifest greater asymmetry compared to normal subjects. For classification, we initially use k-means and fuzzy k-means for unsupervised clustering followed by cluster class prototype assignment based on majority voting. Average classification accuracy of 91:5% and 92:8% are achieved by k-mean and fuzzy k-mean framework for frontal face. The corresponding metrics for profile face are 93:4% and 95%. Combining features of frontal and profile faces, average accuracies are increased to 96:2% and 97:6% respectively for k-means and fuzzy k-means framework.

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.

Infrared moving small target detection based on saliency extraction and image sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Xiaomin; Ren, Kan; Gao, Jin; Li, Chaowei; Gu, Guohua; Wan, Minjie

2016-10-01

Moving small target detection in infrared image is a crucial technique of infrared search and tracking system. This paper present a novel small target detection technique based on frequency-domain saliency extraction and image sparse representation. First, we exploit the features of Fourier spectrum image and magnitude spectrum of Fourier transform to make a rough extract of saliency regions and use a threshold segmentation system to classify the regions which look salient from the background, which gives us a binary image as result. Second, a new patch-image model and over-complete dictionary were introduced to the detection system, then the infrared small target detection was converted into a problem solving and optimization process of patch-image information reconstruction based on sparse representation. More specifically, the test image and binary image can be decomposed into some image patches follow certain rules. We select the target potential area according to the binary patch-image which contains salient region information, then exploit the over-complete infrared small target dictionary to reconstruct the test image blocks which may contain targets. The coefficients of target image patch satisfy sparse features. Finally, for image sequence, Euclidean distance was used to reduce false alarm ratio and increase the detection accuracy of moving small targets in infrared images due to the target position correlation between frames.

Point target detection utilizing super-resolution strategy for infrared scanning oversampling system

NASA Astrophysics Data System (ADS)

Wang, Longguang; Lin, Zaiping; Deng, Xinpu; An, Wei

2017-11-01

To improve the resolution of remote sensing infrared images, infrared scanning oversampling system is employed with information amount quadrupled, which contributes to the target detection. Generally the image data from double-line detector of infrared scanning oversampling system is shuffled to a whole oversampled image to be post-processed, whereas the aliasing between neighboring pixels leads to image degradation with a great impact on target detection. This paper formulates a point target detection method utilizing super-resolution (SR) strategy concerning infrared scanning oversampling system, with an accelerated SR strategy proposed to realize fast de-aliasing of the oversampled image and an adaptive MRF-based regularization designed to achieve the preserving and aggregation of target energy. Extensive experiments demonstrate the superior detection performance, robustness and efficiency of the proposed method compared with other state-of-the-art approaches.

Decision-level fusion of SAR and IR sensor information for automatic target detection

NASA Astrophysics Data System (ADS)

Cho, Young-Rae; Yim, Sung-Hyuk; Cho, Hyun-Woong; Won, Jin-Ju; Song, Woo-Jin; Kim, So-Hyeon

2017-05-01

We propose a decision-level architecture that combines synthetic aperture radar (SAR) and an infrared (IR) sensor for automatic target detection. We present a new size-based feature, called target-silhouette to reduce the number of false alarms produced by the conventional target-detection algorithm. Boolean Map Visual Theory is used to combine a pair of SAR and IR images to generate the target-enhanced map. Then basic belief assignment is used to transform this map into a belief map. The detection results of sensors are combined to build the target-silhouette map. We integrate the fusion mass and the target-silhouette map on the decision level to exclude false alarms. The proposed algorithm is evaluated using a SAR and IR synthetic database generated by SE-WORKBENCH simulator, and compared with conventional algorithms. The proposed fusion scheme achieves higher detection rate and lower false alarm rate than the conventional algorithms.

Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems-a New Paradigm.

PubMed

Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu

2017-10-01

We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance. The pretrained algorithms were sequentially trained in the order of processing of the components. In this study, two different datasets, brain MRA with cerebral aneurysms and chest CT with lung nodules, were collected to develop two different types of CADe systems in the framework. The performances of the developed CADe systems were evaluated by threefold cross-validation. The CADe systems for detecting cerebral aneurysms in brain MRAs and for detecting lung nodules in chest CTs were successfully developed using the respective datasets. The framework was shown to be feasible by the successful development of the two different types of CADe systems. The feasibility of this framework shows promise for a new paradigm in the development of CADe systems: development of CADe systems without any lesion specific algorithm designing.

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.

A Parallel Finite Set Statistical Simulator for Multi-Target Detection and Tracking

NASA Astrophysics Data System (ADS)

Hussein, I.; MacMillan, R.

2014-09-01

Finite Set Statistics (FISST) is a powerful Bayesian inference tool for the joint detection, classification and tracking of multi-target environments. FISST is capable of handling phenomena such as clutter, misdetections, and target birth and decay. Implicit within the approach are solutions to the data association and target label-tracking problems. Finally, FISST provides generalized information measures that can be used for sensor allocation across different types of tasks such as: searching for new targets, and classification and tracking of known targets. These FISST capabilities have been demonstrated on several small-scale illustrative examples. However, for implementation in a large-scale system as in the Space Situational Awareness problem, these capabilities require a lot of computational power. In this paper, we implement FISST in a parallel environment for the joint detection and tracking of multi-target systems. In this implementation, false alarms and misdetections will be modeled. Target birth and decay will not be modeled in the present paper. We will demonstrate the success of the method for as many targets as we possibly can in a desktop parallel environment. Performance measures will include: number of targets in the simulation, certainty of detected target tracks, computational time as a function of clutter returns and number of targets, among other factors.

High-resolution remotely sensed small target detection by imitating fly visual perception mechanism.

PubMed

Huang, Fengchen; Xu, Lizhong; Li, Min; Tang, Min

2012-01-01

The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

RNase H-assisted RNA-primed rolling circle amplification for targeted RNA sequence detection.

PubMed

Takahashi, Hirokazu; Ohkawachi, Masahiko; Horio, Kyohei; Kobori, Toshiro; Aki, Tsunehiro; Matsumura, Yukihiko; Nakashimada, Yutaka; Okamura, Yoshiko

2018-05-17

RNA-primed rolling circle amplification (RPRCA) is a useful laboratory method for RNA detection; however, the detection of RNA is limited by the lack of information on 3'-terminal sequences. We uncovered that conventional RPRCA using pre-circularized probes could potentially detect the internal sequence of target RNA molecules in combination with RNase H. However, the specificity for mRNA detection was low, presumably due to non-specific hybridization of non-target RNA with the circular probe. To overcome this technical problem, we developed a method for detecting a sequence of interest in target RNA molecules via RNase H-assisted RPRCA using padlocked probes. When padlock probes are hybridized to the target RNA molecule, they are converted to the circular form by SplintR ligase. Subsequently, RNase H creates nick sites only in the hybridized RNA sequence, and single-stranded DNA is finally synthesized from the nick site by phi29 DNA polymerase. This method could specifically detect at least 10 fmol of the target RNA molecule without reverse transcription. Moreover, this method detected GFP mRNA present in 10 ng of total RNA isolated from Escherichia coli without background DNA amplification. Therefore, this method can potentially detect almost all types of RNA molecules without reverse transcription and reveal full-length sequence information.

Computational optimisation of targeted DNA sequencing for cancer detection

NASA Astrophysics Data System (ADS)

Martinez, Pierre; McGranahan, Nicholas; Birkbak, Nicolai Juul; Gerlinger, Marco; Swanton, Charles

2013-12-01

Despite recent progress thanks to next-generation sequencing technologies, personalised cancer medicine is still hampered by intra-tumour heterogeneity and drug resistance. As most patients with advanced metastatic disease face poor survival, there is need to improve early diagnosis. Analysing circulating tumour DNA (ctDNA) might represent a non-invasive method to detect mutations in patients, facilitating early detection. In this article, we define reduced gene panels from publicly available datasets as a first step to assess and optimise the potential of targeted ctDNA scans for early tumour detection. Dividing 4,467 samples into one discovery and two independent validation cohorts, we show that up to 76% of 10 cancer types harbour at least one mutation in a panel of only 25 genes, with high sensitivity across most tumour types. Our analyses demonstrate that targeting ``hotspot'' regions would introduce biases towards in-frame mutations and would compromise the reproducibility of tumour detection.

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

Analysis of Infrared Signature Variation and Robust Filter-Based Supersonic Target Detection

PubMed Central

Sun, Sun-Gu; Kim, Kyung-Tae

2014-01-01

The difficulty of small infrared target detection originates from the variations of infrared signatures. This paper presents the fundamental physics of infrared target variations and reports the results of variation analysis of infrared images acquired using a long wave infrared camera over a 24-hour period for different types of backgrounds. The detection parameters, such as signal-to-clutter ratio were compared according to the recording time, temperature and humidity. Through variation analysis, robust target detection methodologies are derived by controlling thresholds and designing a temporal contrast filter to achieve high detection rate and low false alarm rate. Experimental results validate the robustness of the proposed scheme by applying it to the synthetic and real infrared sequences. PMID:24672290

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.

PubMed

Mainsah, B O; Reeves, G; Collins, L M; Throckmorton, C S

2017-08-01

The role of a brain-computer interface (BCI) is to discern a user's intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm. By accounting for

Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

Adaptive early detection ML/PDA estimator for LO targets with EO sensors

NASA Astrophysics Data System (ADS)

Chummun, Muhammad R.; Kirubarajan, Thiagalingam; Bar-Shalom, Yaakov

2000-07-01

The batch Maximum Likelihood Estimator, combined with Probabilistic Data (ML-PDA), has been shown to be effective in acquiring low observable (LO) - low SNR - non-maneuvering targets in the presence of heavy clutter. The use of signal strength or amplitude information (AI) in the ML-PDA estimator with AI in a sliding-window fashion, to detect high- speed targets in heavy clutter using electro-optical (EO) sensors. The initial time and the length of the sliding-window are adjusted adaptively according to the information content of the received measurements. A track validation scheme via hypothesis testing is developed to confirm the estimated track, that is, the presence of a target, in each window. The sliding-window ML-PDA approach, together with track validation, enables early detection by rejecting noninformative scans, target reacquisition in case of temporary target disappearance and the handling of targets with speeds evolving over time. The proposed algorithm is shown to detect the target, which is hidden in as many as 600 false alarms per scan, 10 frames earlier than the Multiple Hypothesis Tracking (MHT) algorithm.

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

Passive Coherent Detection and Target Location with Multiple Non-Cooperative Transmitters

DTIC Science & Technology

2015-06-01

to detect, separate, classify, locate, and track sources of emissions in multi-target environments—triggered the development of passive radar...radar capitalizes on transmitters of opportunity to detect and locate sources of transmission or targets without deliberate emissions . The...equipment as all necessary hardware is currently available on most naval ships. 3 Bistatic radar geometry. Figure 1. B. HISTORY The concept of

Tumor detection and elimination by a targeted gallium corrole

PubMed Central

Agadjanian, Hasmik; Ma, Jun; Rentsendorj, Altan; Valluripalli, Vinod; Hwang, Jae Youn; Mahammed, Atif; Farkas, Daniel L.; Gray, Harry B.; Gross, Zeev; Medina-Kauwe, Lali K.

2009-01-01

Sulfonated gallium(III) corroles are intensely fluorescent macrocyclic compounds that spontaneously assemble with carrier proteins to undergo cell entry. We report in vivo imaging and therapeutic efficacy of a tumor-targeted corrole noncovalently assembled with a heregulin-modified protein directed at the human epidermal growth factor receptor (HER). Systemic delivery of this protein-corrole complex results in tumor accumulation, which can be visualized in vivo owing to intensely red corrole fluorescence. Targeted delivery in vivo leads to tumor cell death while normal tissue is spared. These findings contrast with the effects of doxorubicin, which can elicit cardiac damage during therapy and required direct intratumoral injection to yield similar levels of tumor shrinkage compared with the systemically delivered corrole. The targeted complex ablated tumors at >5 times a lower dose than untargeted systemic doxorubicin, and the corrole did not damage heart tissue. Complexes remained intact in serum and the carrier protein elicited no detectable immunogenicity. The sulfonated gallium(III) corrole functions both for tumor detection and intervention with safety and targeting advantages over standard chemotherapeutic agents. PMID:19342490

Detection of Fast Moving and Accelerating Targets Compensating Range and Doppler Migration

DTIC Science & Technology

2014-06-01

Radon -Fourier transform has been introduced to realize long- term coherent integration of the moving targets with range migration [8, 9]. Radon ...2010) Long-time coherent integration for radar target detection base on Radon -Fourier transform, in Proceedings of the IEEE Radar Conference, pp...432–436. 9. Xu, J., Yu, J., Peng, Y. & Xia, X. (2011) Radon -Fourier transform for radar target detection, I: Generalized Doppler filter bank, IEEE

Indoor detection of passive targets recast as an inverse scattering problem

NASA Astrophysics Data System (ADS)

Gottardi, G.; Moriyama, T.

2017-10-01

The wireless local area networks represent an alternative to custom sensors and dedicated surveillance systems for target indoor detection. The availability of the channel state information has opened the exploitation of the spatial and frequency diversity given by the orthogonal frequency division multiplexing. Such a fine-grained information can be used to solve the detection problem as an inverse scattering problem. The goal of the detection is to reconstruct the properties of the investigation domain, namely to estimate if the domain is empty or occupied by targets, starting from the measurement of the electromagnetic perturbation of the wireless channel. An innovative inversion strategy exploiting both the frequency and the spatial diversity of the channel state information is proposed. The target-dependent features are identified combining the Kruskal-Wallis test and the principal component analysis. The experimental validation points out the detection performance of the proposed method when applied to an existing wireless link of a WiFi architecture deployed in a real indoor scenario. False detection rates lower than 2 [%] have been obtained.

Investigating saccade programming in the praying mantis Tenodera aridifolia using distracter interference paradigms.

PubMed

Yamawaki, Yoshifumi

2006-10-01

To investigate the saccadic system in the mantis, I applied distracter interference paradigms. These involved presenting the mantis with a fixation target and one or several distracters supposed to affect saccades towards the target. When a single target was presented, a medium-sized target located in its lower visual field elicited higher rates of saccade response. This preference for target size and position was also observed when a target and a distracter were presented simultaneously. That is, the mantis chose and fixated the target rather than a distracter that was much smaller or larger than the target, or was located above the target. Furthermore, the mantis' preference was not affected by increasing the number of distracters. However, the presence of the distracter decreased the occurrence rate of saccade and increased the response time to saccade. I conclude that distracter interference paradigms are an effective way of investigating the visual processing underlying saccade generation in the mantis. Possible mechanisms of saccade generation in the mantis are discussed.

Hierarchical effects on target detection and conflict monitoring

PubMed Central

Cao, Bihua; Gao, Feng; Ren, Maofang; Li, Fuhong

2016-01-01

Previous neuroimaging studies have demonstrated a hierarchical functional structure of the frontal cortices of the human brain, but the temporal course and the electrophysiological signature of the hierarchical representation remains unaddressed. In the present study, twenty-one volunteers were asked to perform a nested cue-target task, while their scalp potentials were recorded. The results showed that: (1) in comparison with the lower-level hierarchical targets, the higher-level targets elicited a larger N2 component (220–350 ms) at the frontal sites, and a smaller P3 component (350–500 ms) across the frontal and parietal sites; (2) conflict-related negativity (non-target minus target) was greater for the lower-level hierarchy than the higher-level, reflecting a more intensive process of conflict monitoring at the final step of target detection. These results imply that decision making, context updating, and conflict monitoring differ among different hierarchical levels of abstraction. PMID:27561989

Targets Mask U-Net for Wind Turbines Detection in Remote Sensing Images

NASA Astrophysics Data System (ADS)

Han, M.; Wang, H.; Wang, G.; Liu, Y.

2018-04-01

To detect wind turbines precisely and quickly in very high resolution remote sensing images (VHRRSI) we propose target mask U-Net. This convolution neural network (CNN), which is carefully designed to be a wide-field detector, models the pixel class assignment to wind turbines and their context information. The shadow, which is the context information of the target in this study, has been regarded as part of a wind turbine instance. We have trained the target mask U-Net on training dataset, which is composed of down sampled image blocks and instance mask blocks. Some post-processes have been integrated to eliminate wrong spots and produce bounding boxes of wind turbine instances. The evaluation metrics prove the reliability and effectiveness of our method for the average F1-score of our detection method is up to 0.97. The comparison of detection accuracy and time consuming with the weakly supervised targets detection method based on CNN illustrates the superiority of our method.

Camouflaged target detection based on polarized spectral features

NASA Astrophysics Data System (ADS)

Tan, Jian; Zhang, Junping; Zou, Bin

2016-05-01

The polarized hyperspectral images (PHSI) include polarization, spectral, spatial and radiant features, which provide more information about objects and scenes than traditional intensity or spectrum ones. And polarization can suppress the background and highlight the object, leading to the high potential to improve camouflaged target detection. So polarized hyperspectral imaging technique has aroused extensive concern in the last few years. Nowadays, the detection methods are still not very mature, most of which are rooted in the detection of hyperspectral image. And before using these algorithms, Stokes vector is used to process the original four-dimensional polarized hyperspectral data firstly. However, when the data is large and complex, the amount of calculation and error will increase. In this paper, tensor is applied to reconstruct the original four-dimensional data into new three-dimensional data, then, the constraint energy minimization (CEM) is used to process the new data, which adds the polarization information to construct the polarized spectral filter operator and takes full advantages of spectral and polarized information. This way deals with the original data without extracting the Stokes vector, so as to reduce the computation and error greatly. The experimental results also show that the proposed method in this paper is more suitable for the target detection of the PHSI.

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

Salient target detection based on pseudo-Wigner-Ville distribution and Rényi entropy.

PubMed

Xu, Yuannan; Zhao, Yuan; Jin, Chenfei; Qu, Zengfeng; Liu, Liping; Sun, Xiudong

2010-02-15

We present what we believe to be a novel method based on pseudo-Wigner-Ville distribution (PWVD) and Rényi entropy for salient targets detection. In the foundation of studying the statistical property of Rényi entropy via PWVD, the residual entropy-based saliency map of an input image can be obtained. From the saliency map, target detection is completed by the simple and convenient threshold segmentation. Experimental results demonstrate the proposed method can detect targets effectively in complex ground scenes.

A Signal Detection Theory Analysis of Behavioral Pattern Separation Paradigms

ERIC Educational Resources Information Center

Loiotile, Rita E.; Courtney, Susan M.

2015-01-01

Behavioral pattern separation (BPS) paradigms ask participants to discriminate previously encoded (old) stimuli from highly similar (lure) and categorically distinct (novel) stimuli. The lure-old discrimination, thought to uniquely reflect pattern separation in the hippocampal formation, is typically pitted against the traditional novel-old…

Hyperspectral target detection using heavy-tailed distributions

NASA Astrophysics Data System (ADS)

Willis, Chris J.

2009-09-01

One promising approach to target detection in hyperspectral imagery exploits a statistical mixture model to represent scene content at a pixel level. The process then goes on to look for pixels which are rare, when judged against the model, and marks them as anomalies. It is assumed that military targets will themselves be rare and therefore likely to be detected amongst these anomalies. For the typical assumption of multivariate Gaussianity for the mixture components, the presence of the anomalous pixels within the training data will have a deleterious effect on the quality of the model. In particular, the derivation process itself is adversely affected by the attempt to accommodate the anomalies within the mixture components. This will bias the statistics of at least some of the components away from their true values and towards the anomalies. In many cases this will result in a reduction in the detection performance and an increased false alarm rate. This paper considers the use of heavy-tailed statistical distributions within the mixture model. Such distributions are better able to account for anomalies in the training data within the tails of their distributions, and the balance of the pixels within their central masses. This means that an improved model of the majority of the pixels in the scene may be produced, ultimately leading to a better anomaly detection result. The anomaly detection techniques are examined using both synthetic data and hyperspectral imagery with injected anomalous pixels. A range of results is presented for the baseline Gaussian mixture model and for models accommodating heavy-tailed distributions, for different parameterizations of the algorithms. These include scene understanding results, anomalous pixel maps at given significance levels and Receiver Operating Characteristic curves.

System considerations for detection and tracking of small targets using passive sensors

NASA Astrophysics Data System (ADS)

DeBell, David A.

1991-08-01

Passive sensors provide only a few discriminants to assist in threat assessment of small targets. Tracking of the small targets provides additional discriminants. This paper discusses the system considerations for tracking small targets using passive sensors, in particular EO sensors. Tracking helps establish good versus bad detections. Discussed are the requirements to be placed on the sensor system's accuracy, with respect to knowledge of the sightline direction. The detection of weak targets sets a requirement for two levels of tracking in order to reduce processor throughput. A system characteristic is the need to track all detections. For low thresholds, this can mean a heavy track burden. Therefore, thresholds must be adaptive in order not to saturate the processors. Second-level tracks must develop a range estimate in order to assess threat. Sensor platform maneuvers are required if the targets are moving. The need for accurate pointing, good stability, and a good update rate will be shown quantitatively, relating to track accuracy and track association.

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

DTIC Science & Technology

2005-09-30

and Pre- Clustering MVN Test.....................126 4.2.3 Pre- Clustering Detection Results.................................................130...4.2.4 Pre- Clustering Target Influence..................................................134 4.2.5 Statistical Distance Exclusion and Low Contrast...al, 2001] Figure 2.7 ROC Curve Comparison of RX, K-Means, and Bayesian Pre- Clustering Applied to Anomaly Detection [Ashton, 1998] Figure 2.8 ROC

MutScan: fast detection and visualization of target mutations by scanning FASTQ data.

PubMed

Chen, Shifu; Huang, Tanxiao; Wen, Tiexiang; Li, Hong; Xu, Mingyan; Gu, Jia

2018-01-22

Some types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines. Typically, this process can be executed using alignment visualization tools such as IGV or GenomeBrowse. However, these tools are too heavy and therefore unsuitable for validating mutations in ultra-deep sequencing data. We developed MutScan to address problems of sensitive detection and efficient validation for target mutations. MutScan involves highly optimized string-searching algorithms, which can scan input FASTQ files to grab all reads that support target mutations. The collected supporting reads for each target mutation will be piled up and visualized using web technologies such as HTML and JavaScript. Algorithms such as rolling hash and bloom filter are applied to accelerate scanning and make MutScan applicable to detect or visualize target mutations in a very fast way. MutScan is a tool for the detection and visualization of target mutations by only scanning FASTQ raw data directly. Compared to conventional pipelines, this offers a very high performance, executing about 20 times faster, and offering maximal sensitivity since it can grab mutations with even one single supporting read. MutScan visualizes detected mutations by generating interactive pile-ups using web technologies. These can serve to validate target mutations, thus avoiding false positives. Furthermore, MutScan can visualize all mutation records in a VCF file to HTML pages for cloud-friendly VCF validation. MutScan is an open source tool available at GitHub: https://github.com/OpenGene/MutScan.

Detection and recognition of targets by using signal polarization properties

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Peralta-Fabi, Ricardo; Popov, Anatoly V.; Babakov, Mikhail F.

1999-08-01

The quality of radar target recognition can be enhanced by exploiting its polarization signatures. A specialized X-band polarimetric radar was used for target recognition in experimental investigations. The following polarization characteristics connected to the object geometrical properties were investigated: the amplitudes of the polarization matrix elements; an anisotropy coefficient; depolarization coefficient; asymmetry coefficient; the energy of a backscattering signal; object shape factor. A large quantity of polarimetric radar data was measured and processed to form a database of different object and different weather conditions. The histograms of polarization signatures were approximated by a Nakagami distribution, then used for real- time target recognition. The Neyman-Pearson criterion was used for the target detection, and the criterion of the maximum of a posterior probability was used for recognition problem. Some results of experimental verification of pattern recognition and detection of objects with different electrophysical and geometrical characteristics urban in clutter are presented in this paper.

Detection of target-probe oligonucleotide hybridization using synthetic nanopore resistive pulse sensing.

PubMed

Booth, Marsilea Adela; Vogel, Robert; Curran, James M; Harbison, SallyAnn; Travas-Sejdic, Jadranka

2013-07-15

Despite the plethora of DNA sensor platforms available, a portable, sensitive, selective and economic sensor able to rival current fluorescence-based techniques would find use in many applications. In this research, probe oligonucleotide-grafted particles are used to detect target DNA in solution through a resistive pulse nanopore detection technique. Using carbodiimide chemistry, functionalized probe DNA strands are attached to carboxylated dextran-based magnetic particles. Subsequent incubation with complementary target DNA yields a change in surface properties as the two DNA strands hybridize. Particle-by-particle analysis with resistive pulse sensing is performed to detect these changes. A variable pressure method allows identification of changes in the surface charge of particles. As proof-of-principle, we demonstrate that target hybridization is selectively detected at micromolar concentrations (nanomoles of target) using resistive pulse sensing, confirmed by fluorescence and phase analysis light scattering as complementary techniques. The advantages, feasibility and limitations of using resistive pulse sensing for sample analysis are discussed. Copyright © 2013 Elsevier B.V. All rights reserved.

Study on the influence factors of camouflage target polarization detection

NASA Astrophysics Data System (ADS)

Huang, Yanhua; Chen, Lei; Li, Xia; Wu, Wenyuan

2016-10-01

The degree of linear polarization (DOLP) expressions at any polarizer direction (PD) was deduced based on the Stokes vector and Mueller matrix. The outdoors experiments were carried out to demonstrate the expressions. This paper mainly explored the DOLP-image-Contrast (DOLPC) between the target image and the background image, and the PD and RGB waveband that be considered two important influence factors were studied for camouflage target polarization detection. It was found that the DOLPC of target and background was obviously higher than intensity image. When setting the reference direction that polarizer was perpendicular to the incident face, the DOLP image of interval angle 60 degree between PD and reference direction had relatively high DOLPC, the interval angle 45 degree was the second, and the interval angle 35 degree was the third. The outdoors polarization detection experiment of controlling waveband showed that the DOLPC results was significantly different to use 650nm, 550nm and 450nm waveband, and the polarization detection performance by using 650nm band was an optimization method.

A manifold learning approach to target detection in high-resolution hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ziemann, Amanda K.

Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying "targets" such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m << d. In the remote sensing community, this has led to a recent increase in the use of manifold learning, which aims to characterize the embedded lower-dimensional, non-linear manifold upon which the hyperspectral data inherently lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into

Cortical Spatio-Temporal Dynamics Underlying Phonological Target Detection in Humans

ERIC Educational Resources Information Center

Chang, Edward F.; Edwards, Erik; Nagarajan, Srikantan S.; Fogelson, Noa; Dalal, Sarang S.; Canolty, Ryan T.; Kirsch, Heidi E.; Barbaro, Nicholas M.; Knight, Robert T.

2011-01-01

Selective processing of task-relevant stimuli is critical for goal-directed behavior. We used electrocorticography to assess the spatio-temporal dynamics of cortical activation during a simple phonological target detection task, in which subjects press a button when a prespecified target syllable sound is heard. Simultaneous surface potential…

A general magnitude system in human adults: Evidence from a subliminal priming paradigm.

PubMed

Lourenco, Stella F; Ayzenberg, Vladislav; Lyu, Jennifer

2016-08-01

Despite general agreement that number and other magnitudes share analog format, there is disagreement about the extent to which representations of numerical and non-numerical magnitude recruit common cognitive and neural resources. Cross-dimensional interactions between number and other magnitudes on Stroop-like tasks have been taken as evidence for integration across magnitudes, but such effects are subject to alternative interpretations that allow for differentiated representations. Here we use a subliminal priming paradigm to test for interactions between different magnitudes (number and area) when one magnitude is not consciously detectable. Across two experiments, we first provide evidence for the feasibility of this paradigm by demonstrating that transfer occurs within the dimension of number; that is, symbolic numerals (Arabic digits) that were subliminally primed affected judgments of non-symbolic numerosities in target displays. Crucially, we also found transfer across magnitudes-from subliminally primed numerals to target displays of cumulative surface area whether participants made an ordinal judgment (i.e., "which array is larger in area?") or judged whether two arrays were the same or different in area. These findings suggest that representations of number and area are not fully differentiated. Moreover, they provide unique support for a general magnitude system that includes direct connections, or overlap, between the neural codes for numerical and non-numerical magnitudes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Novel fMRI working memory paradigm accurately detects cognitive impairment in multiple sclerosis.

PubMed

Nelson, Flavia; Akhtar, Mohammad A; Zúñiga, Edward; Perez, Carlos A; Hasan, Khader M; Wilken, Jeffrey; Wolinsky, Jerry S; Narayana, Ponnada A; Steinberg, Joel L

2017-05-01

Cognitive impairment (CI) cannot be diagnosed by magnetic resonance imaging (MRI). Functional magnetic resonance imaging (fMRI) paradigms, such as the immediate/delayed memory task (I/DMT), detect varying degrees of working memory (WM). Preliminary findings using I/DMT showed differences in blood oxygenation level dependent (BOLD) activation between impaired (MSCI, n = 12) and non-impaired (MSNI, n = 9) multiple sclerosis (MS) patients. The aim of the study was to confirm CI detection based on I/DMT BOLD activation in a larger cohort of MS patients. The role of T2 lesion volume (LV) and Expanded Disability Status Scale (EDSS) in magnitude of BOLD signal was also sought. A total of 50 patients (EDSS mean ( m) = 3.2, disease duration (DD) m = 12 years, and age m = 40 years) underwent the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) and I/DMT. Working memory activation (WMa) represents BOLD signal during DMT minus signal during IMT. CI was based on MACFIMS. A total of 10 MSNI, 30 MSCI, and 4 borderline patients were included in the analyses. Analysis of variance (ANOVA) showed MSNI had significantly greater WMa than MSCI, in the left prefrontal cortex and left supplementary motor area ( p = 0.032). Regression analysis showed significant inverse correlations between WMa and T2 LV/EDSS in similar areas ( p = 0.005, 0.004, respectively). I/DMT-based BOLD activation detects CI in MS. Larger studies are needed to confirm these findings.

Rare targets are less susceptible to attention capture once detection has begun.

PubMed

Hon, Nicholas; Ng, Gavin; Chan, Gerald

2016-04-01

Rare or low probability targets are detected more slowly and/ or less accurately than higher probability counterparts. Various proposals have implicated perceptual and response-based processes in this deficit. Recent evidence, however, suggests that it is attentional in nature, with low probability targets requiring more attentional resources than high probability ones to detect. This difference in attentional requirements, in turn, suggests the possibility that low and high probability targets may have different susceptibilities to attention capture, which is also known to be resource-dependent. Supporting this hypothesis, we found that, once attentional resources have begun to be engaged by detection processes, low, but not high, probability targets have a reduced susceptibility to capture. Our findings speak to several issues. First, they indicate that the likelihood of attention capture occurring when a given task-relevant stimulus is being processed is dependent, to some extent, on how said stimulus is represented within mental task sets. Second, they provide added support for the idea that the behavioural deficit associated with low probability targets is attention-based. Finally, the current data point to reduced top-down biasing of target templates as a likely mechanism underlying the attentional locus of the deficit in question.

Dolphin biosonar target detection in noise: wrap up of a past experiment.

PubMed

Au, Whitlow W L

2014-07-01

The target detection capability of bottlenose dolphins in the presence of artificial masking noise was first studied by Au and Penner [J. Acoust. Soc. Am. 70, 687-693 (1981)] in which the dolphins' target detection threshold was determined as a function of the ratio of the echo energy flux density and the estimated received noise spectral density. Such a metric was commonly used in human psychoacoustics despite the fact that the echo energy flux density is not compatible with noise spectral density which is averaged intensity per Hz. Since the earlier detection in noise studies, two important parameters, the dolphin integration time applicable to broadband clicks and the dolphin's auditory filter shape, were determined. The inclusion of these two parameters allows for the estimation of the received energy flux density of the masking noise so that the dolphin target detection can now be determined as a function of the ratio of the received energy of the echo over the received noise energy. Using an integration time of 264 μs and an auditory bandwidth of 16.7 kHz, the ratio of the echo energy to noise energy at the target detection threshold is approximately 1 dB.

Determination of target detection limits in hyperspectral data using band selection and dimensionality reduction

NASA Astrophysics Data System (ADS)

Gross, W.; Boehler, J.; Twizer, K.; Kedem, B.; Lenz, A.; Kneubuehler, M.; Wellig, P.; Oechslin, R.; Schilling, H.; Rotman, S.; Middelmann, W.

2016-10-01

Hyperspectral remote sensing data can be used for civil and military applications to robustly detect and classify target objects. High spectral resolution of hyperspectral data can compensate for the comparatively low spatial resolution, which allows for detection and classification of small targets, even below image resolution. Hyperspectral data sets are prone to considerable spectral redundancy, affecting and limiting data processing and algorithm performance. As a consequence, data reduction strategies become increasingly important, especially in view of near-real-time data analysis. The goal of this paper is to analyze different strategies for hyperspectral band selection algorithms and their effect on subpixel classification for different target and background materials. Airborne hyperspectral data is used in combination with linear target simulation procedures to create a representative amount of target-to-background ratios for evaluation of detection limits. Data from two different airborne hyperspectral sensors, AISA Eagle and Hawk, are used to evaluate transferability of band selection when using different sensors. The same target objects were recorded to compare the calculated detection limits. To determine subpixel classification results, pure pixels from the target materials are extracted and used to simulate mixed pixels with selected background materials. Target signatures are linearly combined with different background materials in varying ratios. The commonly used classification algorithms Adaptive Coherence Estimator (ACE) is used to compare the detection limit for the original data with several band selection and data reduction strategies. The evaluation of the classification results is done by assuming a fixed false alarm ratio and calculating the mean target-to-background ratio of correctly detected pixels. The results allow drawing conclusions about specific band combinations for certain target and background combinations. Additionally

A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence Homology

PubMed Central

Durrant, Jacob D.; Amaro, Rommie E.; Xie, Lei; Urbaniak, Michael D.; Ferguson, Michael A. J.; Haapalainen, Antti; Chen, Zhijun; Di Guilmi, Anne Marie; Wunder, Frank; Bourne, Philip E.; McCammon, J. Andrew

2010-01-01

Conventional drug design embraces the “one gene, one drug, one disease” philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology. PMID:20098496

A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology.

PubMed

Durrant, Jacob D; Amaro, Rommie E; Xie, Lei; Urbaniak, Michael D; Ferguson, Michael A J; Haapalainen, Antti; Chen, Zhijun; Di Guilmi, Anne Marie; Wunder, Frank; Bourne, Philip E; McCammon, J Andrew

2010-01-22

Conventional drug design embraces the "one gene, one drug, one disease" philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.

Detecting ship targets in spaceborne infrared image based on modeling radiation anomalies

NASA Astrophysics Data System (ADS)

Wang, Haibo; Zou, Zhengxia; Shi, Zhenwei; Li, Bo

2017-09-01

Using infrared imaging sensors to detect ship target in the ocean environment has many advantages compared to other sensor modalities, such as better thermal sensitivity and all-weather detection capability. We propose a new ship detection method by modeling radiation anomalies for spaceborne infrared image. The proposed method can be decomposed into two stages, where in the first stage, a test infrared image is densely divided into a set of image patches and the radiation anomaly of each patch is estimated by a Gaussian Mixture Model (GMM), and thereby target candidates are obtained from anomaly image patches. In the second stage, target candidates are further checked by a more discriminative criterion to obtain the final detection result. The main innovation of the proposed method is inspired by the biological mechanism that human eyes are sensitive to the unusual and anomalous patches among complex background. The experimental result on short wavelength infrared band (1.560 - 2.300 μm) and long wavelength infrared band (10.30 - 12.50 μm) of Landsat-8 satellite shows the proposed method achieves a desired ship detection accuracy with higher recall than other classical ship detection methods.

Target detection using the background model from the topological anomaly detection algorithm

NASA Astrophysics Data System (ADS)

Dorado Munoz, Leidy P.; Messinger, David W.; Ziemann, Amanda K.

2013-05-01

The Topological Anomaly Detection (TAD) algorithm has been used as an anomaly detector in hyperspectral and multispectral images. TAD is an algorithm based on graph theory that constructs a topological model of the background in a scene, and computes an anomalousness ranking for all of the pixels in the image with respect to the background in order to identify pixels with uncommon or strange spectral signatures. The pixels that are modeled as background are clustered into groups or connected components, which could be representative of spectral signatures of materials present in the background. Therefore, the idea of using the background components given by TAD in target detection is explored in this paper. In this way, these connected components are characterized in three different approaches, where the mean signature and endmembers for each component are calculated and used as background basis vectors in Orthogonal Subspace Projection (OSP) and Adaptive Subspace Detector (ASD). Likewise, the covariance matrix of those connected components is estimated and used in detectors: Constrained Energy Minimization (CEM) and Adaptive Coherence Estimator (ACE). The performance of these approaches and the different detectors is compared with a global approach, where the background characterization is derived directly from the image. Experiments and results using self-test data set provided as part of the RIT blind test target detection project are shown.

Infrared small target detection based on multiscale center-surround contrast measure

NASA Astrophysics Data System (ADS)

Fu, Hao; Long, Yunli; Zhu, Ran; An, Wei

2018-04-01

Infrared(IR) small target detection plays a critical role in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some difficulties remained to the clutter environment. According to the principle of human discrimination of small targets from a natural scene that there is a signature of discontinuity between the object and its neighboring regions, we develop an efficient method for infrared small target detection called multiscale centersurround contrast measure (MCSCM). First, to determine the maximum neighboring window size, an entropy-based window selection technique is used. Then, we construct a novel multiscale center-surround contrast measure to calculate the saliency map. Compared with the original image, the MCSCM map has less background clutters and noise residual. Subsequently, a simple threshold is used to segment the target. Experimental results show our method achieves better performance.

Antenna Allocation in MIMO Radar with Widely Separated Antennas for Multi-Target Detection

PubMed Central

Gao, Hao; Wang, Jian; Jiang, Chunxiao; Zhang, Xudong

2014-01-01

In this paper, we explore a new resource called multi-target diversity to optimize the performance of multiple input multiple output (MIMO) radar with widely separated antennas for detecting multiple targets. In particular, we allocate antennas of the MIMO radar to probe different targets simultaneously in a flexible manner based on the performance metric of relative entropy. Two antenna allocation schemes are proposed. In the first scheme, each antenna is allocated to illuminate a proper target over the entire illumination time, so that the detection performance of each target is guaranteed. The problem is formulated as a minimum makespan scheduling problem in the combinatorial optimization framework. Antenna allocation is implemented through a branch-and-bound algorithm and an enhanced factor 2 algorithm. In the second scheme, called antenna-time allocation, each antenna is allocated to illuminate different targets with different illumination time. Both antenna allocation and time allocation are optimized based on illumination probabilities. Over a large range of transmitted power, target fluctuations and target numbers, both of the proposed antenna allocation schemes outperform the scheme without antenna allocation. Moreover, the antenna-time allocation scheme achieves a more robust detection performance than branch-and-bound algorithm and the enhanced factor 2 algorithm when the target number changes. PMID:25350505

Antenna allocation in MIMO radar with widely separated antennas for multi-target detection.

PubMed

Gao, Hao; Wang, Jian; Jiang, Chunxiao; Zhang, Xudong

2014-10-27

In this paper, we explore a new resource called multi-target diversity to optimize the performance of multiple input multiple output (MIMO) radar with widely separated antennas for detecting multiple targets. In particular, we allocate antennas of the MIMO radar to probe different targets simultaneously in a flexible manner based on the performance metric of relative entropy. Two antenna allocation schemes are proposed. In the first scheme, each antenna is allocated to illuminate a proper target over the entire illumination time, so that the detection performance of each target is guaranteed. The problem is formulated as a minimum makespan scheduling problem in the combinatorial optimization framework. Antenna allocation is implemented through a branch-and-bound algorithm and an enhanced factor 2 algorithm. In the second scheme, called antenna-time allocation, each antenna is allocated to illuminate different targets with different illumination time. Both antenna allocation and time allocation are optimized based on illumination probabilities. Over a large range of transmitted power, target fluctuations and target numbers, both of the proposed antenna allocation schemes outperform the scheme without antenna allocation. Moreover, the antenna-time allocation scheme achieves a more robust detection performance than branch-and-bound algorithm and the enhanced factor 2 algorithm when the target number changes.

Target Detection over the Diurnal Cycle Using a Multispectral Infrared Sensor.

PubMed

Zhao, Huijie; Ji, Zheng; Li, Na; Gu, Jianrong; Li, Yansong

2016-12-29

When detecting a target over the diurnal cycle, a conventional infrared thermal sensor might lose the target due to the thermal crossover, which could happen at any time throughout the day when the infrared image contrast between target and background in a scene is indistinguishable due to the temperature variation. In this paper, the benefits of using a multispectral-based infrared sensor over the diurnal cycle have been shown. Firstly, a brief theoretical analysis on how the thermal crossover influences a conventional thermal sensor, within the conditions where the thermal crossover would happen and why the mid-infrared (3~5 μm) multispectral technology is effective, is presented. Furthermore, the effectiveness of this technology is also described and we describe how the prototype design and multispectral technology is employed to help solve the thermal crossover detection problem. Thirdly, several targets are set up outside and imaged in the field experiment over a 24-h period. The experimental results show that the multispectral infrared imaging system can enhance the contrast of the detected images and effectively solve the failure of the conventional infrared sensor during the diurnal cycle, which is of great significance for infrared surveillance applications.

Target Detection over the Diurnal Cycle Using a Multispectral Infrared Sensor

PubMed Central

Zhao, Huijie; Ji, Zheng; Li, Na; Gu, Jianrong; Li, Yansong

2016-01-01

When detecting a target over the diurnal cycle, a conventional infrared thermal sensor might lose the target due to the thermal crossover, which could happen at any time throughout the day when the infrared image contrast between target and background in a scene is indistinguishable due to the temperature variation. In this paper, the benefits of using a multispectral-based infrared sensor over the diurnal cycle have been shown. Firstly, a brief theoretical analysis on how the thermal crossover influences a conventional thermal sensor, within the conditions where the thermal crossover would happen and why the mid-infrared (3~5 μm) multispectral technology is effective, is presented. Furthermore, the effectiveness of this technology is also described and we describe how the prototype design and multispectral technology is employed to help solve the thermal crossover detection problem. Thirdly, several targets are set up outside and imaged in the field experiment over a 24-h period. The experimental results show that the multispectral infrared imaging system can enhance the contrast of the detected images and effectively solve the failure of the conventional infrared sensor during the diurnal cycle, which is of great significance for infrared surveillance applications. PMID:28036073

Detection technology of polarization target based on curvelet transform in turbid liquid

NASA Astrophysics Data System (ADS)

Zhang, Su; Duan, Jin; Fu, Qiang; Zhan, Juntong; Ma, Wanzhuo

2015-08-01

To suppress the interference of the target detecting in the turbid medium, a kind of polarization detection technology based on Curvelet transform was applied. This method firstly adjusts the angles of polarizing film to get the intensity images of the situations at 0°,60° and 120°, then deduces the images of Stokes vectors, degree of polarization (DOP) and polarization angle (PA) according to the Mueller matrix. At last the DOP and intensity images can be decomposed by Curvelet transform to realize the fusion of the high and low coefficients respectively, after the processed coefficients are reconstructed, the target which is easier to detect can be achieved. To prove this method, many targets in turbid medium have been detected by polarization method and fused their DOP and intensity images with Curvelet transform algorithm. As an example screws in moderate and high concentration liquid are presented respectively, from which we can see the unpolarized targets are less obvious in higher concentration liquid. When the DOP and intensity images are fused by Curvelet transform, the targets are emerged clearly out of the turbid medium, and the values of the quality evaluation parameters in clarity, degree of contract and spatial frequency are prominently enhanced comparing with the unpolarized images, which can show the feasibility of this method.

A novel spatial-temporal detection method of dim infrared moving small target

NASA Astrophysics Data System (ADS)

Chen, Zhong; Deng, Tao; Gao, Lei; Zhou, Heng; Luo, Song

2014-09-01

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial-temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time-space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

Electromagnetic Induction Spectroscopy for the Detection of Subsurface Targets

DTIC Science & Technology

2012-12-01

curves of the proposed method and that of Fails et al.. For the kNN ROC curve, k = 7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81...et al. [6] and Ramachandran et al. [7] both demonstrated success in detecting mines using the k-nearest-neighbor ( kNN ) algorithm based on the EMI...error is also included in the feature vector. The kNN labels an unknown target based on the closest targets in a training set. Collins et al. [2] and

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.

NAIMA: target amplification strategy allowing quantitative on-chip detection of GMOs.

PubMed

Morisset, Dany; Dobnik, David; Hamels, Sandrine; Zel, Jana; Gruden, Kristina

2008-10-01

We have developed a novel multiplex quantitative DNA-based target amplification method suitable for sensitive, specific and quantitative detection on microarray. This new method named NASBA Implemented Microarray Analysis (NAIMA) was applied to GMO detection in food and feed, but its application can be extended to all fields of biology requiring simultaneous detection of low copy number DNA targets. In a first step, the use of tailed primers allows the multiplex synthesis of template DNAs in a primer extension reaction. A second step of the procedure consists of transcription-based amplification using universal primers. The cRNA product is further on directly ligated to fluorescent dyes labelled 3DNA dendrimers allowing signal amplification and hybridized without further purification on an oligonucleotide probe-based microarray for multiplex detection. Two triplex systems have been applied to test maize samples containing several transgenic lines, and NAIMA has shown to be sensitive down to two target copies and to provide quantitative data on the transgenic contents in a range of 0.1-25%. Performances of NAIMA are comparable to singleplex quantitative real-time PCR. In addition, NAIMA amplification is faster since 20 min are sufficient to achieve full amplification.

NAIMA: target amplification strategy allowing quantitative on-chip detection of GMOs

PubMed Central

Morisset, Dany; Dobnik, David; Hamels, Sandrine; Žel, Jana; Gruden, Kristina

2008-01-01

We have developed a novel multiplex quantitative DNA-based target amplification method suitable for sensitive, specific and quantitative detection on microarray. This new method named NASBA Implemented Microarray Analysis (NAIMA) was applied to GMO detection in food and feed, but its application can be extended to all fields of biology requiring simultaneous detection of low copy number DNA targets. In a first step, the use of tailed primers allows the multiplex synthesis of template DNAs in a primer extension reaction. A second step of the procedure consists of transcription-based amplification using universal primers. The cRNA product is further on directly ligated to fluorescent dyes labelled 3DNA dendrimers allowing signal amplification and hybridized without further purification on an oligonucleotide probe-based microarray for multiplex detection. Two triplex systems have been applied to test maize samples containing several transgenic lines, and NAIMA has shown to be sensitive down to two target copies and to provide quantitative data on the transgenic contents in a range of 0.1–25%. Performances of NAIMA are comparable to singleplex quantitative real-time PCR. In addition, NAIMA amplification is faster since 20 min are sufficient to achieve full amplification. PMID:18710880

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

NASA Astrophysics Data System (ADS)

Mainsah, B. O.; Reeves, G.; Collins, L. M.; Throckmorton, C. S.

2017-08-01

Objective. The role of a brain-computer interface (BCI) is to discern a user’s intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. Approach. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. Main results. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional

Targeted Endoscopic Imaging

PubMed Central

Li, Meng; Wang, Thomas D

2011-01-01

Summary Endoscopy has undergone explosive technological growth in over recent years, and with the emergence of targeted imaging, its truly transformative power and impact in medicine lies just over the horizon. Today, our ability to see inside the digestive tract with medical endoscopy is headed toward exciting crossroads. The existing paradigm of making diagnostic decisions based on observing structural changes and identifying anatomical landmarks may soon be replaced by visualizing functional properties and imaging molecular expression. In this novel approach, the presence of intracellular and cell surface targets unique to disease are identified and used to predict the likelihood of mucosal transformation and response to therapy. This strategy can result in the development of new methods for early cancer detection, personalized therapy, and chemoprevention. This targeted approach will require further development of molecular probes and endoscopic instruments, and will need support from the FDA for streamlined regulatory oversight. Overall, this molecular imaging modality promises to significantly broaden the capabilities of the gastroenterologist by providing a new approach to visualize the mucosa of the digestive tract in a manner that has never been seen before. PMID:19423025

Assessment of Schrodinger Eigenmaps for target detection

NASA Astrophysics Data System (ADS)

Dorado Munoz, Leidy P.; Messinger, David W.; Czaja, Wojtek

2014-06-01

Non-linear dimensionality reduction methods have been widely applied to hyperspectral imagery due to its structure as the information can be represented in a lower dimension without losing information, and because the non-linear methods preserve the local geometry of the data while the dimension is reduced. One of these methods is Laplacian Eigenmaps (LE), which assumes that the data lies on a low dimensional manifold embedded in a high dimensional space. LE builds a nearest neighbor graph, computes its Laplacian and performs the eigendecomposition of the Laplacian. These eigenfunctions constitute a basis for the lower dimensional space in which the geometry of the manifold is preserved. In addition to the reduction problem, LE has been widely used in tasks such as segmentation, clustering, and classification. In this regard, a new Schrodinger Eigenmaps (SE) method was developed and presented as a semi-supervised classification scheme in order to improve the classification performance and take advantage of the labeled data. SE is an algorithm built upon LE, where the former Laplacian operator is replaced by the Schrodinger operator. The Schrodinger operator includes a potential term V, that, taking advantage of the additional information such as labeled data, allows clustering of similar points. In this paper, we explore the idea of using SE in target detection. In this way, we present a framework where the potential term V is defined as a barrier potential: a diagonal matrix encoding the spatial position of the target, and the detection performance is evaluated by using different targets and different hyperspectral scenes.

Aerial surveillance based on hierarchical object classification for ground target detection

NASA Astrophysics Data System (ADS)

Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

2015-03-01

Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

NASA Astrophysics Data System (ADS)

Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

2017-04-01

Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

A speeded-up saliency region-based contrast detection method for small targets

NASA Astrophysics Data System (ADS)

Li, Zhengjie; Zhang, Haiying; Bai, Jiaojiao; Zhou, Zhongjun; Zheng, Huihuang

2018-04-01

To cope with the rapid development of the real applications for infrared small targets, the researchers have tried their best to pursue more robust detection methods. At present, the contrast measure-based method has become a promising research branch. Following the framework, in this paper, a speeded-up contrast measure scheme is proposed based on the saliency detection and density clustering. First, the saliency region is segmented by saliency detection method, and then, the Multi-scale contrast calculation is carried out on it instead of traversing the whole image. Second, the target with a certain "integrity" property in spatial is exploited to distinguish the target from the isolated noises by density clustering. Finally, the targets are detected by a self-adaptation threshold. Compared with time-consuming MPCM (Multiscale Patch Contrast Map), the time cost of the speeded-up version is within a few seconds. Additional, due to the use of "clustering segmentation", the false alarm caused by heavy noises can be restrained to a lower level. The experiments show that our method has a satisfied FASR (False alarm suppression ratio) and real-time performance compared with the state-of-art algorithms no matter in cloudy sky or sea-sky background.

The gap-startle paradigm to assess auditory temporal processing: Bridging animal and human research.

PubMed

Fournier, Philippe; Hébert, Sylvie

2016-05-01

The gap-prepulse inhibition of the acoustic startle (GPIAS) paradigm is the primary test used in animal research to identify gap detection thresholds and impairment. When a silent gap is presented shortly before a loud startling stimulus, the startle reflex is inhibited and the extent of inhibition is assumed to reflect detection. Here, we applied the same paradigm in humans. One hundred and fifty-seven normal-hearing participants were tested using one of five gap durations (5, 25, 50, 100, 200 ms) in one of the following two paradigms-gap-embedded in or gap-following-the continuous background noise. The duration-inhibition relationship was observable for both conditions but followed different patterns. In the gap-embedded paradigm, GPIAS increased significantly with gap duration up to 50 ms and then more slowly up to 200 ms (trend only). In contrast, in the gap-following paradigm, significant inhibition-different from 0--was observable only at gap durations from 50 to 200 ms. The finding that different patterns are found depending on gap position within the background noise is compatible with distinct mechanisms underlying each of the two paradigms. © 2016 Society for Psychophysiological Research.

Search and Discovery Strategies for Biotechnology: the Paradigm Shift

PubMed Central

Bull, Alan T.; Ward, Alan C.; Goodfellow, Michael

2000-01-01

Profound changes are occurring in the strategies that biotechnology-based industries are deploying in the search for exploitable biology and to discover new products and develop new or improved processes. The advances that have been made in the past decade in areas such as combinatorial chemistry, combinatorial biosynthesis, metabolic pathway engineering, gene shuffling, and directed evolution of proteins have caused some companies to consider withdrawing from natural product screening. In this review we examine the paradigm shift from traditional biology to bioinformatics that is revolutionizing exploitable biology. We conclude that the reinvigorated means of detecting novel organisms, novel chemical structures, and novel biocatalytic activities will ensure that natural products will continue to be a primary resource for biotechnology. The paradigm shift has been driven by a convergence of complementary technologies, exemplified by DNA sequencing and amplification, genome sequencing and annotation, proteome analysis, and phenotypic inventorying, resulting in the establishment of huge databases that can be mined in order to generate useful knowledge such as the identity and characterization of organisms and the identity of biotechnology targets. Concurrently there have been major advances in understanding the extent of microbial diversity, how uncultured organisms might be grown, and how expression of the metabolic potential of microorganisms can be maximized. The integration of information from complementary databases presents a significant challenge. Such integration should facilitate answers to complex questions involving sequence, biochemical, physiological, taxonomic, and ecological information of the sort posed in exploitable biology. The paradigm shift which we discuss is not absolute in the sense that it will replace established microbiology; rather, it reinforces our view that innovative microbiology is essential for releasing the potential of microbial

Effects of Alzheimer's Disease on Visual Target Detection: A "Peripheral Bias".

PubMed

Vallejo, Vanessa; Cazzoli, Dario; Rampa, Luca; Zito, Giuseppe A; Feuerstein, Flurin; Gruber, Nicole; Müri, René M; Mosimann, Urs P; Nef, Tobias

2016-01-01

Visual exploration is an omnipresent activity in everyday life, and might represent an important determinant of visual attention deficits in patients with Alzheimer's Disease (AD). The present study aimed at investigating visual search performance in AD patients, in particular target detection in the far periphery, in daily living scenes. Eighteen AD patients and 20 healthy controls participated in the study. They were asked to freely explore a hemispherical screen, covering ±90°, and to respond to targets presented at 10°, 30°, and 50° eccentricity, while their eye movements were recorded. Compared to healthy controls, AD patients recognized less targets appearing in the center. No difference was found in target detection in the periphery. This pattern was confirmed by the fixation distribution analysis. These results show a neglect for the central part of the visual field for AD patients and provide new insights by mean of a search task involving a larger field of view.

Target-responsive DNA hydrogel mediated "stop-flow" microfluidic paper-based analytic device for rapid, portable and visual detection of multiple targets.

PubMed

Wei, Xiaofeng; Tian, Tian; Jia, Shasha; Zhu, Zhi; Ma, Yanli; Sun, Jianjun; Lin, Zhenyu; Yang, Chaoyong James

2015-04-21

A versatile point-of-care assay platform was developed for simultaneous detection of multiple targets based on a microfluidic paper-based analytic device (μPAD) using a target-responsive hydrogel to mediate fluidic flow and signal readout. An aptamer-cross-linked hydrogel was used as a target-responsive flow regulator in the μPAD. In the absence of a target, the hydrogel is formed in the flow channel, stopping the flow in the μPAD and preventing the colored indicator from traveling to the final observation spot, thus yielding a "signal off" readout. In contrast, in the presence of a target, no hydrogel is formed because of the preferential interaction of target and aptamer. This allows free fluidic flow in the μPAD, carrying the indicator to the observation spot and producing a "signal on" readout. The device is inexpensive to fabricate, easy to use, and disposable after detection. Testing results can be obtained within 6 min by the naked eye via a simple loading operation without the need for any auxiliary equipment. Multiple targets, including cocaine, adenosine, and Pb(2+), can be detected simultaneously, even in complex biological matrices such as urine. The reported method offers simple, low cost, rapid, user-friendly, point-of-care testing, which will be useful in many applications.

Channel noise enhances signal detectability in a model of acoustic neuron through the stochastic resonance paradigm.

PubMed

Liberti, M; Paffi, A; Maggio, F; De Angelis, A; Apollonio, F; d'Inzeo, G

2009-01-01

A number of experimental investigations have evidenced the extraordinary sensitivity of neuronal cells to weak input stimulations, including electromagnetic (EM) fields. Moreover, it has been shown that biological noise, due to random channels gating, acts as a tuning factor in neuronal processing, according to the stochastic resonant (SR) paradigm. In this work the attention is focused on noise arising from the stochastic gating of ionic channels in a model of Ranvier node of acoustic fibers. The small number of channels gives rise to a high noise level, which is able to cause a spike train generation even in the absence of stimulations. A SR behavior has been observed in the model for the detection of sinusoidal signals at frequencies typical of the speech.

Small target detection using bilateral filter and temporal cross product in infrared images

NASA Astrophysics Data System (ADS)

Bae, Tae-Wuk

2011-09-01

We introduce a spatial and temporal target detection method using spatial bilateral filter (BF) and temporal cross product (TCP) of temporal pixels in infrared (IR) image sequences. At first, the TCP is presented to extract the characteristics of temporal pixels by using temporal profile in respective spatial coordinates of pixels. The TCP represents the cross product values by the gray level distance vector of a current temporal pixel and the adjacent temporal pixel, as well as the horizontal distance vector of the current temporal pixel and a temporal pixel corresponding to potential target center. The summation of TCP values of temporal pixels in spatial coordinates makes the temporal target image (TTI), which represents the temporal target information of temporal pixels in spatial coordinates. And then the proposed BF filter is used to extract the spatial target information. In order to predict background without targets, the proposed BF filter uses standard deviations obtained by an exponential mapping of the TCP value corresponding to the coordinate of a pixel processed spatially. The spatial target image (STI) is made by subtracting the predicted image from the original image. Thus, the spatial and temporal target image (STTI) is achieved by multiplying the STI and the TTI, and then targets finally are detected in STTI. In experimental result, the receiver operating characteristics (ROC) curves were computed experimentally to compare the objective performance. From the results, the proposed algorithm shows better discrimination of target and clutters and lower false alarm rates than the existing target detection methods.

Infrared small target detection in heavy sky scene clutter based on sparse representation

NASA Astrophysics Data System (ADS)

Liu, Depeng; Li, Zhengzhou; Liu, Bing; Chen, Wenhao; Liu, Tianmei; Cao, Lei

2017-09-01

A novel infrared small target detection method based on sky clutter and target sparse representation is proposed in this paper to cope with the representing uncertainty of clutter and target. The sky scene background clutter is described by fractal random field, and it is perceived and eliminated via the sparse representation on fractal background over-complete dictionary (FBOD). The infrared small target signal is simulated by generalized Gaussian intensity model, and it is expressed by the generalized Gaussian target over-complete dictionary (GGTOD), which could describe small target more efficiently than traditional structured dictionaries. Infrared image is decomposed on the union of FBOD and GGTOD, and the sparse representation energy that target signal and background clutter decomposed on GGTOD differ so distinctly that it is adopted to distinguish target from clutter. Some experiments are induced and the experimental results show that the proposed approach could improve the small target detection performance especially under heavy clutter for background clutter could be efficiently perceived and suppressed by FBOD and the changing target could also be represented accurately by GGTOD.

Morphological operators for enhanced polarimetric image target detection

NASA Astrophysics Data System (ADS)

Romano, João. M.; Rosario, Dalton S.

2015-09-01

We introduce an algorithm based on morphological filters with the Stokes parameters that augments the daytime and nighttime detection of weak-signal manmade objects immersed in a predominant natural background scene. The approach features a tailored sequence of signal-enhancing filters, consisting of core morphological operators (dilation, erosion) and higher level morphological operations (e.g., spatial gradient, opening, closing) to achieve a desired overarching goal. Using representative data from the SPICE database, the results show that the approach was able to automatically and persistently detect with a high confidence level the presence of three mobile military howitzer surrogates (targets) in natural clutter.

Comparison of human observer and algorithmic target detection in nonurban forward-looking infrared imagery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.

2005-07-01

We have performed an experiment that compares the performance of human observers with that of a robust algorithm for the detection of targets in difficult, nonurban forward-looking infrared imagery. Our purpose was to benchmark the comparison and document performance differences for future algorithm improvement. The scale-insensitive detection algorithm, used as a benchmark by the Night Vision Electronic Sensors Directorate for algorithm evaluation, employed a combination of contrastlike features to locate targets. Detection receiver operating characteristic curves and observer-confidence analyses were used to compare human and algorithmic responses and to gain insight into differences. The test database contained ground targets, in natural clutter, whose detectability, as judged by human observers, ranged from easy to very difficult. In general, as compared with human observers, the algorithm detected most of the same targets, but correlated confidence with correct detections poorly and produced many more false alarms at any useful level of performance. Though characterizing human performance was not the intent of this study, results suggest that previous observational experience was not a strong predictor of human performance, and that combining individual human observations by majority vote significantly reduced false-alarm rates.

Geographic Object-Based Image Analysis - Towards a new paradigm.

PubMed

Blaschke, Thomas; Hay, Geoffrey J; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis - GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ' per-pixel paradigm ' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar

PubMed Central

Raja Abdullah, Raja Syamsul Azmir; Abdul Aziz, Noor Hafizah; Abdul Rashid, Nur Emileen; Ahmad Salah, Asem; Hashim, Fazirulhisyam

2016-01-01

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system’s capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications. PMID:27690051

Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar.

PubMed

Raja Abdullah, Raja Syamsul Azmir; Abdul Aziz, Noor Hafizah; Abdul Rashid, Nur Emileen; Ahmad Salah, Asem; Hashim, Fazirulhisyam

2016-09-29

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system's capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications.

Small target detection based on difference accumulation and Gaussian curvature under complex conditions

NASA Astrophysics Data System (ADS)

Zhang, He; Niu, Yanxiong; Zhang, Hao

2017-12-01

Small target detection is a significant subject in infrared search and track and other photoelectric imaging systems. The small target is imaged under complex conditions, which contains clouds, horizon and bright part. In this paper, a novel small target detection method is proposed based on difference accumulation, clustering and Gaussian curvature. Difference accumulation varies from regions. Therefore, after obtaining difference accumulations, clustering is applied to determine whether the pixel belongs to the heterogeneous region, and eliminate heterogeneous region. Then Gaussian curvature is used to separate target from the homogeneous region. Experiments are conducted for verification, along with comparisons to several other methods. The experimental results demonstrate that our method has an advantage of 1-2 orders of magnitude on SCRG and BSF than others. Given that the false alarm rate is 1, the detection probability can be approximately 0.9 by using proposed method.

Insect Detection of Small Targets Moving in Visual Clutter

PubMed Central

Barnett, Paul D; O'Carroll, David C

2006-01-01

Detection of targets that move within visual clutter is a common task for animals searching for prey or conspecifics, a task made even more difficult when a moving pursuer needs to analyze targets against the motion of background texture (clutter). Despite the limited optical acuity of the compound eye of insects, this challenging task seems to have been solved by their tiny visual system. Here we describe neurons found in the male hoverfly,Eristalis tenax, that respond selectively to small moving targets. Although many of these target neurons are inhibited by the motion of a background pattern, others respond to target motion within the receptive field under a surprisingly large range of background motion stimuli. Some neurons respond whether or not there is a speed differential between target and background. Analysis of responses to very small targets (smaller than the size of the visual field of single photoreceptors) or those targets with reduced contrast shows that these neurons have extraordinarily high contrast sensitivity. Our data suggest that rejection of background motion may result from extreme selectivity for small targets contrasting against local patches of the background, combined with this high sensitivity, such that background patterns rarely contain features that satisfactorily drive the neuron. PMID:16448249

Using In-vivo Fluorescence Imaging in Personalized Cancer Diagnostics and Therapy, an Image and Treat Paradigm

PubMed Central

Ardeshirpour, Yasaman; Chernomordik, Victor; Capala, Jacek; Hassan, Moinuddin; Zielinsky, Rafal; Griffiths, Gary; Achilefu, Samuel; Smith, Paul; Gandjbakhckhe, Amir

2013-01-01

The major goal in developing drugs targeting specific tumor receptors, such as Monoclonal AntiBodies (MAB), is to make a drug compound that targets selectively the cancer-causing biomarkers, inhibits their functionality, and/or delivers the toxin specifically to the malignant cells. Recent advances in MABs show that their efficacy depends strongly on characterization of tumor biomarkers. Therefore, one of the main tasks in cancer diagnostics and treatment is to develop non-invasive in-vivo imaging techniques for detection of cancer biomarkers and monitoring their down regulation during the treatment. Such methods can potentially result in a new imaging and treatment paradigm for cancer therapy. In this article we have reviewed fluorescence imaging approaches, including those developed in our group, to detect and monitor Human Epidermal Growth Factor 2 (HER2) receptors before and during therapy. Transition of these techniques from the bench to bedside is the ultimate goal of our project. Similar approaches can be used potentially for characterization of other cancer related cell biomarkers. PMID:22066595

Power corrupts co-operation: cognitive and motivational effects in a double EEG paradigm

PubMed Central

Kanso, Riam; Hewstone, Miles; Hawkins, Erin; Waszczuk, Monika; Nobre, Anna Christina

2014-01-01

This study investigated the effect of interpersonal power on co-operative performance. We used a paired electro-encephalogram paradigm: pairs of participants performed an attention task, followed by feedback indicating monetary loss or gain on every trial. Participants were randomly allocated to the power-holder, subordinate or neutral group by creating different levels of control over how a joint monetary reward would be allocated. We found that power was associated with reduced behavioural accuracy. Event-related potential analysis showed that power-holders devoted less motivational resources to their targets than did subordinates or neutrals, but did not differ at the level of early conflict detection. Their feedback potential results showed a greater expectation of rewards but reduced subjective magnitude attributed to losses. Subordinates, on the other hand, were asymmetrically sensitive to power-holders’ targets. They expected fewer rewards, but attributed greater significance to losses. Our study shows that power corrupts balanced co-operation with subordinates. PMID:23160813

Power corrupts co-operation: cognitive and motivational effects in a double EEG paradigm.

PubMed

Kanso, Riam; Hewstone, Miles; Hawkins, Erin; Waszczuk, Monika; Nobre, Anna Christina

2014-02-01

This study investigated the effect of interpersonal power on co-operative performance. We used a paired electro-encephalogram paradigm: pairs of participants performed an attention task, followed by feedback indicating monetary loss or gain on every trial. Participants were randomly allocated to the power-holder, subordinate or neutral group by creating different levels of control over how a joint monetary reward would be allocated. We found that power was associated with reduced behavioural accuracy. Event-related potential analysis showed that power-holders devoted less motivational resources to their targets than did subordinates or neutrals, but did not differ at the level of early conflict detection. Their feedback potential results showed a greater expectation of rewards but reduced subjective magnitude attributed to losses. Subordinates, on the other hand, were asymmetrically sensitive to power-holders' targets. They expected fewer rewards, but attributed greater significance to losses. Our study shows that power corrupts balanced co-operation with subordinates.

The paradigm compiler: Mapping a functional language for the connection machine

NASA Technical Reports Server (NTRS)

Dennis, Jack B.

1989-01-01

The Paradigm Compiler implements a new approach to compiling programs written in high level languages for execution on highly parallel computers. The general approach is to identify the principal data structures constructed by the program and to map these structures onto the processing elements of the target machine. The mapping is chosen to maximize performance as determined through compile time global analysis of the source program. The source language is Sisal, a functional language designed for scientific computations, and the target language is Paris, the published low level interface to the Connection Machine. The data structures considered are multidimensional arrays whose dimensions are known at compile time. Computations that build such arrays usually offer opportunities for highly parallel execution; they are data parallel. The Connection Machine is an attractive target for these computations, and the parallel for construct of the Sisal language is a convenient high level notation for data parallel algorithms. The principles and organization of the Paradigm Compiler are discussed.

Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics

NASA Astrophysics Data System (ADS)

Yi, Weisong; Zhang, Jian; Jiang, Houmin; Zhang, Niya

2014-09-01

Gastric cancer is one of the leading causes of cancer death in the world due to its high morbidity and mortality. Hyperspectral imaging (HSI) is an emerging, non-destructive, cutting edge analytical technology that combines conventional imaging and spectroscopy in one single system. The manuscript has investigated the application of near-infrared hyperspectral imaging (900-1700 nm) (NIR-HSI) for gastric cancer detection with algorithms. Major spectral differences were observed in three regions (950-1050, 1150-1250, and 1400-1500 nm). By inspecting cancerous mean spectrum three major absorption bands were observed around 975, 1215 and 1450 nm. Furthermore, the cancer target detection results are consistent and conformed with histopathological examination results. These results suggest that NIR-HSI is a simple, feasible and sensitive optical diagnostic technology for gastric cancer target detection with chemometrics.

Real-Time Adaptation of Decision Thresholds in Sensor Networks for Detection of Moving Targets (PREPRINT)

DTIC Science & Technology

2010-01-01

target kinematics for multiple sensor detections is referred to as the track - before - detect strategy, and is commonly adopted in multi-sensor surveillance...of moving targets. Wettergren [4] presented an application of track - before - detect strategies to undersea distributed sensor networks. In de- signing...the deployment of a distributed passive sensor network that employs this track - before - detect procedure, it is impera- tive that the placement of

Identifying Minefields and Verifying Clearance: Adapting Statistical Methods for UXO Target Detection

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

Gilbert, Richard O.; O'Brien, Robert F.; Wilson, John E.

2003-09-01

It may not be feasible to completely survey large tracts of land suspected of containing minefields. It is desirable to develop a characterization protocol that will confidently identify minefields within these large land tracts if they exist. Naturally, surveying areas of greatest concern and most likely locations would be necessary but will not provide the needed confidence that an unknown minefield had not eluded detection. Once minefields are detected, methods are needed to bound the area that will require detailed mine detection surveys. The US Department of Defense Strategic Environmental Research and Development Program (SERDP) is sponsoring the development ofmore » statistical survey methods and tools for detecting potential UXO targets. These methods may be directly applicable to demining efforts. Statistical methods are employed to determine the optimal geophysical survey transect spacing to have confidence of detecting target areas of a critical size, shape, and anomaly density. Other methods under development determine the proportion of a land area that must be surveyed to confidently conclude that there are no UXO present. Adaptive sampling schemes are also being developed as an approach for bounding the target areas. These methods and tools will be presented and the status of relevant research in this area will be discussed.« less

Experimental Psychopathology Paradigms for Alcohol Use Disorders: Applications for Translational Research

PubMed Central

Bujarski, Spencer; Ray, Lara A.

2016-01-01

In spite of high prevalence and disease burden, scientific consensus on the etiology and treatment of Alcohol Use Disorder (AUD) has yet to be reached. The development and utilization of experimental psychopathology paradigms in the human laboratory represents a cornerstone of AUD research. In this review, we describe and critically evaluate the major experimental psychopathology paradigms developed for AUD, with an emphasis on their implications, strengths, weaknesses, and methodological considerations. Specifically we review alcohol administration, self-administration, cue-reactivity, and stress-reactivity paradigms. We also provide an introduction to the application of experimental psychopathology methods to translational research including genetics, neuroimaging, pharmacological and behavioral treatment development, and translational science. Through refining and manipulating key phenotypes of interest, these experimental paradigms have the potential to elucidate AUD etiological factors, improve the efficiency of treatment developments, and refine treatment targets thus advancing precision medicine. PMID:27266992

Experimental psychopathology paradigms for alcohol use disorders: Applications for translational research.

PubMed

Bujarski, Spencer; Ray, Lara A

2016-11-01

In spite of high prevalence and disease burden, scientific consensus on the etiology and treatment of Alcohol Use Disorder (AUD) has yet to be reached. The development and utilization of experimental psychopathology paradigms in the human laboratory represents a cornerstone of AUD research. In this review, we describe and critically evaluate the major experimental psychopathology paradigms developed for AUD, with an emphasis on their implications, strengths, weaknesses, and methodological considerations. Specifically we review alcohol administration, self-administration, cue-reactivity, and stress-reactivity paradigms. We also provide an introduction to the application of experimental psychopathology methods to translational research including genetics, neuroimaging, pharmacological and behavioral treatment development, and translational science. Through refining and manipulating key phenotypes of interest, these experimental paradigms have the potential to elucidate AUD etiological factors, improve the efficiency of treatment developments, and refine treatment targets thus advancing precision medicine. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effect of inherent location uncertainty on detection of stationary targets in noisy image sequences.

PubMed

Manjeshwar, R M; Wilson, D L

2001-01-01

The effect of inherent location uncertainty on the detection of stationary targets was determined in noisy image sequences. Targets were thick and thin projected cylinders mimicking arteries, catheters, and guide wires in medical imaging x-ray fluoroscopy. With the use of an adaptive forced-choice method, detection contrast sensitivity (the inverse of contrast) was measured both with and without marker cues that directed the attention of observers to the target location. With the probability correct clamped at 80%, contrast sensitivity increased an average of 77% when the marker was added to the thin-cylinder target. There was an insignificant effect on the thick cylinder. The large enhancement with the thin cylinder was obtained even though the target was located exactly in the center of a small panel, giving observers the impression that it was well localized. Psychometric functions consisting of d' plotted as a function of the square root of the signal-energy-to-noise-ratio gave a positive x intercept for the case of the thin cylinder without a marker. This x intercept, characteristic of uncertainty in other types of detection experiments, disappeared when the marker was added or when the thick cylinder was used. Inherent location uncertainty was further characterized by using four different markers with varying proximity to the target. Visual detection by human observers increased monotonically as the markers better localized the target. Human performance was modeled as a matched-filter detector with an uncertainty in the placement of the template. The removal of a location cue was modeled by introducing a location uncertainty of approximately equals 0.4 mm on the display device or only 7 microm on the retina, a size on the order of a single photoreceptor field. We conclude that detection is affected by target location uncertainty on the order of cellular dimensions, an observation with important implications for detection mechanisms in humans. In medical

A new EMI system for detection and classification of challenging targets

NASA Astrophysics Data System (ADS)

Shubitidze, F.; Fernández, J. P.; Barrowes, B. E.; O'Neill, K.

2013-06-01

Advanced electromagnetic induction (EMI) sensors currently feature multi-axis illumination of targets and tri-axial vector sensing (e.g., MetalMapper), or exploit multi-static array data acquisition (e.g., TEMTADS). They produce data of high density, quality, and diversity, and have been combined with advanced EMI models to provide superb classification performance relative to the previous generation of single-axis, monostatic sensors. However, these advances yet have to improve significantly our ability to classify small, deep, and otherwise challenging targets. Particularly, recent live-site discrimination studies at Camp Butner, NC and Camp Beale, CA have revealed that it is more challenging to detect and discriminate small munitions (with calibers ranging from 20 mm to 60 mm) than larger ones. In addition, a live-site test at the Massachusetts Military Reservation, MA highlighted the difficulties for current sensors to classify large, deep, and overlapping targets with high confidence. There are two main approaches to overcome these problems: 1) adapt advanced EMI models to the existing systems and 2) improve the detection limits of current sensors by modifying their hardware. In this paper we demonstrate a combined software/hardware approach that will provide extended detection range and spatial resolution to next-generation EMI systems; we analyze and invert EMI data to extract classification features for small and deep targets; and we propose a new system that features a large transmitter coil.

A novel infrared small moving target detection method based on tracking interest points under complicated background

NASA Astrophysics Data System (ADS)

Dong, Xiabin; Huang, Xinsheng; Zheng, Yongbin; Bai, Shengjian; Xu, Wanying

2014-07-01

Infrared moving target detection is an important part of infrared technology. We introduce a novel infrared small moving target detection method based on tracking interest points under complicated background. Firstly, Difference of Gaussians (DOG) filters are used to detect a group of interest points (including the moving targets). Secondly, a sort of small targets tracking method inspired by Human Visual System (HVS) is used to track these interest points for several frames, and then the correlations between interest points in the first frame and the last frame are obtained. Last, a new clustering method named as R-means is proposed to divide these interest points into two groups according to the correlations, one is target points and another is background points. In experimental results, the target-to-clutter ratio (TCR) and the receiver operating characteristics (ROC) curves are computed experimentally to compare the performances of the proposed method and other five sophisticated methods. From the results, the proposed method shows a better discrimination of targets and clutters and has a lower false alarm rate than the existing moving target detection methods.

An Efficient Moving Target Detection Algorithm Based on Sparsity-Aware Spectrum Estimation

PubMed Central

Shen, Mingwei; Wang, Jie; Wu, Di; Zhu, Daiyin

2014-01-01

In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results. PMID:25222035

Reliable motion detection of small targets in video with low signal-to-clutter ratios

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

Nichols, S.A.; Naylor, R.B.

1995-07-01

Studies show that vigilance decreases rapidly after several minutes when human operators are required to search live video for infrequent intrusion detections. Therefore, there is a need for systems which can automatically detect targets in live video and reserve the operator`s attention for assessment only. Thus far, automated systems have not simultaneously provided adequate detection sensitivity, false alarm suppression, and ease of setup when used in external, unconstrained environments. This unsatisfactory performance can be exacerbated by poor video imagery with low contrast, high noise, dynamic clutter, image misregistration, and/or the presence of small, slow, or erratically moving targets. This papermore » describes a highly adaptive video motion detection and tracking algorithm which has been developed as part of Sandia`s Advanced Exterior Sensor (AES) program. The AES is a wide-area detection and assessment system for use in unconstrained exterior security applications. The AES detection and tracking algorithm provides good performance under stressing data and environmental conditions. Features of the algorithm include: reliable detection with negligible false alarm rate of variable velocity targets having low signal-to-clutter ratios; reliable tracking of targets that exhibit motion that is non-inertial, i.e., varies in direction and velocity; automatic adaptation to both infrared and visible imagery with variable quality; and suppression of false alarms caused by sensor flaws and/or cutouts.« less

Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling

PubMed Central

Zhao, Liang; Chen, Feng; Dai, Jing; Hua, Ting; Lu, Chang-Tien; Ramakrishnan, Naren

2014-01-01

Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach. PMID:25350136

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.

A statistical approach to detection of copy number variations in PCR-enriched targeted sequencing data.

PubMed

Demidov, German; Simakova, Tamara; Vnuchkova, Julia; Bragin, Anton

2016-10-22

Multiplex polymerase chain reaction (PCR) is a common enrichment technique for targeted massive parallel sequencing (MPS) protocols. MPS is widely used in biomedical research and clinical diagnostics as the fast and accurate tool for the detection of short genetic variations. However, identification of larger variations such as structure variants and copy number variations (CNV) is still being a challenge for targeted MPS. Some approaches and tools for structural variants detection were proposed, but they have limitations and often require datasets of certain type, size and expected number of amplicons affected by CNVs. In the paper, we describe novel algorithm for high-resolution germinal CNV detection in the PCR-enriched targeted sequencing data and present accompanying tool. We have developed a machine learning algorithm for the detection of large duplications and deletions in the targeted sequencing data generated with PCR-based enrichment step. We have performed verification studies and established the algorithm's sensitivity and specificity. We have compared developed tool with other available methods applicable for the described data and revealed its higher performance. We showed that our method has high specificity and sensitivity for high-resolution copy number detection in targeted sequencing data using large cohort of samples.

A Refined Computer Harassment Paradigm: Validation, and Test of Hypotheses about Target Characteristics

ERIC Educational Resources Information Center

Siebler, Frank; Sabelus, Saskia; Bohner, Gerd

2008-01-01

A refined computer paradigm for assessing sexual harassment is presented, validated, and used for testing substantive hypotheses. Male participants were given an opportunity to send sexist jokes to a computer-simulated female chat partner. In Study 1 (N = 44), the harassment measure (number of sexist jokes sent) correlated positively with…

Aircraft target detection algorithm based on high resolution spaceborne SAR imagery

NASA Astrophysics Data System (ADS)

Zhang, Hui; Hao, Mengxi; Zhang, Cong; Su, Xiaojing

2018-03-01

In this paper, an image classification algorithm for airport area is proposed, which based on the statistical features of synthetic aperture radar (SAR) images and the spatial information of pixels. The algorithm combines Gamma mixture model and MRF. The algorithm using Gamma mixture model to obtain the initial classification result. Pixel space correlation based on the classification results are optimized by the MRF technique. Additionally, morphology methods are employed to extract airport (ROI) region where the suspected aircraft target samples are clarified to reduce the false alarm and increase the detection performance. Finally, this paper presents the plane target detection, which have been verified by simulation test.

New target and detection methods: active detectors

NASA Astrophysics Data System (ADS)

Mittig, W.; Savajols, H.; Demonchy, C. E.; Giot, L.; Roussel-Chomaz, P.; Wang, H.; Ter-Akopian, G.; Fomichev, A.; Golovkov, M. S.; Stepansov, S.; Wolski, R.; Alamanos, N.; Drouart, A.; Gillibert, A.; Lapoux, V.; Pollacco, E.

2003-07-01

The study of nuclei far from stability interacting with simple target nuclei, such as protons, deuterons, 3He and 4He implies the use of inverse kinematics. The very special kinematics, together with the low intensities of the beams calls for special techniques. In july 2002 we tested a new detector, in which the detector gas is the target. This allows in principle a 4π solid angle of the detection, and a big effective target thickness without loss of resolution. The detector developped, called Maya, used isobuthane C4H10 as gas in present tests, and other gases are possible. The multiplexed electronics of more than 1000channels allows the reconstruction of the events occuring between the incoming particle and the detector gas atoms in 3D. Here we were interested in the elastic scattering of 8He on protons for the study of the isobaric analogue states (IAS) of 9He. The beam, in this case, is stopped in the detector. The resonance energy is determined by the place of interaction and the energy of the recoiling proton. The design of the detector is shown, together with some preliminary results are discussed.

Location detection and tracking of moving targets by a 2D IR-UWB radar system.

PubMed

Nguyen, Van-Han; Pyun, Jae-Young

2015-03-19

In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.

Key parameters design of an aerial target detection system on a space-based platform

NASA Astrophysics Data System (ADS)

Zhu, Hanlu; Li, Yejin; Hu, Tingliang; Rao, Peng

2018-02-01

To ensure flight safety of an aerial aircraft and avoid recurrence of aircraft collisions, a method of multi-information fusion is proposed to design the key parameter to realize aircraft target detection on a space-based platform. The key parameters of a detection wave band and spatial resolution using the target-background absolute contrast, target-background relative contrast, and signal-to-clutter ratio were determined. This study also presented the signal-to-interference ratio for analyzing system performance. Key parameters are obtained through the simulation of a specific aircraft. And the simulation results show that the boundary ground sampling distance is 30 and 35 m in the mid- wavelength infrared (MWIR) and long-wavelength infrared (LWIR) bands for most aircraft detection, and the most reasonable detection wavebands is 3.4 to 4.2 μm and 4.35 to 4.5 μm in the MWIR bands, and 9.2 to 9.8 μm in the LWIR bands. We also found that the direction of detection has a great impact on the detection efficiency, especially in MWIR bands.

Algorithm for Automatic Detection, Localization and Characterization of Magnetic Dipole Targets Using the Laser Scalar Gradiometer

DTIC Science & Technology

2016-06-01

TECHNICAL REPORT Algorithm for Automatic Detection, Localization and Characterization of Magnetic Dipole Targets Using the Laser Scalar...Automatic Detection, Localization and Characterization of Magnetic Dipole Targets Using the Laser Scalar Gradiometer Leon Vaizer, Jesse Angle, Neil...of Magnetic Dipole Targets Using LSG i June 2016 TABLE OF CONTENTS INTRODUCTION

Visual working memory modulates low-level saccade target selection: Evidence from rapidly generated saccades in the global effect paradigm

PubMed Central

Hollingworth, Andrew; Matsukura, Michi; Luck, Steven J.

2013-01-01

In three experiments, we examined the influence of visual working memory (VWM) on the metrics of saccade landing position in a global effect paradigm. Participants executed a saccade to the more eccentric object in an object pair appearing on the horizontal midline, to the left or right of central fixation. While completing the saccade task, participants maintained a color in VWM for an unrelated memory task. Either the color of the saccade target matched the memory color (target match), the color of the distractor matched the memory color (distractor match), or the colors of neither object matched the memory color (no match). In the no-match condition, saccades tended to land at the midpoint between the two objects: the global, or averaging, effect. However, when one of the two objects matched VWM, the distribution of landing position shifted toward the matching object, both for target match and for distractor match. VWM modulation of landing position was observed even for the fastest quartile of saccades, with a mean latency as low as 112 ms. Effects of VWM on such rapidly generated saccades, with latencies in the express-saccade range, indicate that VWM interacts with the initial sweep of visual sensory processing, modulating perceptual input to oculomotor systems and thereby biasing oculomotor selection. As a result, differences in memory match produce effects on landing position similar to the effects generated by differences in physical salience. PMID:24190909

Targeted Feature Detection for Data-Dependent Shotgun Proteomics.

PubMed

Weisser, Hendrik; Choudhary, Jyoti S

2017-08-04

Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known

Infrared maritime target detection using the high order statistic filtering in fractional Fourier domain

NASA Astrophysics Data System (ADS)

Zhou, Anran; Xie, Weixin; Pei, Jihong

2018-06-01

Accurate detection of maritime targets in infrared imagery under various sea clutter conditions is always a challenging task. The fractional Fourier transform (FRFT) is the extension of the Fourier transform in the fractional order, and has richer spatial-frequency information. By combining it with the high order statistic filtering, a new ship detection method is proposed. First, the proper range of angle parameter is determined to make it easier for the ship components and background to be separated. Second, a new high order statistic curve (HOSC) at each fractional frequency point is designed. It is proved that maximal peak interval in HOSC reflects the target information, while the points outside the interval reflect the background. And the value of HOSC relative to the ship is much bigger than that to the sea clutter. Then, search the curve's maximal target peak interval and extract the interval by bandpass filtering in fractional Fourier domain. The value outside the peak interval of HOSC decreases rapidly to 0, so the background is effectively suppressed. Finally, the detection result is obtained by the double threshold segmenting and the target region selection method. The results show the proposed method is excellent for maritime targets detection with high clutters.

Poverty eradication: a new paradigm.

PubMed

Pethe, V P

1998-08-01

This article offers a new paradigm for eradicating poverty in India. It was assumed incorrectly by Mahatma Gandhi that a good society without mass poverty would follow after independence. India copied Western models of development and developed giant factories, big dams, and megacities. Agriculture did not expand the number of jobs for people. The Western paradigm failed in India because of the false assumption of "trickle down" of income to the masses. The targeted programs to the poor did not directly benefit enough of the poor. Mega-industrialization led to reduced employment and higher skill needs. The model failed mainly because it was a proxy and relied on indirect ways of reaching the poor. The models failed to be adapted to conditions in India. The Swadeshi paradigm is a direct model for addressing mass poverty. Poverty is affected by immediate, intermediate, and ultimate determinants. Poverty begets social and economic problems, such as ignorance, ill health, high fertility, unemployment, and crime. In India and developing countries, mass poverty results from under use of human resources; lack of equal opportunities; and an outdated non-egalitarian social structure, an unjust global economic order, human cruelty, and erosion of ethical values. Indians are squandering their precious resources mimicking Western consumerism. Poverty leads to rapid population growth. People become productive assets with universal literacy, compulsory and free education, health services and sanitation, vocational training, and work ethics. India needs people-oriented policies with less emphasis on capital accumulation.

Staircase-scene-based nonuniformity correction in aerial point target detection systems.

PubMed

Huo, Lijun; Zhou, Dabiao; Wang, Dejiang; Liu, Rang; He, Bin

2016-09-01

Focal-plane arrays (FPAs) are often interfered by heavy fixed-pattern noise, which severely degrades the detection rate and increases the false alarms in airborne point target detection systems. Thus, high-precision nonuniformity correction is an essential preprocessing step. In this paper, a new nonuniformity correction method is proposed based on a staircase scene. This correction method can compensate for the nonlinear response of the detector and calibrate the entire optical system with computational efficiency and implementation simplicity. Then, a proof-of-concept point target detection system is established with a long-wave Sofradir FPA. Finally, the local standard deviation of the corrected image and the signal-to-clutter ratio of the Airy disk of a Boeing B738 are measured to evaluate the performance of the proposed nonuniformity correction method. Our experimental results demonstrate that the proposed correction method achieves high-quality corrections.

Three plot correlation-based small infrared target detection in dense sun-glint environment for infrared search and track

NASA Astrophysics Data System (ADS)

Kim, Sungho; Choi, Byungin; Kim, Jieun; Kwon, Soon; Kim, Kyung-Tae

2012-05-01

This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based infrared search and track (IRST) problem in dense sun-glint environment. It is critical to detect small infrared targets such as sea-skimming missiles or asymmetric small ships for national defense. On the sea surface, sun-glint clutters degrade the detection performance. Furthermore, if we have to detect true targets using only three images with a low frame rate camera, then the problem is more difficult. We propose a novel three plot correlation filter and statistics based clutter reduction method to achieve robust small target detection rate in dense sun-glint environment. We validate the robust detection performance of the proposed method via real infrared test sequences including synthetic targets.

Target-protecting dumbbell molecular probe against exonucleases digestion for sensitive detection of ATP and streptavidin.

PubMed

Chen, Jinyang; Liu, Yucheng; Ji, Xinghu; He, Zhike

2016-09-15

In this work, a versatile dumbbell molecular (DM) probe was designed and employed in the sensitively homogeneous bioassay. In the presence of target molecule, the DM probe was protected from the digestion of exonucleases. Subsequently, the protected DM probe specifically bound to the intercalation dye and resulted in obvious fluorescence signal which was used to determine the target molecule in return. This design allows specific and versatile detection of diverse targets with easy operation and no sophisticated fluorescence labeling. Integrating the idea of target-protecting DM probe with adenosine triphosphate (ATP) involved ligation reaction, the DM probe with 5'-end phosphorylation was successfully constructed for ATP detection, and the limitation of detection was found to be 4.8 pM. Thanks to its excellent selectivity and sensitivity, this sensing strategy was used to detect ATP spiked in human serum as well as cellular ATP. Moreover, the proposed strategy was also applied in the visual detection of ATP in droplet-based microfluidic platform with satisfactory results. Similarly, combining the principle of target-protecting DM probe with streptavidin (SA)-biotin interaction, the DM probe with 3'-end biotinylation was developed for selective and sensitive SA determination, which demonstrated the robustness and versatility of this design. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Automatic detection of small surface targets with electro-optical sensors in a harbor environment

NASA Astrophysics Data System (ADS)

Bouma, Henri; de Lange, Dirk-Jan J.; van den Broek, Sebastiaan P.; Kemp, Rob A. W.; Schwering, Piet B. W.

2008-10-01

In modern warfare scenarios naval ships must operate in coastal environments. These complex environments, in bays and narrow straits, with cluttered littoral backgrounds and many civilian ships may contain asymmetric threats of fast targets, such as rhibs, cabin boats and jet-skis. Optical sensors, in combination with image enhancement and automatic detection, assist an operator to reduce the response time, which is crucial for the protection of the naval and land-based supporting forces. In this paper, we present our work on automatic detection of small surface targets which includes multi-scale horizon detection and robust estimation of the background intensity. To evaluate the performance of our detection technology, data was recorded with both infrared and visual-light cameras in a coastal zone and in a harbor environment. During these trials multiple small targets were used. Results of this evaluation are shown in this paper.

Hand-held optical imager (Gen-2): improved instrumentation and target detectability

PubMed Central

Gonzalez, Jean; DeCerce, Joseph; Erickson, Sarah J.; Martinez, Sergio L.; Nunez, Annie; Roman, Manuela; Traub, Barbara; Flores, Cecilia A.; Roberts, Seigbeh M.; Hernandez, Estrella; Aguirre, Wenceslao; Kiszonas, Richard

2012-01-01

Abstract. Hand-held optical imagers are developed by various researchers towards reflectance-based spectroscopic imaging of breast cancer. Recently, a Gen-1 handheld optical imager was developed with capabilities to perform two-dimensional (2-D) spectroscopic as well as three-dimensional (3-D) tomographic imaging studies. However, the imager was bulky with poor surface contact (∼30%) along curved tissues, and limited sensitivity to detect targets consistently. Herein, a Gen-2 hand-held optical imager that overcame the above limitations of the Gen-1 imager has been developed and the instrumentation described. The Gen-2 hand-held imager is less bulky, portable, and has improved surface contact (∼86%) on curved tissues. Additionally, the forked probe head design is capable of simultaneous bilateral reflectance imaging of both breast tissues, and also transillumination imaging of a single breast tissue. Experimental studies were performed on tissue phantoms to demonstrate the improved sensitivity in detecting targets using the Gen-2 imager. The improved instrumentation of the Gen-2 imager allowed detection of targets independent of their location with respect to the illumination points, unlike in Gen-1 imager. The developed imager has potential for future clinical breast imaging with enhanced sensitivity, via both reflectance and transillumination imaging. PMID:23224163

Effect of radar frequency on the detection of shaped (low RCS) targets

NASA Astrophysics Data System (ADS)

Moraitis, D.; Alland, S.

The use of shaping to reduce the radar cross-section (RCS) of aircraft and missiles can result in the RCS varying significantly with radar operating frequency. This RCS sensitivity to frequency should be considered when selecting radar frequency and should be accounted for when evaluating radar performance. A detection range increase for shaped (low RCS) targets of a factor of two or greater can be realized for lower frequency radar (e.g., UHF-Band or L-Band) when compared to higher frequency radar (C-Band or X-Band). For low flying (sea skimming) targets, the RCS variation with frequency for shaped (low RCS) targets neutralizes the advantage that higher radar frequencies realize in multipath propagation resulting in approximately the same detection range across the radar bands from UHF to X-Band.

Drivers' detection of roadside targets when driving vehicles with three headlight systems during high beam activation.

PubMed

Reagan, Ian J; Brumbelow, Matthew L

2017-02-01

A previous open-road experiment indicated that curve-adaptive HID headlights driven with low beams improved drivers' detection of low conspicuity targets compared with fixed halogen and fixed HID low beam systems. The current study used the same test environment and targets to assess whether drivers' detection of targets was affected by the same three headlight systems when using high beams. Twenty drivers search and responded for 60 8×12inch targets of high or low reflectance that were distributed evenly across straight and curved road sections as they drove at 30 mph on an unlit two-lane rural road. The results indicate that target detection performance was generally similar across the three systems. However, one interaction indicated that drivers saw low reflectance targets on straight road sections from further away when driving with the fixed halogen high beam condition compared with curve-adaptive HID high beam headlights and also indicated a possible benefit for the curve-adaptive HID high beams for high reflectance targets placed on the inside of curves. The results of this study conflict with the previous study of low beams, which showed a consistent benefit for the curve-adaptive HID low beams for targets placed on curves compared with fixed HID and fixed halogen low beam conditions. However, a comparison of mean detection distances from the two studies indicated uniformly longer mean target detection distances for participants driving with high beams and implicates the potential visibility benefits for systems that optimize proper high beam use. Copyright © 2016 Elsevier Ltd. All rights reserved.

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array.

PubMed

Li, Miao; Li, Jun; Zhou, Yiyu

2015-12-08

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts-MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing.

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array

PubMed Central

Li, Miao; Li, Jun; Zhou, Yiyu

2015-01-01

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts—MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing. PMID:26670234

Research on the algorithm of infrared target detection based on the frame difference and background subtraction method

NASA Astrophysics Data System (ADS)

Liu, Yun; Zhao, Yuejin; Liu, Ming; Dong, Liquan; Hui, Mei; Liu, Xiaohua; Wu, Yijian

2015-09-01

As an important branch of infrared imaging technology, infrared target tracking and detection has a very important scientific value and a wide range of applications in both military and civilian areas. For the infrared image which is characterized by low SNR and serious disturbance of background noise, an innovative and effective target detection algorithm is proposed in this paper, according to the correlation of moving target frame-to-frame and the irrelevance of noise in sequential images based on OpenCV. Firstly, since the temporal differencing and background subtraction are very complementary, we use a combined detection method of frame difference and background subtraction which is based on adaptive background updating. Results indicate that it is simple and can extract the foreground moving target from the video sequence stably. For the background updating mechanism continuously updating each pixel, we can detect the infrared moving target more accurately. It paves the way for eventually realizing real-time infrared target detection and tracking, when transplanting the algorithms on OpenCV to the DSP platform. Afterwards, we use the optimal thresholding arithmetic to segment image. It transforms the gray images to black-white images in order to provide a better condition for the image sequences detection. Finally, according to the relevance of moving objects between different frames and mathematical morphology processing, we can eliminate noise, decrease the area, and smooth region boundaries. Experimental results proves that our algorithm precisely achieve the purpose of rapid detection of small infrared target.

Identification of letters in the predesignated target paradigm: a word superiority effect for the common word the.

PubMed

Peterzell, D H; Sinclair, G P; Healy, A F; Bourne, L E

1990-01-01

The size of the perceptual unit used in reading was addressed using the predesignated target paradigm. Sixteen subjects viewed the following stimuli in random order: the words tee, the, tie, and toe; the nonwords eet, eht, eit, and eot; and the letters e, h, i, and o. Subjects fixated on the location of the center letter and identified the letter as e, h, i, or o, alternatives which were known to them at the onset. A word superiority effect was obtained for the common word the but not for the less common words tee, tie, and toe. The word superiority effect was attributable to bias rather than discriminability: Subjects exhibited a bias to perceive the words in this experiment as the (i.e., there was a bias to perceive h in the t e stimulus presentations). These results suggest that the common word the is processed in reading units that are larger than the letter, and that the system is biased to perceive common rather than uncommon words in data-limited conditions.

An Optimized Hidden Node Detection Paradigm for Improving the Coverage and Network Efficiency in Wireless Multimedia Sensor Networks.

PubMed

Alanazi, Adwan; Elleithy, Khaled

2016-09-07

Successful transmission of online multimedia streams in wireless multimedia sensor networks (WMSNs) is a big challenge due to their limited bandwidth and power resources. The existing WSN protocols are not completely appropriate for multimedia communication. The effectiveness of WMSNs varies, and it depends on the correct location of its sensor nodes in the field. Thus, maximizing the multimedia coverage is the most important issue in the delivery of multimedia contents. The nodes in WMSNs are either static or mobile. Thus, the node connections change continuously due to the mobility in wireless multimedia communication that causes an additional energy consumption, and synchronization loss between neighboring nodes. In this paper, we introduce an Optimized Hidden Node Detection (OHND) paradigm. The OHND consists of three phases: hidden node detection, message exchange, and location detection. These three phases aim to maximize the multimedia node coverage, and improve energy efficiency, hidden node detection capacity, and packet delivery ratio. OHND helps multimedia sensor nodes to compute the directional coverage. Furthermore, an OHND is used to maintain a continuous node- continuous neighbor discovery process in order to handle the mobility of the nodes. We implement our proposed algorithms by using a network simulator (NS2). The simulation results demonstrate that nodes are capable of maintaining direct coverage and detecting hidden nodes in order to maximize coverage and multimedia node mobility. To evaluate the performance of our proposed algorithms, we compared our results with other known approaches.

An Optimized Hidden Node Detection Paradigm for Improving the Coverage and Network Efficiency in Wireless Multimedia Sensor Networks

PubMed Central

Alanazi, Adwan; Elleithy, Khaled

2016-01-01

Successful transmission of online multimedia streams in wireless multimedia sensor networks (WMSNs) is a big challenge due to their limited bandwidth and power resources. The existing WSN protocols are not completely appropriate for multimedia communication. The effectiveness of WMSNs varies, and it depends on the correct location of its sensor nodes in the field. Thus, maximizing the multimedia coverage is the most important issue in the delivery of multimedia contents. The nodes in WMSNs are either static or mobile. Thus, the node connections change continuously due to the mobility in wireless multimedia communication that causes an additional energy consumption, and synchronization loss between neighboring nodes. In this paper, we introduce an Optimized Hidden Node Detection (OHND) paradigm. The OHND consists of three phases: hidden node detection, message exchange, and location detection. These three phases aim to maximize the multimedia node coverage, and improve energy efficiency, hidden node detection capacity, and packet delivery ratio. OHND helps multimedia sensor nodes to compute the directional coverage. Furthermore, an OHND is used to maintain a continuous node– continuous neighbor discovery process in order to handle the mobility of the nodes. We implement our proposed algorithms by using a network simulator (NS2). The simulation results demonstrate that nodes are capable of maintaining direct coverage and detecting hidden nodes in order to maximize coverage and multimedia node mobility. To evaluate the performance of our proposed algorithms, we compared our results with other known approaches. PMID:27618048

Target Detection of Quantum Illumination Receiver Based on Photon-subtracted Entanglement State

NASA Astrophysics Data System (ADS)

Chi, Jiao; Liu, HongJun; Huang, Nan; Wang, ZhaoLu

2017-12-01

We theoretically propose a quantum illumination receiver based on the ideal photon-subtracted two-mode squeezed state (PSTMSS) to efficiently detect the noise-hidden target. This receiver is generated by applying an optical parametric amplifier (OPA) to the cross correlation detection. With analyzing the output performance, it is found that OPA as a preposition technology of the receiver can contribute to the PSTMSS by significantly reducing the error probability than that of the general two-mode squeezed state (TMSS). Comparing with TMSS, the signal-to-noise ratio of quantum illumination based on ideal PSTMSS and OPA is improved more than 4 dB under an optimal gain of OPA. This work may provide a potential improvement in the application of accurate target detection when two kinds of resource have the identical real squeezing parameter.

Multisensor fusion for 3D target tracking using track-before-detect particle filter

NASA Astrophysics Data System (ADS)

Moshtagh, Nima; Romberg, Paul M.; Chan, Moses W.

2015-05-01

This work presents a novel fusion mechanism for estimating the three-dimensional trajectory of a moving target using images collected by multiple imaging sensors. The proposed projective particle filter avoids the explicit target detection prior to fusion. In projective particle filter, particles that represent the posterior density (of target state in a high-dimensional space) are projected onto the lower-dimensional observation space. Measurements are generated directly in the observation space (image plane) and a marginal (sensor) likelihood is computed. The particles states and their weights are updated using the joint likelihood computed from all the sensors. The 3D state estimate of target (system track) is then generated from the states of the particles. This approach is similar to track-before-detect particle filters that are known to perform well in tracking dim and stealthy targets in image collections. Our approach extends the track-before-detect approach to 3D tracking using the projective particle filter. The performance of this measurement-level fusion method is compared with that of a track-level fusion algorithm using the projective particle filter. In the track-level fusion algorithm, the 2D sensor tracks are generated separately and transmitted to a fusion center, where they are treated as measurements to the state estimator. The 2D sensor tracks are then fused to reconstruct the system track. A realistic synthetic scenario with a boosting target was generated, and used to study the performance of the fusion mechanisms.

Hyperspectral data collection for the assessment of target detection algorithms: the Viareggio 2013 trial

NASA Astrophysics Data System (ADS)

Rossi, Alessandro; Acito, Nicola; Diani, Marco; Corsini, Giovanni; De Ceglie, Sergio Ugo; Riccobono, Aldo; Chiarantini, Leandro

2014-10-01

Airborne hyperspectral imagery is valuable for military and civilian applications, such as target identification, detection of anomalies and changes within multiple acquisitions. In target detection (TD) applications, the performance assessment of different algorithms is an important and critical issue. In this context, the small number of public available hyperspectral data motivated us to perform an extensive measurement campaign including various operating scenarios. The campaign was organized by CISAM in cooperation with University of Pisa, Selex ES and CSSN-ITE, and it was conducted in Viareggio, Italy in May, 2013. The Selex ES airborne hyperspectral sensor SIM.GA was mounted on board of an airplane to collect images over different sites in the morning and afternoon of two subsequent days. This paper describes the hyperspectral data collection of the trial. Four different sites were set up, representing a complex urban scenario, two parking lots and a rural area. Targets with dimensions comparable to the sensor ground resolution were deployed in the sites to reproduce different operating situations. An extensive ground truth documentation completes the data collection. Experiments to test anomalous change detection techniques were set up changing the position of the deployed targets. Search and rescue scenarios were simulated to evaluate the performance of anomaly detection algorithms. Moreover, the reflectance signatures of the targets were measured on the ground to perform spectral matching in varying atmospheric and illumination conditions. The paper presents some preliminary results that show the effectiveness of hyperspectral data exploitation for the object detection tasks of interest in this work.

Small maritime target detection through false color fusion

NASA Astrophysics Data System (ADS)

Toet, Alexander; Wu, Tirui

2008-04-01

We present an algorithm that produces a fused false color representation of a combined multiband IR and visual imaging system for maritime applications. Multispectral IR imaging techniques are increasingly deployed in maritime operations, to detect floating mines or to find small dinghies and swimmers during search and rescue operations. However, maritime backgrounds usually contain a large amount of clutter that severely hampers the detection of small targets. Our new algorithm deploys the correlation between the target signatures in two different IR frequency bands (3-5 and 8-12 μm) to construct a fused IR image with a reduced amount of clutter. The fused IR image is then combined with a visual image in a false color RGB representation for display to a human operator. The algorithm works as follows. First, both individual IR bands are filtered with a morphological opening top-hat transform to extract small details. Second, a common image is extracted from the two filtered IR bands, and assigned to the red channel of an RGB image. Regions of interest that appear in both IR bands remain in this common image, while most uncorrelated noise details are filtered out. Third, the visual band is assigned to the green channel and, after multiplication with a constant (typically 1.6) also to the blue channel. Fourth, the brightness and colors of this intermediate false color image are renormalized by adjusting its first order statistics to those of a representative reference scene. The result of these four steps is a fused color image, with naturalistic colors (bluish sky and grayish water), in which small targets are clearly visible.

Two-dimensional hidden semantic information model for target saliency detection and eyetracking identification

NASA Astrophysics Data System (ADS)

Wan, Weibing; Yuan, Lingfeng; Zhao, Qunfei; Fang, Tao

2018-01-01

Saliency detection has been applied to the target acquisition case. This paper proposes a two-dimensional hidden Markov model (2D-HMM) that exploits the hidden semantic information of an image to detect its salient regions. A spatial pyramid histogram of oriented gradient descriptors is used to extract features. After encoding the image by a learned dictionary, the 2D-Viterbi algorithm is applied to infer the saliency map. This model can predict fixation of the targets and further creates robust and effective depictions of the targets' change in posture and viewpoint. To validate the model with a human visual search mechanism, two eyetrack experiments are employed to train our model directly from eye movement data. The results show that our model achieves better performance than visual attention. Moreover, it indicates the plausibility of utilizing visual track data to identify targets.

Target Detection and Classification Using Seismic and PIR Sensors

DTIC Science & Technology

2012-06-01

time series analysis via wavelet - based partitioning,” Signal Process...regard, this paper presents a wavelet - based method for target detection and classification. The proposed method has been validated on data sets of...The work reported in this paper makes use of a wavelet - based feature extraction method , called Symbolic Dynamic Filtering (SDF) [12]–[14]. The

A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature

NASA Astrophysics Data System (ADS)

Xu, Minpeng; Qi, Hongzhi; Wan, Baikun; Yin, Tao; Liu, Zhipeng; Ming, Dong

2013-04-01

Objective. Hybrid brain-computer interfaces (BCIs) have been proved to be more effective in mental control by combining another channel of physiologic control signals. Among those studies, little attention has been paid to the combined use of a steady-state visual evoked potential (SSVEP) and P300 potential, both providing the fastest and the most reliable EEG based BCIs. In this paper, a novel hybrid BCI speller is developed to elicit P300 potential and SSVEP blocking (SSVEP-B) distinctly and simultaneously with the same target stimulus. Approach. Twelve subjects were involved in the study and every one performed offline spelling twice in succession with two different speller paradigms for comparison: hybrid speller and control P300-speller. Feature analysis was adopted from the view of time domain, frequency domain and spatial distribution; the performances were evaluated by character accuracy and information transfer rate (ITR). Main results. Signal analysis of the hybrid paradigm shows that SSVEPs are an evident EEG component during the nontarget phase but are dismissed and replaced by P300 potentials after target stimuli. The absence of an SSVEP, called SSVEP-B, mostly appearing in channel Oz, presents a sharp distinction between target responses and nontarget responses. The r2 value of SSVEP-B in channel Oz is comparable to that of P300 in channel Cz. Compared with the control P300-speller, the hybrid speller achieves significantly higher accuracy and ITR with combined features. Significance. The results indicate that the combination of P300 with an SSVEP-B improves target discrimination greatly; the proposed hybrid paradigm is superior to the control paradigm in spelling performance. Thus, our findings provide a new approach to improve BCI performances.

Attention and eye-movement control in reading: The selective reading paradigm.

PubMed

Reingold, Eyal M; Sheridan, Heather; Meadmore, Katie L; Drieghe, Denis; Liversedge, Simon P

2016-12-01

We introduced a novel paradigm for investigating covert attention and eye-movement control in reading. In 2 experiments, participants read sentence words (shown in blue color) while ignoring interleaved distractor strings (shown in orange color). Each single-line text display contained a target word and a critical distractor. Critical distractors were located just prior to the target in the text and were either words or symbol strings (e.g., @#%&). Target word availability for parafoveal processing (i.e., preview validity) was also manipulated. The results indicated much shallower processing of distractors than targets, and this pattern was more pronounced for symbol than word distractors. The influences of word frequency and fixation location on first-pass fixation durations on distractors were dramatically different than the well-documented pattern obtained in normal reading. Robust preview benefits were demonstrated both when the critical distractors were fixated and when the critical distractors were skipped. Finally, with the exception of larger preview benefits that were obtained in the condition in which the target and critical distractor were identical, the magnitude of the preview effect was largely unaffected by the nature of the critical distractor. Implications of the present paradigm and findings to the study of eye-movement control in reading are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Switchable DNA interfaces for the highly sensitive detection of label-free DNA targets.

PubMed

Rant, Ulrich; Arinaga, Kenji; Scherer, Simon; Pringsheim, Erika; Fujita, Shozo; Yokoyama, Naoki; Tornow, Marc; Abstreiter, Gerhard

2007-10-30

We report a method to detect label-free oligonucleotide targets. The conformation of surface-tethered probe nucleic acids is modulated by alternating electric fields, which cause the molecules to extend away from or fold onto the biased surface. Binding (hybridization) of targets to the single-stranded probes results in a pronounced enhancement of the layer-height modulation amplitude, monitored optically in real time. The method features an exceptional detection limit of <3 x 10(8) bound targets per cm(2) sensor area. Single base-pair mismatches in the sequences of DNA complements may readily be identified; moreover, binding kinetics and binding affinities can be determined with high accuracy. When driving the DNA to oscillate at frequencies in the kHz regime, distinct switching kinetics are revealed for single- and double-stranded DNA. Molecular dynamics are used to identify the binding state of molecules according to their characteristic kinetic fingerprints by using a chip-compatible detection format.

Switchable DNA interfaces for the highly sensitive detection of label-free DNA targets

PubMed Central

Rant, Ulrich; Arinaga, Kenji; Scherer, Simon; Pringsheim, Erika; Fujita, Shozo; Yokoyama, Naoki; Tornow, Marc; Abstreiter, Gerhard

2007-01-01

We report a method to detect label-free oligonucleotide targets. The conformation of surface-tethered probe nucleic acids is modulated by alternating electric fields, which cause the molecules to extend away from or fold onto the biased surface. Binding (hybridization) of targets to the single-stranded probes results in a pronounced enhancement of the layer-height modulation amplitude, monitored optically in real time. The method features an exceptional detection limit of <3 × 108 bound targets per cm2 sensor area. Single base-pair mismatches in the sequences of DNA complements may readily be identified; moreover, binding kinetics and binding affinities can be determined with high accuracy. When driving the DNA to oscillate at frequencies in the kHz regime, distinct switching kinetics are revealed for single- and double-stranded DNA. Molecular dynamics are used to identify the binding state of molecules according to their characteristic kinetic fingerprints by using a chip-compatible detection format. PMID:17951434

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Shimozaki, Steven S.; Abbey, Craig K.

2002-01-01

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Twin target self-amplification-based DNA machine for highly sensitive detection of cancer-related gene.

PubMed

Xu, Huo; Jiang, Yifan; Liu, Dengyou; Liu, Kai; Zhang, Yafeng; Yu, Suhong; Shen, Zhifa; Wu, Zai-Sheng

2018-06-29

The sensitive detection of cancer-related genes is of great significance for early diagnosis and treatment of human cancers, and previous isothermal amplification sensing systems were often based on the reuse of target DNA, the amplification of enzymatic products and the accumulation of reporting probes. However, no reporting probes are able to be transformed into target species and in turn initiate the signal of other probes. Herein we reported a simple, isothermal and highly sensitive homogeneous assay system for tumor suppressor p53 gene detection based on a new autonomous DNA machine, where the signaling probe, molecular beacon (MB), was able to execute the function similar to target DNA besides providing the common signal. In the presence of target p53 gene, the operation of DNA machine can be initiated, and cyclical nucleic acid strand-displacement polymerization (CNDP) and nicking/polymerization cyclical amplification (NPCA) occur, during which the MB was opened by target species and cleaved by restriction endonuclease. In turn, the cleaved fragments could activate the next signaling process as target DNA did. According to the functional similarity, the cleaved fragment was called twin target, and the corresponding fashion to amplify the signal was named twin target self-amplification. Utilizing this newly-proposed DNA machine, the target DNA could be detected down to 0.1 pM with a wide dynamic range (6 orders of magnitude) and single-base mismatched targets were discriminated, indicating a very high assay sensitivity and good specificity. In addition, the DNA machine was not only used to screen the p53 gene in complex biological matrix but also was capable of practically detecting genomic DNA p53 extracted from A549 cell line. This indicates that the proposed DNA machine holds the potential application in biomedical research and early clinical diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Efficacy of sacubitril/valsartan vs. enalapril at lower than target doses in heart failure with reduced ejection fraction: the PARADIGM-HF trial.

PubMed

Vardeny, Orly; Claggett, Brian; Packer, Milton; Zile, Michael R; Rouleau, Jean; Swedberg, Karl; Teerlink, John R; Desai, Akshay S; Lefkowitz, Martin; Shi, Victor; McMurray, John J V; Solomon, Scott D

2016-10-01

In this analysis, we utilized data from PARADIGM-HF to test the hypothesis that participants who exhibited any dose reduction during the trial would have similar benefits from lower doses of sacubitril/valsartan relative to lower doses of enalapril. In a post-hoc analysis from PARADIGM-HF, we characterized patients by whether they received the maximal dose (200 mg sacubitril/valsartan or 10 mg enalapril twice daily) throughout the trial or had any dose reduction to lower doses (100/50/0 mg sacubitril/valsartan or 5/2.5/0 mg enalapril twice daily). The treatment effect for the primary outcome was estimated, stratified by dose level using time-updated Cox regression models. In the two treatment arms, participants with a dose reduction (43% of those randomized to enalapril and 42% of those randomized to sacubitril/valsartan) had similar baseline characteristics and similar baseline predictors of the need for dose reduction. In a time-updated analysis, any dose reduction was associated with a higher subsequent risk of the primary event [hazard ratio (HR) 2.5, 95% confidence interval (CI) 2.2-2.7]. However, the treatment benefit of sacubitril/valsartan over enalapril following a dose reduction was similar (HR 0.80, 95% CI 0.70-0.93, P < 0.001) to that observed in patients who had not experienced any dose reduction (HR 0.79, 95% CI 0.71-0.88, P < 0.001). In PARADIGM-HF, study medication dose reduction identified patients at higher risk of a major cardiovascular event. The magnitude of benefit for patients on lower doses of sacubitril/valsartan relative to those on lower doses of enalapril was similar to that of patients who remained on target doses of both drugs. © 2016 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

Rivaling paradigms in psychiatric neurosurgery: adjustability versus quick fix versus minimal-invasiveness.

PubMed

Müller, Sabine; Riedmüller, Rita; van Oosterhout, Ansel

2015-01-01

In the wake of deep brain stimulation (DBS) development, ablative neurosurgical procedures are seeing a comeback, although they had been discredited and nearly completely abandoned in the 1970s because of their unethical practice. Modern stereotactic ablative procedures as thermal or radiofrequency ablation, and particularly radiosurgery (e.g., Gamma Knife) are much safer than the historical procedures, so that a re-evaluation of this technique is required. The different approaches of modern psychiatric neurosurgery refer to different paradigms: microsurgical ablative procedures is based on the paradigm 'quick fix,' radiosurgery on the paradigm 'minimal-invasiveness,' and DBS on the paradigm 'adjustability.' From a mere medical perspective, none of the procedures is absolutely superior; rather, they have different profiles of advantages and disadvantages. Therefore, individual factors are crucial in decision-making, particularly the patients' social situation, individual preferences, and individual attitudes. The different approaches are not only rivals, but also enriching mutually. DBS is preferable for exploring new targets, which may become candidates for ablative microsurgery or radiosurgery.

Targeted Feature Detection for Data-Dependent Shotgun Proteomics

PubMed Central

2017-01-01

Label-free quantification of shotgun LC–MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification (“FFId”), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between “internal” and “external” (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the “uncertain” feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards

Epidermal Growth Factor Receptor targeting in non-small cell lung cancer: revisiting different strategies against the same target.

PubMed

Castañón, Eduardo; Martín, Patricia; Rolfo, Christian; Fusco, Juan P; Ceniceros, Lucía; Legaspi, Jairo; Santisteban, Marta; Gil-Bazo, Ignacio

2014-01-01

Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) have changed the paradigm of treatment in non-small cell lung cancer (NSCLC). The molecular biology study of EGFR has led to clinical trials that select patients more accurately, regarding the presence of EGFR activating mutations. Nonetheless, a lack of response or a temporary condition of the response has been detected in patients on EGFR TKIs. This has urged to study potential resistance mechanisms underneath. The most important ones are the presence of secondary mutations in EGFR, such as T790M, or the overexpression of mesenchymal-epithelial transition factor (MET) that may explain why patients who initially respond to EGFR TKIs, may ultimately become refractory. Several approaches have been taken and new drugs both targeting EGFR resistance-mutation or MET are currently being developed. Here we review and update the EGFR biological pathway as well as the clinical data leading to approval of the EGFR TKIs currently in the market. New compounds under investigation targeting resistance mutations or dually targeting EGFR and other relevant receptors are also reviewed and discussed.

Clarifying the Narrative Paradigm.

ERIC Educational Resources Information Center

Fisher, Walter R.

1989-01-01

Replies to Rowland's article (same issue) on Fisher's views of the narrative paradigm. Clarifies the narrative paradigm by discussing three senses in which "narration" can be understood, and by indicating what the narrative paradigm is not. (SR)

Challenging paradigms in estuarine ecology and management

NASA Astrophysics Data System (ADS)

Elliott, M.; Whitfield, A. K.

2011-10-01

For many years, estuarine science has been the 'poor relation' in aquatic research - freshwater scientists ignored estuaries as they tended to get confused by salt and tides, and marine scientists were more preoccupied by large open systems. Estuaries were merely regarded by each group as either river mouths or sea inlets respectively. For the past four decades, however, estuaries (and other transitional waters) have been regarded as being ecosystems in their own right. Although often not termed as such, this has led to paradigms being generated to summarise estuarine structure and functioning and which relate to both the natural science and management of these systems. This paper defines, details and affirms these paradigms that can be grouped into those covering firstly the science (definitions, scales, linkages, productivity, tolerances and variability) and secondly the management (pressures, valuation, health and services) of estuaries. The more 'science' orientated paradigms incorporate the development and types of ecotones, the nature of stressed and variable systems (with specific reference to resilience and redundancy), the relationship between generalists and specialists produced by environmental tolerance, the relevance of scale in relation to functioning and connectivity, the sources of production and degree of productivity, the biodiversity-ecosystem functioning and the stress-subsidy debates. The more 'management' targeted paradigms include the development and effects of exogenic unmanaged pressures and endogenic managed pressures, the perception of health and the ability to manage estuaries (related to internal and external influences), and the influence of all of these on the production of ecosystem services and societal benefits.

Multispectral photoacoustic decomposition with localized regularization for detecting targeted contrast agent

NASA Astrophysics Data System (ADS)

Tavakoli, Behnoosh; Chen, Ying; Guo, Xiaoyu; Kang, Hyun Jae; Pomper, Martin; Boctor, Emad M.

2015-03-01

Targeted contrast agents can improve the sensitivity of imaging systems for cancer detection and monitoring the treatment. In order to accurately detect contrast agent concentration from photoacoustic images, we developed a decomposition algorithm to separate photoacoustic absorption spectrum into components from individual absorbers. In this study, we evaluated novel prostate-specific membrane antigen (PSMA) targeted agents for imaging prostate cancer. Three agents were synthesized through conjugating PSMA-targeting urea with optical dyes ICG, IRDye800CW and ATTO740 respectively. In our preliminary PA study, dyes were injected in a thin wall plastic tube embedded in water tank. The tube was illuminated with pulsed laser light using a tunable Q-switch ND-YAG laser. PA signal along with the B-mode ultrasound images were detected with a diagnostic ultrasound probe in orthogonal mode. PA spectrums of each dye at 0.5 to 20 μM concentrations were estimated using the maximum PA signal extracted from images which are obtained at illumination wavelengths of 700nm-850nm. Subsequently, we developed nonnegative linear least square optimization method along with localized regularization to solve the spectral unmixing. The algorithm was tested by imaging mixture of those dyes. The concentration of each dye was estimated with about 20% error on average from almost all mixtures albeit the small separation between dyes spectrums.

Emerging paradigms in anti-infective drug design.

PubMed

Barrett, Michael P; Croft, Simon L

2014-01-01

The need for new drugs to treat microbial infections is pressing. The great progress made in the middle part of the twentieth Century was followed by a period of relative inactivity as the medical needs relating to infectious disease in the wealthier nations receded. Growing realisation that anti-infectives are needed in many parts of the world, to treat neglected diseases as well as to combat the burgeoning risk of resistance to existing drugs, has galvanised a new wave of research into anti-microbial drugs. The transfer of knowledge from the Pharmaceutical industry relating to the importance of understanding how to target drugs successfully within the body, and improved understanding of how pathogens interact with their hosts, is driving a series of new paradigms in anti-infective drug design. Here we provide an overview of those processes as an introduction to a series of articles from experts in this area that emerged from a meeting entitled "Emerging Paradigms in Anti-Infective Drug Design" held in London on the 17th and 18th September 2012. The symposium was organised jointly by British Society for Parasitology (BSP) and the Biological & Medicinal Chemistry sector of the Royal Society of Chemistry (RSC) and held at the London School of Hygiene & Tropical Medicine. The symposium set out to cover all aspects of the identification of new therapeutic modalities for the treatment of neglected and tropical diseases. We aimed to bring together leading scientists from all the disciplines working in this field and cover the pharmacology, medicinal chemistry and drug delivery of potential new medicines. Sessions were held on: "Target diseases and targets for drugs", "Target based medicinal chemistry", "Bioavailability and chemistry", "Targeting intracellular microbes", "Alternative approaches and models", and "New anti-infectives - how do we get there?" This symposium was organised by Simon Croft (LSHTM) and Mike Barrett (University of Glasgow) for the BSP, and David

Oscillatory signatures of crossmodal congruence effects: An EEG investigation employing a visuotactile pattern matching paradigm.

PubMed

Göschl, Florian; Friese, Uwe; Daume, Jonathan; König, Peter; Engel, Andreas K

2015-08-01

Coherent percepts emerge from the accurate combination of inputs from the different sensory systems. There is an ongoing debate about the neurophysiological mechanisms of crossmodal interactions in the brain, and it has been proposed that transient synchronization of neurons might be of central importance. Oscillatory activity in lower frequency ranges (<30Hz) has been implicated in mediating long-range communication as typically studied in multisensory research. In the current study, we recorded high-density electroencephalograms while human participants were engaged in a visuotactile pattern matching paradigm and analyzed oscillatory power in the theta- (4-7Hz), alpha- (8-13Hz) and beta-bands (13-30Hz). Employing the same physical stimuli, separate tasks of the experiment either required the detection of predefined targets in visual and tactile modalities or the explicit evaluation of crossmodal stimulus congruence. Analysis of the behavioral data showed benefits for congruent visuotactile stimulus combinations. Differences in oscillatory dynamics related to crossmodal congruence within the two tasks were observed in the beta-band for crossmodal target detection, as well as in the theta-band for congruence evaluation. Contrasting ongoing activity preceding visuotactile stimulation between the two tasks revealed differences in the alpha- and beta-bands. Source reconstruction of between-task differences showed prominent involvement of premotor cortex, supplementary motor area, somatosensory association cortex and the supramarginal gyrus. These areas not only exhibited more involvement in the pre-stimulus interval for target detection compared to congruence evaluation, but were also crucially involved in post-stimulus differences related to crossmodal stimulus congruence within the detection task. These results add to the increasing evidence that low frequency oscillations are functionally relevant for integration in distributed brain networks, as demonstrated for

Paradigm Change: Targeting Enemy Leadership in a Complex Environment

DTIC Science & Technology

2011-06-10

hunt Pablo Escobar, Centra Spike needed patience and supporting networks to uncover the puzzle of the Medellin Cartel. Although US and British... Medellin Cartel; who calls the shots? Eventually, Escobar stood out, but he was not the first target. Jose Rodriquez Gacha’s outspoken attitude...Alzate, the hired suicide bomber, flipped the switch and 110 passengers were dead. The Medellin Cartel missed their target, but the intent was loud and

Aspects of detection and tracking of ground targets from an airborne EO/IR sensor

NASA Astrophysics Data System (ADS)

Balaji, Bhashyam; Sithiravel, Rajiv; Daya, Zahir; Kirubarajan, Thiagalingam

2015-05-01

An airborne EO/IR (electro-optical/infrared) camera system comprises of a suite of sensors, such as a narrow and wide field of view (FOV) EO and mid-wave IR sensors. EO/IR camera systems are regularly employed on military and search and rescue aircrafts. The EO/IR system can be used to detect and identify objects rapidly in daylight and at night, often with superior performance in challenging conditions such as fog. There exist several algorithms for detecting potential targets in the bearing elevation grid. The nonlinear filtering problem is one of estimation of the kinematic parameters from bearing and elevation measurements from a moving platform. In this paper, we developed a complete model for the state of a target as detected by an airborne EO/IR system and simulated a typical scenario with single target with 1 or 2 airborne sensors. We have demonstrated the ability to track the target with `high precision' and noted the improvement from using two sensors on a single platform or on separate platforms. The performance of the Extended Kalman filter (EKF) is investigated on simulated data. Image/video data collected from an IR sensor on an airborne platform are processed using an image tracking by detection algorithm.

Infrared images target detection based on background modeling in the discrete cosine domain

NASA Astrophysics Data System (ADS)

Ye, Han; Pei, Jihong

2018-02-01

Background modeling is the critical technology to detect the moving target for video surveillance. Most background modeling techniques are aimed at land monitoring and operated in the spatial domain. A background establishment becomes difficult when the scene is a complex fluctuating sea surface. In this paper, the background stability and separability between target are analyzed deeply in the discrete cosine transform (DCT) domain, on this basis, we propose a background modeling method. The proposed method models each frequency point as a single Gaussian model to represent background, and the target is extracted by suppressing the background coefficients. Experimental results show that our approach can establish an accurate background model for seawater, and the detection results outperform other background modeling methods in the spatial domain.

Detecting Moving Targets by Use of Soliton Resonances

NASA Technical Reports Server (NTRS)

Zak, Michael; Kulikov, Igor

2003-01-01

A proposed method of detecting moving targets in scenes that include cluttered or noisy backgrounds is based on a soliton-resonance mathematical model. The model is derived from asymptotic solutions of the cubic Schroedinger equation for a one-dimensional system excited by a position-and-time-dependent externally applied potential. The cubic Schroedinger equation has general significance for time-dependent dispersive waves. It has been used to approximate several phenomena in classical as well as quantum physics, including modulated beams in nonlinear optics, and superfluids (in particular, Bose-Einstein condensates). In the proposed method, one would take advantage of resonant interactions between (1) a soliton excited by the position-and-time-dependent potential associated with a moving target and (2) eigen-solitons, which represent dispersive waves and are solutions of the cubic Schroedinger equation for a time-independent potential.

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.

Engineered Peptides for Applications in Cancer-Targeted Drug Delivery and Tumor Detection.

PubMed

Soudy, R; Byeon, N; Raghuwanshi, Y; Ahmed, S; Lavasanifar, A; Kaur, K

2017-01-01

Cancer-targeting peptides as ligands for targeted delivery of anticancer drugs or drug carriers have the potential to significantly enhance the selectivity and the therapeutic benefit of current chemotherapeutic agents. Identification of tumor-specific biomarkers like integrins, aminopeptidase N, and epidermal growth factor receptor as well as the popularity of phage display techniques along with synthetic combinatorial methods used for peptide design and structure optimization have fueled the advancement and application of peptide ligands for targeted drug delivery and tumor detection in cancer treatment, detection and guided therapy. Although considerable preclinical data have shown remarkable success in the use of tumor targeting peptides, peptides generally suffer from poor pharmacokinetics, enzymatic instability, and weak receptor affinity, and they need further structural modification before successful translation to clinics is possible. The current review gives an overview of the different engineering strategies that have been developed for peptide structure optimization to confer selectivity and stability. We also provide an update on the methods used for peptide ligand identification, and peptide- receptor interactions. Additionally, some applications for the use of peptides in targeted delivery of chemotherapeutics and diagnostics over the past 5 years are summarized. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics.

PubMed

Wang, Zhengfang; Jablonski, Joseph E

2016-01-01

Economically motivated adulteration (EMA) of lemon juice was detected by LC-MS and principal component analysis (PCA). Twenty-two batches of freshly squeezed lemon juice were adulterated by adding an aqueous solution containing 5% citric acid and 6% sucrose to pure lemon juice to obtain 30%, 60% and 100% lemon juice samples. Their total titratable acidities, °Brix and pH values were measured, and then all the lemon juice samples were subject to LC-MS analysis. Concentrations of hesperidin and eriocitrin, major phenolic components of lemon juice, were quantified. The PCA score plots for LC-MS datasets were used to preview the classification of pure and adulterated lemon juice samples. Results showed a large inherent variability in the chemical properties among 22 batches of 100% lemon juice samples. Measurement or quantitation of one or several chemical properties (targeted detection) was not effective in detecting lemon juice adulteration. However, by using the LC-MS datasets, including both chromatographic and mass spectrometric information, 100% lemon juice samples were successfully differentiated from adulterated samples containing 30% lemon juice in the PCA score plot. LC-MS coupled with chemometric analysis can be a complement to existing methods for detecting juice adulteration.

Geographic Object-Based Image Analysis – Towards a new paradigm

PubMed Central

Blaschke, Thomas; Hay, Geoffrey J.; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm. PMID:24623958

Analyzing the Web Services and UniFrame Paradigms

DTIC Science & Technology

2003-04-01

paradigm from a centralized one to a distributed one. Hence, the target environment is no more a centrally managed, but concerned with collaboration...lever (business logic level) and provide a new platform to build software for a distributed environment . UniFrame is a research project that aims to...EAI solutions provide tends to be complex and expensive, despite improving the overall communication. In addition, the EAI interfaces are not reusable

Target detection, shape discrimination, and signal characteristics of an echolocating false killer whale (Pseudorca crassidens).

PubMed

Brill, R L; Pawloski, J L; Helweg, D A; Au, W W; Moore, P W

1992-09-01

This study demonstrated the ability of a false killer whale (Pseudorca crassidens) to discriminate between two targets and investigated the parameters of the whale's emitted signals for changes related to test conditions. Target detection performance comparable to the bottlenose dolphin's (Tursiops truncatus) has previously been reported for echolocating false killer whales. No other echolocation capabilities have been reported. A false killer whale, naive to conditioned echolocation tasks, was initially trained to detect a cylinder in a "go/no-go" procedure over ranges of 3 to 8 m. The transition from a detection task to a discrimination task was readily achieved by introducing a spherical comparison target. Finally, the cylinder was successfully compared to spheres of two different sizes and target strengths. Multivariate analyses were used to evaluate the parameters of emitted signals. Duncan's multiple range tests showed significant decreases (df = 185, p less than 0.05) in both source level and bandwidth in the transition from detection to discrimination. Analysis of variance revealed a significant decrease in the number of clicks over test conditions [F(5.26) = 5.23, p less than 0.0001]. These data suggest that the whale relied on cues relevant to target shape as well as target strength, that changes in source level and bandwidth were task-related, that the decrease in clicks was associated with learning experience, and that Pseudorca's ability to discriminate shapes using echolocation may be comparable to that of Tursiops truncatus.

Effects of Alzheimer’s Disease on Visual Target Detection: A “Peripheral Bias”

PubMed Central

Vallejo, Vanessa; Cazzoli, Dario; Rampa, Luca; Zito, Giuseppe A.; Feuerstein, Flurin; Gruber, Nicole; Müri, René M.; Mosimann, Urs P.; Nef, Tobias

2016-01-01

Visual exploration is an omnipresent activity in everyday life, and might represent an important determinant of visual attention deficits in patients with Alzheimer’s Disease (AD). The present study aimed at investigating visual search performance in AD patients, in particular target detection in the far periphery, in daily living scenes. Eighteen AD patients and 20 healthy controls participated in the study. They were asked to freely explore a hemispherical screen, covering ±90°, and to respond to targets presented at 10°, 30°, and 50° eccentricity, while their eye movements were recorded. Compared to healthy controls, AD patients recognized less targets appearing in the center. No difference was found in target detection in the periphery. This pattern was confirmed by the fixation distribution analysis. These results show a neglect for the central part of the visual field for AD patients and provide new insights by mean of a search task involving a larger field of view. PMID:27582704

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

Mitochondrial DNA Targets Increase Sensitivity of Malaria Detection Using Loop-Mediated Isothermal Amplification ▿

PubMed Central

Polley, Spencer D.; Mori, Yasuyoshi; Watson, Julie; Perkins, Mark D.; González, Iveth J.; Notomi, Tsugunori; Chiodini, Peter L.; Sutherland, Colin J.

2010-01-01

Loop-mediated isothermal amplification (LAMP) of DNA offers the ability to detect very small quantities of pathogen DNA following minimal tissue sample processing and is thus an attractive methodology for point-of-care diagnostics. Previous attempts to diagnose malaria by the use of blood samples and LAMP have targeted the parasite small-subunit rRNA gene, with a resultant sensitivity for Plasmodium falciparum of around 100 parasites per μl. Here we describe the use of mitochondrial targets for LAMP-based detection of any Plasmodium genus parasite and of P. falciparum specifically. These new targets allow routine amplification from samples containing as few as five parasites per μl of blood. Amplification is complete within 30 to 40 min and is assessed by real-time turbidimetry, thereby offering rapid diagnosis with greater sensitivity than is achieved by the most skilled microscopist or antigen detection using lateral flow immunoassays. PMID:20554824

DEspR roles in tumor vasculo-angiogenesis, invasiveness, CSC-survival and anoikis resistance: a 'common receptor coordinator' paradigm.

PubMed

Herrera, Victoria L; Decano, Julius L; Tan, Glaiza A; Moran, Ann M; Pasion, Khristine A; Matsubara, Yuichi; Ruiz-Opazo, Nelson

2014-01-01

A priori, a common receptor induced in tumor microvessels, cancer cells and cancer stem-like cells (CSCs) that is involved in tumor angiogenesis, invasiveness, and CSC anoikis resistance and survival, could underlie contemporaneous coordination of these events rather than assume stochasticity. Here we show that functional analysis of the dual endothelin1/VEGFsignal peptide receptor, DEspR, (formerly named Dear, Chr.4q31.2) supports the putative common receptor paradigm in pancreatic ductal adenocarcinoma (PDAC) and glioblastoma (GBM) selected for their invasiveness, CD133+CSCs, and polar angiogenic features. Unlike normal tissue, DEspR is detected in PDAC and GBM microvessels, tumor cells, and CSCs isolated from PDAC-Panc1 and GBM-U87 cells. DEspR-inhibition decreased angiogenesis, invasiveness, CSC-survival and anoikis resistance in vitro, and decreased Panc1-CSC and U87-CSC xenograft tumor growth, vasculo-angiogenesis and invasiveness in nude(nu/nu) rats, suggesting that DEspR activation would coordinate these tumor progression events. As an accessible, cell-surface 'common receptor coordinator', DEspR-inhibition defines a novel targeted-therapy paradigm for pancreatic cancer and glioblastoma.

Highly sensitive and specific colorimetric detection of cancer cells via dual-aptamer target binding strategy.

PubMed

Wang, Kun; Fan, Daoqing; Liu, Yaqing; Wang, Erkang

2015-11-15

Simple, rapid, sensitive and specific detection of cancer cells is of great importance for early and accurate cancer diagnostics and therapy. By coupling nanotechnology and dual-aptamer target binding strategies, we developed a colorimetric assay for visually detecting cancer cells with high sensitivity and specificity. The nanotechnology including high catalytic activity of PtAuNP and magnetic separation & concentration plays a vital role on the signal amplification and improvement of detection sensitivity. The color change caused by small amount of target cancer cells (10 cells/mL) can be clearly distinguished by naked eyes. The dual-aptamer target binding strategy guarantees the detection specificity that large amount of non-cancer cells and different cancer cells (10(4) cells/mL) cannot cause obvious color change. A detection limit as low as 10 cells/mL with detection linear range from 10 to 10(5) cells/mL was reached according to the experimental detections in phosphate buffer solution as well as serum sample. The developed enzyme-free and cost effective colorimetric assay is simple and no need of instrument while still provides excellent sensitivity, specificity and repeatability, having potential application on point-of-care cancer diagnosis. Copyright © 2015 Elsevier B.V. All rights reserved.

Moving target detection for frequency agility radar by sparse reconstruction

NASA Astrophysics Data System (ADS)

Quan, Yinghui; Li, YaChao; Wu, Yaojun; Ran, Lei; Xing, Mengdao; Liu, Mengqi

2016-09-01

Frequency agility radar, with randomly varied carrier frequency from pulse to pulse, exhibits superior performance compared to the conventional fixed carrier frequency pulse-Doppler radar against the electromagnetic interference. A novel moving target detection (MTD) method is proposed for the estimation of the target's velocity of frequency agility radar based on pulses within a coherent processing interval by using sparse reconstruction. Hardware implementation of orthogonal matching pursuit algorithm is executed on Xilinx Virtex-7 Field Programmable Gata Array (FPGA) to perform sparse optimization. Finally, a series of experiments are performed to evaluate the performance of proposed MTD method for frequency agility radar systems.

Moving target detection for frequency agility radar by sparse reconstruction.

PubMed

Quan, Yinghui; Li, YaChao; Wu, Yaojun; Ran, Lei; Xing, Mengdao; Liu, Mengqi

2016-09-01

Frequency agility radar, with randomly varied carrier frequency from pulse to pulse, exhibits superior performance compared to the conventional fixed carrier frequency pulse-Doppler radar against the electromagnetic interference. A novel moving target detection (MTD) method is proposed for the estimation of the target's velocity of frequency agility radar based on pulses within a coherent processing interval by using sparse reconstruction. Hardware implementation of orthogonal matching pursuit algorithm is executed on Xilinx Virtex-7 Field Programmable Gata Array (FPGA) to perform sparse optimization. Finally, a series of experiments are performed to evaluate the performance of proposed MTD method for frequency agility radar systems.

Functional MRI Motor Imagery Tasks to Detect Command Following in Traumatic Disorders of Consciousness.

PubMed

Bodien, Yelena G; Giacino, Joseph T; Edlow, Brian L

2017-01-01

Severe traumatic brain injury impairs arousal and awareness, the two components of consciousness. Accurate diagnosis of a patient's level of consciousness is critical for determining treatment goals, access to rehabilitative services, and prognosis. The bedside behavioral examination, the current clinical standard for diagnosis of disorders of consciousness, is prone to misdiagnosis, a finding that has led to the development of advanced neuroimaging techniques aimed at detection of conscious awareness. Although a variety of paradigms have been used in functional magnetic resonance imaging (fMRI) to reveal covert consciousness, the relative accuracy of these paradigms in the patient population is unknown. Here, we compare the rate of covert consciousness detection by hand squeezing and tennis playing motor imagery paradigms in 10 patients with traumatic disorders of consciousness [six male, six acute, mean ± SD age = 27.9 ± 9.1 years, one coma, four unresponsive wakefulness syndrome, two minimally conscious without language function, and three minimally conscious with language function, per bedside examination with the Coma Recovery Scale-Revised (CRS-R)]. We also tested the same paradigms in 10 healthy subjects (nine male, mean ± SD age = 28.5 ± 9.4 years). In healthy subjects, the hand squeezing paradigm detected covert command following in 7/10 and the tennis playing paradigm in 9/10 subjects. In patients who followed commands on the CRS-R, the hand squeezing paradigm detected covert command following in 2/3 and the tennis playing paradigm in 0/3 subjects. In patients who did not follow commands on the CRS-R, the hand squeezing paradigm detected command following in 1/7 and the tennis playing paradigm in 2/7 subjects. The sensitivity, specificity, and accuracy (ACC) of detecting covert command following in patients who demonstrated this behavior on the CRS-R was 66.7, 85.7, and 80% for the hand squeezing paradigm and 0, 71.4, and

Functional MRI Motor Imagery Tasks to Detect Command Following in Traumatic Disorders of Consciousness

PubMed Central

Bodien, Yelena G.; Giacino, Joseph T.; Edlow, Brian L.

2017-01-01

Severe traumatic brain injury impairs arousal and awareness, the two components of consciousness. Accurate diagnosis of a patient’s level of consciousness is critical for determining treatment goals, access to rehabilitative services, and prognosis. The bedside behavioral examination, the current clinical standard for diagnosis of disorders of consciousness, is prone to misdiagnosis, a finding that has led to the development of advanced neuroimaging techniques aimed at detection of conscious awareness. Although a variety of paradigms have been used in functional magnetic resonance imaging (fMRI) to reveal covert consciousness, the relative accuracy of these paradigms in the patient population is unknown. Here, we compare the rate of covert consciousness detection by hand squeezing and tennis playing motor imagery paradigms in 10 patients with traumatic disorders of consciousness [six male, six acute, mean ± SD age = 27.9 ± 9.1 years, one coma, four unresponsive wakefulness syndrome, two minimally conscious without language function, and three minimally conscious with language function, per bedside examination with the Coma Recovery Scale-Revised (CRS-R)]. We also tested the same paradigms in 10 healthy subjects (nine male, mean ± SD age = 28.5 ± 9.4 years). In healthy subjects, the hand squeezing paradigm detected covert command following in 7/10 and the tennis playing paradigm in 9/10 subjects. In patients who followed commands on the CRS-R, the hand squeezing paradigm detected covert command following in 2/3 and the tennis playing paradigm in 0/3 subjects. In patients who did not follow commands on the CRS-R, the hand squeezing paradigm detected command following in 1/7 and the tennis playing paradigm in 2/7 subjects. The sensitivity, specificity, and accuracy (ACC) of detecting covert command following in patients who demonstrated this behavior on the CRS-R was 66.7, 85.7, and 80% for the hand squeezing paradigm and 0, 71.4, and

Targeted Cancer Therapy: Vital Oncogenes and a New Molecular Genetic Paradigm for Cancer Initiation Progression and Treatment.

PubMed

Willis, Rudolph E

2016-09-14

It has been declared repeatedly that cancer is a result of molecular genetic abnormalities. However, there has been no working model describing the specific functional consequences of the deranged genomic processes that result in the initiation and propagation of the cancer process during carcinogenesis. We no longer need to question whether or not cancer arises as a result of a molecular genetic defect within the cancer cell. The legitimate questions are: how and why? This article reviews the preeminent data on cancer molecular genetics and subsequently proposes that the sentinel event in cancer initiation is the aberrant production of fused transcription activators with new molecular properties within normal tissue stem cells. This results in the production of vital oncogenes with dysfunctional gene activation transcription properties, which leads to dysfunctional gene regulation, the aberrant activation of transduction pathways, chromosomal breakage, activation of driver oncogenes, reactivation of stem cell transduction pathways and the activation of genes that result in the hallmarks of cancer. Furthermore, a novel holistic molecular genetic model of cancer initiation and progression is presented along with a new paradigm for the approach to personalized targeted cancer therapy, clinical monitoring and cancer diagnosis.

The Crossmodal Facilitation of Visual Object Representations by Sound: Evidence from the Backward Masking Paradigm

ERIC Educational Resources Information Center

Chen, Yi-Chuan; Spence, Charles

2011-01-01

We report a series of experiments designed to demonstrate that the presentation of a sound can facilitate the identification of a concomitantly presented visual target letter in the backward masking paradigm. Two visual letters, serving as the target and its mask, were presented successively at various interstimulus intervals (ISIs). The results…

Improved training for target detection using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Elbakary, M. I.; Alam, M. S.; Aslan, M. S.

2008-03-01

In a FLIR image sequence, a target may disappear permanently or may reappear after some frames and crucial information such as direction, position and size related to the target are lost. If the target reappears at a later frame, it may not be tracked again because the 3D orientation, size and location of the target might be changed. To obtain information about the target before disappearing and to detect the target after reappearing, distance classifier correlation filter (DCCF) is trained manualy by selecting a number of chips randomly. This paper introduces a novel idea to eliminates the manual intervention in training phase of DCCF. Instead of selecting the training chips manually and selecting the number of the training chips randomly, we adopted the K-means algorithm to cluster the training frames and based on the number of clusters we select the training chips such that a training chip for each cluster. To detect and track the target after reappearing in the field-ofview ,TBF and DCCF are employed. The contduced experiemnts using real FLIR sequences show results similar to the traditional agorithm but eleminating the manual intervention is the advantage of the proposed algorithm.

Infants with complex congenital heart diseases show poor short-term memory in the mobile paradigm at 3 months of age.

PubMed

Chen, Chao-Ying; Harrison, Tondi; Heathcock, Jill

2015-08-01

The purpose of this study was to examine learning, short-term memory and general development including cognitive, motor, and language domains in infants with Complex Congenital Heart Defects (CCDH). Ten infants with CCHD (4 males, 6 females) and 14 infants with typical development (TD) were examined at 3 months of age. The mobile paradigm, where an infant's leg is tethered to an overhead mobile, was used to evaluate learning and short-term memory. The Bayley Scales of Infant Development 3rd edition (Bayley-III) was used to evaluate general development in cognitive, motor, and language domains. Infants with CCHD and infants with TD both showed learning with significant increase in kicking rate (p<0.001) across periods of the mobile paradigm, but only infants with TD demonstrated short-term memory (p=0.017) in the mobile paradigm. There were no differences on cognitive, motor, and language development between infants with CCHD and infants with TD on the Bayley-III. Early assessment is necessary to guide targeted treatment in infants with CCHD. One-time assessment may fail to detect potential cognitive impairments during early infancy in infants with CCHD. Supportive intervention programs for infants with CCHD that focuses on enhancing short-term memory are recommended. Copyright © 2015 Elsevier Inc. All rights reserved.

An eye-tracking paradigm for analyzing the processing time of sentences with different linguistic complexities.

PubMed

Wendt, Dorothea; Brand, Thomas; Kollmeier, Birger

2014-01-01

An eye-tracking paradigm was developed for use in audiology in order to enable online analysis of the speech comprehension process. This paradigm should be useful in assessing impediments in speech processing. In this paradigm, two scenes, a target picture and a competitor picture, were presented simultaneously with an aurally presented sentence that corresponded to the target picture. At the same time, eye fixations were recorded using an eye-tracking device. The effect of linguistic complexity on language processing time was assessed from eye fixation information by systematically varying linguistic complexity. This was achieved with a sentence corpus containing seven German sentence structures. A novel data analysis method computed the average tendency to fixate the target picture as a function of time during sentence processing. This allowed identification of the point in time at which the participant understood the sentence, referred to as the decision moment. Systematic differences in processing time were observed as a function of linguistic complexity. These differences in processing time may be used to assess the efficiency of cognitive processes involved in resolving linguistic complexity. Thus, the proposed method enables a temporal analysis of the speech comprehension process and has potential applications in speech audiology and psychoacoustics.

An Eye-Tracking Paradigm for Analyzing the Processing Time of Sentences with Different Linguistic Complexities

PubMed Central

Wendt, Dorothea; Brand, Thomas; Kollmeier, Birger

2014-01-01

An eye-tracking paradigm was developed for use in audiology in order to enable online analysis of the speech comprehension process. This paradigm should be useful in assessing impediments in speech processing. In this paradigm, two scenes, a target picture and a competitor picture, were presented simultaneously with an aurally presented sentence that corresponded to the target picture. At the same time, eye fixations were recorded using an eye-tracking device. The effect of linguistic complexity on language processing time was assessed from eye fixation information by systematically varying linguistic complexity. This was achieved with a sentence corpus containing seven German sentence structures. A novel data analysis method computed the average tendency to fixate the target picture as a function of time during sentence processing. This allowed identification of the point in time at which the participant understood the sentence, referred to as the decision moment. Systematic differences in processing time were observed as a function of linguistic complexity. These differences in processing time may be used to assess the efficiency of cognitive processes involved in resolving linguistic complexity. Thus, the proposed method enables a temporal analysis of the speech comprehension process and has potential applications in speech audiology and psychoacoustics. PMID:24950184

In situ targeted MRI detection of Helicobacter pylori with stable magnetic graphitic nanocapsules

PubMed Central

Li, Yunjie; Hu, Xiaoxiao; Ding, Ding; Zou, Yuxiu; Xu, Yiting; Wang, Xuewei; Zhang, Yin; Chen, Long; Chen, Zhuo; Tan, Weihong

2017-01-01

Helicobacter pylori infection is implicated in the aetiology of many diseases. Despite numerous studies, a painless, fast and direct method for the in situ detection of H. pylori remains a challenge, mainly due to the strong acidic/enzymatic environment of the gastric mucosa. Herein, we report the use of stable magnetic graphitic nanocapsules (MGNs), for in situ targeted magnetic resonance imaging (MRI) detection of H. pylori. Several layers of graphene as the shell effectively protect the magnetic core from corrosion while retaining the superior contrast effect for MRI in the gastric environment. Boronic-polyethylene glycol molecules were synthesized and modified on the MGN surface for targeted MRI detection. In a mouse model of H. pylori-induced infection, H. pylori was specifically detected through both T2-weighted MR imaging and Raman gastric mucosa imaging using functionalized MGNs. These results indicated that enhancement of MRI using MGNs may be a promising diagnostic and bioimaging platform for very harsh conditions. PMID:28643777

In situ targeted MRI detection of Helicobacter pylori with stable magnetic graphitic nanocapsules

NASA Astrophysics Data System (ADS)

Li, Yunjie; Hu, Xiaoxiao; Ding, Ding; Zou, Yuxiu; Xu, Yiting; Wang, Xuewei; Zhang, Yin; Chen, Long; Chen, Zhuo; Tan, Weihong

2017-06-01

Helicobacter pylori infection is implicated in the aetiology of many diseases. Despite numerous studies, a painless, fast and direct method for the in situ detection of H. pylori remains a challenge, mainly due to the strong acidic/enzymatic environment of the gastric mucosa. Herein, we report the use of stable magnetic graphitic nanocapsules (MGNs), for in situ targeted magnetic resonance imaging (MRI) detection of H. pylori. Several layers of graphene as the shell effectively protect the magnetic core from corrosion while retaining the superior contrast effect for MRI in the gastric environment. Boronic-polyethylene glycol molecules were synthesized and modified on the MGN surface for targeted MRI detection. In a mouse model of H. pylori-induced infection, H. pylori was specifically detected through both T2-weighted MR imaging and Raman gastric mucosa imaging using functionalized MGNs. These results indicated that enhancement of MRI using MGNs may be a promising diagnostic and bioimaging platform for very harsh conditions.

Highly sensitive detection of target molecules using a new fluorescence-based bead assay

NASA Astrophysics Data System (ADS)

Scheffler, Silvia; Strauß, Denis; Sauer, Markus

2007-07-01

Development of immunoassays with improved sensitivity, specificity and reliability are of major interest in modern bioanalytical research. We describe the development of a new immunomagnetic fluorescence detection (IM-FD) assay based on specific antigen/antibody interactions and on accumulation of the fluorescence signal on superparamagnetic PE beads in combination with the use of extrinsic fluorescent labels. IM-FD can be easily modified by varying the order of coatings and assay conditions. Depending on the target molecule, antibodies (ABs), entire proteins, or small protein epitopes can be used as capture molecules. The presence of target molecules is detected by fluorescence microscopy using fluorescently labeled secondary or detection antibodies. Here, we demonstrate the potential of the new assay detecting the two tumor markers IGF-I and p53 antibodies in the clinically relevant concentration range. Our data show that the fluorescence-based bead assay exhibits a large dynamic range and a high sensitivity down to the subpicomolar level.

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.

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.

The word class effect in the picture–word interference paradigm

PubMed Central

Janssen, Niels; Melinger, Alissa; Mahon, Bradford Z.; Finkbeiner, Matthew; Caramazza, Alfonso

2010-01-01

The word class effect in the picture–word interference paradigm is a highly influential finding that has provided some of the most compelling support for word class constraints on lexical selection. However, methodological concerns called for a replication of the most convincing of those effects. Experiment 1 was a direct replication of Pechmann and Zerbst (2002; Experiment 4). Participants named pictures of objects in the context of noun and adverb distractors. Naming took place in bare noun and sentence frame contexts. A word class effect emerged in both bare noun and sentence frame naming conditions, suggesting a semantic origin of the effect. In Experiment 2, participants named objects in the context of noun and verb distractors whose word class relationship to the target and imageability were orthogonally manipulated. As before, naming took place in bare noun and sentence frame naming contexts. In both naming contexts, distractor imageability but not word class affected picture naming latencies. These findings confirm the sensitivity of the picture–word interference paradigm to distractor imageability and suggest the paradigm is not sensitive to distractor word class. The results undermine the use of the word class effect in the picture–word interference paradigm as supportive of word class constraints during lexical selection. PMID:19998070

Detection and quantification of bacterial biofilms combining high-frequency acoustic microscopy and targeted lipid microparticles

PubMed Central

2014-01-01

Background Immuno-compromised patients such as those undergoing cancer chemotherapy are susceptible to bacterial infections leading to biofilm matrix formation. This surrounding biofilm matrix acts as a diffusion barrier that binds up antibiotics and antibodies, promoting resistance to treatment. Developing non-invasive imaging methods that detect biofilm matrix in the clinic are needed. The use of ultrasound in conjunction with targeted ultrasound contrast agents (UCAs) may provide detection of early stage biofilm matrix formation and facilitate optimal treatment. Results Ligand-targeted UCAs were investigated as a novel method for pre-clinical non-invasive molecular imaging of early and late stage biofilms. These agents were used to target, image and detect Staphylococcus aureus biofilm matrix in vitro. Binding efficacy was assessed on biofilm matrices with respect to their increasing biomass ranging from 3.126 × 103 ± 427 UCAs per mm2 of biofilm surface area within 12 h to 21.985 × 103 ± 855 per mm2 of biofilm matrix surface area at 96 h. High-frequency acoustic microscopy was used to ultrasonically detect targeted UCAs bound to a biofilm matrix and to assess biofilm matrix mechanoelastic physical properties. Acoustic impedance data demonstrated that biofilm matrices exhibit impedance values (1.9 MRayl) close to human tissue (1.35 - 1.85 MRayl for soft tissues). Moreover, the acoustic signature of mature biofilm matrices were evaluated in terms of integrated backscatter (0.0278 - 0.0848 mm-1 × sr-1) and acoustic attenuation (3.9 Np/mm for bound UCAs; 6.58 Np/mm for biofilm alone). Conclusions Early diagnosis of biofilm matrix formation is a challenge in treating cancer patients with infection-associated biofilms. We report for the first time a combined optical and acoustic evaluation of infectious biofilm matrices. We demonstrate that acoustic impedance of biofilms is similar to the impedance of human tissues, making in vivo imaging and detection of biofilm

Detection and quantification of bacterial biofilms combining high-frequency acoustic microscopy and targeted lipid microparticles.

PubMed

Anastasiadis, Pavlos; Mojica, Kristina D A; Allen, John S; Matter, Michelle L

2014-07-06

Immuno-compromised patients such as those undergoing cancer chemotherapy are susceptible to bacterial infections leading to biofilm matrix formation. This surrounding biofilm matrix acts as a diffusion barrier that binds up antibiotics and antibodies, promoting resistance to treatment. Developing non-invasive imaging methods that detect biofilm matrix in the clinic are needed. The use of ultrasound in conjunction with targeted ultrasound contrast agents (UCAs) may provide detection of early stage biofilm matrix formation and facilitate optimal treatment. Ligand-targeted UCAs were investigated as a novel method for pre-clinical non-invasive molecular imaging of early and late stage biofilms. These agents were used to target, image and detect Staphylococcus aureus biofilm matrix in vitro. Binding efficacy was assessed on biofilm matrices with respect to their increasing biomass ranging from 3.126 × 103 ± 427 UCAs per mm(2) of biofilm surface area within 12 h to 21.985 × 103 ± 855 per mm(2) of biofilm matrix surface area at 96 h. High-frequency acoustic microscopy was used to ultrasonically detect targeted UCAs bound to a biofilm matrix and to assess biofilm matrix mechanoelastic physical properties. Acoustic impedance data demonstrated that biofilm matrices exhibit impedance values (1.9 MRayl) close to human tissue (1.35 - 1.85 MRayl for soft tissues). Moreover, the acoustic signature of mature biofilm matrices were evaluated in terms of integrated backscatter (0.0278 - 0.0848 mm(-1) × sr(-1)) and acoustic attenuation (3.9 Np/mm for bound UCAs; 6.58 Np/mm for biofilm alone). Early diagnosis of biofilm matrix formation is a challenge in treating cancer patients with infection-associated biofilms. We report for the first time a combined optical and acoustic evaluation of infectious biofilm matrices. We demonstrate that acoustic impedance of biofilms is similar to the impedance of human tissues, making in vivo imaging and detection of biofilm matrices difficult

A Search Model for Imperfectly Detected Targets

NASA Technical Reports Server (NTRS)

Ahumada, Albert

2012-01-01

Under the assumptions that 1) the search region can be divided up into N non-overlapping sub-regions that are searched sequentially, 2) the probability of detection is unity if a sub-region is selected, and 3) no information is available to guide the search, there are two extreme case models. The search can be done perfectly, leading to a uniform distribution over the number of searches required, or the search can be done with no memory, leading to a geometric distribution for the number of searches required with a success probability of 1/N. If the probability of detection P is less than unity, but the search is done otherwise perfectly, the searcher will have to search the N regions repeatedly until detection occurs. The number of searches is thus the sum two random variables. One is N times the number of full searches (a geometric distribution with success probability P) and the other is the uniform distribution over the integers 1 to N. The first three moments of this distribution were computed, giving the mean, standard deviation, and the kurtosis of the distribution as a function of the two parameters. The model was fit to the data presented last year (Ahumada, Billington, & Kaiwi, 2 required to find a single pixel target on a simulated horizon. The model gave a good fit to the three moments for all three observers.

Golay Complementary Waveforms in Reed–Müller Sequences for Radar Detection of Nonzero Doppler Targets

PubMed Central

Wang, Xuezhi; Huang, Xiaotao; Suvorova, Sofia; Moran, Bill

2018-01-01

Golay complementary waveforms can, in theory, yield radar returns of high range resolution with essentially zero sidelobes. In practice, when deployed conventionally, while high signal-to-noise ratios can be achieved for static target detection, significant range sidelobes are generated by target returns of nonzero Doppler causing unreliable detection. We consider signal processing techniques using Golay complementary waveforms to improve radar detection performance in scenarios involving multiple nonzero Doppler targets. A signal processing procedure based on an existing, so called, Binomial Design algorithm that alters the transmission order of Golay complementary waveforms and weights the returns is proposed in an attempt to achieve an enhanced illumination performance. The procedure applies one of three proposed waveform transmission ordering algorithms, followed by a pointwise nonlinear processor combining the outputs of the Binomial Design algorithm and one of the ordering algorithms. The computational complexity of the Binomial Design algorithm and the three ordering algorithms are compared, and a statistical analysis of the performance of the pointwise nonlinear processing is given. Estimation of the areas in the Delay–Doppler map occupied by significant range sidelobes for given targets are also discussed. Numerical simulations for the comparison of the performances of the Binomial Design algorithm and the three ordering algorithms are presented for both fixed and randomized target locations. The simulation results demonstrate that the proposed signal processing procedure has a better detection performance in terms of lower sidelobes and higher Doppler resolution in the presence of multiple nonzero Doppler targets compared to existing methods. PMID:29324708

Optoacoustic detection of viral antigens using targeted gold nanorods

NASA Astrophysics Data System (ADS)

Maswadi, Saher; Woodward, Lee; Glickman, Randolph D.; Barsalou, Norman

2009-02-01

We are detecting antigens (Ag), isolated from infectious organisms, utilizing laser optoacoustic spectroscopy and antibody-coupled gold nanorod (NR) contrast agents specifically targeted to the antigen of interest. We have detected, in clinical ocular samples, both Herpes Simplex Virus Type 1 and 2 (HSV-1 and HSV-2) . A monoclonal antibody (Ab) specific to both HSV-1 and HSV-2 was conjugated to gold nanorods to produce a targeted contrast agent with a strong optoacoustic signal. Elutions obtained from patient corneal swabs were adsorbed in standard plastic micro-wells. An immunoaffinity reaction was then performed with the functionalized gold nanorods, and the results were probed with an OPO laser, emitting wavelengths at the peak absorptions of the nanorods. Positive optoacoustic responses were obtained from samples containing authentic (microbiologically confirmed) HSV-1 and HSV-2. To obtain an estimate of the sensitivity of the technique, serial dilutions from 1 mg/ml to 1 pg/ml of a C. trachomatis surface Ag were prepared, and were probed with a monoclonal Ab, specific to the C. trachomatis surface Ag, conjugated to gold nanorods. An optoacoustic response was obtained, proportional to the concentration of antigen, and with a limit of detection of about 5 pg/ml. The optoacoustic signals generated from micro-wells containing albumin or saline were similar to those from blank wells. The potential benefit of this method is identify viral agents more rapidly than with existing techniques. In addition, the sensitivity of the assay is comparable or superior to existing colorimetric- or fluorometric-linked immunoaffinity assays.

Cancer research in need of a scientific revolution: Using 'paradigm shift' as a method of investigation.

PubMed

Wion, Didier; Appaix, Florence; Burruss, Meriwether; Berger, Francois; van der Sanden, Boudewijn

2015-09-01

Despite important human and financial resources and considerable accumulation of scientific publications, patents, and clinical trials, cancer research has been slow in achieving a therapeutic revolution similar to the one that occurred in the last century for infectious diseases. It has been proposed that science proceeds not only by accumulating data but also through paradigm shifts. Here, we propose to use the concept of 'paradigm shift' as a method of investigation when dominant paradigms fail to achieve their promises. The first step in using the 'paradigm shift' method in cancer research requires identifying its founding paradigms. In this review, two of these founding paradigms will be discussed: (i) the reification of cancer as a tumour mass and (ii) the translation of the concepts issued from infectious disease in cancer research. We show how these founding paradigms can generate biases that lead to over-diagnosis and over-treatment and also hamper the development of curative cancer therapies. We apply the 'paradigm shift' method to produce perspective reversals consistent with current experimental evidence. The 'paradigm shift' method enlightens the existence of a tumour physiologic-prophylactic-pathologic continuum. It integrates the target/antitarget concept and that cancer is also an extracellular disease. The 'paradigm shift' method has immediate implications for cancer prevention and therapy. It could be a general method of investigation for other diseases awaiting therapy.

Verbal short-term memory as an articulatory system: evidence from an alternative paradigm.

PubMed

Cheung, Him; Wooltorton, Lana

2002-01-01

In a series of experiments, the role of articulatory rehearsal in verbal [corrected] short-term memory was examined via a shadowing-plus-recall paradigm. In this paradigm, subjects shadowed a word target presented closely after an auditory memory list before they recalled the list. The phonological relationship between the shadowing target and the final item on the memory list was manipulated. Experiments 1 and 2 demonstrated that targets sounding similar to the list-final memory item generally took longer to shadow than unrelated targets. This inhibitory effect of phonological relatedness was more pronounced with tense- than lax-vowel pseudoword recall lists. The interaction between vowel tenseness and phonological relatedness was replicated in Experiment 3 using shorter lists of real words. In Experiment 4, concurrent articulation was applied during list learning to block rehearsal; consequently, neither the phonological relatedness effect nor its interaction with vowel tenseness emerged. Experiments 5 and 6 manipulated the occurrence frequencies and lexicality of the recall items, respectively, instead of vowel tenseness. Unlike vowel tenseness, these non-articulatory memory factors failed to interact with the phonological relatedness effect. Experiment 7 orthogonally manipulated the vowel tenseness and frequencies of the recall items; slowing in shadowing times due to phonological relatedness was modulated by vowel tenseness but not frequency. Taken together, these results suggest that under the present paradigm, the modifying effect of vowel tenseness on the magnitude of slowing in shadowing due to phonological relatedness is indicative of a prominent articulatory component in verbal short-term retention. The shadowing-plus-recall approach avoids confounding overt recall into internal memory processing, which is an inherent problem of the traditional immediate serial recall and span tasks.

Vascular Targeting of Nanocarriers: Perplexing Aspects of the Seemingly Straightforward Paradigm

PubMed Central

2015-01-01

Targeted nanomedicine holds promise to find clinical use in many medical areas. Endothelial cells that line the luminal surface of blood vessels represent a key target for treatment of inflammation, ischemia, thrombosis, stroke, and other neurological, cardiovascular, pulmonary, and oncological conditions. In other cases, the endothelium is a barrier for tissue penetration or a victim of adverse effects. Several endothelial surface markers including peptidases (e.g., ACE, APP, and APN) and adhesion molecules (e.g., ICAM-1 and PECAM) have been identified as key targets. Binding of nanocarriers to these molecules enables drug targeting and subsequent penetration into or across the endothelium, offering therapeutic effects that are unattainable by their nontargeted counterparts. We analyze diverse aspects of endothelial nanomedicine including (i) circulation and targeting of carriers with diverse geometries, (ii) multivalent interactions of carrier with endothelium, (iii) anchoring to multiple determinants, (iv) accessibility of binding sites and cellular response to their engagement, (v) role of cell phenotype and microenvironment in targeting, (vi) optimization of targeting by lowering carrier avidity, (vii) endocytosis of multivalent carriers via molecules not implicated in internalization of their ligands, and (viii) modulation of cellular uptake and trafficking by selection of specific epitopes on the target determinant, carrier geometry, and hydrodynamic factors. Refinement of these aspects and improving our understanding of vascular biology and pathology is likely to enable the clinical translation of vascular endothelial targeting of nanocarriers. PMID:24787360

Quantum illumination for enhanced detection of Rayleigh-fading targets

NASA Astrophysics Data System (ADS)

Zhuang, Quntao; Zhang, Zheshen; Shapiro, Jeffrey H.

2017-08-01

Quantum illumination (QI) is an entanglement-enhanced sensing system whose performance advantage over a comparable classical system survives its usage in an entanglement-breaking scenario plagued by loss and noise. In particular, QI's error-probability exponent for discriminating between equally likely hypotheses of target absence or presence is 6 dB higher than that of the optimum classical system using the same transmitted power. This performance advantage, however, presumes that the target return, when present, has known amplitude and phase, a situation that seldom occurs in light detection and ranging (lidar) applications. At lidar wavelengths, most target surfaces are sufficiently rough that their returns are speckled, i.e., they have Rayleigh-distributed amplitudes and uniformly distributed phases. QI's optical parametric amplifier receiver—which affords a 3 dB better-than-classical error-probability exponent for a return with known amplitude and phase—fails to offer any performance gain for Rayleigh-fading targets. We show that the sum-frequency generation receiver [Zhuang et al., Phys. Rev. Lett. 118, 040801 (2017), 10.1103/PhysRevLett.118.040801]—whose error-probability exponent for a nonfading target achieves QI's full 6 dB advantage over optimum classical operation—outperforms the classical system for Rayleigh-fading targets. In this case, QI's advantage is subexponential: its error probability is lower than the classical system's by a factor of 1 /ln(M κ ¯NS/NB) , when M κ ¯NS/NB≫1 , with M ≫1 being the QI transmitter's time-bandwidth product, NS≪1 its brightness, κ ¯ the target return's average intensity, and NB the background light's brightness.

Added value of second biopsy target in screen-detected widespread suspicious breast calcifications.

PubMed

Falkner, Nathalie M; Hince, Dana; Porter, Gareth; Dessauvagie, Ben; Jeganathan, Sanjay; Bulsara, Max; Lo, Glen

2018-06-01

There is controversy on the optimal work-up of screen-detected widespread breast calcifications: whether to biopsy a single target or multiple targets. This study evaluates agreement between multiple biopsy targets within the same screen-detected widespread (≥25 mm) breast calcification to determine if the second biopsy adds value. Retrospective observational study of women screened in a statewide general population risk breast cancer mammographic screening program from 2009 to 2016. Screening episodes recalled for widespread calcifications where further views indicated biopsy, and two or more separate target areas were sampled within the same lesion were included. Percentage agreement and Cohen's Kappa were calculated. A total of 293317 women were screened during 761124 separate episodes with recalls for widespread calcifications in 2355 episodes. In 171 women, a second target was biopsied within the same lesion. In 149 (86%) cases, the second target biopsy result agreed with the first biopsy (κ = 0.6768). Agreement increased with increasing mammography score (85%, 86% and 92% for score 3, 4 and 5 lesions). Same day multiple biopsied lesions were three times more likely to yield concordant results compared to post-hoc second target biopsy cases. While a single target biopsy is sufficient to discriminate a benign vs. malignant diagnosis in most cases, in 14% there is added value in performing a second target biopsy. Biopsies performed prospectively are more likely to yield concordant results compared to post-hoc second target biopsy cases, suggesting a single prospective biopsy may be sufficient when results are radiological-pathological concordant; discordance still requires repeat sampling. © 2018 The Royal Australian and New Zealand College of Radiologists.

Acute Alcohol Consumption Impairs Controlled but Not Automatic Processes in a Psychophysical Pointing Paradigm

PubMed Central

Johnston, Kevin; Timney, Brian; Goodale, Melvyn A.

2013-01-01

Numerous studies have investigated the effects of alcohol consumption on controlled and automatic cognitive processes. Such studies have shown that alcohol impairs performance on tasks requiring conscious, intentional control, while leaving automatic performance relatively intact. Here, we sought to extend these findings to aspects of visuomotor control by investigating the effects of alcohol in a visuomotor pointing paradigm that allowed us to separate the influence of controlled and automatic processes. Six male participants were assigned to an experimental “correction” condition in which they were instructed to point at a visual target as quickly and accurately as possible. On a small percentage of trials, the target “jumped” to a new location. On these trials, the participants’ task was to amend their movement such that they pointed to the new target location. A second group of 6 participants were assigned to a “countermanding” condition, in which they were instructed to terminate their movements upon detection of target “jumps”. In both the correction and countermanding conditions, participants served as their own controls, taking part in alcohol and no-alcohol conditions on separate days. Alcohol had no effect on participants’ ability to correct movements “in flight”, but impaired the ability to withhold such automatic corrections. Our data support the notion that alcohol selectively impairs controlled processes in the visuomotor domain. PMID:23861934

Infrared dim and small target detecting and tracking method inspired by Human Visual System

NASA Astrophysics Data System (ADS)

Dong, Xiabin; Huang, Xinsheng; Zheng, Yongbin; Shen, Lurong; Bai, Shengjian

2014-01-01

Detecting and tracking dim and small target in infrared images and videos is one of the most important techniques in many computer vision applications, such as video surveillance and infrared imaging precise guidance. Recently, more and more algorithms based on Human Visual System (HVS) have been proposed to detect and track the infrared dim and small target. In general, HVS concerns at least three mechanisms including contrast mechanism, visual attention and eye movement. However, most of the existing algorithms simulate only a single one of the HVS mechanisms, resulting in many drawbacks of these algorithms. A novel method which combines the three mechanisms of HVS is proposed in this paper. First, a group of Difference of Gaussians (DOG) filters which simulate the contrast mechanism are used to filter the input image. Second, a visual attention, which is simulated by a Gaussian window, is added at a point near the target in order to further enhance the dim small target. This point is named as the attention point. Eventually, the Proportional-Integral-Derivative (PID) algorithm is first introduced to predict the attention point of the next frame of an image which simulates the eye movement of human being. Experimental results of infrared images with different types of backgrounds demonstrate the high efficiency and accuracy of the proposed method to detect and track the dim and small targets.

The CommonGround Visual Paradigm for Biosurveillance

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

Livnat, Yarden; Jurrus, Elizabeth R.; Gundlapalli, Adi V.

2013-06-14

Biosurveillance is a critical area in the intelligence community for real-time detection of disease outbreaks. Identifying epidemics enables analysts to detect and monitor disease outbreaks that might be spread from natural causes or from possible biological warfare attacks. Containing these events and disseminating alerts requires the ability to rapidly find, classify and track harmful biological signatures. In this paper, we describe a novel visual paradigm to conduct biosurveillance using an Infectious Disease Weather Map. Our system provides a visual common ground in which users can view, explore and discover emerging concepts and correlations such as symptoms, syndromes, pathogens, and geographicmore » locations.« less

A retrospective detection algorithm for extraction of weak targets in clutter and interference environments

NASA Astrophysics Data System (ADS)

Prengaman, R. J.; Thurber, R. E.; Bath, W. G.

The usefulness of radar systems depends on the ability to distinguish between signals returned from desired targets and noise. A retrospective processor uses all contacts (or 'plots') from several past radar scans, taking into account all possible target trajectories formed from stored contacts for each input detection. The processor eliminates many false alarms, while retaining those contacts describing resonable trajectories. The employment of a retrospective processor makes it, therefore, possible to obtain large improvements in detection sensitivity in certain important clutter environments. Attention is given to the retrospective processing concept, a theoretical analysis of the multiscan detection process, the experimental evaluation of retrospective data filter, and aspects of retrospective data filter hardware implementation.

Detection and Tracking of Moving Targets Behind Cluttered Environments Using Compressive Sensing

NASA Astrophysics Data System (ADS)

Dang, Vinh Quang

Detection and tracking of moving targets (target's motion, vibration, etc.) in cluttered environments have been receiving much attention in numerous applications, such as disaster search-and-rescue, law enforcement, urban warfare, etc. One of the popular techniques is the use of stepped frequency continuous wave radar due to its low cost and complexity. However, the stepped frequency radar suffers from long data acquisition time. This dissertation focuses on detection and tracking of moving targets and vibration rates of stationary targets behind cluttered medium such as wall using stepped frequency radar enhanced by compressive sensing. The application of compressive sensing enables the reconstruction of the target space using fewer random frequencies, which decreases the acquisition time. Hardware-accelerated parallelization on GPU is investigated for the Orthogonal Matching Pursuit reconstruction algorithm. For simulation purpose, two hybrid methods have been developed to calculate the scattered fields from the targets through the wall approaching the antenna system, and to convert the incoming fields into voltage signals at terminals of the receive antenna. The first method is developed based on the plane wave spectrum approach for calculating the scattered fields of targets behind the wall. The method uses Fast Multiple Method (FMM) to calculate scattered fields on a particular source plane, decomposes them into plane wave components, and propagates the plane wave spectrum through the wall by integrating wall transmission coefficients before constructing the fields on a desired observation plane. The second method allows one to calculate the complex output voltage at terminals of a receiving antenna which fully takes into account the antenna effects. This method adopts the concept of complex antenna factor in Electromagnetic Compatibility (EMC) community for its calculation.

Accuracy of software-assisted detection of tumour feeders in transcatheter hepatic chemoembolization using three target definition protocols.

PubMed

Iwazawa, J; Ohue, S; Hashimoto, N; Mitani, T

2014-02-01

To compare the accuracy of computer software analysis using three different target-definition protocols to detect tumour feeder vessels for transarterial chemoembolization of hepatocellular carcinoma. C-arm computed tomography (CT) data were analysed for 81 tumours from 57 patients who had undergone chemoembolization using software-assisted detection of tumour feeders. Small, medium, and large-sized targets were manually defined for each tumour. The tumour feeder was verified when the target tumour was enhanced on selective C-arm CT of the investigated vessel during chemoembolization. The sensitivity, specificity, and accuracy of the three protocols were evaluated and compared. One hundred and eight feeder vessels supplying 81 lesions were detected. The sensitivity of the small, medium, and large target protocols was 79.8%, 91.7%, and 96.3%, respectively; specificity was 95%, 88%, and 50%, respectively; and accuracy was 87.5%, 89.9%, and 74%, respectively. The sensitivity was significantly higher for the medium (p = 0.003) and large (p < 0.001) target protocols than for the small target protocol. The specificity and accuracy were higher for the small (p < 0.001 and p < 0.001, respectively) and medium (p < 0.001 and p < 0.001, respectively) target protocols than for the large target protocol. The overall accuracy of software-assisted automated feeder analysis in transarterial chemoembolization for hepatocellular carcinoma is affected by the target definition size. A large target definition increases sensitivity and decreases specificity in detecting tumour feeders. A target size equivalent to the tumour size most accurately predicts tumour feeders. Copyright © 2013 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Fluorescence turn-on detection of target sequence DNA based on silicon nanodot-mediated quenching.

PubMed

Zhang, Yanan; Ning, Xinping; Mao, Guobin; Ji, Xinghu; He, Zhike

2018-05-01

We have developed a new enzyme-free method for target sequence DNA detection based on the dynamic quenching of fluorescent silicon nanodots (SiNDs) toward Cy5-tagged DNA probe. Fascinatingly, the water-soluble SiNDs can quench the fluorescence of cyanine (Cy5) in Cy5-tagged DNA probe in homogeneous solution, and the fluorescence of Cy5-tagged DNA probe can be restored in the presence of target sequence DNA (the synthetic target miRNA-27a). Based on this phenomenon, a SiND-featured fluorescent sensor has been constructed for "turn-on" detection of the synthetic target miRNA-27a for the first time. This newly developed approach possesses the merits of low cost, simple design, and convenient operation since no enzymatic reaction, toxic reagents, or separation procedures are involved. The established method achieves a detection limit of 0.16 nM, and the relative standard deviation of this method is 9% (1 nM, n = 5). The linear range is 0.5-20 nM, and the recoveries in spiked human fluids are in the range of 90-122%. This protocol provides a new tactic in the development of the nonenzymic miRNA biosensors and opens a promising avenue for early diagnosis of miRNA-associated disease. Graphical abstract The SiND-based fluorescent sensor for detection of S-miR-27a.

Evaluation of Targeted Next-Generation Sequencing for Detection of Bovine Pathogens in Clinical Samples.

PubMed

Anis, Eman; Hawkins, Ian K; Ilha, Marcia R S; Woldemeskel, Moges W; Saliki, Jeremiah T; Wilkes, Rebecca P

2018-07-01

The laboratory diagnosis of infectious diseases, especially those caused by mixed infections, is challenging. Routinely, it requires submission of multiple samples to separate laboratories. Advances in next-generation sequencing (NGS) have provided the opportunity for development of a comprehensive method to identify infectious agents. This study describes the use of target-specific primers for PCR-mediated amplification with the NGS technology in which pathogen genomic regions of interest are enriched and selectively sequenced from clinical samples. In the study, 198 primers were designed to target 43 common bovine and small-ruminant bacterial, fungal, viral, and parasitic pathogens, and a bioinformatics tool was specifically constructed for the detection of targeted pathogens. The primers were confirmed to detect the intended pathogens by testing reference strains and isolates. The method was then validated using 60 clinical samples (including tissues, feces, and milk) that were also tested with other routine diagnostic techniques. The detection limits of the targeted NGS method were evaluated using 10 representative pathogens that were also tested by quantitative PCR (qPCR), and the NGS method was able to detect the organisms from samples with qPCR threshold cycle ( C T ) values in the 30s. The method was successful for the detection of multiple pathogens in the clinical samples, including some additional pathogens missed by the routine techniques because the specific tests needed for the particular organisms were not performed. The results demonstrate the feasibility of the approach and indicate that it is possible to incorporate NGS as a diagnostic tool in a cost-effective manner into a veterinary diagnostic laboratory. Copyright © 2018 Anis et al.

Detection of unknown targets from aerial camera and extraction of simple object fingerprints for the purpose of target reacquisition

NASA Astrophysics Data System (ADS)

Mundhenk, T. Nathan; Ni, Kang-Yu; Chen, Yang; Kim, Kyungnam; Owechko, Yuri

2012-01-01

An aerial multiple camera tracking paradigm needs to not only spot unknown targets and track them, but also needs to know how to handle target reacquisition as well as target handoff to other cameras in the operating theater. Here we discuss such a system which is designed to spot unknown targets, track them, segment the useful features and then create a signature fingerprint for the object so that it can be reacquired or handed off to another camera. The tracking system spots unknown objects by subtracting background motion from observed motion allowing it to find targets in motion, even if the camera platform itself is moving. The area of motion is then matched to segmented regions returned by the EDISON mean shift segmentation tool. Whole segments which have common motion and which are contiguous to each other are grouped into a master object. Once master objects are formed, we have a tight bound on which to extract features for the purpose of forming a fingerprint. This is done using color and simple entropy features. These can be placed into a myriad of different fingerprints. To keep data transmission and storage size low for camera handoff of targets, we try several different simple techniques. These include Histogram, Spatiogram and Single Gaussian Model. These are tested by simulating a very large number of target losses in six videos over an interval of 1000 frames each from the DARPA VIVID video set. Since the fingerprints are very simple, they are not expected to be valid for long periods of time. As such, we test the shelf life of fingerprints. This is how long a fingerprint is good for when stored away between target appearances. Shelf life gives us a second metric of goodness and tells us if a fingerprint method has better accuracy over longer periods. In videos which contain multiple vehicle occlusions and vehicles of highly similar appearance we obtain a reacquisition rate for automobiles of over 80% using the simple single Gaussian model compared

Organizational Paradigm Shifts.

ERIC Educational Resources Information Center

National Association of College and University Business Officers, Washington, DC.

This collection of essays explores a new paradigm of higher education. The first essay, "Beyond Re-engineering: Changing the Organizational Paradigm" (L. Edwin Coate), suggests a model of quality process management and a structure for managing organizational change. "Thinking About Consortia" (Mary Jo Maydew) discusses…

Long-Term Memories Bias Sensitivity and Target Selection in Complex Scenes

PubMed Central

Patai, Eva Zita; Doallo, Sonia; Nobre, Anna Christina

2014-01-01

In everyday situations we often rely on our memories to find what we are looking for in our cluttered environment. Recently, we developed a new experimental paradigm to investigate how long-term memory (LTM) can guide attention, and showed how the pre-exposure to a complex scene in which a target location had been learned facilitated the detection of the transient appearance of the target at the remembered location (Summerfield, Lepsien, Gitelman, Mesulam, & Nobre, 2006; Summerfield, Rao, Garside, & Nobre, 2011). The present study extends these findings by investigating whether and how LTM can enhance perceptual sensitivity to identify targets occurring within their complex scene context. Behavioral measures showed superior perceptual sensitivity (d′) for targets located in remembered spatial contexts. We used the N2pc event-related potential to test whether LTM modulated the process of selecting the target from its scene context. Surprisingly, in contrast to effects of visual spatial cues or implicit contextual cueing, LTM for target locations significantly attenuated the N2pc potential. We propose that the mechanism by which these explicitly available LTMs facilitate perceptual identification of targets may differ from mechanisms triggered by other types of top-down sources of information. PMID:23016670

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.

Risk maps for targeting exotic plant pest detection programs in the United States

Treesearch

R.D. Magarey; D.M. Borchert; J.S. Engle; M Garcia-Colunga; Frank H. Koch; et al

2011-01-01

In the United States, pest risk maps are used by the Cooperative Agricultural Pest Survey for spatial and temporal targeting of exotic plant pest detection programs. Methods are described to create standardized host distribution, climate and pathway risk maps for the top nationally ranked exotic pest targets. Two examples are provided to illustrate the risk mapping...

Time reversal optical tomography and decomposition methods for detection and localization of targets in highly scattering turbid media

NASA Astrophysics Data System (ADS)

Wu, Binlin

New near-infrared (NIR) diffuse optical tomography (DOT) approaches were developed to detect, locate, and image small targets embedded in highly scattering turbid media. The first approach, referred to as time reversal optical tomography (TROT), is based on time reversal (TR) imaging and multiple signal classification (MUSIC). The second approach uses decomposition methods of non-negative matrix factorization (NMF) and principal component analysis (PCA) commonly used in blind source separation (BSS) problems, and compare the outcomes with that of optical imaging using independent component analysis (OPTICA). The goal is to develop a safe, affordable, noninvasive imaging modality for detection and characterization of breast tumors in early growth stages when those are more amenable to treatment. The efficacy of the approaches was tested using simulated data, and experiments involving model media and absorptive, scattering, and fluorescent targets, as well as, "realistic human breast model" composed of ex vivo breast tissues with embedded tumors. The experimental arrangements realized continuous wave (CW) multi-source probing of samples and multi-detector acquisition of diffusely transmitted signal in rectangular slab geometry. A data matrix was generated using the perturbation in the transmitted light intensity distribution due to the presence of absorptive or scattering targets. For fluorescent targets the data matrix was generated using the diffusely transmitted fluorescence signal distribution from the targets. The data matrix was analyzed using different approaches to detect and characterize the targets. The salient features of the approaches include ability to: (a) detect small targets; (b) provide three-dimensional location of the targets with high accuracy (~within a millimeter or 2); and (c) assess optical strength of the targets. The approaches are less computation intensive and consequently are faster than other inverse image reconstruction methods that

A new paradigm of dielectric relaxation spectroscopy for non-invasive detection of breast abnormalities: a preliminary feasibility analysis

NASA Astrophysics Data System (ADS)

Dhurjaty, Sreeram; Qiu, Yuchen; Tan, Maxine; Qian, Wei; Zheng, Bin

2016-03-01

In order to improve efficacy of screening mammography, in recent years, we have been investigating the feasibility of applying a resonance-frequency based electrical impedance spectroscopy (REIS) technology to noninvasively detect breast abnormalities that may lead to the development of cancer in the near-term. Despite promising study-results, we found that REIS suffered from relatively poor reproducibility due to perturbations in electrode placement, contact pressure variation on the breast, as well as variation of the resonating inductor. To overcome this limitation, in this study, we propose and analyze a new paradigm of Dielectric Relaxation Spectroscopy (DRS) that measures polarization-lag of dielectric signals in breast-capacitance when excited by the pulses or sine waves. Unlike conventional DRS that operates using the signals at very high frequencies (GHz) to examine changes in polarization, our new method detects and characterizes the dielectric properties of tissue at low frequencies (<=10 MHz) due to the advent of inexpensive oscillators that are accurate to 1 pico-second (used in GPS receivers) as well as measurement of amplitudes of 1 ppm or better. From theoretical analysis, we have proved that the sensitivity of new DRS in detecting permittivity of water increased by >=80 times as compared to conventional DRS, which operates at frequencies around 4GHz. By analyzing and comparing the relationship between the new DRS and REIS, we found that this DRS has potential advantages in enhancing repeatability from various readings, including temperature-insensitive detection, and yielding higher resolution or sensitivity (up to 100 Femtofarads).

Target-responsive DNAzyme cross-linked hydrogel for visual quantitative detection of lead.

PubMed

Huang, Yishun; Ma, Yanli; Chen, Yahong; Wu, Xuemeng; Fang, Luting; Zhu, Zhi; Yang, Chaoyong James

2014-11-18

Because of the severe health risks associated with lead pollution, rapid, sensitive, and portable detection of low levels of Pb(2+) in biological and environmental samples is of great importance. In this work, a Pb(2+)-responsive hydrogel was prepared using a DNAzyme and its substrate as cross-linker for rapid, sensitive, portable, and quantitative detection of Pb(2+). Gold nanoparticles (AuNPs) were first encapsulated in the hydrogel as an indicator for colorimetric analysis. In the absence of lead, the DNAzyme is inactive, and the substrate cross-linker maintains the hydrogel in the gel form. In contrast, the presence of lead activates the DNAzyme to cleave the substrate, decreasing the cross-linking density of the hydrogel and resulting in dissolution of the hydrogel and release of AuNPs for visual detection. As low as 10 nM Pb(2+) can be detected by the naked eye. Furthermore, to realize quantitative visual detection, a volumetric bar-chart chip (V-chip) was used for quantitative readout of the hydrogel system by replacing AuNPs with gold-platinum core-shell nanoparticles (Au@PtNPs). The Au@PtNPs released from the hydrogel upon target activation can efficiently catalyze the decomposition of H2O2 to generate a large volume of O2. The gas pressure moves an ink bar in the V-chip for portable visual quantitative detection of lead with a detection limit less than 5 nM. The device was able to detect lead in digested blood with excellent accuracy. The method developed can be used for portable lead quantitation in many applications. Furthermore, the method can be further extended to portable visual quantitative detection of a variety of targets by replacing the lead-responsive DNAzyme with other DNAzymes.