Enhanced MMW and SMMW/THz imaging system performance prediction and analysis tool for concealed weapon detection and pilotage obstacle avoidance.

The U.S. Army Research Laboratory has continued to develop and enhance a millimeter-wave (MMW) and submillimeter-wave (SMMW)/terahertz (THz)-band imaging system performance prediction and analysis tool for both the detection and identification of concealed weaponry and for pilotage obstacle avoidance. The details of the MATLAB-based model that accounts for the effects of all critical sensor and display components, for the effects of atmospheric attenuation, concealment material attenuation, active illumination, target and background orientation, target and background thermal emission, and various imaging system architectures have been reported on in 2005, 2007, and 2011. This paper provides a comprehensive review of a newly enhanced MMW and SMMW/THz imaging system analysis and design tool that now includes an improved noise submodel for more accurate and reliable performance predictions, the capability to account for postcapture image contrast enhancement, and the capability to account for concealment material backscatter with active-illumination-based systems. Present plans for additional expansion of the model's predictive capabilities are also outlined.

Target identification and navigation performance modeling of a passive millimeter wave imager.

Human task performance using a passive interferometric millimeter wave imaging sensor is modeled using a task performance modeling approach developed by the U.S. Army Night Vision and Electronic Sensors Directorate. The techniques used are illustrated for an imaging system composed of an interferometric antenna array, optical upconversion, and image formation using a shortwave infrared focal plane array. Two tasks, target identification and pilotage, are modeled. The effects of sparse antenna arrays on task performance are considered. Applications of this model include system trade studies for concealed weapon identification, navigation in fog, and brownout conditions.

Modeling the target acquisition performance of active imaging systems.

Recent development of active imaging system technology in the defense and security community have driven the need for a theoretical understanding of its operation and performance in military applications such as target acquisition. In this paper, the modeling of active imaging systems, developed at the U.S. Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate, is presented with particular emphasis on the impact of coherent effects such as speckle and atmospheric scintillation. Experimental results from human perception tests are in good agreement with the model results, validating the modeling of coherent effects as additional noise sources. Example trade studies on the design of a conceptual active imaging system to mitigate deleterious coherent effects are shown.

A 3D model-based simulation of demyelination to understand its effects on diffusion tensor imaging.

Demyelination is the progressive damage to the myelin sheath, a protective covering that surrounds a nerve's axon. Due to its high sensitivity to microscopic tissue changes, diffusion tensor imaging (DTI) is a powerful means of detecting signs of demyelination and axonal injury. In this paper, we present a 3D virtual model capable of simulating the complex Brownian motion of water molecules in a bundle of myelinated axons and glial cells for the purpose of synthesizing DTI data, characterizing and verifying the impact of demyelination on DTI. Our model consists of a highly detailed and realistic 3D representation of biological fiber bundles, with a myelin sheath covering the axons and glial cells in between them. The system simulates the Brownian motion of molecules to extract diffusion data. We perform our experiment for progressive stages of demyelination and demonstrate its effect on DTI measurements.

Hardware-efficient robust biometric identification from 0.58 second template and 12 features of limb (Lead I) ECG signal using logistic regression classifier.

The electrocardiogram (ECG), widely known as a cardiac diagnostic signal, has recently been proposed for biometric identification of individuals; however reliability and reproducibility are of research interest. In this paper, we propose a template matching technique with 12 features using logistic regression classifier that achieved high reliability and identification accuracy. Non-invasive ECG signals were captured using our custom-built ambulatory EEG/ECG embedded device (NeuroMonitor). ECG data were collected from healthy subjects (10), between 25-35 years, for 10 seconds per trial. The number of trials from each subject was 10. From each trial, only 0.58 seconds of Lead I ECG data were used as template. Hardware-efficient fiducial point detection technique was implemented for feature extraction. To obtain repeated random sub-sampling validation, data were randomly separated into training and testing sets at a ratio of 80:20. Test data were used to find the classification accuracy. ECG template data with 12 extracted features provided the best performance in terms of accuracy (up to 100%) and processing complexity (computation time of 1.2ms). This work shows that a single limb (Lead I) ECG can robustly identify an individual quickly and reliably with minimal contact and data processing using the proposed algorithm.

Principal component analysis of knee kinematics and kinetics after anterior cruciate ligament reconstruction.

This study compared the gait of 10 subjects with unilateral anterior cruciate ligament (ACL) reconstruction to a group of 12 height- and weight-matched control subjects. The analysis was based on knee flexion, adduction, and internal rotation angles and moments. The objective was to use principal component analysis (PCA) to identify knees of the ACL reconstructed subjects that fell outside normal ranges as determined by control subjects. Gait data were collected on all subjects in a motion analysis laboratory. Principal component (PC) models were developed for each gait measure based on the control subjects' data and used to assess gait waveforms of ACL reconstructed subjects. PCA allows analysis of entire gait waveforms for comparisons. In a sample of 10 ACL reconstructed subjects (7 years after surgery, on average), six of the ACL reconstructed knees had not returned to normal following surgery and eight of the contralateral knees functioned differently from controls. A majority of the differences were noted to occur in the abduction-adduction knee moment with corresponding infrequency in the differences seen in abduction-adduction rotation. PCA enabled us to identify subjects with abnormal gait waveforms as outliers relative to the normal control group.

Terahertz imaging system performance model for concealed-weapon identification.

The U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) and the U.S. Army Research Laboratory have developed a terahertz (THz) -band imaging system performance model for detection and identification of concealed weaponry. The MATLAB-based model accounts for the effects of all critical sensor and display components and for the effects of atmospheric attenuation, concealment material attenuation, and active illumination. The model is based on recent U.S. Army NVESD sensor performance modeling technology that couples system design parameters to observer-sensor field performance by using the acquire methodology for weapon identification performance predictions. This THz model has been developed in support of the Defense Advanced Research Project Agencies' Terahertz Imaging Focal-Plane Technology (TIFT) program and is currently being used to guide the design and development of a 0.650 THz active-passive imaging system. This paper will describe the THz model in detail, provide and discuss initial modeling results for a prototype THz imaging system, and outline plans to calibrate and validate the model through human perception testing.

Adaptive deblurring of noisy images.

We propose a practical sensor deblurring filtering method for images that are contaminated with noise. A sensor blurring function is usually modeled via a Gaussian-like function having a bell shape. The straightforward inverse function results in the magnification of noise at high frequencies. To address this issue, we apply a special spectral window to the inverse blurring function. This special window is called the power window, which is a Fourier-based smoothing window that preserves most of the spatial frequency components in the passband and attenuates quickly at the transition band. The power window is differentiable at the transition point, which gives a desired smooth property and limits the ripple effect. Utilizing the properties of the power window, we design the deblurring filter adaptively by estimating the energy of the signal and the noise of the image to determine the passband and the transition band of the filter. The deblurring filter design criteria are (a) the filter magnitude is less than 1 at the frequencies where the noise is stronger than the desired signal (the transition band), and (b) the filter magnitude is greater than 1 at the other frequencies (the passband). Therefore the adaptively designed deblurring filter is able to deblur the image by a desired amount based on the estimated or known blurring function while suppressing the noise in the output image. The deblurring filter performance is demonstrated by a human perception experiment in which 10 observers are to identify 12 military targets with 12 aspect angles. The results of comparing target identification probabilities with blurred and deblurred images and adding two levels of noise to blurred and deblurred noisy images are reported.