Image registration is an important pre-processing step for many image exploitation algorithms such as geo-location, object recognition, vision-aided navigation, and image fusion. The utility and effectiveness of downstream exploitation algorithms depends on reliable image registration. Image registration failure can corrupt processing and performance of downstream algorithms by mis-associating image features. For example, feature mis-association leads to generation of incorrect target geo-coordinates in aerial surveillance applications, or erroneous vision-based measurements in vision aided navigation. Accurate and dependable registration failure detection mitigates deleterious effects of erroneous registration. However, in autonomous operation modes, with no human in-the-loop, and in the absence of registration solution groundtruth knowledge, verifying registration solutions is problematic. In this paper we present a machine learning based image registration verification system that operates autonomously, without ground-truth. We train a machine learning algorithm to identify correct registration solutions, even for difficult multi-modal image registration in which sensor phenomenology differences produce different feature manifestation. The verification approach includes techniques for mitigation of falsealarms that may arise due to feature ambiguity. We present examples of feature ambiguity for correlation-related registration techniques. We describe Radon transform processing, covariance estimation, and fusion techniques for feature ambiguity detection. We present numerical verification performance results from a small pilot study designed to investigate the feasibility of using machine learning for reliable registration verification.

Effective infantry training requires accurate pointing direction knowledge of soldier-carried weapons to determine hit or miss scores. Infantry training solutions, which transition seamlessly to operational use, enables soldiers to train with what they use in combat, thereby increasing training efficacy and soldier lethality. To meet these dual needs, BAE Systems is developing the High-precision Infantry Training System (HITS). HITS provides a day/night, weapon-mounted ballistics point-of-impact detection system, to be used for live force-on-force training at Combat Training Centers (CTCs). HITS is hardware agnostic, but presently leverages BAE Systems’ fielded FWSI (Family-of-Weapons Sights-Individual) IR camera optic system to provide a ruggedized, low-SWaP, weapon-mounted platform for high-precision gun barrel pointing angle determination. The magnetometer and Inertial Measurement Unit (IMU) sensors, housed in the FWS-I gunsight, provide approximate pointing direction information while the FWS-I IR camera produces IR imagery with a field-of-view centered on the pointing direction of the gun. HITS uses innovative computer-vision processing to calculate the gun’s correct pointing angles. HITS performs geometric alignment of the FWS-I acquired IR image to a reference image of the view. The reference image is created by projecting a pre-generated reference model of the training site, consisting of geo-located 3-D LIDAR point-cloud data, onto the image plane. Using the gunsight location information, obtained from a GPS-RTK, the correct pointing angles are calculated from the image alignment. The system has an initial pointing accuracy goal of two milliradians. In this paper, we provide an overview of the HITS algorithm components including reference image generation, image registration, and pointing angle determination.

In vision-aided navigation, several challenges affect algorithm performance in different ways. These challenges include terrain type, sensor technology, cloud coverage, sensor malfunction, and environmental conditions such as illumination. Currently, ordinance such as precision-guided munitions, may experience any of these challenges over the course of the mission. Each challenge has an associated probability-of-occurrence, e.g. probability that clouds will obscure the field-of-view, or that sensor hardware will induce image blurring or other artifacts that impede scene imaging ability. Different navigation algorithms respond differently to each challenge. By capturing these outcomes for a given algorithm and understanding the likelihood that such challenges occur during a mission, we can derive the overall mission-reliability and performance ranking of that algorithm. In this context, reliability refers to the ability of an algorithm to maintain a required Circle of Equal Probability at the target location that ensures mission success. Moreover, in multi-vendor system development efforts, critical algorithms such as navigation are frequently provided as a proprietary "black-box," with no visibility into internal functioning. The absence of internal visibility makes performance evaluation more challenging. In this paper we present our novel analysis approach, and results from application to a black-box state-of-the-art navigation algorithm developed for navigation in UPS-denied environments. Our approach identifies possible technologies used, and delineates hypothesized challenges affecting algorithm performance. It identifies all algorithm inputs and develops an evaluation framework where all challenges can be applied individually and successively to evaluate mission worthiness, i.e., mission reliability. Challenges refer to realistic, confounding stressors, arising in-mission, such as terrain type differences, attitude errors, imager faults, cloud coverage, and timing errors. [1] Our approach uses the RAPSODE methodology (cf. ReliAbility PhySics based On Dynamic causal nEtworks, http://ewh.ieee.org/r1/boston/r1/files/boston_rs_meeting_mar16.pdf). The methodology was developed to evaluate high-reliability complex systems.

Accurate and successful image registration is a key enabling technology in applications such as image fusion, matching and pattern recognition. Knowledge of registration solution quality and accuracy can help prevent an inaccurate registration from degrading or corrupting performance of downstream image processing applications. However, estimating the spatial accuracy of image registration solutions can be difficult in the absence of ground-truth information on feature content or fiducial marker correspondences. This paper presents an automated spatial registration accuracy measurement for estimating and quantifying the spatial accuracy of correlation-based image registration in the absence of ground-truth information. For correlation surfaces exhibiting a single dominant peak, the approach consists of fitting an appropriate region of the correlation surface, about the peak coefficient, with a two-dimensional Gaussian. It then uses the covariance of the Gaussian to model the registration spatial error covariance. Use of a fitted Gaussian provides an intuitive probabilistic interpretation to the registration solution; the Gaussian function value at a spatial offset from the Gaussian peak gives the likelihood of that offset value. For more complicated regions containing multiple correlation local peak values, we extend the approach to fit a Gaussian mixture model to the region and use the mixture model covariance for the spatial accuracy metric. We describe an energy-based method for choosing the model region of the correlation surface. We discuss implementation subtleties and provide perturbation methods for handling numerically illconditioned matrices. We present numerical spatial error estimation results generated from registration of real video imagery acquired from a UAV platform.

Image correlation has proven useful for image filtering, matching, pattern recognition, and image registration over many decades. The two classical correlation forms, amplitude and phase correlation, display different properties. Amplitude correlation often provides a low, broad peak in the correlation domain. The broadness of the peak provides robustness to matching imagery exhibiting non-translational geometric offsets, such as rotation or scale differences. By contrast, phase correlation tends to provide a high, narrow peak. The high peak signifies high matching confidence while the narrow peak width provides accurate shift localization. However, the phase correlation peak degrades rapidly when matching against images with non-translational geometric offset. To provide tradeoffs between properties of these traditional correlation forms, in this paper we present a general, flexible form of correlation called Spectrally-Shaped Correlation (SSC). SSC provides control over the Fourier domain normalization of the correlation components. We apply SSC to the problem of image registration. We show how SSC contains the classical amplitude, phase, and phase-only correlation forms as special cases. First, we present the general theory of Fourier transforms for multi-channel imagery, modeled as hypercomplex-valued imagery. We present mathematical details of the transform techniques and develop the SSC approach. We then present numerical results demonstrating registration of real image data, acquired from a UAV operating in an urban environment, to reference imagery. We demonstrate a performance improvement of the SSC over the classical forms of correlation.

While Machine Learning (ML) Automatic Target Recognition (ATR) represents the state-of-the art in target recognition, model-based ATR plays a valuable role. Model-based ATR complements machine learning ATR approaches by filling a near-term niche. While explainable Artificial Intelligence (AI) is not yet fully realized, model-based ATR serves to validate machine learning recognition decisions, and thus instills confidence in ML target calls. Alternatively, model-based ATR can act as a stand-alone ATR component, particularly in scenarios in which a small number of targets are of interest, e.g., "target-of-the-day" engagements. Model-based ATR approaches need no training data, and thus provide an alternative to machine learning approaches in the absence of sufficient quantities of real, or sufficiently high-fidelity synthetic, training data. In this paper, we present an approach to model-based ATR, called Shape-Based ATR (SB-ATR), which captures salient target shape information for recognizing targets in wide-area satellite imagery. SB-ATR finds the right blend of coarse 3-D target shape abstraction and target realism to provide robustness against target variations and environmental operating conditions, while simultaneously providing high-performance target recognition. The approach uses newer, robust forms of image correlation for matching a predicted target shape against the image. Shape prediction searches over target pose, and uses satellite metadata and solar geometry to generate realistic target shape and shadow predictions. The correlation matchers provide tolerance to illumination variations, moderate occlusions, image distortions and noise, and geometric differences between models and real targets. We present technical details of the shape-based approach, and provide numerical target recognition results on real-world satellite imagery demonstrating performance.

The pervasiveness of small Unmanned Aerial Vehicles (UAVs), due to low cost, ease of control, and portability, opens the possibility of their use in urban environments for illegal or adversarial purposes. Such use includes unauthorized surveillance, reconnaissance, and weaponization. Detecting adversarial UAVs in urban environments is difficult. Urban canyons provide shielding from visibility. The small size of quadcopter-type UAVs limits the number of object pixels available for processing, which reduces standoff detection performance. UAVs fly against a background of ground motion clutter which can mask their motion. One possible solution to small UAV detection in urban environments uses low-cost UAV surveillance platforms, equipped with optical sensors, together with computer vision algorithms to detect adversarial UAVs in video data. In this paper we adapt the astronomical technique of transit photometry to detect small UAVs, operating in urban environments, in video data. Transit photometry, typically used for exo-planet discovery, detects small changes in background brightness due to a transiting object. As the UAV traverses across a bright background region, for example, the vehicle occludes the background and reduces the perceived brightness. This brightness dip may be used to infer the existence of a potential UAV passing across the background. The transit photometry curve, resulting from this brightness dip, reveals information about the traversing vehicle. We investigate mathematical properties of the transit photometry curve and derive a closed-form expression for it. We present numerical results demonstrating the technique on real video data acquired from a small UAV operating in an urban environment.

In the marine environment agar degradation is assured by bacteria that contain large agarolytic systems with enzymes acting in various endo- and exo-modes. Agarase A (AgaA) is an endo-glycoside hydrolase of family 16 considered to initiate degradation of agarose. Agaro-oligosaccharide binding at a unique surface binding site (SBS) in AgaA from Zobellia galactanivorans was investigated by computational methods in conjunction with a structure/sequence guided approach of site-directed mutagenesis probed by surface plasmon resonance binding analysis of agaro-oligosaccharides of DP 4-10. The crystal structure has shown that agaro-octaose interacts via H-bonds and aromatic stacking along 7 subsites (L through R) of the SBS in the inactive catalytic nucleophile mutant AgaA-E147S. D271 is centrally located in the extended SBS where it forms H-bonds to galactose and 3,6-anhydrogalactose residues of agaro-octaose at subsites O and P. We propose D271 is a key residue in ligand binding to the SBS. Thus AgaA-E147S/D271A gave slightly decreasing KD values from 625 ± 118 to 468 ± 13 μM for agaro-hexaose, -octaose, and -decaose, which represent 3- to 4-fold reduced affinity compared with AgaA-E147S. Molecular dynamics simulations and interaction analyses of AgaA-E147S/D271A indicated disruption of an extended H-bond network supporting that D271 is critical for the functional SBS. Notably, neither AgaA-E147S/W87A nor AgaA-E147S/W277A, designed to eliminate stacking with galactose residues at subsites O and Q, respectively, were produced in soluble form. W87 and W277 may thus control correct folding and structural integrity of AgaA.

Here we present a water-in-air droplet platform for micro-compartmentalization for single molecule guided synthesis and analysis consisting of a flow-system hosting dense arrays of aqueous microdroplets on a glass surface surrounded by air.

We combine altimeter, IMU-provided orientation, and along-path to reference imagery registration to provide airborne platform navigation solutions in GPS-denied environments, at an accuracy and precision primarily gated by the quality of locally available digital elevation maps (DEM) and reference imagery. Our image-to-image registration approach, based on an enhancement to phase correlation which greatly increases its efficacy, significantly outperforms cornerbased methods. Sensor orientation, required for image projection, is provided by even inexpensive IMUs, which experience limited error indefinitely, independent of the amount of accumulated drift in position. When feature-rich imagery becomes available (e.g. after transitioning from over-water to over-land flight), a limited iteration over rotation parameters of the vehicle allows the recapture of the pose. For standard errors in altitude, IMU orientation, and reference DEMs and imagery, our approach typically provides combined navigation accuracy to within several meters, often within 1 meter- thus in many cases outperforming GPS.

Modern satellites tag their images with geolocation information using GPS and star tracking systems. Depending on the quality of the geopositioning equipment, errors may range from a few meters to tens of meters on the ground. At the current state of art, there is no established method to automatically correct these errors limiting the large-scale joint utilization of cross-platform satellite images. In this paper, an automatic geolocation correction framework that corrects images from multiple satellites simultaneously is presented. As a result of the proposed correction process, all the images are effectively registered to the same absolute geodetic coordinate frame. The usability and the quality of the correction framework are demonstrated through a 3-D surface reconstruction application. The 3-D surface models given by original satellite geopositioning metadata, and the corrected metadata, are compared. The quality difference is measured through an entropy-based metric applied to the orthographic height maps given by the 3-D surface models. Measuring the absolute accuracy of the framework is harder due to lack of publicly available high-precision ground surveys. However, the geolocation of images of exemplar satellites from different parts of the globe are corrected, and the road networks given by OpenStreetMap are projected onto the images using original and corrected metadata to demonstrate the improved quality of alignment.

High-resolution and accurate Digital Elevation Model (DEM) generation from satellite imagery is a challenging problem. In this work, a stereo 3-D reconstruction framework is outlined that is applicable to nonstereoscopic satellite image pairs that may be captured by different satellites. The orthographic height maps given by stereo reconstruction are compared to height maps given by a multiview approach based on Probabilistic Volumetric Representation (PVR). Height map qualities are measured in comparison to manually prepared ground-truth height maps in three sites from different parts of the world with urban, semi-urban and rural features. The results along with strengths and weaknesses of the two techniques are summarized.

Mutational analysis of Sulfolobus solfataricus class II α-mannosidase was focused on side chains that interact with the hydroxyls of the -1 mannosyl of the substrate (Asp-534) or form ligands to the active site divalent metal ion (His-228 and His-533) judged from crystal structures of homologous enzymes. D534A and D534N appeared to be completely inactive. When compared to the wild-type enzyme, the mutant enzymes in general showed only small changes in K(M) for the substrate, p-nitrophenyl-α-mannoside, but elevated activation constants, K(A), for the divalent metal ion (Co²⁺, Zn²⁺, Mn²⁺, or Cd²⁺). Some mutant enzyme forms displayed an altered preference for the metal ion compared to that of the wild type-enzyme. Furthermore, the H228Q, H533E, and H533Q enzymes were inhibited at increasing Zn²⁺ concentrations. The catalytic rate was reduced for all enzymes compared to that of the wild-type enzyme, although less dramatically with some activating metal ions. No major differences in the pH dependence between wild-type and mutant enzymes were found in the presence of different metal ions. The pH optimum was 5, but enzyme instability was observed at pH <4.5; therefore, only the basic limb of the bell-shaped pH profile was analyzed.

The stability of serine proteases is of major importance for their application in industrial processes. Here we study the determinants of the stability of a Nocardiopsis prasina serine protease using fast residual activity assays, a feature classification algorithm, and structure-based energy calculation algorithms for 121 micropurified mutant enzyme clones containing multiple point mutations. Using a multivariate regression analysis, we deconvolute the data for the mutant clones and find that mutations of residues Asn47 and Pro124 are deleterious to the stability of the enzyme. Both of these residues are situated in loops that are known to be important for the stability of the highly homologous α-lytic protease. Structure-based energy calculations with PEATSA give a good general agreement with the trend of experimentally measured values but also identify a number of clones that the algorithm fails to predict correctly. We discuss the significance of the results in relation to the structure and function of closely related proteases, comment on the optimal experimental design when performing high-throughput experiments for characterizing the determinants of protein stability, and discuss the performance of structure-based energy calculations with complex data sets such as the one presented here.

On arrival at the Emergency Department (ED), 30% (n=30) of patients presented with an airway obstruction unrelieved in the pre-hospital setting. The major cause of airway obstruction was blood in the airway (23%). A total of 36.5% (n=19) of patients with a GCS of 3-4 (n=50), presented at ED with an airway obstruction. On arrival at ED 10.0% of patients had an oxygen saturation level of less than 85% (n=10), 2.0% (n=2) with saturation between 85-89, 9.0% (n=9) with saturation between 90-94 and 67.0% (n=67) with saturation between 95-100%. In twelve cases, the oxygen saturation levels were not documented at ED. Of those patients with a GCS of 3-4 (n=50), 18% received no airway adjunct, 60% received an oropharyngeal airway, 8% a nasopharyngeal airway and 14% were intubated. Outcome was measured using the Glasgow Outcome Scale (GOS) at discharge from hospital. A total of 37.4% of patients died, whilst 6.1% of patients were in a persistent vegetative state. Severe disability was experienced by 17.2% of patients; moderate

Large amounts of data are being generated annually on the connection between the sequence, structure and function of proteins using site-directed mutagenesis, protein design and directed evolution techniques. These data provide the fundamental building blocks for our understanding of protein function, molecular biology and living organisms in general. However, much experimental data are never deposited in databases and is thus 'lost' in journal publications or in PhD theses. At the same time theoretical scientists are in need of large amounts of experimental data for benchmarking and calibrating novel predictive algorithms, and theoretical progress is therefore often hampered by the lack of suitable data to validate or disprove a theoretical assumption. We present PEAT (Protein Engineering Analysis Tool), an application that integrates data deposition, storage and analysis for researchers carrying out protein engineering projects or biophysical characterization of proteins. PEAT contains modules for DNA sequence manipulation, primer design, fitting of biophysical characterization data (enzyme kinetics, circular dichroism spectroscopy, NMR titration data, etc.), and facilitates sharing of experimental data and analyses for a typical university-based research group. PEAT is freely available to academic researchers at http://enzyme.ucd.ie/PEAT.

NMR‐monitored pH titration experiments are routinely used to measure site‐specific protein p K a values. Accurate experimental p K a values are essential in dissecting enzyme catalysis, in studying the pH‐dependence of protein stability and ligand binding, in benchmarking p K a prediction algorithms, and ultimately in understanding electrostatic effects in proteins. However, due to the complex ways in which pH‐dependent electrostatic and structural changes manifest themselves in NMR spectra, reported apparent p K a values are often dependent on the way that NMR pH‐titration curves are analyzed. It is therefore important to retain the raw NMR spectroscopic data to allow for documentation and possible re‐interpretation. We have constructed a database of primary NMR pH‐titration data, which is accessible via a web interface. Here, we report statistics of the database contents and analyze the data with a global perspective to provide guidelines on best practice for fitting NMR titration curves. Titration_DB is available at http://enzyme.ucd.ie/Titration_DB . Proteins 2010. © 2009 Wiley‐Liss, Inc.

Site‐specific pKa values measured by NMR spectroscopy provide essential information on protein electrostatics, the pH‐dependence of protein structure, dynamics and function, and constitute an important benchmark for protein pKa calculation algorithms. Titration curves can be measured by tracking the NMR chemical shifts of several reporter nuclei versus sample pH. However, careful analysis of these curves is needed to extract residue‐specific pKa values since pH‐dependent chemical shift changes can arise from many sources, including through‐bond inductive effects, through‐space electric field effects, and conformational changes. We have re‐measured titration curves for all carboxylates and His 15 in Hen Egg White Lysozyme (HEWL) by recording the pH‐dependent chemical shifts of all backbone amide nitrogens and protons, Asp/Glu side chain protons and carboxyl carbons, and imidazole protonated carbons and protons in this protein. We extracted pKa values from the resulting titration curves using standard fitting methods, and compared these values to each other, and with those measured previously by 1H NMR (Bartik et al., Biophys J 1994;66:1180–1184). This analysis gives insights into the true accuracy associated with experimentally measured pKa values. We find that apparent pKa values frequently differ by 0.5–1.0 units depending upon the nuclei monitored, and that larger differences occasionally can be observed. The variation in measured pKa values, which reflects the difficulty in fitting and assigning pH‐dependent chemical shifts to specific ionization equilibria, has significant implications for the experimental procedures used for measuring protein pKa values, for the benchmarking of protein pKa calculation algorithms, and for the understanding of protein electrostatics in general. Proteins 2011. © 2010 Wiley‐Liss, Inc.