Mark Rahmes

Fingerprint Reconstruction Method Using Partial Differential Equation and Exemplar-Based Inpainting Methods

Manual latent fingerprint reconstruction to restore missing ridges is a tedious, time consuming, and expensive process. Latent fingerprint ridges are typically partially smudged, partially missing, aged, etc. This type of fingerprint cannot be used in the court of law directly to garner a conviction unless it can be matched to a known fingerprint. However, latent prints minimize the search for potential suspects and finding missing people. We propose an automated reconstruction method which minimizes manual restoration. Our nonlinear partial differential equation (PDE) and exemplar inpainting processes can aid the fingerprint expert. Larger missing regions are repaired using our coherent-based exemplar inpainting algorithm. PDE inpainting is used to fill small fissures in ridge structure. Ridge-lines are sharpened with anisotropic diffusion filters. These technologies improve latent fingerprint computer matching by allowing more minutiae. Accuracy assessment for inpainting missing ridges is described.

Autonomous selection of PDE inpainting techniques vs. exemplar inpainting techniques for void fill of high resolution digital surface models

High resolution Digital Surface Models (DSMs) may contain voids (missing data) due to the data collection process used to obtain the DSM, inclement weather conditions, low returns, system errors/malfunctions for various collection platforms, and other factors. DSM voids are also created during bare earth processing where culture and vegetation features have been extracted. The Harris LiteSiteTM Toolkit handles these void regions in DSMs via two novel techniques. We use both partial differential equations (PDEs) and exemplar based inpainting techniques to accurately fill voids. The PDE technique has its origin in fluid dynamics and heat equations (a particular subset of partial differential equations). The exemplar technique has its origin in texture analysis and image processing. Each technique is optimally suited for different input conditions. The PDE technique works better where the area to be void filled does not have disproportionately high frequency data in the neighborhood of the boundary of the void. Conversely, the exemplar based technique is better suited for high frequency areas. Both are autonomous with respect to detecting and repairing void regions. We describe a cohesive autonomous solution that dynamically selects the best technique as each void is being repaired.

A game theory model for situation awareness and management

We describe a model for determining strategies for making decisions. Decision making involves a model with several possible actions, state of the world with a probability, and a metric of how well the best decision was made. The ability to perform data mining and discover patterns to automatically predict likelihood of reaction to specific events and situational awareness is enhanced from multiple social media inputs. We discuss development of a method for determining actionable information to efficiently propitiate manpower, equipment assets, or propaganda responses. Our solution combines a variety of textual content information in different formats to help with a decision process to include sources, systems, and services that control and influence a situation. Different viewpoints need to be understood that are points involved in the event. Our FeatureSEARCHTM tool is helpful for rapidly parsing text that has been extracted with an intelligent algorithm in order to evaluate the population sentiment for the targeted area. Our tool allows for calculating optimal strategies provides greater knowledge about the state of the world and increases the likelihood of a decision maker making the best decision. We discuss game theory using linear programming methods to solve for multiple possible strategies that are known. The decision makers success depends upon his ability to correctly and automatically judge the multiple psychological and rational factors. The goal of our system, called GlobalSite, is to deliver trustworthy threat analysis systems and services that understand situations, while being a vital tool for continuing mission operations information.

Multi-dimensional reward volumes for sensor priority strategies

We describe a model for determining strategies for making decisions for sensor prioritization strategies. We combine operations research methods and remote sensing for decision making with several possible actions, state of the world, and a mixed probability metric. We perform data mining and discover patterns to automatically enhance situational awareness from multiple sensor inputs. Our solution has been developed for a method for determining actionable information to efficiently manage remote sensing assets and use open source information. Our tool allows for calculating optimal strategies, provides greater knowledge about the state of the world, and increases the likelihood of a decision maker making the best decision. We discuss multi-dimensional game theory using linear programming methods to solve for multiple possible strategies. We discuss a new concept of reward volumes. The decision makers success depends upon his ability to correctly and automatically judge the multiple factors. The goal of our system, called GlobalSite, is to deliver trustworthy threat analysis systems and services that understand situations, while being a vital tool for continuing mission operations.

Matching social network biometrics using geo-analytical behavioral modeling

Social patterns and graphical representation of geospatial activity is important for describing a persons typical behavior. We discuss a framework using social media and GPS smart phone to track an individual and establish normal activity with a network biometric. An individuals daily routine may include visiting many locations - home, work, shopping, entertainment and other destinations. All of these activities pose a routine or status quo of expected behavior. What has always been difficult, however, is predicting a change to the status quo, or predicting unusual behavior. We propose taking the knowledge of location information over a relatively long period of time and marrying that with modern analytical capabilities. The result is a biometric that can be fused and correlated with anothers behavioral biometric to determine relationships. Our solution is based on the analytical environment to support the ingestion of many data sources and the integration of analytical algorithms such as feature extraction, crowd source analysis, open source data mining, trends, pattern analysis and linear game theory optimization. Our framework consists of a hierarchy of data, space, time, and knowledge entities. We exploit such statistics to predict behavior or activity based on past observations. We use multivariate mutual information as a measure to compare behavioral biometrics.

Optimal multi-dimensional fusion model for sensor allocation and accuracy assessment

I We describe a multi-dimensional model for fusion of activity based intelligence (ABI) hypothesis-driven evidence through optimal sensor management. We determine decision-making strategies based upon ability to perform data mining and pattern discovery utilizing open source, actionable information to prepare for specific events or situations from multiple information sources. Our solution is based on an analytical framework using game theory to support ingestion of data sources (evidence); integration of analytical algorithms for feature extraction, crowd source analysis, open source data mining, trends, and pattern analysis and linear game theory optimization to support multiple hypothesis analysis. This solution may also save money by offering a Pareto efficient, repeatable process for resource management. We combine operations research methods and remote sensing for decision-making with several possible actions, state of world, and a mixed pro bability metric. Our tool allows for calculating optimal strategies, provides greater knowledge about remote sensing access times and increases likelihood of a decision-maker making best decision. We fuse evidence using Dempsters Rule and Nash Equilibrium (NE) for allocation of demands by sensor modality. We discuss a method for calculating optimal detector to determine accuracy of resource allocation. By calculating all NE possibilities per period, optimization of sensor allocation is achieved for overall higher system efficiency. We model impact of decision-making on accuracy by adding more dimensions to decision-making process as sensitivity analysis. Future work is to implement the design on a distributed processing platform to support real-world-sized scenarios and simulations.

Multi-disciplinary ontological geo-analytical incident modeling

Cultural patterns and representations of crime areas are important in geo-analytics. We discuss a multi-disciplinary ontology that provides an analytical, architectural framework to apply a broad spectrum of analytical capabilities to changes the world faces on a daily basis to determine an optimal allocation of resources for continuous enrichment of Foundation GEOINT content. All of these changes such as food shortages, weather, environmental hazards, traffic congestion, economic crises, political unrest, un-employment, and specifically crime, pose a constant struggle in societies to maintain status quo. What has always been difficult to predict is change to status quo, or predicting regions at risk or undergoing stress. We propose taking knowledge of a location and society, and marrying that with modern analytical capabilities, including linear game theory, to pit forces of maintaining status quo against forces within society that desire to force a drastic change. The result is a probabilistic output of potential outcomes that can be further analyzed to predict most likely and most dangerous outcomes. Our solution is based on analytical environment to support ingestion of many data sources and integration of analytical algorithms such as feature extraction, crowd source analysis, open source data mining, trends, pattern analysis and linear game theory optimization. Our framework consists of a hierarchy of data, space, time and knowledge entities. We exploit such crime statistics to predict crime activity based on past observations. We also show simulations based on minimum mean squared error (mmse) and pseudo estimators of crime activity.

A multi-resolution fractal additive scheme for blind watermarking of 3D point data

We present a fractal feature space for 3D point watermarking to make geospatial systems more secure. By exploiting the self similar nature of fractals, hidden information can be spatially embedded in point cloud data in an acceptable manner as described within this paper. Our method utilizes a blind scheme which provides automatic retrieval of the watermark payload without the need of the original cover data. Our method for locating similar patterns and encoding information in LiDAR point cloud data is accomplished through a look-up table or code book. The watermark is then merged into the point cloud data itself resulting in low distortion effects. With current advancements in computing technologies, such as GPGPUs, fractal processing is now applicable for processing of big data which is present in geospatial as well as other systems. This watermarking technique described within this paper can be important for systems where point data is handled by numerous aerial collectors including analysts use for systems such as a National LiDAR Data Layer.

A qualitative and quantitative method for predicting sentiment toward deployed U.S. forces

The ability to automatically predict likelihood of reaction to specific events and situational awareness is important to many military and commercial applications. Gauging population sentiment for targeted response areas and having the ability to predict or control sentiment within these areas is invaluable. Review of reception towards deployed forces must be analyzed, especially in areas vital for U.S. national interests. Predicting population behavior is critical for success and must include a qualitative as well as a quantitative solution. Additionally, a feedback mechanism is needed for periodically updating reception towards presence of U.S. Forces over time. We propose a method for predicting sentiment towards deployed U.S. Forces in near real time, to efficiently propitiate manpower resources, allocate equipment assets, and reduce cost of analyses. Sentiment prediction is becoming an increasingly important and feasible task based on social media, open source data, physical imagery and abundance of video data feeds. Predicting reaction to events can be time consuming. Locating the most likely affected areas is very tedious, requiring much human labor effort, and it is often difficult to obtain the best information on a timely basis. An efficient tool would be helpful to rapidly parse text that has been extracted from an intelligent algorithm in order to evaluate the population sentiment for the targeted area. Multiple data inputs and artificial intelligence (AI) algorithms are required in order to support sound decision making theory. The goal of our system, called GlobalSite, is to deliver trustworthy threat analysis systems and services that understand situations, while being a vital tool for continuing mission operations information.

Cooperative cognitive electronic warfare UAV game modeling for frequency hopping radar

In modern warfare concepts, the use of wireless communications and network-centric topologies with unmanned aerial vehicles (UAVs) creates an opportunity to combine the familiar concepts of wireless beamforming in opportunistic random arrays and swarm UAVs. Similar in concept to the collaborative beamforming used in ground-based randomly distributed array systems, our novel approach improves wireless beamforming performance by leveraging cooperative location and function knowledge. This enables the capabilities of individual UAVs to be enhanced, using swarming and cooperative beamforming techniques, for more-effective support of complex radar jamming and deception missions. In addition, a dedicated System Oversight function can be used to optimize the number of beamforming UAVs required to jam a given target and manage deception assets.