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To mymargin evaluate nitrogen-doped diamond as a candidate for Photoconductive Semiconductor Switches (PCSS) triggered in the sub-bandgap visible range, we have fabricated and tested diamond PCSS from a set of diamond grades of varying nitrogen concentration. The nitrogen-doped diamond PCSS have higher responsivity than other diamond optoelectronic devices in the visible range. We present a novel demonstration of sub-bandgap triggered diamond PCSS, which when scaled to higher voltage, can compete with and even surpass performance of existing ultraviolet-triggered diamond PCSS. This work suggests that nitrogen-doped diamond is a promising candidate for high-power, fast optoelectronic devices.

Random nanotexturing of optics and semiconductors has proven to be a highly effective way to produce high transmittivity, low reflective surfaces. Here, the authors report a one-step technique using SF6 reactive ion etching to produce nanotexturing of SiC. The effect of etching process power and time is optimized to demonstrate sub-1% specular reflectance and below 5% total reflectance over the 400–2000 nm spectral range. The technique is applied to compare measurements of a photoconductive switch with untreated and treated surfaces. Using a white light source emitting in the range 400–750 nm, the authors show that the decrease in specular reflectivity results in a 20% increase in the photocurrent response.

In this chapter, we provide an overview of Cyber Situational Awareness, an emerging research area in the broad field of cyber security, and discuss, at least at a high level, how to gain Cyber Situation Awareness. Our discussion focuses on answering the following questions: What is Cyber Situation Awareness? Why is research needed? What are the current research objectives and inspiring scientific principles? Why should one take a multidisciplinary approach? How could one take an end-to-end holistic approach? What are the future research directions?

Situation analysis for activities such as crisis management, military situation awareness, homeland security, or environmental monitoring is both enabled and challenged by access to enormous data sets. The advent of new sensing capabilities, advanced computing and tools available via cloud services, intelligent interconnections to mobile devices, and global interconnectivity with ever-increasing bandwidths provide unprecedented access to data and to computing. In addition, emerging digital natives freely share data and collaboration. Thus, on one hand situation analysts have great opportunities to access unprecedented amounts of information from sensors, human observers and online sources to assist in understanding an evolving situation. On the other hand, this access to huge data sources and computing can create a type of intelligence attention-deficit disorder, in which analysts are overwhelmed by the urgent, but lack the ability to focus on important data. This chapter provides a summary of this dilemma, describes a new analysis paradigm that links data-driven and hypothesis driven approaches, introduces a new prototype analyst workbench, and discusses an educational approach to empower the next generation of analyst.

We have previously argued that a combination of trends in information technology (IT) and changing habits of people using IT provide opportunities for the emergence of a new generation of analysts that can perform effective intelligence, surveillance and reconnaissance (ISR) on a “do it yourself” (DIY) or “armchair” approach (see D.L. Hall and J. Llinas (2014)). Key technology advances include: i) new sensing capabilities including the use of micro-scale sensors and ad hoc deployment platforms such as commercial drones, ii) advanced computing capabilities in mobile devices that allow advanced signal and image processing and modeling, iii) intelligent interconnections due to advances in “web N” capabilities, and iv) global interconnectivity and increasing bandwidth. In addition, the changing habits of the digital natives reflect new ways of collecting and reporting information, sharing information, and collaborating in dynamic teams. This paper provides a survey and assessment of tools and resources to support this emerging analysis approach. The tools range from large-scale commercial tools such as IBM i2 Analyst Notebook, Palantir, and GeoSuite to emerging open source tools such as GeoViz and DECIDE from university research centers. The tools include geospatial visualization tools, social network analysis tools and decision aids. A summary of tools is provided along with links to web sites for tool access.

Clonidine has recently been used as a pre-operative medication and sedative/anxiolytic drug. Its extended duration of action makes it suitable for longer procedures. In this randomized, crossover, placebo-controlled clinical trial, we characterized the effects of oral clonidine pre-treatment on intravenous diazepam/meperidine sedation using the bi-spectral index (BIS) in 13 participants. Clonidine significantly increased the numbers of BIS-depressed readings and percent memory loss during sedation, while reducing total diazepam and post-operative analgesic dosages by 44% and 55%, respectively. Systolic, diastolic, and mean arterial blood pressures, as well as pulse rates, were reduced. Respiratory rate, oxygen saturation, end-tidal CO2, and recovery from sedation were unchanged. Participants, surgeons, and sedationists preferred clonidine over the placebo. Clonidine pre-treatment increased and prolonged sedation and amnesia and stabilized vital signs while significantly decreasing diazepam and post-operative analgesic usage. These results suggest that pre-operative clonidine administration could be a useful supplement to intravenous sedation for dental procedures of long duration.

Computerized oral brush biopsy became commercially available in 1999. The objectives of this study were to provide senior dental students with the clinical competency to perform oral brush biopsy and to evaluate the extent to which students used this technique and the outcomes of their biopsy techniques. During the 2004–05 academic year, 114 senior dental students at The Ohio State University College of Dentistry participated in a brush biopsy instructional program. Brush biopsy training was part of their outreach and engagement experience. Technique instruction included 1) completion of thirteen interactive online educational modules, 2) discussion of a written protocol including clinical photographs and an oral pathology position paper, and 3) performance of a trial demonstration brush biopsy on each other. Eighty-one students submitted brush biopsies on clinical patients. Lesions were found in approximately one out of every twenty patients. Seventy-four biopsy results (91 percent) were diagnostic although seventeen (21 percent) were limited by suboptimal cellular representation and seven (9 percent) were inadequate. The high percentage (71 percent) of students who actually applied their training in a true clinical setting indicates that the program was successful in providing dental students the information necessary to perform this procedure on their own. However, the number of incomplete specimens suggests the need for technique refinement and additional clinical experience.

Clonidine is a preferential alpha-2 agonist drug that has been used for over 35 years to treat hypertension. Recently, it has also been used as a preoperative medication and as a sedative/anxiolytic drug. This randomized, double-blind, placebo-controlled crossover clinical trial characterized the effects of oral clonidine pretreatment on intravenous catheter placement in 13 patients. Parameters measured included the bispectral index (BIS), Observer's Assessment of Alertness/Sedation Scale (OAA/S), frontal temporal electromyogram (EMG), 30-Second Blink Count (Blink), Digit Symbol Substitution Test (DSST), State Anxiety Inventory (SAI), fingertip versus forearm skin temperatures, and multiple questionnaires. Oral clonidine significantly decreased SAI scores, OAA/S, EMG, and Blink, but did not cause statistically significant BIS or DSST reductions. Subjects preferred oral clonidine pretreatment prior to venipuncture compared to placebo. Questionnaires also indicated that clonidine provided minimal sedation, considerable anxiolysis, and some analgesia. Fingertip versus forearm skin temperature differentials were decreased. Reduced fingertip versus forearm temperature differentials suggest increased peripheral cutaneous blood flow prior to venous cannulation. Oral clonidine pretreatment not only helped control patient anxiety and pain but also provided cardiovascular stability.

Customer-driven sensor management advocates applying e-commerce concepts and advances to sensor management. In e-commerce, customer wants essentially drive the production process. Sensor management has traditionally followed a much less capitalistic process, producing information "goods" based on predefined system goals and priorities. We explore here some of the possibilities and advantages of incorporating a customer-driven market-based approach to sensor management.This article is part of a special issue on Self-Managing Systems.

This paper describes a scaled-world simulation developed to conduct empirical research on team cognition, communication, and decision-making within a distributed environment. The NeoCITIES simulation is an advancement of the CITIES task, which was designed to study group decision-making within a command, control, and communications (C3) setting (Wellens & Ergener, 1988). Studying group decision-making is a two-fold problem involving team cognition and team communication. According to McNeese (2003), team cognition is constructed through distributed and emerging activities via several sources. A majority of studies examining distributed decision-making have involved militaristic, battlefield engagement, or urban warfare settings. In that same spirit, NeoCITIES was designed for emergency crisis management teams undergoing terrorist attacks within a college-town. Thus, NeoCITIES is a new and operationally relevant scaled world that emulates the complexities and emergent decision-making attributes resident in a 9/11-type of terrorist scenario. Through the use of NeoCITIES, we anticipate the assessment of a number of cognitive tools to support distributed cognition (e.g., problem-based decomposition) as well as advancing adaptive intelligent interfaces.

This paper emphasizes the use of cognitive task analysis to gain significant insight into the unique domain of intelligence analysts, how intelligence analysts view this domain, and how this domain can be replicated in a controlled simulation environment in which innovative tools and procedures can be empirically tested. Details of two comprehensive knowledge elicitation sessions involving intelligence analysts are provided as an example of using the Advanced Knowledge Acquisition and Design (AKADAM) methodology to obtain contextually relevant information for use in developing a homeland defense-oriented simulation/experimental task. Several distinctive characteristics of intelligence analyst functionality were discovered, including the multi-source integration of relevant information, complex cognitive analysis, and team collaboration in decision-making. Additional themes such as social interaction and the limitations of current analysis tools were identified.

Complex problems such as analysis of military situation assessment, homeland defense, diagnosis of the health of complex systems, medical diagnosis, and environmental monitoring require the ability to utilize a wide variety of data such as signals, images, textual information, and scalar data. The rapid evolution of micro-scale sensors, wideband communications, and microprocessors enables the collection and dissemination of huge amounts of data to be provided to a human analyst. Unfortunately, the analyst cannot directly understand nor process the data. Instead, analysts reason about high-level abstractions via language. A challenge exists to decompose general problems into detailed models that link to specific types of data (viz., problem centered decomposition) and to compose data into meaningful relationships to assist the understanding of semantic representations of abstract concepts. This paper discusses the challenge of problem-centered analysis (including problem centered decomposition and problem centered composition) and describes our efforts to develop cognitive aids to assist the analysis process for improved understanding of complex problems.

Intelligence analysis depends on data fusion systems to provide capabilities of detecting and tracking important objects, events, and their relationships in connection to an analytical situation. However, automated data fusion technologies are not mature enough to offer reliable and trustworthy information for situation awareness. Given the trend of increasing sophistication of data fusion algorithms and loss of transparency in data fusion process, analysts are left out of the data fusion process cycle with little to no control and confidence on the data fusion outcome. Following the recent rethinking of data fusion as human-centered process, this paper proposes a conceptual framework towards developing alternative data fusion architecture. This idea is inspired by the recent advances in our understanding of human cognitive systems, the science of visual analytics, and the latest thinking about human-centered data fusion. Our conceptual framework is supported by an analysis of the limitation of existing fully automated data fusion systems where the effectiveness of important algorithmic decisions depend on the availability of expert knowledge or the knowledge of the analyst’s mental state in an investigation. The success of this effort will result in next generation data fusion systems that can be better trusted while maintaining high throughput.

(2001). Just how provincial is Western philosophy? 'Truth' in comparative context. Social Epistemology: Vol. 15, No. 4, pp. 285-297.

We describe here an interdisciplinary lab science course for non-majors using the history of science as a curricular guide. Our experience with diverse instructors underscores the importance of the teachers and classroom dynamics, beyond the curriculum. Moreover, the institutional political context is central: are courses for non-majors valued and is support given to instructors to innovate? Two sample projects are profiled.

The following essay is a somewhat preliminary effort to construct a vocabulary of Chinese gardens. To this end we shall focus upon some very general notions which characterize the ‘cosmological’ setting within which the Chinese garden perforce must find its place. These remarks will presume that there are illumining relationships between gardens as aesthetic creations and their cosmological setting. And, though we shall not be concerned to detail these relationships in every case, more than enough evidence for these relationships may be found in the companion essays in this present issue. In any case, our speculative venture is intended to be justified solely on pragmatic grounds. If resort to the considerations provided in the following paragraphs allows those involved in the investigation of Chinese gardens to understand their subject better, and to express that understanding in a more coherent fashion, we shall have succeeded in our primary task.

Condition-based maintenance (CBM) is an emerging technology which seeks to develop sensors and processing systems aimed at monitoring the operation of complex machinery such as turbine engines, rotor craft drive trains, or industrial equipment. The goal of CBM systems is to determine the state of the equipment (i.e., the mechanical health and status), and to predict the remaining useful life for the system being monitored. The success of such systems depends upon a number of factors including: (1) the ability to design or use robust sensors for measuring relevant phenomena such as vibration, acoustic spectra, infrared emissions, oil debris, etc.; (2) real time processing of the sensor data to extract useful information (such as features or data characteristics) in a noisy environment and to detect parametric changes which might be indicative of impending failure conditions; (3) fusion of multi-sensor data to obtain improved information beyond that available to a single sensor; (4) micro and macro level models which predict the temporal evolution of failure phenomena; and finally, (5) the capability to perform automated approximate reasoning to interpret the results of the sensor measurements, processed data, and model predictions in the context of an operational environment. The latter capability is the focus of this paper. Although numerous techniques have emerged from the discipline of artificial intelligence for automated reasoning (e.g., rule-based expert systems, blackboard systems, case-based reasoning, neural networks, etc.), none of these techniques are able to satisfy all of the requirements for reasoning about condition-based maintenance. This paper provides an assessment of automated reasoning techniques for CBM and identifies a particular problem for CBM, namely, the ability to reason with negative information (viz., data which by it’s absence is indicative of mechanical status and health). A general architecture is introduced for CBM automated reasoning, which hierarchically combines implicit and explicit reasoning techniques. Initial experiments with fuzzy logic are also described.Copyright © 1996 by ASME

This study aimed to teach dental students to use an INRatio® (HemoSense) device, to apply the results to patients, and to evaluate the outcomes.This was a retrospective convenience data study that used student surveys.The research was conducted at The Ohio State University College of Dentistry Geriatric Dental Program in Columbus, Ohio.A total of 105 senior dental students participated in an INRatio training program.The instructions involved the following: 1) a table showing INRatio protocol and dental procedures versus international normalized ratio (INR) guidelines; 2) an online technique training course, 3) a trial INRatio test on another senior dental student; and 4) INRatio for a dental patient.The clinical outcomes including INR values were a retrospective convenience data study. Instructional outcomes were obtained from student surveys.A total of 79% of students indicated they would use portable INR testing in their future practices; however, 73% of students had difficulty obtaining an adequate blood sample. All patient surgical procedures were completed successfully without serious complications.This study indicates the INRatio analysis can be included in dental student training, providing safe, efficient, and successful patient care. Difficulty obtaining blood samples suggests the need for extra training, additional clinical experience, and INR devices requiring smaller blood samples.

Although the capability of computer-based artificial intelligence techniques for decision-making and situational awareness has seen notable improvement over the last several decades, the current state-of-the-art still falls short of creating computer systems capable of autonomously making complex decisions and judgments in many domains where data is nuanced and accountability is high. However, there is a great deal of potential for hybrid systems in which software applications augment human capabilities by focusing the analyst’s attention to relevant information elements based on both a priori knowledge of the analyst’s goals and the processing/correlation of a series of data streams too numerous and heterogeneous for the analyst to digest without assistance. Researchers at Penn State University are exploring ways in which an information framework influenced by Klein’s (Recognition Primed Decision) RPD model, Endsley’s model of situational awareness, and the Joint Directors of Laboratories (JDL) data fusion process model can be implemented through a novel combination of Complex Event Processing (CEP) and Multi-Agent Software (MAS). Though originally designed for stock market and financial applications, the high performance data-driven nature of CEP techniques provide a natural compliment to the proven capabilities of MAS systems for modeling naturalistic decision-making, performing process adjudication, and optimizing networked processing and cognition via the use of “mobile agents.” This paper addresses the challenges and opportunities of such a framework for augmenting human observational capability as well as enabling the ability to perform collaborative context-aware reasoning in both human teams and hybrid human / software agent teams.

A method is presented for including motional and eddy current effects when analytically modeling electrical impedance functions of cylindrical magnetostrictive transducers. To approximate eddy current effects, onedimensional analytical constant parameter linear electromagnetic models of a cylindrical magnetostrictive transducer are developed. Maxwell's equations are solved for the magnetic field strength as a function of radial position. Closed form expressions for magnetic flux as a function of radial position are then derived, from which transducer electrical impedance functions are formulated. Two different physical models of the transducer are considered. The first model results in the classic eddy current solution for a rod in a wound wire solenoid. The second physical model includes the effects of a conducting external cylindrical housing. Motional effects are incorporated into the electromagnetic models via the magnetomechanical model, which is a frequency and load dependent, complex valued expression for the "dynamic magnetic permeability" of the magnetostrictive material within the transducer. The functional form for this dynamic magnetic permeability is derived by comparing the transduction equations for the magnetostrictive material with those for the transducer containing the material. Electrical impedance functions for both physical models are compared with an experimental measurement for a particular magnetostrictive transducer (using Terfenol-D) in its low signal linear range of operation. The second physical model was found to offer the better simulation of the experimental measurement of the transducer's electrical impedance, with errors in magnitude or phase of less than + 5% for excitation frequencies between 100 and 10,000 Hz (the asrun mechanical resonant frequency was 8800 Hz). Experience has shown that measured material parameters typically yield simulations of electrical impedance functions within i 10%. Predictions of electrical impedance functions for Terfenol-D transducers, calculated based on estimated or published Terfenol-D parameters, can be in error by well over 40%. Thus, the accuracy of model results seem to be controlled primarily by the accuracy with which magnetostrictive material parameters are known.

Current generation mechanical diagnostic equipment is designed to identify individual events or trends in the output of sensors mounted on a mechanical component, subsystem, or system. Such equipment can provide a useful indication that a failure condition may be developing, but it cannot provide reliable predictions of the remaining safe or operational life. Typically, these diagnostic systems simply compare the output of individual sensors against a priori thresholds to establish a measure of the system’s health. Two problems result from this approach: (1) there is no advantage taken of possible synergy among the sensors, i.e., the determination of health is one dimensional; and (2) the diagnosis provides only a statement regarding the current equipment health, but docs not provide a prediction of the time remaining to failure. This often leads to an operational environment in which diagnostic equipment outputs arc either ignored because of frequent false alarms or frequent (and costly) time-based preventive maintenance is performed to avoid hazardous failures. This paper describes a new approach to the development of a more robust diagnosis and prognostic capability. It is based on the fusion of sensor-based and model-based information. Sensor-based information is the essence of current diagnostic systems. Model-based information combines dynamic models of individual mechanical components with micromechanical models of relevant failure mechanisms, such as fracture and crack propagation. These micromechanical models must account for initial flaw size distribution and other microstructural parameters describing initial component condition. A specific application of this approach is addressed, the diagnosis of mechanical failure in meshing gears. Four specific issues are considered: (a) how to couple a validated numerical simulation of gear transmission error (due to tooth spacing irregularity, contour irregularity, or material inhomogeneity) with physically and empirically-based descriptions of fatigue crack growth to predict a failure precursor signature at the component level; (b) how to predict the manifestation of this signature at the subsystem or system level where sensors are located; (c) how to fuse this model-based information with the corresponding sensor-based information to predict remaining safe or operational life of a gear; and (d) issues associated with extending this methodology to bearings and other rotating machinery components.

Performance data from a magnetostrictive broadband (100 Hz-10 kHz) vibration source is presented. An original design for a magnetostrictive transducer was built and tested. The transducer used a rod of ETREMA Terfenol-DTM (EDGE Technologies, Ames, Iowa) as the motion source (rod dimensions: 51 mm long x 6.35 mm diameter). This communication will convey the characteristics of magnetostrictive transducer behavior. The shaker is considered to be a single input (electric current) single output (force) system. For low signal operation it behaves in a linear fashion; output force is proportional to input electric current and harmonic content is low. Comparisons with a commercially available permanent magnet shaker show that the magnetostrictive shaker's linear region extends to high enough forces (or displacements) to be of use as a vibration excitation source. Higher input currents eventually lead to degradation of the magnetostrictive shaker's linear behavior; output harmonics become appreciable, force levels increase disproportionately and, of course, linear systems analysis techniques become inappropriate. The magnetostrictive shaker discussed is capable of producing peak acceleration amplitudes in excess of 1960 m/s2 (200 g's, at frequencies greater than 4 kHz with a 24 gram load using sinusoidal excitation). Experimental results are presented for: 1) output force as a function of frequency, load, and current amplitude (30 Hz-10 kHz with sinusoidal excitation); 2) typical electrical impedance as a function of frequency; 3) frequency response functions as Newtons per ampere, Newtons per volt, and meter per ampere; and 4) performance comparisons made with a commercially available permanent magnet shaker. Experimental results 2, 3, and 4 are for operation of the magnetostrictive shaker in its linear range (linear in a least squares sense).

Utilization of human participants as "soft sensors" is becoming increasingly important for gathering information related to a wide range of phenomena including natural and man-made disasters, environmental changes over time, crime prevention, and other roles of the "citizen scientist." The ubiquity of advanced mobile devices is facilitating the role of humans as "hybrid sensor platforms", allowing them to gather data (e.g. video, still images, GPS coordinates), annotate it based on their intuitive human understanding, and upload it using existing infrastructure and social networks. However, this new paradigm presents many challenges related to source characterization, effective tasking, and utilization of massive quantities of physical sensor, human-based, and hybrid hard/soft data in a manner that facilitates decision making instead of simply amplifying information overload. In the Joint Directors of Laboratories (JDL) data fusion process model, "level 4" fusion is a meta-process that attempts to improve performance of the entire fusion system through effective source utilization. While there are well-defined approaches for tasking and categorizing physical sensors, these methods fall short when attempting to effectively utilize a hybrid group of physical sensors and human observers. While physical sensor characterization can rely on statistical models of performance (e.g. accuracy, reliability, specificity, etc.) under given conditions, "soft" sensors add the additional challenges of characterizing human performance, tasking without inducing bias, and effectively balancing strengths and weaknesses of both human and physical sensors. This paper addresses the challenges of the evolving human-centric fusion paradigm and presents cognitive, perceptual, and other human factors that help to understand, categorize, and augment the roles and capabilities of humans as observers in hybrid systems.

Information fusion is becoming increasingly human-centric. While past systems typically relegated humans to the role of analyzing a finished fusion product, current systems are exploring the role of humans as integral elements in a modular and extensible distributed framework where many tasks can be accomplished by either human or machine performers. For example, "participatory sensing" campaigns give humans the role of "soft sensors" by uploading their direct observations or as "soft sensor platforms" by using mobile devices to record human-annotated, GPS-encoded high quality photographs, video, or audio. Additionally, the role of "human-in-the-loop", in which individuals or teams using advanced human computer interface (HCI) tools such as stereoscopic 3D visualization, haptic interfaces, or aural "sonification" interfaces can help to effectively engage the innate human capability to perform pattern matching, anomaly identification, and semantic-based contextual reasoning to interpret an evolving situation. The Pennsylvania State University is participating in a Multi-disciplinary University Research Initiative (MURI) program funded by the U.S. Army Research Office to investigate fusion of hard and soft data in counterinsurgency (COIN) situations. In addition to the importance of this research for Intelligence Preparation of the Battlefield (IPB), many of the same challenges and techniques apply to health and medical informatics, crisis management, crowd-sourced "citizen science", and monitoring environmental concerns. One of the key challenges that we have encountered is the development of data formats, protocols, and methodologies to establish an information architecture and framework for the effective capture, representation, transmission, and storage of the vastly heterogeneous data and accompanying metadata -- including capabilities and characteristics of human observers, uncertainty of human observations, "soft" contextual data, and information pedigree. This paper describes our findings and offers insights into the role of data representation in hard/soft fusion.

In this paper, we describe the construction of a soundtrack that fuses stock market data with information taken from tweets. This soundtrack, or auditory display, presents the numerical and text data in such a way that anomalous events may be readily detected, even by untrained listeners. The soundtrack generation is flexible, allowing an individual listener to create a unique audio mix from the available information sources. Properly constructed, the display exploits the auditory system's sensitivities to periodicities, to dynamic changes, and to patterns. This type of display could be valuable in environments that demand high levels of situational awareness based on multiple sources of incoming information.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Computer networks no longer simply enable military and civilian operations, but have become vital infrastructures for all types of operations ranging from sensing and command/control to logistics, power distribution, and many other functions. Consequently, network attacks have become weapons of choice for adversaries engaged in asymmetric warfare. Traditionally, data and information fusion techniques were developed to improve situational awareness and threat assessment by combining data from diverse sources, and have recently been extended to include both physical (“hard”) sensors and human observers (acting as “soft” sensors). This chapter provides an introduction to traditional data fusion models and adapts them to the domain of cyber security. Recent advances in hard and soft information fusion are summarized and applied to the cyber security domain. Research on the use of sound for human-in-the-loop pattern recognition (sonification) is also introduced. Finally, perspectives are provided on the future for data fusion in cyber security research.

Current needs in tactical situational awareness require a new type of infrastructure to encode, transmit, store, fuse, and display vastly heterogeneous data that may include "hard" sensor types including video, radar, multispectral, acoustic sensor array, 3D flash LIDAR, and "soft" sensor inputs such as textual reports from trained and untrained personnel, unsolicited and solicited open source web information, and hybrid "hard/soft" data such as human-annotated image or video data - which can be highly useful, but difficult to categorize and exploit. While the demand for scalability, rapid deployment, and decentralized access to data and services grows, the need for data security and integrity is as critical as ever. Methods for handling the conflicting needs between access and security are addressed. Furthermore, the evolving role of humans in data fusion systems must be addressed by the infrastructure. In addition to systems enhancing human data analysis capabilities through advanced visualization and sonification techniques, the data itself is more likely to contain information about humans - which is not always a task well suited to conventional data storage and retrieval methods. This paper describes a multi-agent approach to designing a secure, distributed, service-oriented infrastructure to support human-centric hard and soft information fusion.

In this paper we explore image fusion methods for 3D LIDAR sensors, thermal sensors, and visible color sensors. Traditional display methods are demonstrated in contrast to the proposed robust representations. The new fused representations make full use of the display gamut of a color monitor. In addition, a data transformation on 3D LIDAR points is demonstrated which ports hard sensor data into information space. The LIDAR data is classified and clustered in a hierarchical fashion, which allows temporal and spatial coherent fusion with soft sensor data.

There is an emerging demand for the development of data fusion techniques and algorithms that are capable of combining conventional "hard" sensor inputs such as video, radar, and multispectral sensor data with "soft" data including textual situation reports, open-source web information, and "hard/soft" data such as image or video data that includes human-generated annotations. New techniques that assist in sense-making over a wide range of vastly heterogeneous sources are critical to improving tactical situational awareness in counterinsurgency (COIN) and other asymmetric warfare situations. A major challenge in this area is the lack of realistic datasets available for test and evaluation of such algorithms. While "soft" message sets exist, they tend to be of limited use for data fusion applications due to the lack of critical message pedigree and other metadata. They also lack corresponding hard sensor data that presents reasonable "fusion opportunities" to evaluate the ability to make connections and inferences that span the soft and hard data sets. This paper outlines the design methodologies, content, and some potential use cases of a COIN-based synthetic soft and hard dataset created under a United States Multi-disciplinary University Research Initiative (MURI) program funded by the U.S. Army Research Office (ARO). The dataset includes realistic synthetic reports from a variety of sources, corresponding synthetic hard data, and an extensive supporting database that maintains "ground truth" through logical grouping of related data into "vignettes." The supporting database also maintains the pedigree of messages and other critical metadata.

Building on our previous work, we extend sonification techniques to common network security data. In this current work, we examine packet flow and the creation of socket connections between a requestor's IP address and port number with the server's IP address and port number. Our goals for the aural rendering are twofold: to make certain conditions immediately apparent to untrained listeners, and to create a sound model capable of enough nuance that there is the possibility of unexpected patterns becoming apparent to a seasoned listener. This system could be used to potentially provide better cognitive refinement capabilities for data fusion systems, especially when multiple sources of data at various levels of refinement are presented to the human analyst.

This project is focused on development of a research framework for dynamic integration of information from hard (electronic) and soft (human) sensors. This framework includes representation of 2nd order uncertainty. The overall approach is based on three pillars: (1) traditional sensing resources ("S-space"), (2) dynamic communities of observers ("H-space") and (3) resources such as archived sensor data, blogs, reports, dynamic news reports from citizen reporters via the internet ("I-space). During this project we have developed an overall framework, implemented a distributed cyber infrastructure (an Extreme Events Laboratory) for conducting human in the loop experiments, performed literature reviews, and designed initial experiments related to focus of attention of human observers. In addition, we have developed some mathematical models for characterizing soft sensors and for knowledge representation.

However much the Catholic Church may wish to free the peoples of the world from the excessive atheistic rationalism of the Englihtenment that has pitted science against religion, it is still in most other ways solidly on the side of modernity.Centesimus Annus endorses aform of democracy, akind of capitalism, asort of technological development, all of which are strongly undergirded by a resolute belief in human beings as rights-bearing individuals possessed of individual autonomy and a legitimate appetite for private property. The themes of liberal democracy, capitalist free enterprise, and the proliferation of rational technologies form the common focus of both the Enlightenment and Anti-Enlightenment sensibilities.

Results are presented summarizing dynamic per formance capabilities for three different magnetostrictive actuators with demonstration of magnetostriction used for control of struc tural vibrations in a beam. Project goals were to design a magne tostrictive actuator for use as a broadband vibration source (using magnetostriction for generation of dynamic forces), to fabricate a working prototype system, and to demonstrate magnetostrictive ac tuator use for vibration control. The vibration control work is modeled after analog proportional gain control techniques. A magnetostrictive actuator is used to oppose structural vibrations that are sensed with an accelerometer. The magnetostrictive ele ment of the actuator is permeated by a magnetic field that fluc tuates in direction at a rate proportional to the inverted accel erometer output. The resultant actuator strain is proportional in amplitude and 180° out of phase with the structure's vibratory mo tion. Use of a magnetostrictive actuator to reduce beam vibrations at frequencies up to 4000 Hz is shown. Also, broadband actuator frequency responses are shown that demonstrate practical imple mentation of magnetostrictive material in vibration generation systems which should carry over to use in vibration control ap plications at frequencies up to 10,000 Hz and above.

Increasing interest in human-centered information fusion systems involves; (1) humans as sensors (viz., “soft sensors”), (2) humans performing pattern recognition and participating in the fusion cognitive process, and (3) human groups performing collaborative analysis (viz., “crowd-sourcing” of analysis). Test and evaluation of such systems is challenging because we must develop both representative test data (involving both physical sensors and human observers) and test environments to evaluate the performance of the hardware, software and humans-in-the-loop. This paper describes an experimental facility called an extreme events laboratory, a test and evaluation approach, and evolving test data sets for evaluation of human-centered information fusion systems for situation awareness. The data sets include both synthetic data as well as data obtained using human subjects in campus wide experiments.

The University at Buffalo (UB) Center for Multisource Information Fusion (CMIF) along with a team including the Pennsylvania State University (PSU), Iona College (Iona), and Tennessee State University (TSU) is conducting research to develop a generalized framework, mathematical techniques, and test and evaluation methods to address the ingestion and harmonized fusion of Hard and Soft information in a distributed Level 1 and Level 2 data fusion environment. The primary Research Thrusts addressed are framed around the major functional components of the JDL Fusion Process; these include: 1. Source Characterization of Soft Data input streams including; human observation-direct, indirect, open source inputs, linguistic framing, and text processing. 2. Common Referencing and Alignment of Hard and Soft Data, especially strategies and methods for meta-data generation for Hard-Soft data normalization. 3. Generalized Data Association Strategies and Algorithms for Hard and Soft Data. Robust Estimation Methods that exploit associated Hard and Soft Data. 5. Dynamic Network-based Effects on Hard-Soft Data Fusion Architectures and Methods. 6. Test and Evaluation Methodology Development to include Human-in-the-Loop. 7. Extensibility, Adaptability, and Robustness Assessment. 8. Fusion Process Framework. 9. Technology Concept of Employment. This program is a large, 5-year effort and considered distinctive in being a major academic thrust into the complexities of the hard and soft fusion problem. This paper summarizes the research strategy, the early technology decisions made, and the very early results of both design approaches and prototyping.

Traditionally, information or data fusion has sought to combine information from multiple physical sensors, such as radar and acoustic sensors, to improve our knowledge of the physical environment. Recent changes in information fusion involve; (1) an interest in characterizing the human landscape as well as the physical landscape, (2) use of humans as observers or "soft" sensors, (3) explicit use of human pattern recognition via advanced visualization and sonficiation, and (4) use of multiple humans for dynamic, ad hoc, collaboration for analysis. This paper describes these new trends and describes on-going research at the Pennsylvania State University in all of these areas.

Detection of intrusions is a continuing problem in network security. Due to the large volumes of data recorded in Web server logs, analysis is typically forensic, taking place only after a problem has occurred. This paper describes a novel method of representing Web log information through multi-channel sound, while simultaneously visualizing network activity using a 3-D immersive environment. We are exploring the detection of intrusion signatures and patterns, utilizing human aural and visual pattern recognition ability to detect intrusions as they occur. IP addresses and return codes are mapped to an informative and unobtrusive listening environment to act as a situational sound track of Web traffic. Web log data is parsed and formatted using Python, then read as a data array by the synthesis language SuperCollider [1], which renders it as a sonification. This can be done either for the study of pre-existing data sets or in monitoring Web traffic in real time. Components rendered aurally include IP address, geographical information, and server Return Codes. Users can interact with the data, speeding or slowing the speed of representation (for pre-existing data sets) or "mixing" sound components to optimize intelligibility for tracking suspicious activity.

In this paper, we describe the design and development of the NeoCITIES Simulation task environment. The enhanced NeoCITIES environment allows repeatable experiments in which artifacts are introduced to improve team performance and measure quantities such as inference accuracy as a function of crisis tempo, data rate, decision complexity and individual factors such as induced stress. NeoCITIES was developed to study the effectiveness of cognitive artifacts within a simulated command and control environment. This paper describes the initial results of a human in the loop experiment to quantify the effects of data overload on human analyst performance. The experiment involves the introduction of cognitive aids to support improved team coordination and understanding of team-member interactions in a simulated extreme events scenario.

Distributed Sensor management, the process of managing or coordinating the use of sensing resources in a distributed environment, is a multi-objective optimization problem. In our earlier work, we proposed MASM (Market-Architecture for Sensor Management), a market-based approach to allocate sensor resources in real-time to various resource requestors. MASM models the multi-objective sensor management problem as a combinatorial-auction based market where the network resources sell goods to the resource requestors. To allow the resource requestors to participate in the market, MASM grants “budgets” to these resource requestors based on their priority to the overall mission. However, for a given budget, self-interested resource requestors or buyers can learn from market-data and adapt their bidding behavior. This paper presents results of an initial experimental study, where the learning behavior of resource requestors is modeled and their effect on market performance is examined.