This PDF file contains the front matter associated with SPIE Proceedings Volume 10206, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

The ever increasing biocompatibility and pervasive nature of wearable and implantable devices demand novel sustainable solutions to realize their connectivity, which can impact broad application scenarios such as in the defense, biomedicine, and entertainment fields. Where wireless electromagnetic communications are facing challenges such as device miniaturization, energy scarcity, limited range, and possibility of interception, solutions not only inspired but also based on natural communication means might result into valid alternatives. In this paper, a communication paradigm where digital information is propagated through the nervous system is proposed and analyzed on the basis of achievable information rates. In particular, this paradigm is based on an analytical framework where the response of a system based on haptic (tactile) information transmission and ElectroEncephaloGraphy (EEG)-based reception is modeled and characterized. Computational neuroscience models of the somatosensory signal representation in the brain, coupled with models of the generation and propagation of somatosensory stimulation from skin mechanoreceptors, are employed in this paper to provide a proof-of-concept evaluation of achievable performance in encoding information bits into tactile stimulation, and decoding them from the recorded brain activity. Based on these models, the system is simulated and the resulting data are utilized to train a Support Vector Machine (SVM) classifier, which is finally used to provide a proof-of-concept validation of the system performance in terms of information rates against bit error probability at the reception.

The paper deals with a speech quality monitoring system using probes placed on the individual network nodes operating VoIP services. Information on speech quality is measured periodically and the results are then stored on the central server which provides visualization in a form of graph respecting a topology of the probes. Article provides overall description of the technology and algorithms used in the speech quality monitoring system and results achieved in this applied research are verified in real operation. Contribution of the work lies in a proposal of the new multi-agent system enabling speech quality monitoring and in own implementation and its verification in Czech academic network.

This paper proposes a new hybrid algorithm for security, which incorporates both distributed and hierarchal approaches. It uses a mobile data collector (MDC) to collect information in order to save energy of sensor nodes in a wireless sensor network (WSN) as, in most networks, these sensor nodes have limited energy. Wireless sensor networks are prone to security problems because, among other things, it is possible to use a rogue sensor node to eavesdrop on or alter the information being transmitted. To prevent this, this paper introduces a security algorithm for MDC-based WSNs. A key use of this algorithm is to protect the confidentiality of the information sent by the sensor nodes. The sensor nodes are deployed in a random fashion and form group structures called clusters. Each cluster has a cluster head. The cluster head collects data from the other nodes using the time-division multiple access protocol. The sensor nodes send their data to the cluster head for transmission to the base station node for further processing. The MDC acts as an intermediate node between the cluster head and base station. The MDC, using its dynamic acyclic graph path, collects the data from the cluster head and sends it to base station. This approach is useful for applications including warfighting, intelligent building and medicine. To assess the proposed system, the paper presents a comparison of its performance with other approaches and algorithms that can be used for similar purposes.

Wireless data rates have doubled every eighteen months for the last three decades. Following this trend, Terabit-per-second links will become a reality within the next five years. In this context, Terahertz (THz) band (0.1-10 THz) communication is envisioned as a key technology of the next decade. Despite major progress towards developing THz sources, compact signal generators above 1 THz able to efficiently work at room temperature are still missing. Recently, the use of hybrid graphene/semiconductor high-electron-mobility transistors (HEMT) has been proposed as a way to generate Surface Plasmon Polariton (SPP) waves at THz frequencies. Compact size, room-temperature operation and tunability of the graphene layer, in addition to possibility for large scale integration, motivate the exploration of this approach. In this paper, a simulation model of hybrid graphene/semiconductor HEMT-based THz sources is developed. More specifically, first, the necessary conditions for the so-called Dyakonov-Shur instability to arise within the HEMT channel are derived, and the impact of imperfect boundary conditions is analyzed. Second, the required conditions for coupling between a confined plasma wave in the HEMT channel and a SPP wave in graphene are derived, by starting from the coupling analysis between two 2DEG. Multi-physics simulation are conducted by integrating the hydrodynamic equations for the description of the HEMT device with Maxwell’s equations for SPP modeling. Extensive results are provided to analyze the impact of different design elements on the THz signal source. This work will guide the experimental fabrication and characterization of the devices.

In vivo Wireless Nanosensor Networks (iWNSNs) consist of nano-sized communicating devices with unprece- dented sensing and actuation capabilities, which are able to operate inside the human body. iWNSNs are a disruptive technology that enables the monitoring and control of biological processes at the cellular and sub- cellular levels. Compared to ex vivo measurements, which are conducted on samples extracted from the human body, iWNSNs can track (sub) cellular processes when and where they occur. Major progress in the field of na- noelectronics, nanophotonics and wireless communication is enabling the interconnection of nanosensors. Among others, plasmonic nanolasers with sub-micrometric footprint, plasmonic nano-antennas able to confine light in nanometric structures, and single-photon detectors with unrivaled sensitivity, enable the communication among implanted nanosensors in the near infrared and optical transmission windows. Motivated by these results, in this paper, an optofluidic channel model is developed to investigate the communication properties and temporal dynamics between a pair of in vivo nanosensors in the human blood. The developed model builds upon the authors’ recent work on light propagation modeling through multi-layered single cells and cell assemblies and takes into account the geometric, electromagnetic and microfluidic properties of red blood cells in the human circulatory system. The proposed model guides the development of practical communication strategies among nanosensors, and paves the way through new nano-biosensing strategies able to identify diseases by detecting the slight changes in the channel impulse response, caused by either the change in shape of the blood cells or the presence of pathogens.

Terahertz (THz) communication is envisioned as a key wireless technology to satisfy the need for 1000x faster wireless data rates. To date, major progress on both electronic and photonic technologies are finally closing the so-called THz gap. Among others, graphene-based plasmonic nano-devices have been proposed as a way to enable ultra-broadband communications above 1THz. The unique dynamic complex conductivity of graphene enables the propagation of Surface Plasmon Polariton (SPP) waves at THz frequencies. In addition, the conductivity of graphene and, thus, the SPP propagation properties, can be dynamically tuned by means of electrostatic biasing or material doping. This result opens the door to frequency-tunable devices for THz communications. In this paper, the temporal dynamics of graphene-enhanced metallic grating structures used for excitation and detection of SPP waves at THz frequencies are analytically and numerically modeled. More specifically, the response of a metallic grating structure built on top of a graphene-based heterostructure is analyzed by taking into account the grating period and duty cycle and the Fermi energy of the graphene layer. Then, the interfacial charge transfer between a metallic back-gate and the graphene layer in a metal/dielectric/graphene stack is analytically modeled, and the range of achievable Fermi energies is determined. Finally, the rate at which the Fermi energy in graphene can be tuned is estimated starting from the transmission line model of graphene. Extensive numerical and simulation results with COMSOL Multi-physics are provided. The results show that the proposed structure enables dynamic frequency systems with THz bandwidths, thus, enabling resilient communication techniques such as time-hopping THz modulations.

Many forms of malware and security breaches exist today. One type of breach downgrades a cryptographic program by employing a man-in-the-middle attack. In this work, we explore the utilization of hardware events in conjunction with machine learning algorithms to detect which version of OpenSSL is being run during the encryption process. This allows for the immediate detection of any unknown downgrade attacks in real time. Our experimental results indicated this detection method is both feasible and practical. When trained with normal TLS and SSL data, our classifier was able to detect which protocol was being used with 99.995% accuracy. After the scope of the hardware event recording was enlarged, the accuracy diminished greatly, but to 53.244%. Upon removal of TLS 1.1 from the data set, the accuracy returned to 99.905%.

The speed of IoT devices currently connected in our daily lives has drastically accelerated in the last couple of years. The lack of standardization, regulation, and an efficient process to integrate these devices to our ecosystem has led to a relaxed security and an ineffective use of the data generated. This paper presents a new approach to the IoT ecosystem management that improves data sharing and security by categorizing and micromanaging the connected devices. The use of micromanaging multiple access points (M2AP) allows the architecture to respond faster and efficiently to events and attacks to the digital hive. The “local beehive”/ ”master beehive” approach seals a compromise of delegating tasks and improving the network management capacity. Finally, an efficient data storage and compact reports of the raw information collected can then be transmitted to cloud services for further analysis if required.

The abstract is not available

The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP’s host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

Many novel DoD missions, from disaster relief to cyber reconnaissance, require teams of humans and machines with diverse capabilities. Current solutions do not account for heterogeneity of agent capabilities, uncertainty of team knowledge, and dynamics of and dependencies between tasks and agent roles, resulting in brittle teams. Most importantly, the state-of-the-art team design solutions are either centralized, imposing role and relation assignment onto agents, or completely distributed, suitable for only homogeneous organizations such as swarms. Centralized design models can’t provide insights for team’s self-organization, i.e. adapting team structure over time in distributed collaborative manner by team members with diverse expertise and responsibilities. In this paper we present an information-theoretic formalization of team composition and structure adaptation using a minimization of variational free energy. The structure adaptation is obtained in an iterative distributed and collaborative manner without the need for centralized control. We show that our model is lightweight, predictive, and produces team structures that theoretically approximate an optimal policy for team adaptation. Our model also provides a unique coupling between the structure and action policy, and captures three essential processes of learning, perception, and control.

Existing distributed ledger implementations – specifically, several blockchain implementations – embody a cacophony of divergent capabilities augmenting innovations of cryptographic hashes, consensus mechanisms, and asymmetric cryptography in a wide variety of applications. Whether specifically designed for cryptocurrency or otherwise, several distributed ledgers rely upon modular mechanisms such as consensus or smart contracts. These components, however, can vary substantially among implementations; differences involving proof-of-work, practical byzantine fault tolerance, and other consensus approaches exemplify distinct distributed ledger variations. Such divergence results in unique combinations of modules, performance, latency, and fault tolerance. As implementations continue to develop rapidly due to the emerging nature of blockchain technologies, this paper encapsulates a snapshot of sensor and internet of things (IoT) specific implementations of blockchain as of the end of 2016. Several technical risks and divergent approaches preclude standardization of a blockchain for sensors and IoT in the foreseeable future; such issues will be assessed alongside the practicality of IoT applications among Hyperledger, Iota, and Ethereum distributed ledger implementations suggested for IoT. This paper contributes a comparison of existing distributed ledger implementations intended for practical sensor and IoT utilization. A baseline for characterizing distributed ledger implementations in the context of IoT and sensors is proposed. Technical approaches and performance are compared considering IoT size, weight, and power limitations. Consensus and smart contracts, if applied, are also analyzed for the respective implementations’ practicality and security. Overall, the maturity of distributed ledgers with respect to sensor and IoT applicability will be analyzed for enterprise interoperability.

Modeling large military targets is a challenge as they can be complex systems encompassing myriad combinations of human, technological, and social elements that interact, leading to complex behaviors. Moreover, such targets have multiple components and structures, extending across multiple spatial and temporal scales, and are in a state of change, either in response to events in the environment or changes within the system. Complex adaptive system (CAS) theory can help in capturing the dynamism, interactions, and more importantly various emergent behaviors, displayed by the targets. However, a key stumbling block is incorporating information from various intelligence, surveillance and reconnaissance (ISR) sources, while dealing with the inherent uncertainty, incompleteness and time criticality of real world information. To overcome these challenges, we present a probabilistic reasoning network based framework called complex adaptive Bayesian Knowledge Base (caBKB). caBKB is a rigorous, overarching and axiomatic framework that models two key processes, namely information aggregation and information composition. While information aggregation deals with the union, merger and concatenation of information and takes into account issues such as source reliability and information inconsistencies, information composition focuses on combining information components where such components may have well defined operations. Since caBKBs can explicitly model the relationships between information pieces at various scales, it provides unique capabilities such as the ability to de-aggregate and de-compose information for detailed analysis. Using a scenario from the Network Centric Operations (NCO) domain, we will describe how our framework can be used for modeling targets with a focus on methodologies for quantifying NCO performance metrics.

Ever increasing adoption of cloud technology scales up the activities like creation, exchange, and alteration of cloud data objects, which create challenges to track malicious activities and security violations. Addressing this issue requires implementation of data provenance framework so that each data object in the federated cloud environment can be tracked and recorded but cannot be modified. The blockchain technology gives a promising decentralized platform to build tamper-proof systems. Its incorruptible distributed ledger/blockchain complements the need of maintaining cloud data provenance. In this paper, we present a cloud based data provenance framework using block chain which traces data record operations and generates provenance data. We anchor provenance data records into block chain transactions, which provide validation on provenance data and preserve user privacy at the same time. Once the provenance data is uploaded to the global block chain network, it is extremely challenging to tamper the provenance data. Besides, the provenance data uses hashed user identifiers prior to uploading so the blockchain nodes cannot link the operations to a particular user. The framework ensures that the privacy is preserved. We implemented the architecture on ownCloud, uploaded records to blockchain network, stored records in a provenance database and developed a prototype in form of a web service.

The purpose of this study is to determine the ability of an eye-tracker to detect changes in vigilance performance compared to the common method of using cerebral blood flow velocities (CBFV). Sixteen subjects completed this study. Each participant performed a 40-minute vigilance task while wearing an eye-tracker and a transcranial doppler (TCD) on each of four separate days. The results indicate that percentage of eye closure (PERCLOS) measured by the eye-tracker increased as vigilance performance declined and right CBFV as measured by the TCD decreased as performance declined. The results indicate that PERCLOS (left eye r=-.72 right eye r=-.67) more strongly correlated with changes in performance when compared to CBFV (r=.54). We conclude that PERCLOS, as measured by a head-worn eye tracking system, may serve as a compelling alternative (or supplemental) indicator of impending or concurrent performance declines in operational settings where sustained attention or vigilance is required. Such head-worn or perhaps even offbody oculometric sensor systems could potentially overcome some of the practical disadvantages inherent with TCD data collection for operational purposes. If portability and discomfort challenges with TCD can be overcome, both TCD and eye tracking might be advantageously combined for even greater performance monitoring than can be offered by any single device.

We present a proof-of-concept investigation into the use of sensor networks for tracking of WiFi users in outdoor urban environments. Sensors are fixed, and are capable of measuring signal power from users’ WiFi devices. We derive a maximum likelihood estimate for user location based on instantaneous sensor power measurements. The algorithm takes into account the effects of power control, and is self-calibrating in that the signal power model used by the location algorithm is adjusted and improved as part of the operation of the network. Simulation results to verify the system’s performance are presented. The simulation scenario is based on a 1.5 km2 area of lower Manhattan, The self-calibration mechanism was verified for initial rms (root mean square) errors of up to 12 dB in the channel power estimates: rms errors were reduced by over 60% in 300 track-hours, in systems with limited power control. Under typical operating conditions with (without) power control, location rms errors are about 8.5 (5) meters with 90% accuracy within 9 (13) meters, for both pedestrian and vehicular users. The distance error distributions for smaller distances (<30 m) are well-approximated by an exponential distribution, while the distributions for large distance errors have fat tails. The issue of optimal sensor placement in the sensor network is also addressed. We specify a linear programming algorithm for determining sensor placement for networks with reduced number of sensors. In our test case, the algorithm produces a network with 18.5% fewer sensors with comparable accuracy estimation performance. Finally, we discuss future research directions for improving the accuracy and capabilities of sensor network systems in urban environments.

The development of the next generation of multi-domain unmanned semi and fully autonomous C4ISR systems involves a multitude of security concerns and interoperability challenges. Conceptual solutions to capability shortfalls and gaps can be identified through Concept Development and Experimentation (CD and E) cycles. Modelling and Simulation (M and S) is a key tool in supporting unmanned autonomous systems (UAxS) CD and E activities and addressing associated security challenges. This paper serves to illustrate the application of M and S to UAxS development and highlight initiatives made by the North Atlantic Treaty Organization (NATO) M and S Centre of Excellence (CoE) to facilitate interoperability. The NATO M and S CoE collaborates with other NATO and Nations bodies in order to develop UAxS projects such as the Allied Command for Transformation Counter Unmanned Autonomous Systems (CUAxS) project or the work of Science and Technology Organization (STO) panels. Some initiatives, such as Simulated Interactive Robotics Initiative (SIRI) made the baseline for further developments and to study emerging technologies in M and S and robotics fields. Artificial Intelligence algorithm modelling, Robot Operating Systems (ROS), network operations, cyber security, interoperable languages and related data models are some of the main aspects considered in this paper. In particular, the implementation of interoperable languages like C-BML and NIEM MilOps are discussed in relation to a Command and Control - Simulation Interoperability (C2SIM) paradigm. All these technologies are used to build a conceptual architecture to support UAxS CD and E.In addition, other projects that the NATO M and S CoE is involved in, such as the NATO Urbanization Project could provide credible future operational environments and benefit UAxS project development, as dual application of UAxS technology in large urbanized areas.In conclusion, this paper contains a detailed overview regarding how applying Modelling and Simulation to support CD and E activities is a valid approach to develop and validate future capabilities requirements in general and next generation UAxS.

Monte Carlo (MC) methods have been shown to capture the ultrafast dynamics of carriers and resulting luminescence in photo-excited GaN. These calculations have assumed a homogeneous material under uniform illumination. We investigate the development of surrogate models to represent the carrier dynamics with fewer degrees of freedom, with the ultimate goal of computing the response of structured materials under inhomogeneous illumination profiles. These models are presently based on the extraction of mean and variance information from single data sets, through the use of fits to s piece-wise linear basis. Two models are then developed, one based on the mean and variance directly, and one on a Gaussian process regression model. New carrier populations with similar statistical profiles are then generated from these models to determine the amount of data reduction that can be obtained.

A fully integrated Mission-Level Radar model is in development as part of a multi-year effort under the Northrop Grumman Mission Systems (NGMS) sector’s Model Based Engineering (MBE) initiative to digitally interconnect and unify previously separate performance and cost models. In 2016, an NGMS internal research and development (IR and D) funded multidisciplinary team integrated radio frequency (RF), power, control, size, weight, thermal, and cost models together using a commercial-off-the-shelf software, ModelCenter, for an Active Electronically Scanned Array (AESA) radar system. Each represented model was digitally connected with standard interfaces and unified to allow end-to-end mission system optimization and trade studies. The radar model was then linked to the Air Force’s own mission modeling framework (AFSIM). The team first had to identify the necessary models, and with the aid of subject matter experts (SMEs) understand and document the inputs, outputs, and behaviors of the component models. This agile development process and collaboration enabled rapid integration of disparate models and the validation of their combined system performance. This MBE framework will allow NGMS to design systems more efficiently and affordably, optimize architectures, and provide increased value to the customer. The model integrates detailed component models that validate cost and performance at the physics level with high-level models that provide visualization of a platform mission. This connectivity of component to mission models allows hardware and software design solutions to be better optimized to meet mission needs, creating cost-optimal solutions for the customer, while reducing design cycle time through risk mitigation and early validation of design decisions.

Domain-specific languages are a useful tool for productivity allowing domain experts to program using familiar concepts and vocabulary while benefiting from performance choices made by computing experts. Embedding the domain specific language into an existing language allows easy interoperability with non-domain-specific code and use of standard compilers and build systems. In C++, this is enabled through the template and preprocessor features. C++ embedded domain specific languages (EDSLs) allow the user to write simple, safe, performant, domain specific code that has access to all the low-level functionality that C and C++ offer as well as the diverse set of libraries available in the C/C++ ecosystem. In this paper, we will discuss several tools available for building EDSLs in C++ and show examples of projects successfully leveraging EDSLs. Modern C++ has added many useful new features to the language which we have leveraged to further extend the capability of EDSLs. At EM Photonics, we have used EDSLs to allow developers to transparently benefit from using high performance computing (HPC) hardware. We will show ways EDSLs combine with existing technologies and EM Photonics high performance tools and libraries to produce clean, short, high performance code in ways that were not previously possible.

Security analysts are tasked with identifying and mitigating network service vulnerabilities. A common problem associated with in-depth testing of network protocols is the availability of software that communicates across disparate protocols. Many times, the software required to communicate with these services is not publicly available. Developing this software is a time-consuming undertaking that requires expertise and understanding of the protocol specification. The work described in this paper aims at developing a software package that is capable of automatically creating communication clients by using packet capture (pcap) and TShark dissectors. Currently, our focus is on simple protocols with fixed fields. The methodologies developed as part of this work will extend to other complex protocols such as the Gateway Load Balancing Protocol (GLBP), Port Aggregation Protocol (PAgP), and Open Shortest Path First (OSPF). Thus far, we have architected a modular pipeline for an automatic traffic-based software generator. We start the transformation of captured network traffic by employing TShark to convert packets into a Packet Details Markup Language (PDML) file. The PDML file contains a parsed, textual, representation of the packet data. Then, we extract field data, types, along with inter and intra-packet dependencies. This information is then utilized to construct an XML file that encompasses the protocol state machine and field vocabulary. Finally, this XML is converted into executable code. Using our methodology, and as a starting point, we have succeeded in automatically generating software that communicates with other hosts using an automatically generated Internet Control Message Protocol (ICMP) client program.

We describe the development of a centimeter-scale resolution simulation framework for a theoretical tree canopy that includes rainfall deposition, evaporation, and thermal infrared emittance. Rainfall is simulated as discrete raindrops with specified rate. The individual droplets will either fall through the canopy and intersect the ground; adhere to a leaf; bounce or shatter on impact with a leaf resulting in smaller droplets that are propagated through the canopy. Surface physical temperatures are individually determined by surface water evaporation, spatially varying within canopy wind velocities, solar radiation, and water vapor pressure. Results are validated by theoretical canopy gap and gross rainfall interception models.

Scene simulations of optical signature properties using signature codes normally requires input of various parameterized measurement data of surfaces and coatings in order to achieve realistic scene object features. Some of the most important parameters are used in the model of the Bidirectional Reflectance Distribution Function (BRDF) and are normally determined by surface reflectance and scattering measurements. Reflectance measurements of the spectral Directional Hemispherical Reflectance (DHR) at various incident angles can normally be performed in most spectroscopy labs, while measuring the BRDF is more complicated or may not be available at all in many optical labs. We will present a method in order to achieve the necessary BRDF data directly from DHR measurements for modeling software using the Sandford-Robertson BRDF model. The accuracy of the method is tested by modeling a test surface by comparing results from using estimated and measured BRDF data as input to the model. These results show that using this method gives no significant loss in modeling accuracy.

The Terahertz Time Domain Reflection Spectroscopy (THz-TDS) method of paint layer diagnostics is a non-contact electromagnetic technique analogous to pulsed-ultrasound with the added capability of spectroscopic characterization. The THz-TDS sensor emits a near-single cycle electromagnetic pulse with a bandwidth from 0.1 to 3 THz. This wide bandwidth pulse is focused on the coating, and echo pulses are generated from each interface (air-coating, layer-layer, coating-substrate). In this paper, the THz-TDS method is applied to specialty aircraft coatings. The THz-TDS method is able to penetrate the whole coating stack and sample the properties of each layer. Because the reflected pulses from individual layers typically overlap in time, the complex permittivity function and thickness of each layer is determined by a best fit of the measured reflection (either in time or frequency domain) to a layered model of the paint. The THz- TDS method is applied to specialty coatings prior to and during accelerated aging on a series of test coupons. The coupons are also examined during aging using ATR (attenuated total reflectance)-FTIR spectroscopy, Raman scattering spectroscopy, and Scanning Electron Microscopy (SEM) to ascertain, quantify, and understand the breakdown mechanisms of the coatings. In addition, the same samples are characterized using THz-TDS techniques to determine if the THz-TDS method can be utilized as a non-destructive evaluation technique to sense degradation of the coatings. Our results suggest that the degradation mechanism begins in the top coat layer. In this layer, 254 nm UV illumination in combination with the presence of moisture works partially with oxides as catalysts to decompose the polymer matrix thereby creating porosity in the top coat layer. Since the catalytic effect is partial, loss of the oxides by chemical reaction can also occur. As the topcoat layer becomes more porous, it allows water vapor to permeate the topcoat layer and interact with the rain erosion layer via carbonization of the polymer matrix in the rain erosion layer. The presence of the salt accelerates the pitting degradation. The goal of this paper is to determine if THz-TDS can be used to sense degradation of the coating.

This study describes a parametric model for inverse analysis of diffuse IR reflectance from explosive materials that are sparsely distributed upon a surface. This model, which adopts the the Kubelka-Munk theory of diffuse reflectance as ansatz, permits extraction of scattering coefficients correlated with the level of surface coverage by an explosive. In general, this model establishes ground-work for development of a prediction tool that will enable rapid and quantitative band contrast matching of spectral features for detection of trace levels of explosive material on surfaces. This study considers a prototype system comprising a sparse distribution of RDX particles on a soda-lime glass surface. The results of this study provide validation of the parametric model within reasonable error, for practical applications including simulating IR spectral responses corresponding to sparse surface distributions of explosives or other particular matter.