This PDF file contains the front matter associated with SPIE Proceedings Volume 11869, including the Title Page, Copyright information, and Table of Contents

Welcome and Introduction to Conference 11869

In this work, we report on the trace detection of an explosive molecule, picric acid (PA), and a dye molecule, malachite green (MG), using surface enhanced Raman scattering (SERS) technique. We have synthesized porous Silicon (PSi) by a simple electrochemical etching method and anisotropic gold nanostars (AuNSs) using chemical reduction of the gold salt (HAuCl₄). Rough PSi acts as a suitable platform for generating SERS hotspots upon the addition of these AuNSs. The average particle size was found to be <50 nm with a strong absorption peak in the near infra-red (NIR) spectral region. PSi substrates along with AuNSs on its surface are used to explore their detection performance for PA and MG at different concentrations. Furthermore, we have compared the Raman signal intensities of Malachite Green (MG) on Si, PSi with and without Au NSs. Without Au NSs, bare PSi was found to exhibit a low Raman signal as compared to bare Si due to its hindering effect of an analyte molecule in the pore structures. However, this signal is enhanced by employing AuNSs onto the roughened porous surface. A portable Raman spectrometer (BWTEK) was used for all the SERS measurements with an excitation wavelength of 785 nm. We have achieved detection of MG at nanomolar (10^-9 M) and PA at micromolar (10^-6 M) concentrations using these hybrid SERS substrates. The enhancement factor was estimated to be in the range of 10⁴-10⁵. We believe that the optimization of porosity in PSi and sizes of AuNSs will improve the limit of detection further.

The issue of civil security and prevention of terrorist attacks in public places is becoming more and more actual every year. In this regard, increased attention is paid to detection of explosives. Of particular interest are methods to detect trinitrotoluene (TNT), hexogen (RDX), penthrite (PETN), octogen (HMX). Recently, gas-analytical, nuclear-physical, electromagnetic, terahertz, and biological detection methods have been developed. The lowest detection limit was achieved using gas-analytical methods, namely the non-linear ion mobility spectrometry method, with a limit of detection of 5.10^-15 g/cm³. However, the question of feasibility of using these methods in real conditions is increasingly raised. There is an opinion that it is much more effective to detect explosives by traces than by vapor. In this work we investigated the possibility of detecting vapors of pure explosives with low saturated pressure of vapors. By the example of pure and faсtory hexogen, using the method of thermal-programmed desorption and mass spectrometry, it was shown that it is hexogen vapor, and not technological impurities or additives with saturated vapor pressure exceeding the saturated vapor pressure of hexogen, that are registered in the gas phase by ion mobility spectrometry. A technique was developed and proposed to concentrate RDX vapors. Using temperature-programmed desorption, the minimal time of concentration and surface fill factor were determined.

Currently, one of the most important application of flow cytometry is the real-time analysis of aerosols, in particular, to ensure biosafety. In most cases, such analysis is aimed at detecting fluorescent signals from aerosol particles corresponding to the light emission of tryptophan and nicotinamide adenine dinucleotide (NADH). Further development of the method is largely related to the improvement of the light detecting systems for recording and processing of fluorescence and scattered light signals. In this work, a comparative analysis of flow cytometers for bioaerosols detection based on photo-multiplier tubes (PMT) and avalanche photodiodes (APD) operating in analog and photon-counting modes was carried out. The limit of detection (LOD) of bioaerosols, response time and ability to detect particles with low scattering and fluorescence cross section were calculated and examined. The calculations were carried out for the well-known optical scheme of fluorescence detection based on discrete photodetectors and dichroic mirrors combined with an air flow chamber equipped with elliptical and spherical mirrors. An ultraviolet light emission diode (LED) was used as a model source of exciting radiation. To estimate the optical properties of aerosol particles, experimental results obtained for a model bovine serum albumin bioaerosol and published data on various other bioaerosols were used. The calculation of the total number of fluorescent photons, emitted by particles of various sizes while passing the flow chamber was carried out. The obtained data were compared with parameters of photodetectors operating in analog and photon-counting modes. The critical particle size was determined for the effective registration in a photon-counting mode. Considering the size distribution of aerosol particles, it was concluded that application of the photon-counting mode will reduce the LOD of bioaerosols by more than an order of magnitude.

The presence of peculiarities in terahertz spectra of many organic compounds allows the use of THz imaging and spectroscopy for the detection of various hazardous and explosive substances. This work is devoted to the study of the detection of trace amounts of 1,3,5-Trinitro-1,3,5-triazinane (RDX) in the form of particles localized in millimeter and submillimeter sizes using THz imaging with spectral resolution. As a result of the work, images of trace amounts of RDX in reflected THz radiation were obtained. The contrast in these images made it possible to detect single particles of the powdery substance. The difference in contrast for RDX and polyethylene (PE) in the obtained terahertz images makes it possible to use THz imaging with spectral resolution not only for detection, but also for the identification of chemical compounds.

In the past decade, consumption of illegal and controlled street drugs has steadily increased. According to the latest World Drug Report, released by the United Nations Office on Drugs and Crime (UNODC)¹, more people are using drugs, and there are more drugs, and more types of drugs, than ever. Around 269 million people used drugs worldwide in 2018, which is 30 per cent more than in 20091. The growth in global drug supply and demand poses challenges to law enforcement, compounds health risks and complicates efforts to prevent and treat drug use disorders. Due to COVID-19, traffickers may have to find new routes and methods and opioid shortages may result in people seeking out more readily available substances such as alcohol, benzodiazepines or mixing with synthetic drugs.¹ Herein, we study the use of Raman SORS technology for rapid identification of narcotics in a range of concentrations – from pure form (as it is smuggled or transported) to street forms and products, often mixed with conventional cutting agents, with the potential to improve safety, efficiency and critical decision making in incident management, search operations, policing and ports and border operations.

In this paper we study the influence of the carrier and drift gas composition on ionization processes taking place inside drift chamber of field asymmetric ion mobility spectrometer with laser ionization. Solid state nanosecond laser of YAG:Nd 3+ type with fourth harmonic unit (λ = 266 nm, τpulse = 6 ns, E pulse = 700 – 2500 μJ, ν = 10 – 20 Hz) was used for negative ion generation. In this study we experimentally discover the features of laser ionization of four nitro-compounds: cyclotrimethylenetrinitramine (RDX), cyclotetramethylenetetranitramine (HMX), pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT) explosives. Drift and sample carrier gas were prepared by mixing purified air with different amounts of water vapor and organic dopants. Ion mobility increments were calculated after calibration of field asymmetric ion mobility spectrometer (FAIMS) based on published data for TNT and Iodine and measured alternating separation field waveform. The experimental setup also included drift time ion mobility spectrometer (IMS) which was used to verify linear ion mobility spectra to supplement ion mobility increment values, obtained by FAIMS. Previous studies of laser ionization with optimization of intensity and pulse repetition rates gave LOD values well below 10−15 g/cm3: 3 × 10−15 g/cm3 for RDX, 8 × 10−15 g/cm3 for PETN and less than 3 × 10−15 g/cm3 for HMX. Common ideas about ionization mechanisms of nitro-based explosives propose that indirect processes with ion-molecular reactions substantially contribute to negative ion formation as well as resonant enhanced multi photon ionization (REMPI) direct processes. Ionization process starts with electron generation by organic impurities in atmospheric air. These organic compounds have low ionization energy and require less than two photons to ionize. Current research involves doping air sample with such substances as: toluene acetone, naphthalene and chloroform at different UV irradiation modes. Such compounds can act as electron source for rising TNT and RDX ion signal levels above background. Such selectivity enhancement can be a step on the way to achieving even lower detection limits to sense trace explosive vapor concentrations in real conditions.

Trinitrotoluene (TNT) is a highly explosive nitroaromatic compound that is used for military and terrorist activities such as the development of improvised explosive devices (IEDs), landmines and is the main charge or explosive in most of the anti-personal and anti-vehicle mines. Different chemicals/ contaminants associated with TNT in soils near buried land mines comprise the microbial transformation products of TNT (2-amino-4,6-dinitrotoluene [2-Am-DNT] and 4-amino-2,6-dinitrotoluene [4-Am-DNT]), manufacturing impurities of TNT (2,4-DNT, 2,6- DNT, and 1,3-DNB), and TNT. Time, cost, and casualties associated with demining have necessitated the demand for improved detection techniques with reduced false positives by directly detecting the explosive material, rather than casing material of mines. Different analytical methods used to detect trace level of explosives in soil include ion mobility mass spectrometry, gas chromatography-mass spectrometry (GC-MS), and liquid chromatographymass spectrometry (LC-MS) that require samples to be collected from hazardous sites to laboratories. This is extremely unsafe, time consuming, involve large and expensive instrumentation cost and specially trained staff. Thus, detecting chemical signatures of these nitroaromatics in soil infected with these chemicals due to leaked TNT mines can provide location of landmines/ landmine prone zones to aide humanitarian demining process. This paper illustrates soil analysis for explosives and selected contaminants by Raman spectroscopy as a chemical, nondestructive, remote sensing method. As with advancement of Raman-based standoff detection techniques, fieldportable instruments and UAV deployable probes, this technique can be effectively employed in detecting buried landmines based on specific chemical signatures of target analyte. In this present study, TNT-based nitroaromatic was assessed in contaminated soil samples using Raman spectroscopy, where uncontaminated soil was used as background and matrix for spiking target contaminants at different concentrations.

Aerosol samplers with a recirculating liquid film are promising devices for remote biological monitoring. The presence of the liquid film provides a high survival rate for biological objects. The relatively simpler design allows portability to the sampler, which will make it possible to conduct tests outside the laboratory. In this study an analytical expression, describing the capturing efficiency of aerosol particles in the water film, taking into account the friction forces arising from interaction of water and air in a cyclone-based aerosol collector, was obtained. A new element, took over from the theory of centrifugal sprayers - a vortex chamber, was added to the theory and design of the collector. It allows increasing the initial angular moment of the elements of air volume entering the collector, which leads in appropriately an increase of maximal height of rising liquid film and particle capturing efficiency. To analyze the obtained expressions, graphs of particle capturing efficiency on basic parameters of modified cyclone collector and volumetric air flow were calculated. The graphs made it possible to determine the optimal geometric parameters for the portable cyclone-based collector. The introduced dependence on viscosity made it possible to estimate more accurately the efficiency of the device at various temperatures (including negative temperatures). For the selected parameters, graphs particle capturing efficiency were plotted. Water-alcohol solution and Novec 1230 fluid were used as fluids capable of operating at subzero temperatures. To check the operability of the sampler, tests were carried out to collect samples of sprayed inactivated adenovirus in a microbiological safety box at the Gamaleya Institute. The results of tests are discussed.

This paper discusses the vulnerability across Brazilian borders considering illicit trade. Smuggling affronts the public administration by bringing prohibited goods into the country. The typical characteristic of smuggling is the lack of collected taxes on goods transported across the border. Since the intelligence analysis estimates the events that are taking place for the movement of illicit goods, it can provide subsidies for agencies to make decisions to fight crime. The policy of the Brazilian State seeks to foster technologies that safeguard Brazil's immense land border with ten of the South American countries. The collection of satellite images is used for analyzes involving the recognition of paths that can be taken in the country. A technological trend is the periodic updating of the images to identify changes in the terrain. The analysis of the images allows monitoring of traffic routes known by the authorities, as well as identifying new routes. Because of their characteristics, satellite images allow, with good precision, the calculation of the distances traveled on each road segment, official or otherwise, and the estimation of the maximum speed that vehicles can travel according to the characteristics observed for the roughness and sinuosity of the road. It is considered unfeasible to observe the Brazilian borders with almost 17 thousand kilometers in length using only the military force. The authorities recognize the need to use equipment such as radios, antennas, radars, film cameras, X-ray sensors, UAVs, etc. to improve surveillance of these borders. Even so, the improvement of the information system speeds the planning of traffic control actions. The illicit drugs reaching at consumer center puts a flag in the information system. The combinations of these flags are triggered, and a control plan is mitigated. The more intelligence is used in this system, the greater the success of actions to combat illicit products.

The cycle of violence perpetrated from using targeted killing techniques contributes to the creation of new extremists. Targeting, profiling, harassing, and killing suspected extremists by opposing groups promote terrorism, feed into the narrative of oppressed versus oppressor, and fuel the passion for revenge. Until it is revealed why martyrdom and extremism are so attractive to potential terrorist recruits, the allurement continues without resolution. Terrorism has been a growing threat over the last two decades, with recruits growing daily. While there have been numerous worldwide strategies to combat terrorism, the results are disproportionate. This research examines targeting tactics that catapult fear, paranoia, and despair in the Federally Administrated Tribal Areas of Pakistan. It identifies the impact of the western world’s view and how targeted killing potentially affect the future of Islamic extremism. It critically examines the cycle of terror through the lens of terror management theory via first-hand media reports, interviews, and compares those to contrast to recruitment tactics and attacks.

The European Integrated Border Management (IBM) Strategy addresses the aspect of the “four-tier access control model” in order to develop and implement IBM at national and European Union (EU) level: (i) measures undertaken with third countries or service providers; (ii) cooperation with neighboring countries; (iii) border control and counter-smuggling measures and (iv) control measures within the area of free movement. Within this challenging objective lies the need for a well defined framework and technical platform supporting cross-agency and cross-country collaboration and information exchange. In this paper we present research results in the context of an innovative risk-based border management paradigm shift and exchange of information related to risks and alerts perceived and reported across all involved stakeholders (Border Guards, Custom Authorities and Border Control Point (BCP) Operators), both within EU Member States and Schengen area, as well as collaboration with third countries. The presented framework includes an integrated platform implementation demonstrated in a relevant operational environment of air, land and sea BCP.

Authentication of travel documents (e.g., passports) and breeder documents (e.g., birth certificates) is important to facilitate legal movement of passengers and to prevent cross-border crime, such as terrorism, smuggling, illegal migration and human trafficking. However, it is time consuming and difficult to verify all security features, the border guards differ in experience and expertise, and it is hard to stay alert every minute of a working day. New (artificial-intelligence based) technologies can assist in the automated fraud detection in travel and breeder documents, which may lead to faster and more consistent checks. This paper presents five categories of new technologies in automated document authentication to overcome the limitations of current document analysis systems in automated and non-automated border control scenarios. The first category consists of techniques related to the verification of visual security features on the holder page of travel documents. This category includes the verification of KINEGRAMs and other Optically Variable features under different light sources and lighting angles, and the analysis of printing techniques. The second category consists of techniques related to the analysis of breeder documents. This analysis can be at detail level (e.g., investigation of stamps) and at tactical level (e.g., verification of a check digit in a document number). The third category concerns the analysis of travel patterns, using information from the visa pages in passports. The stamps on these pages can be used to extract a travel pattern to support risk assessments and to detect anomalies. The fourth category is an analysis of the border-guard inspection history based upon a distributed ledger and blockchain technology that enables secure storage and prevents undesired manipulations. The last category analyzes the electronic chip of a passport. The software analyses document signer and country signer certificates on the chip to detect vulnerable cryptographic keys and tactical anomalies.

The paper describes a method for human behaviour recognition in surveillance applications. The method is based on the long short-term memory (LSTM), which is a form of recurrent neural network (RNN). LSTM has a memory function that can learn long-term dependencies by remembering short-term information for a long time. LSTM is therefore suitable for the events that are of interest in this paper. Except for the LSTM, the method also includes a simulation model for producing training and validation datasets. The simulation model describes a real environment with streets, buildings and squares and the motion patterns are represented with mathematical spline curves. The amount of training data are expanded further by creating different small variations of the spline curves. Output from the simulation model are matrices consisting of position, velocity and acceleration for a selected simulation time and sampling frequency. Before the data are used as input data to the LSTM a scaling is done so that the data patterns representing position, velocity and acceleration can contribute fully with useful information to the training process. The paper presents and discusses the method as well as results in the form of recognition accuracy of some simulated surveillance scenarios.

Deep neural networks, especially convolutional deep neural networks, are state-of-the-art methods to classify, segment or even generate images, movies, or sounds. However, these methods lack of a good semantic understanding of what happens internally. The question, why a COVID-19 detector has classified a stack of lung-ct images as positive, is sometimes more interesting than the overall specificity and sensitivity. Especially when human domain expert knowledge disagrees with the given output. This way, human domain experts could also be advised to reconsider their choice, regarding the information pointed out by the system. In addition, the deep learning model can be controlled, and a present dataset bias can be found. Currently, most explainable AI methods in the computer vision domain are purely used on image classification, where the images are ordinary images in the visible spectrum. As a result, there is no comparison on how the methods behave with multimodal image data, as well as most methods have not been investigated on how they behave when used for object detection. This work tries to close the gaps by investigating three saliency map generator methods on how their maps differ in the different spectra. This is achieved via an accurate and systematic training. Additionally, we examine how they perform when used for object detection. As a practical problem, we chose object detection in the infrared and visual spectrum for autonomous driving. The dataset used in this work, is the Multispectral Object Detection Dataset,1 where each scene is available in the long-wave (FIR), mid-wave (MIR) and short-wave (NIR) infrared as well as the visual (RGB) spectrum. The results show, that there are differences between the infrared and visual activation maps. Further, an advanced training with both, the infrared and visual data not only improves the network's output, it also leads to more focused spots in the saliency maps.

Terrorism is an international security challenge. The early detection of threats (e.g., explosives or firearms) could provide a valuable contribution to the ability to prevent, protect and respond to terrorism. This paper presents a system for the management of a plurality of sensors to improve the threat-detection capabilities without disrupting the flow of passengers. The system improves the prevention capabilities of soft targets (such as airports, undergrounds and railway stations) with a high number of daily commuters. The system architecture consists of three main components. The first component is 2D video tracking and re-identification (Re-ID), which allows the labelling and tracking of commuters in a small area. Thereby, it supports the fusion of sensors at different locations. The Re-ID has a smart training strategy with anonymized snippets to increase flexibility for new environments. The second component is 3D video tracking with a stereo camera, which gives a more accurate location measurement than 2D video. Location prediction is used to compensate for latency in the control of active elements in the threat detection sensor. Recurrent neural networks for location prediction were trained by using real 3D tracking data from a railway station. The performance is evaluated with a ground-truth based on Ultra-Wide Band (UWB) radio positioning and a coordinate conversion method was created to compensate for identified inaccuracies. The third component is Command & Control (C&C), which consists of three submodules: message broker, data-fusion and security client. The message broker is a publish-subscribe middleware layer to enable flexible integration of the various sensors and components. The data-fusion combines outputs of multiple sensors. In case of a suspect person, the security client triggers an alarm and a comprehensive report is sent to the security guards.

In addition to conventional camera networks, the deployment of drones allows for increased flexibility in surveillance tasks. Key components in modern analysis systems required to quickly assess a large amount of recorded aerial video data are the detection, tracking, and re-identification of persons. Each of the components is influenced by the characteristics of the aerial data and must be robust against challenges such as different flight altitudes and varying acquisition views and angles. In this work, we introduce a fast and efficient framework for person search which is specifically tailored to the characteristics of aerial data recorded by drones. In contrast to most of the works on person search and re-identification, we incorporate a tracking technique to add relevant context information about persons' movements to the retrieval results. In general, we focus on the three pipeline stages person detection, tracking, and re-identification as itself as well as the interplay between the components. For this, we adapt current state-of-the-art approaches for detection to the specific characteristics of aerial data and speed up the inference time by several modifications. Next, we apply a deep learning-based tracking approach, namely Deep SORT, to generate person tracks based on the detections. For the re-identification stage, we employ a lightweight re-identification model which is applied to generate features for both tracking and re-identification. To demonstrate the suitability of our proposed video analysis pipeline, we evaluate each component as well as their interplay on the P-DESTRE dataset.

Thermal face recognition has various applications in security, video-surveillance, military and government industries, etc. In contrast to the visible-light domain case, there is a lack of sufficiently large databases of thermal-face images. Therefore, it is difficult to construct a good face recognition system directly through learning procedures. We address this problem by using the synthesis approach, in which artificial visible-domain images of faces are generated from thermal face images and then identified in the visible-light domain. The generation of the artificial face images is done using a conditional Generative Adversarial Network, and the face identification is done by another, separate, deep neural network. Generating visible face images from thermal ones using DNNs requires large datasets of pairs of thermal and visible face images of the same persons, taken from the same position, time and imaging conditions, with two different cameras. The available datasets of such pairs are of relatively very low amount of subjects. Therefore, as in other similar studies, we approach this problem as a closed-set face recognition task, in which all testing identities are predefined in the training set. We train the face identification network itself using the synthesized images together with the original ground-truth visible-domain images to create a recognition system that is suitable to the characteristics of the artificially generated images. Thus, the recognition network will be optimized specifically for the thermal face recognition.

The North Atlantic Treaty Organization (NATO) SET-237 Task Group (TG) was formed in January 2016 to develop and recommend to NATO a reference standard methodology (or methodologies) for fabricating quantifiable surface standards for the evaluation of stand-off active and passive optical systems. The purpose of this task group, which was led by the DEVCOM-Chemical Biological Center, was to utilise different printing technologies to produce explosives test standards for the evaluation of effective standoff threat detection capabilities for force protection, IED detection and threat diagnostics. The TG also developed the procedures to characterize the samples (crystalline phase of material, morphology) and to estimate accuracy and precision information like surface coverage, particle size distribution, areal density, etc. of the prepared samples. Fraunhofer ICT was one of the organizations fabricating quantifiable surface standards using a Nanoplotter NP 2.1 from GeSim GmbH, Germany, a piezo-based drop on demand printer. In collaboration with DEVCOM-CBC, Fraunhofer ICT and ENEA, an ImageJ Standard Operating Procedures (SOP) for calculating particle statistics was established. ENEA acted as an “independent laboratory” characterising the coupons produced by the other partners to develop standard protocols to be included in the certificate of analysis of the coupons produced with ink-jet printers. A NATO field trial was organised by ENEA at its test site in Frascati (Rome) in 2019 to assess the printed samples against multiple field instruments for the test standard’s ability to meet the NATO SET-237 mission requirements. The poster will present samples prepared with Inkjet printer as well as protocols for their evaluation together with a new plug-in for the software ImageJ to process micrographs of the samples. The results of the work done in this TG will be utilised to support the work within the NATO-SPS project DEXTER and the EU Horizon 2020 project RISEN.

This paper offers a glimpse into some innovative methods that how to spot, track, and intercept hypersonic boost-glide and cruise missiles. These innovative methods are the implementation of enhanced joint armature of a railgun and reflective optical Multilateration system that had never been discussed prior to the current date. A hybrid optical and enhanced railgun defense system that consists of a steering 2D optical phased array of phased arrays that scans vertically, its above space (z-axis) as an optical wall of laser light in z-y plane plus several detectors, functioning like a Multilateration (MLAT) system and an enhanced active armature railgun system. The former is capable of spotting, tracking, and intercepting the target and the latter is a very fast device to intercept hypersonic missiles. A two-dimensional phased array of phased arrays (PAPA) scans vertically in its upper space (z-axis). The output power of the PAPA can reach easily several Mega Watts and at least three optical sensors are located close to the optical system that is operational in the mid-phase or in the terminal phase (close to the target area) and scans the approach space. By crossing the missile through the optical wall (x-axis) that scans the space having several MHz frequencies, the light is scattered toward around and to sensors as well, the three sensors receive the light that spots the location of the missile by MLAT detection principles. On the other hand, the system is able to deflect in space (in the direction of the y-axis) to follow the target and destroy it. The enhanced armature railgun is a new concept that can increase the railgun projectile to tens of Mach speed, using very low electrical energy.