Preventing terrorist attacks at soft targets has become a priority for our society. The realization of sensor systems for automatic threat detection in crowded spaces, such as airports and metro stations, is challenged by the limited sensing coverage capability of the devices in place due to the variety of dangerous materials, to the scanning rate of the devices, and to the detection area covered. In this context, effectiveness of the physical configuration of the system based on the detectors used, the coordination of the sensor data collection, and the real time data analysis for threat identification and localization to enable timely reactions by the security guards are essential requirements for such integrated sensor-based applications. This paper describes a modular distributed architecture of a command-and-control software, which is independent from the specific detectors and where sensor data fusion is supported by two intelligent video systems. Furthermore, the system installation can be replicated at different locations of a public space. Person tracking and later re-identification in a separate area, and tracking hand-over between different video components, provide the command-and-control with localization information of threats to timely activate alarm management and support the activity of subsequent detectors. The architecture has been implemented for the NATO-funded DEXTER program and has been successfully tested in a big city trial at a metro station in Rome both when integrated with two real detectors of weapons and explosives and as a stand-alone system. The discussion focuses on the software functions of the command-and-control and on the flexibility and re-use of the system in wider settings.

Counter terrorism is a huge challenge for public spaces. Therefore, it is essential to support early detection of threats, such as weapons or explosives. An integrated fusion engine was developed for the management of a plurality of sensors to detect threats without disrupting the flow of commuters. The system improves security of soft targets (such as airports, undergrounds and railway stations) by providing security operators with real-time information of the threat combined with image and position data of each person passing the monitored area. This paper describes the results of the fusion engine in a public-space trial in a metro station in Rome. The system consists of 2D-video tracking, person re-identification, 3D-video tracking, and command and control (C&C) formulating two co-existing data pipelines: one for visualization on smart glasses and another for hand-over to another sensor. Over multiple days, 586 commuters participated in the trial. The results of the trial show overall accuracy scores of 97.4% and 97.6% for the visualization and hand-over pipelines, respectively, and each component reached high accuracy values (2D Video = 98.0%, Re-identification = 100.0%, 3D Video = 99.7% and C&C = 99.5%).

Frequency correlations are a versatile and powerful tool which can be exploited to perform spectral analysis of objects whose direct measurement might be unfeasible. This is achieved through a so-called ghost spectrometer that can be implemented with quantum and classical resources alike. While there are some known advantages associated to either choice, an analysis of their metrological capabilities has not yet been performed. Here we report on the metrological comparison between a quantum and a classical ghost spectrometer. We perform the estimation of the transmittivity of a bandpass filter using frequency-entangled photon pairs. Our results show that a quantum advantage is achievable, depending on the values of the transmittivity and on the number of frequency modes analyzed.

In the framework of the H2020 project INCLUDING (www.including-cluster.eu), the Piraeus port commercial terminal has hosted a field exercise on the identification and recovery of two orphan sources inside a cargo container. The primary objective of the field exercise was to test the integration of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) in the response plan coordinated by the CBRN experts of the Hellenic Ministry of Defence and the first operational verification of a platform under development in the project for the management of the mobilized resources on the incident scene. The activity marks a solid step ahead in introducing innovation in the exercise and training activities in the nuclear security domain and in improving sharing of resources at EU level. In this article is described the concept of operation of the INCLUDING platform, its architecture and its use in the different phase of the Piraeus port exercise, that is one of the eight Joint Action planned during the INCLUDING project duration.

A. Chiuri,1 I. Gianani,2 V. Cimini,2, 3 L. De Dominicis,1 M.G. Genoni,4 and M. Barbieri2, 5 1ENEA Centro Ricerche Frascati, via E. Fermi 45, 00044 Frascati, Italy 2Dipartimento di Scienze, Universitá degli Studi Roma Tre, Via della Vasca Navale 84, 00146, Rome, Italy 3Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy 4Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, 20133, Milan, Italy 5Istituto Nazionale di Ottica (INO-CNR), L.go E. Fermi 6, I-50125 Firenze, Italy (Dated: May 21, 2021)

Accurate and timely assessment of displacements and/or structural damages in nuclear reactor vessels’ components is a key action in routine inspections for planning maintenance and repairs but also in emergency situations for mitigating consequences of nuclear incidents. Nevertheless, all these components are maintained underwater and reside in high-radiation fields thus imposing harsh operative conditions to inspection devices which must cope with effects such as Cerenkov radiation background, Total Ionizing Radiation (TID), and occlusions in the detectors’ field of view. To date, ultrasonic techniques and video cameras are in use for inspection of components’ integrity and with measurements of volumetric and surface crack opening displacements, respectively. The present work reports the realization of a radiation tolerant laser scanner and the results of tests in a nuclear research reactor vessel for acquisition of 3D models of critical components. The device, qualified for underwater operation and for withstanding up to 1 MGy of TID, is based on a 515 nm laser diode and a fast-scanning electro-optic unit. To evaluate performances in a significant but controlled environment, the device has been deployed in the vessel of a research reactor operated by ENEA in the Casaccia Research Centre in Rome (Italy). A 3D model of the fuel rods assembly through a cooling water column of 7 m has been acquired. The system includes proprietary postprocessing software that automatically recognizes components of interest and provides dimensional analysis. Possible application fields of the system stretch to dimensional analysis also in spent nuclear fuel storage pools.

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.

Here, we describe an innovative Integrated Laser Sensor (ILS) that combines four spectroscopic techniques and two vision systems into a unique, transportable device. The instrument performs Raman and Laser-Induced Fluorescence (LIF) spectroscopy excited at 355 nm and Laser-Induced Breakdown Spectroscopy (LIBS) excited at 1064 nm, and it also detects Laser Scattering (LS) from the target under illumination at 650 nm. The combination of these techniques supplies information about: material change from one scanning point to another, the presence of surface contaminants, the molecular and elemental composition of top target layers. Switching between the spectroscopic techniques and the laser wavelengths is fully automatic. The instrument is equipped with an autofocus and it performs scanning with a chosen grid density over an interactively-selected target area. Alternative to the spectroscopic measurements, it is possible to switch the instrument to a high magnification target viewing. The working distances tested until now are between 8.5 and 30 m. The instrument is self-powered and remotely controlled via wireless communication. The ILS has been fully developed at ENEA for security applications and it was successfully tested in two outdoor campaigns where an automatic recognition of areas containing explosives in traces had been implemented. The strategies for the identification of nitro-compounds placed on various substrates as fingerprints and the results obtained at a working distance of 10 m are discussed in the following.

Optical and spectroscopic techniques offer unique possibilities for non destructive or micro-destructive characterization of surfaces, with widespread applications, starting from in-line monitoring of industrial processes. A significant group of industrial applications concerns nuclear grade material characterization and plasma diagnostics relevant to the thermonuclear fusion process. However, technologies and methodologies originally developed for specific in-vessel utilization, can easily find additional in-situ and remote application to environmental and cultural heritage (CH) diagnostics addressed to preventive conservation and restoration of surfaces. At ENEA Frascati different prototypes have been developed and patented to collect reflectance and fluorescence images excited at different ultraviolet and visible laser wavelengths, Raman and LIBS signals. Portable integrated instruments suitable for operation at different distances from a 1.5 to 30 m, have been assembled and operated in laboratory on multilayered samples and in field campaigns for Security and on CH painted surfaces. Significant results relevant to the cross fertilization among different applications will be presented and discussed.

Inspired by retinex theory, we propose a novel method for selecting key points from a depth map of a 3D freeform shape; we also use these key points as a basis for shape registration. To find key points, first, depths are transformed using the Hotelling method and normalized to reduce their dependence on a particular viewpoint. Adaptive smoothing is then applied using weights which decrease with spatial gradient and local inhomogeneity; this preserves local features such as edges and corners while ensuring smoothed depths are not reduced. Key points are those with locally maximal depths, faithfully capturing shape. We show how such key points can be used in an efficient registration process, using two state-of-the-art iterative closest point variants. A comparative study with leading alternatives, using real range images, shows that our approach provides informative, expressive, and repeatable points leading to the most accurate registration results.

Underwater three‐dimensional (3D) laser imaging is a technique that generates faithful 3D models of submerged targets by means of a sensor emitting a laser beam. The technique is the transposition to the underwater environment of the mature technology currently applied routinely on emerged land. Nevertheless, underwater 3D laser imaging has specific scientific and technological challenges to face and its solution is key for bridging the gap with the homologous counterpart of the technique in air.

We report the results of the application of Laser-Induced Breakdown Spectroscopy (LIBS) for the detection of some common military explosives and theirs precursors deposited on white varnished car's external and black car's internal or external plastic. The residues were deposited by an artificial silicon finger, to simulate material manipulation by terrorists when preparing a car bomb, leaving traces of explosives on the parts of a car.LIBS spectra were acquired by using a first prototype laboratory stand-off device, developed in the framework of the EU FP7 313077 project EDEN (End-user driven DEmo for CBRNe). The system operates at working distances 8-30 m and collects the LIBS in the spectral range 240-840 nm. In this configuration, the target was moved precisely in X-Y direction to simulate the scanning system, to be implemented successively.The system is equipped with two colour cameras, one for wide scene view and another for imaging with a very high magnification, capable to discern fingerprints on a target.The spectral features of each examined substance were identified and compared to those belonging to the substrate and the surrounding air, and those belonging to possible common interferents.These spectral differences are discussed and interpreted.The obtained results show that the detection and discrimination of nitro-based compounds like RDX, PETN, ammonium nitrate (AN), and urea nitrate (UN) from organic interfering substances like diesel, greasy lubricants, greasy adhesives or oils in fingerprint concentration, at stand-off distance of some meters or tenths of meters is feasible.

Feature extraction and matching provide the basis of many methods for object registration, modeling, retrieval, and recognition. However, this approach typically introduces false matches, due to lack of features, noise, occlusion, and cluttered backgrounds. In registration, these false matches lead to inaccurate estimation of the underlying transformation that brings the overlapping shapes into best possible alignment. In this paper, we propose a novel boosting-inspired method to tackle this challenging task. It includes three key steps: (i) underlying transformation estimation in the weighted least squares sense, (ii) boosting parameter estimation and regularization via Tsallis entropy, and (iii) weight re-estimation and regularization via Shannon entropy and update with a maximum fusion rule. The process is iterated. The final optimal underlying transformation is estimated as a weighted average of the transformations estimated from the latest iterations, with weights given by the boosting parameters. A comparative study based on real shape data shows that the proposed method outperforms four other state-of-the-art methods for evaluating the established point matches, enabling more accurate and stable estimation of the underlying transformation.

The European EDEN project marks an unprecedented effort of the European Commission to support Research & Development actions in the field of CBRNe. Building upon the results and lessons learnt in previous national and international projects, EDEN aims at bringing to a next level of integration tools and practices for preventing and facing emergency situations. The solutions sought will be validated through in-field exercises with simulated situations resembling real conditions as far as possible. ENEA is leading one of these exercises, indicated in the project as Thematic Demos and devoted to the realization of a 3D laser scanner for improved inspection in the wet environments of a nuclear facility (nuclear vessel and storage pool). The present contribution outlines the guidelines of this action scheduled for September 2015, which puts ENEA at the forefront of the research in the field of innovative inspection methods for nuclear industry. A short description of the EDEN project concept and structure is given for better framing the action.

Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight re-estimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed.

While today 3D digitisation techniques are commonly applied in several areas of cultural heritage, introducing new ways for monitoring, cataloguing, and studying masterpieces, the use of these technologies is not always worthwhile in terms of costs/benefits. The ENEA UTAPRAD-DIM laboratory has developed opto-electronic devices for cultural heritage applications. Two different 3D laser scanners for terrestrial and underwater inspection have been the subject of laboratory research for the last decade and a new technique known as imaging topological radar (ITR) has been developed and patented. The ITR system is based on the superimposition of three amplitude-modulated laser sources for the simultaneous acquisition of data related to colour and structure. This approach opens new scenarios for colour measurement and remote/non-invasive analysis, reducing the gap between costs and benefits from the technology. Several factors affect the quality of data collected by ITR, such as the precision of the scanner mechanism, the material and shape of the work studied, and the geometry of scanning (i.e. distance and angular dependencies). This paper explores the effect of the geometry of scanning on point cloud quality, focussing attention on data correction algorithms and their practical application. The data collected during the digitisation of the Sistine Chapel using the RGB-ITR scanner serve as a case study for validating the theoretical assumptions, models, and algorithms.

Inspired by retinex theory, we propose a novel method for selecting key points from a depth map of a 3D freeform shape; we also use these key points as a basis for shape registration. To find key points, first, depths are transformed using the Hotelling method and normalized to reduce their dependence on a particular viewpoint. Adaptive smoothing is then applied using weights which decrease with spatial gradient and local inhomogeneity; this preserves local features such as edges and corners while ensuring smoothed depths are not reduced. Key points are those with locally maximal depths, faithfully capturing shape. We show how such key points can be used in an efficient registration process, using two state-of-the-art iterative closest point variants. A comparative study with leading alternatives, using real range images, shows that our approach provides informative, expressive, and repeatable points leading to the most accurate registration results.

A philosophical thought experiment that raises questions regarding observation and knowledge of reality declaims: "If a tree falls in a forest and no one is around to hear it, does it make any sound?". A similar question could be formulated in terms of color perception: "If no light illuminates an object, does the color still exist?". A technology called RGB-ITR (Red Green Blue - Imaging Topological Radar), based on the amplitude modulation of n-lasers stimuli, has been developing in UTAPRADDIM lab of C.R. ENEA Frascati for several years now. One of the major application of this technology is remote monitoring and digitization of artistic goods in Cultural Heritage (CH) environment: the exclusive use of lasers/photo-diodes for the acquisition of both color and distance information permits to obtain not only a very accurate 3D model of the investigated target, but also hyper-photos free from external light sources influence and with calibrated whites in all the instrument working range. By performing several case studies in real environment, this work aims at demonstrating the versatility of this technique in several fields of CH sector, like remote structure and color monitoring, cataloging and fruition. Recently the significant effort has been spent on looking for novel applications of this technology in the underwater environment for archaeological and industrial purposes. The lab results, reported in this work, demonstrate the potential of this technology to bypass the barrier created by the water back-scattering, especially in case of turbid environment.

An impressive number of laser-based sensors for 3D vision for terrestrial applications have been developed so far, making it a mature and expanding market worth billions of euros. When it comes to the subsea environment the presence of water, combined with the demand to operate at depth, acts as game-changing factors and considerable scientific and technological challenges arise. This chapter concentrates on selected techniques for subsea 3D vision and ranging using laser-based sensors, with priority given to projects where the efforts have been focused on developing devices ready to be deployed in real scenarios.