A review of X-ray explosives detection techniques for checked baggage

Enhancing substance identification by Raman spectroscopy using deep neural convolutional networks with an attention mechanism

Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.

Fluorescent Carbon Nitride Nanoparticles for Picric Acid Sensing

Detection of nitroaromatic explosives is essential in the area of environmental safety. Fluorescent carbon nitride nanoparticles is a promising material for this purpose. Herein, we have prepared fluorescent carbon nitride nanoparticles (CNNPs) by one step thermal treatment of formamide. These fluorescent CNNPs is sensitive towards picric acid (PA) than other analytes both in aqueous medium and on test paper which is witnessed by fluorescence quenching based on inner filter effect (IFE). The PA detection with the fluorescent CNNPs is observed in the concentration ranges, 0 µM to 60 µM with linear range of 10 nM to 25 µM. The minimum detection limit in aqueous medium and solid phase are determined to be 26.20 nM and 10 µM respectively. Finally, the fluorescent CNNPs is applied for detection of PA in real water samples. The recoveries are in the ranges from 99.54 to 116.35% with relative standard deviation less than 3.85%. This proposed fluorescent method can act as suitable analytical technique to monitored PA concentration in water samples.

Thermal control of organic semiconductors for trace detection of explosives

Organic semiconductors can be applied as ultra-sensitive fluorescent sensors for detecting trace vapours of explosives. The detection of explosives is manifest by the fluorescence quenching of the sensors. However, for many organic fluorescent sensors, the fluorescence quenching is irreversible and imposes a limitation in terms of reusability. Here we present a study of the thermal control of thin-film fluorescent sensors made from the commercial fluorescent polymer Super Yellow (SY). Thermal control of the sensor's temperature results in the desorption of the absorbed analytes, nitroaromatic explosives (2,4-DNT and DNB), and a taggant molecule (DMDNB). The amount of photoluminescence (PL) quenching and the desorption temperature of analytes provides a route to discriminate between the analytes, and additonally make the SY sensors reusable.

Halide Perovskites Films for Ionizing Radiation Detection: An Overview of Novel Solid-State Devices

Halide perovskites are a novel class of semiconductors that have attracted great interest in recent decades due to their peculiar properties of interest for optoelectronics. In fact, their use ranges from the field of sensors and light emitters to ionizing radiation detectors. Since 2015, ionizing radiation detectors exploiting perovskite films as active media have been developed. Recently, it has also been demonstrated that such devices can be suitable for medical and diagnostic applications. This review collects most of the recent and innovative publications regarding solid-state devices for the detection of X-rays, neutrons, and protons based on perovskite thin and thick films in order to show that this type of material can be used to design a new generation of devices and sensors. Thin and thick films of halide perovskites are indeed excellent candidates for low-cost and large-area device applications, where the film morphology allows the implementation on flexible devices, which is a cutting-edge topic in the sensor sector.

Sparse interleaved sampling for high resolution focal construct geometry X-ray tomography

We demonstrate interleaved sampling by multiplexing conical subshells within the tomosynthesis and raster scanning a phantom through a 150 kV shell X-ray beam. Each view comprises pixels sampled on a regular 1 mm grid, which is then upscaled by padding with null pixels before tomosynthesis. We show that upscaled views comprising 1% sample pixels and 99% null pixels increase the contrast transfer function (CTF) computed from constructed optical sections from approximately 0.6 line pairs/mm to 3 line pairs/mm. The driver of our method is to complement work concerning the application of conical shell beams to the measurement of diffracted photons for materials identification. Our approach is relevant to time-critical, and dose-sensitive analytical scanning applications in security screening, process control and medical imaging.

Renal Clearable Mo-Based Polyoxometalate Nanoclusters: A Promising Radioprotectant against Ionizing Irradiation

In response to diffused ionizing radiation damage throughout the body caused by nuclear leaks and inaccurate radiotherapy, radioprotectants with considerable free radical scavenging capacities, along with negligible adverse effects, are highly regarded. Herein, unlike being performed as toxic chemotherapeutic drug candidates, molybdenum-based polyoxometalate nanoclusters (Mo-POM NCs) were developed as a non-toxic potent radioprotectant with impressive free radical scavenging capacities for ionizing radiation protection. In comparison to the clinically used radioprotectant drug amifostine (AM), the as-prepared Mo-POM NCs exhibited effective shielding capacity by virtue of their antioxidant properties resulting from a valence shift of molybdenum ions, alleviating not only ionizing radiation-induced DNA damage but also disruption of the radiation-sensitive hematopoietic system. More encouragingly, without trouble with long-term retention in the body, ultra-small sized Mo-POM NCs prepared by the mimetic Folin-Ciocalteu assay can be removed from the body through the renal-urinary pathway and the hepato-enteral excretory system after completing the mission of radiation protection. This work broadened the biological applications of metal-based POM chemotherapeutic drugs to act as a neozoic radioprotectant.

Conical shell illumination incorporating a moving aperture for depth-resolved high-energy X-ray diffraction

In many applications, the main limitation of X-ray absorption methods is that the signals measured are a function of the attenuation coefficient, which tells us almost nothing about the chemical or crystallographic nature of objects under inspection. To calculate fundamental crystallographic parameters requires the measurement of diffracted photons from a sample. Standard laboratory diffraction methods have been refined for well over a century and provide 'gold standard' structural models for well-prepared samples and single crystals but have little applicability for thick heterogeneous samples as demanded by many screening applications. We present a new high-energy X-ray diffraction probe, which in comparison with previous depth-resolving hollow beam techniques, requires a single beam, point detector and a simple swept aperture to resolve sample signatures at unknown locations within an inspection space. We perform Monte Carlo simulations to support experiments on both single- and multiple-material localisation and identification. The new probe is configured and tested using low-cost commercial components to provide a rapid and cost-effective solution for applications including explosives detection, process control and diagnostics.

Scintillation Response of Nd-Doped LaMgAl11O19 Single Crystals Emitting NIR Photons for High-Dose Monitoring

The Nd-doped LaMgAl₁₁O₁₉ single crystals were synthesized by the floating zone method, and the photoluminescence and scintillation properties were evaluated. Under X-ray irradiation, several sharp emission peaks due to the 4f-4f transitions of Nd³⁺ were observed at 900, 1060, and 1340 nm in the near-infrared range, and the decay curves show the typical decay time for Nd³⁺. The samples show good afterglow properties comparable with practical X-ray scintillators. The 1% and 3% Nd-doped LaMgAl₁₁O₁₉ samples show a good linearity in the dynamic range from 6-60,000 mGy/h.

Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks

X-ray imaging has been boosted by the introduction of phase-based methods. Detail visibility is enhanced in phase contrast images, and dark-field images are sensitive to inhomogeneities on a length scale below the system's spatial resolution. Here we show that dark-field creates a texture which is characteristic of the imaged material, and that its combination with conventional attenuation leads to an improved discrimination of threat materials. We show that remaining ambiguities can be resolved by exploiting the different energy dependence of the dark-field and attenuation signals. Furthermore, we demonstrate that the dark-field texture is well-suited for identification through machine learning approaches through two proof-of-concept studies. In both cases, application of the same approaches to datasets from which the dark-field images were removed led to a clear degradation in performance. While the small scale of these studies means further research is required, results indicate potential for a combined use of dark-field and deep neural networks in security applications and beyond.

Halide perovskites and perovskite related materials for particle radiation detection

Radiation detectors are widely used in physics, materials science, chemistry, and biology. Halide perovskites are known for their superior properties including tunable bandgaps and chemical compositions, high defect tolerance, solution-processable synthesis of films and crystals, and high carrier diffusion length. Recently, halide perovskites have attracted enormous interest as particle radiation detectors for both charged (α and β) and uncharged (neutrons) particles. Solid-state detectors based on single crystal perovskites can detect α particles and thermal neutrons with energy-resolved spectra. Halide perovskite scintillators are also able to detect β particles and fast neutrons. In this review, we briefly introduce the fundamentals of radiation detection and summarize the recent progress on halide perovskite detectors for particle radiation.

Significance of the spectral correction of photon counting detector response in material classification from spectral x-ray CT

Purpose: Photon counting imaging detectors (PCD) has paved the way for spectral x-ray computed tomography (spectral CT), which simultaneously measures a sample's linear attenuation coefficient (LAC) at multiple energies. However, cadmium telluride (CdTe)-based PCDs working under high flux suffer from detector effects, such as charge sharing and photon pileup. These effects result in the severe spectral distortions of the measured spectra and significant deviation of the extracted LACs from the reference attenuation curve. We analyze the influence of the spectral distortion correction on material classification performance. Approach: We employ a spectral correction algorithm to reduce the primary spectral distortions. We use a method for material classification that measures system-independent material properties, such as electron density, ρ e , and effective atomic number, Z eff . These parameters are extracted from the LACs using attenuation decomposition and are independent of the scanner specification. The classification performance with the raw and corrected data is tested on different numbers of energy bins and projections and different radiation dose levels. We use experimental data with a broad range of materials in the range of 6 ≤ Z eff ≤ 15 , acquired with a custom laboratory instrument for spectral CT. Results: We show that using the spectral correction leads to an accuracy increase of 1.6 and 3.8 times in estimating ρ e and Z eff , respectively, when the image reconstruction is performed from only 12 projections and the 15 energy bins approach is used. Conclusions: The correction algorithm accurately reconstructs the measured attenuation curve and thus gives better classification performance.

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The present study provides an overview of the existing and developing techniques for non-intrusive inspection control, current research trends, and future challenges in the field. Both traditional and developing methods, techniques, and technologies were analyzed with the use of traditional and novel sensor types. Finally, it was concluded that the improvement of non-intrusive inspection experience could be gained with the additional use of novel types of sensors (such as biosensors) combined with traditional techniques (X-ray inspection).

One-dimensional Europium-coordination polymer as luminescent sensor for highly selective and sensitive detection of 2,4,6-trinitrophenol

Three isostructural lanthanide coordination polymers (LnCPs), [Ln(L)₆(DMF)]_n {HL = 2-(2-formylphenoxy) acetic acid, Ln = Sm (1); Eu (2); Tb (3)} have been synthesized by solvothermal reaction and characterized. Single crystal analyses revealed that the architectures of these LnCPs own one dimensional chain which can be further packed into two-dimensional architectures by hydrogen bonds. Moreover, these LnCPs can offer strategically placed uncoordinated formyl groups, which may act as hydrogen-bond acceptor in the sensing of nitro explosives. Luminescence measurements reveal that LnCPs 2 and 3 exhibit strong luminescence in solid states. LnCP 2 shows quick, highly selective and sensitive detection of 2,4,6-trinitrophenol (TNP) with the high quenching constant (2.6 × 10⁴ M^-1) and low detection limit (3.39 μM), which indicates that LnCP 2 is more efficient than most of Eu-based coordination polymers for the sensing of TNP. Furthermore, LnCP 2 represents the first example of one-dimensional Eu-based sensors with formyl group as hydrogen-bonding site in the detection of TNP.

Iterative difference deblurring algorithm for linear computed laminography

Linear Computed Laminography (LCL) is used to yield slice images of plate-like objects (PLO) for the advantage of short exposure time, high control precision and low cost. Shift and Add (SAA) is a widely used reconstruction algorithm for LCL. One limitation of SAA is that the reconstructed image of the in-focus layer (IFL) contains information from off-focus layers (OFL), resulting in inter-slice aliasing and blurring. In this paper, an Iterative Difference Deblurring (IDD) algorithm based on LCL is proposed to reduce the blur in reconstructed images. The core idea of the IDD algorithm is: contributions from OFL are subtracted from the projection data to remove the blur from the IFL. The corrected projections are then reconstructed using the SAA to remove the superimposed contributions of OFL from the IFL. An iterative approach is utilized to adjust a weighting factor applied during the subtraction stage. The results demonstrate that IDD algorithm can achieve PLO reconstruction in the LCL system under extremely sparse sampling conditions, and can effectively reduce the inter-slice aliasing and blurring.

Field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network

With the advanced development of miniaturization and integration of instruments, Raman spectroscopy (RS) has demonstrated its great significance because of its non-invasive property and fingerprint identification ability, and extended its applications in public security, especially for hazardous chemicals. However, the fast and accurate RS analysis of hazardous chemicals in field test by non-professionals is still challenging due to the lack of an effective and timely spectral-based chemical-discriminating solution. In this study, a platform was developed for the field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network (DASN) incorporated into a cloud server. With the Raman spectra of 300 chemicals, DASN stands out with identification accuracy of 100% and outweighs other machine learning and deep learning methods. The network feature maps for the spectra of methamphetamine and ketamine focus on the main peaks of 1001 and 652 cm^-1, which indicates the powerful feature extraction capability of DASN. Its receiver operating characteristic (ROC) curve completely encloses the other models, and the area under the curve is up to 1, implying excellent robustness. With the well-built platform combining RS, DASN, and cloud server, one test process including Raman measurement and identification can be performed in tens of seconds. Hence, the developed platform is simple, fast, accurate, and could be considered as a promising tool for hazardous chemical identification in public security on the scene.

Large-area TaN superconducting microwire single photon detectors for X-ray detection

With the development of superconducting nanowire single photon detectors, increasing numbers of important applications are being explored, covering not only low-energy optical photon detection but also high-energy photon and particle detection. In this work, 100-nm-thick TaN superconducting microwire single photon detectors (SMSPDs) with large active areas were prepared for X-ray detection, and their response characteristics to X-rays were studied. The results showed that our TaN SMSPDs were able to detect X-rays at a wide range of bias currents and working temperatures. The detectors could distinguish different energy X-rays under suitable working conditions, and the energy resolving power was strongly related to the bias current.

DEBISim: A simulation pipeline for dual energy CT-based baggage inspection systems1

BACKGROUND

Materials characterization made possible by dual energy CT (DECT) scanners is expected to considerably improve automatic detection of hazardous objects in checked and carry-on luggage at our airports. Training a computer to identify the hazardous items from DECT scans however implies training on a baggage dataset that can represent all the possible ways a threat item can packed inside a bag. Practically, however, generating such data is made challenging by the logistics (and the permissions) related to the handling of the hazardous materials.

OBJECTIVE

The objective of this study is to present a software simulation pipeline that eliminates the need for a human to handle dangerous materials and that allows for virtually unlimited variability in the placement of such materials in a bag alongside benign materials.

METHODS

In this paper, we present our DEBISim software pipeline that carries out an end-to-end simulation of a DECT scanner for virtual bags. The key highlights of DEBISim are: (i) A 3D user-interactive graphics editor for constructing a virtual 3D bag with manual placement of different types of objects in it; (ii) An automated virtual bag generation algorithm for creating randomized baggage datasets; (iii) An ability to spawn deformable sheets and liquid-filled containers in a virtual bag to represent plasticized and liquid explosives; and (iv) A GPU-based X-ray forward modelling block for spiral cone-beam scanners used in checked baggage screening.

RESULTS

We have tested our simulator using two standard CT phantoms: the American College of Radiology (ACR) phantom and the NIST security screening phantom as well as on a set of reference materials representing commonly encountered items in checked baggage. For these phantoms, we have assessed the quality of the simulator by comparing the simulated data reconstructions with real CT scans of the same phantoms. The comparison shows that the material-specific properties as well as the CT artifacts in the scans generated by DEBISim are close to those produced by an actual scanner.

CONCLUSION

DEBISim is an end-to-end simulation framework for rapidly generating X-ray baggage data for dual energy cone-beam scanners.

Calculation of effective atomic numbers using a rational polynomial approximation method with a dual-energy X-ray imaging system

The study aims to develop a rational polynomial approximation method for improving the accuracy of the effective atomic number calculation with a dual-energy X-ray imaging system. This method is based on a multi-materials calibration model with iterative optimization, which can improve the calculation accuracy of the effective atomic number by adding a rational term without increasing the computation time. The performance of the proposed rational polynomial approximation method is demonstrated and validated by both simulated and experimental studies. The twelve reference materials are used to establish the effective atomic number calibration model, and the value of the effective atomic numbers are between 5.444 and 22. For the accuracy of the effective atomic number calculation, the relative differences between calculated and experimental values are less than 8.5%for all sample cases in this study. The average calculation accuracy of the method proposed in this study can be improved by about 40%compared with the conventional polynomial approximation method. Additionally, experimental quality assurance phantom imaging result indicates that the proposed method is compliant with the international baggage inspection standards for detecting the explosives. Moreover, the experimental imaging results reveal that the difference of color between explosives and the surrounding materials is in significant contrast for the dual-energy image with the proposed method.

Aggregation-directed High Fidelity Sensing of Picric Acid by a Perylenediimide-based Luminogen

Widespread use of picric acid (PA) in chemical industries and deadly explosives poses dreadful impact on all living creatures as well as the natural environment and has raised global concerns that necessitate the development of fast and efficient sensing platforms. To address this issue, herein, we report a perylenediimide-peptide conjugate, PDI-1, for detection of PA in methanol. The probe displays typical aggregation caused quenching (ACQ) behaviour and exhibits a fluorescence "turn-off" sensory response towards PA which is unaffected by the presence of other interfering nitroaromatic compounds. The sensing mechanism involves PA induced aggregation of the probe into higher order tape like structures which leads to quenching of emission. The probe possesses a low detection limit of 5.6 nM or 1.28 ppb and a significantly high Stern-Volmer constant of 6.87×10⁴  M^-1 . It also exhibits conducting properties in the presence of PA vapours and thus represents a prospective candidate for vapour phase detection of PA. This is, to the best of our knowledge, the first example of a perylenediimide based probe that demonstrates extremely specific, selective and sensitive detection of PA and thus grasps the potential for application in practical scenarios.

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F₁ score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F₁ on the SIXray and GDXray dataset, respectively.

Automation reliability, human-machine system performance, and operator compliance: A study with airport security screeners supported by automated explosives detection systems for cabin baggage screening

Using a simulated X-ray screening task, we tested 122 airport security screeners working with the support of explosives detection systems for cabin baggage screening (EDSCB) as low-level automation. EDSCB varied systematically on three automation reliability measures: accuracy, d', and positive predictive value (PPV). Results showed that when unaided performance was high, operator confidence was high, and automation provided only small benefits. When unaided performance was lower, operator confidence was lower, and automation with higher d' provided large benefits. Operator compliance depended on the PPV of automation: We found lower compliance for lower PPV. Automation with a high false alarm rate of 20% and a low PPV of .3 resulted in operators ignoring about one-half of the true automation alarms on difficult targets-a strong cry-wolf effect. Our results suggest that automation reliability described by d' and PPV is more valid than using accuracy alone. When the PPV is below .5, operators should receive clear instructions on how to respond to automation alarms.

A sensitive and robust thin-film x-ray detector using 2D layered perovskite diodes

Solid-state radiation detectors, using crystalline semiconductors to convert radiation photons to electrical charges, outperform other technologies with high detectivity and sensitivity. Here, we demonstrate a thin-film x-ray detector comprised with highly crystalline two-dimensional Ruddlesden-Popper phase layered perovskites fabricated in a fully depleted p-i-n architecture. It shows high diode resistivity of 1012 ohm·cm in reverse-bias regime leading to a high x-ray detecting sensitivity up to 0.276 C Gyair -1 cm-3. Such high signal is collected by the built-in potential underpinning operation of primary photocurrent device with robust operation. The detectors generate substantial x-ray photon-induced open-circuit voltages that offer an alternative detecting mechanism. Our findings suggest a new generation of x-ray detectors based on low-cost layered perovskite thin films for future x-ray imaging technologies.

An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening

BACKGROUND

The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work.

OBJECTIVE

In this paper, we present a solution to AATR based on such X-ray CT baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects).

METHODS

We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multi-class support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT baggage image datasets specifically collected for the AATR study.

RESULTS

Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%.

CONCLUSIONS

Our AATR shows the capabilities of adapting to varying types of materials, even the unknown materials which are not available in the training data, adapting to varying required probability of detection and adapting to varying scales of the threat object.

Development of Inert, Polymer-Bonded Simulants for Explosives Detection Systems Based on Transmission X-ray

Explosives detection systems (EDS) based on X-ray are used at airports to screen baggage for the presence of explosives. In Europe and the United States, EDS equipment is tested extensively by specialist test centres prior to approval for operational use in airports. Once EDS are installed in airports, however, it can be challenging to test the EDS equipment and verify that it continues to perform at the highest level, because of the impracticality of introducing bulk explosives into civil aviation airports. We have developed inert, non-toxic polymer-bonded simulants and validated them against real explosives using EDS equipment. The accuracy of our simulants is within 1% of the target bulk density, and within 2% of the target effective atomic number, and the materials have a stability of at least 4 years, with an uncertainty of 0.5%. The simulants generate alarms in almost 100% of cases on a wide range of commercial EDS models, and we consider the simulants fit for purpose for use during testing of EDS equipment at airports.

Identification method of EDXRD spectra for illicit substance detection

The energy-dispersive X-ray diffraction technique can be more practical and accurate for security applications such as detecting drugs and explosives. Here, an accurate multivariate discriminant analysis (MDA) method is used to identify the energy-dispersive X-ray diffraction spectra of illicit contraband. MDA is a comprehensive algorithm based on the principal component analysis algorithm, spectral angle matching method, and correlation coefficient method. Experiments are performed to acquire the diffracted spectra of drugs and common daily necessities. The accurate identification of models for an unknown substance can indicate the substance type in an already established database. Even in the case of shielding, the concealed object can be correctly identified, and the identification accuracy improved much compared with other algorithms.

Work design for airport security officers: Effects of rest break schedules and adaptable automation

This study investigated whether there is empirical support for the current EU regulation mandating breaks of at least 10 min after each period of 20 min continuously reviewing X-ray images in airport security screening. As a second goal, it examined whether providing more autonomy to airport security officers (in the form of spontaneous rest breaks and adaptable automation) would improve their performance and subjective state. Seventy-two student participants had to indicate the presence (or absence) of a threat item (either a gun or a knife) in a series of grey-scaled X-ray images of cabin baggage. Three work-rest schedules were examined: spontaneous breaks (i.e. participants could take breaks at any time), two 5-min breaks and two 10-min breaks during a 1-h testing session. Furthermore, half of the participants were assisted in their task by an adaptable support system offering three levels of automation: (1) no support, (2) cues indicating the presence of a potential threat item, and (3) cues indicating the exact location of a potential threat item. Results showed no performance differences between break regimes, which suggests that there may be viable alternatives to the current EU regulations. It also emerged that providing participants with adaptable automation did not lead to better detection performance but resulted in a less positive response bias than participants without automatic support. Implications for current aviation security regulations are discussed.

Combined X-ray diffraction and absorption tomography using a conical shell beam

We combine diffraction and absorption tomography by raster scanning samples through a hollow cone of pseudo monochromatic X-rays with a mean energy of 58.4 keV. A single image intensifier takes 90x90 (x,y) snapshots during the scan. We demonstrate a proof-of-principle of our technique using a heterogeneous three-dimensional (x,y,z) phantom (90x90x170 mm³) comprised of different material phases, i.e., copper and sodium chlorate. Each snapshot enables the simultaneous measurement of absorption contrast and diffracted flux. The axial resolution was ~1 mm along the (x,y) orthogonal scan directions and ~7 mm along the z-axis. The tomosynthesis of diffracted flux measurements enable the calculation of d-spacing values with ~0.1 Å full width at half maximum (FWHM) at ~2 Å. Thus the identified materials may be color-coded in the absorption optical sections. Characterization of specific material phases is of particular interest in security screening for the identification of narcotics and a wide range of homemade explosives concealed within complex "everyday objects." Other potential application areas include process control and biological imaging.

Confocal energy-dispersive X-ray diffraction tomography employing a conical shell beam

We introduce a new high-energy X-ray diffraction tomography technique for volumetric materials characterization. In this method, a conical shell beam is raster scanned through the samples. A central aperture optically couples the diffracted flux from the samples onto a pixelated energy-resolving detector. Snapshot measurements taken during the scan enable the construction of depth-resolved dark-field section images. The calculation of d-spacing values enables the mapping of material phase in a volumetric image. We demonstrate our technique using five ~15 mm thick, axially separated samples placed within a polymer tray of the type used routinely in airport security stations. Our method has broad analytical utility due to scalability in both scan size and X-ray energy. Additional application areas include medical diagnostics, materials science, and process control.

Experts EEG Cognitive Analysis and Substance Classification in Security Inspection

In recent years, the workload of security offers has increased along with the requirement of anti-terrorism. In the paper, a series of evaluation index of security inspection based on the EEG signals of the security officers were proposed to improve the accuracy of dangerous instances detection and decrease the workload of the officers. We performed an experiment to record the EEG data of security officers when they were watching the picture with or without the dangerous item in the uncovered and obscured scenes. Brain network analysis based on graph theory was applied to generate the indexes from the EEG induced by the parcel picture of security inspection, and is a new perspective on the classification of the parcel composition. The paper studied the low-frequency, multi-channel experts EEG signals, calculated the phase locking value (PLV) between every two channels to construct the topological functional brain network (FBN). The appropriate binary FBNs were obtained by setting the thresholds, and then the complex brain network parameters were estimated by the graph-theoretic methods, which were used for classification with 10-fold cross-validation and the average accuracy was 83.3[Formula: see text][Formula: see text][Formula: see text]97.78%. The method was effectively applied to the substance classification and would further improve the recognition accuracy of the target by combining this method with the existing detection technology.

Detecting Bombs in X-Ray Images of Hold Baggage: 2D Versus 3D Imaging

OBJECTIVE

This study compared the visual inspection performance of airport security officers (screeners) when screening hold baggage with state-of-the-art 3D versus older 2D imaging.

BACKGROUND

3D imaging based on computer tomography features better automated detection of explosives and higher baggage throughput than older 2D X-ray imaging technology. Nonetheless, some countries and airports hesitate to implement 3D systems due to their lower image quality and the concern that screeners will need extensive and specific training before they can be allowed to work with 3D imaging.

METHOD

Screeners working with 2D imaging (2D screeners) and screeners working with 3D imaging (3D screeners) conducted a simulated hold baggage screening task with both types of imaging. Differences in image quality of the imaging systems were assessed with the standard procedure for 2D imaging.

RESULTS

Despite lower image quality, screeners' detection performance with 3D imaging was similar to that with 2D imaging. 3D screeners revealed higher detection performance with both types of imaging than 2D screeners.

CONCLUSION

Features of 3D imaging systems (3D image rotation and slicing) seem to compensate for lower image quality. Visual inspection competency acquired with one type of imaging seems to transfer to visual inspection with the other type of imaging.

APPLICATION

Replacing older 2D with newer 3D imaging systems can be recommended. 2D screeners do not need extensive and specific training to achieve comparable detection performance with 3D imaging. Current image quality standards for 2D imaging need revision before they can be applied to 3D imaging.

Expertise, Automation and Trust in X-Ray Screening of Cabin Baggage

X-ray screening of passenger baggage is a key component in aviation security. The current study investigated how experts and novices performed in an X-ray baggage screening task while being assisted by an adaptable diagnostic aid. Furthermore, it examined how both groups operated and trusted this automated system. 30 experts (certified screeners) and 31 novices (students) had to indicate whether a target item (either a knife or a gun) was present in a series of X-ray images of cabin baggage. Half of the participants could choose between three different support levels of the diagnostic aid (DA): (1) no support, (2) a cue indicating the presence of a potential target without locating it, or (3) a cue indicating the presence of a potential target by surrounding it with a red frame. As expected, experts achieved higher detection performance (d'), were more self-confident and felt more competent in achieving the task than novices. Furthermore, experts experienced less time pressure and fatigue. Although both groups used the DA in a comparable way (in terms of support level used and frequency of level switches), results showed a performance increase for novices working with the DA compared to novices without support. This benefit of DA was not observed for experts. Interestingly, despite no difference in perceived trust ratings, experts were more compliant (i.e., following DA recommendations when it indicated the presence of a target) and reliant (i.e., following DA recommendations when it indicated the absence of a target) than novices. Altogether, the results of the present study suggested that novices benefited more from a DA than experts. Furthermore, compliance and reliance on DA seemed to depend on expertise with the task. Since experts should be better at assessing the reliability of the DA than novices, they may have used the DA as 'back-up' to confirm their decisions based on expertise (confirmatory function), while novices may have used it as a guide to base their decisions on (support function). Finally, trust towards a DA was associated with the degree to which participants found the DA useful.

Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability

The present study evaluated three automation modes for improving performance in an X-ray luggage screening task. One hundred and forty participants were asked to detect the presence of prohibited items in X-ray images of cabin luggage. Twenty participants conducted this task without automatic support (control group), whereas the others worked with either indirect cues (system indicated the target presence without specifying its location), or direct cues (system pointed out the exact target location) or adaptable automation (participants could freely choose between no cue, direct and indirect cues). Furthermore, automatic support reliability was manipulated (low versus high). The results showed a clear advantage for direct cues regarding detection performance and response time. No benefits were observed for adaptable automation. Finally, high automation reliability led to better performance and higher operator trust. The findings overall confirmed that automatic support systems for luggage screening should be designed such that they provide direct, highly reliable cues.Practitioner summary: The present study confirmed previous findings showing better detection performance in X-ray images of luggage when supported by automation providing direct, highly reliable cues. Furthermore, participants used adaptable automation only to select their preferred level of automation. This behaviour did not provide the benefits expected under adaptable automation.

Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection

Bomb attacks on civil aviation make detecting improvised explosive devices and explosive material in passenger baggage a major concern. In the last few years, explosive detection systems for cabin baggage screening (EDSCB) have become available. Although used by a number of airports, most countries have not yet implemented these systems on a wide scale. We investigated the benefits of EDSCB with two different levels of automation currently being discussed by regulators and airport operators: automation as a diagnostic aid with an on-screen alarm resolution by the airport security officer (screener) or EDSCB with an automated decision by the machine. The two experiments reported here tested and compared both scenarios and a condition without automation as baseline. Participants were screeners at two international airports who differed in both years of work experience and familiarity with automation aids. Results showed that experienced screeners were good at detecting improvised explosive devices even without EDSCB. EDSCB increased only their detection of bare explosives. In contrast, screeners with less experience (tenure < 1 year) benefitted substantially from EDSCB in detecting both improvised explosive devices and bare explosives. A comparison of all three conditions showed that automated decision provided better human-machine detection performance than on-screen alarm resolution and no automation. This came at the cost of slightly higher false alarm rates on the human-machine system level, which would still be acceptable from an operational point of view. Results indicate that a wide-scale implementation of EDSCB would increase the detection of explosives in passenger bags and automated decision instead of automation as diagnostic aid with on screen alarm resolution should be considered.

Recent Developments in Spectroscopic Techniques for the Detection of Explosives

Trace detection of explosives has been an ongoing challenge for decades and has become one of several critical problems in defense science; public safety; and global counter-terrorism. As a result, there is a growing interest in employing a wide variety of approaches to detect trace explosive residues. Spectroscopy-based techniques play an irreplaceable role for the detection of energetic substances due to the advantages of rapid, automatic, and non-contact. The present work provides a comprehensive review of the advances made over the past few years in the fields of the applications of terahertz (THz) spectroscopy; laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy; and ion mobility spectrometry (IMS) for trace explosives detection. Furthermore, the advantages and limitations of various spectroscopy-based detection techniques are summarized. Finally, the future development for the detection of explosives is discussed.

Study of the adhesion of explosive residues to the finger and transfer to clothing and luggage

It is important to understand the extent of transfer of explosive particles to different surfaces in order to better evaluate potential cross-contamination by explosives in crowded security controls such as those at airports. This work investigated the transfer of nine explosive residues (ANFO, dynamite, black powder, TNT, HMTD, PETN, NH₄NO₃, KNO₃, NaClO₃) through fingerprints from one surface to another. First, the extent of adhesion of explosive residues from different surfaces to the bare finger, nitrile and latex gloves was studied. Then, the transfer of explosive residues from one surface to another through fingerprints was investigated. Cotton fabric (hereinafter referred to as cotton) as clothing material and polycarbonate plastic (hereinafter referred to as polycarbonate) as luggage material were chosen for the experiments. These surfaces containing explosive particles were imaged using a reflex camera before and after the particles were transferred. Afterwards the images were processed in MATLAB where pixels corresponding to explosive residues were quantified. Results demonstrated that transfer of explosive residues frequently occurred with certain differences among materials. Generally, the amount of explosive particles adhered to the finger decreased in the following order: skin>latex>nitrile, while the transfer of particles from the finger to another surface was the opposite. The adhesion of explosive residues from polycarbonate to the finger was found to be better compared to cotton, while the amount of particles transferred to cotton was higher.

Highly selective detection of 2,4,6-trinitrophenol by using newly developed terbium-doped blue carbon dots

The detection of nitroaromatic explosives is of great importance owing to their strong explosive power and harmfulness in terms of the environment, homeland security and public safety. Herein, rare earth-doped carbon dots with multifunctional features were firstly prepared by simply keeping the mixture of terbium(iii) nitrate pentahydrate and citric acid at 190 °C for 30 min. The as-prepared terbium doped carbon dots (Tb-CDs), through a rapid and simple direct carbonization route, have a size of about 3 nm, and exhibit excitation wavelength dependent emission of blue fluorescence, are stable, and can be applied for the selective and colorimetric detection of 2,4,6-trinitrophenol (TNP) in the range of 500 nM-100 μM with a limit of detection of 200 nM based on the inner filtering effect (IFE) of the excitation and emission bands of Tb-CDs by TNP and the electron transfer (ET) from Tb-CDs to TNP, giving a precise and highly reproducible result for detecting complex water samples.