Nicholas Bowring

A Review of Concealed Weapon Detection and Research in Perspective

This paper reviews recent developments in the area of concealed weapon detection using largely electromagnetic methods including metal detection, magnetic field distortion, electromagnetic resonance, acoustic and ultrasonic inspection, millimetre waves, Terahertz imaging, Infrared, X-ray etc. The advantages and disadvantages of these approaches are discussed. Research challenges are presented. Future research perspectives are presented and our joint research project has also been introduced.

A high-performance computing method for data allocation in distributed database systems

Enhancing the performance of the DDBs (Distributed Database system) can be done by speeding up the computation of the data allocation, leading to higher speed allocation decisions and resulting in smaller data redundancy and shorter processing time. This paper deals with an integrated method for grouping the distributed sites into clusters and customizing the database fragments allocation to the clusters and their sites. We design a high speed clustering and allocating method to determine which fragments would be allocated to which cluster and site so as to maintain data availability and a constant systemic reliability, and evaluate the performance achieved by this method and demonstrate its efficiency by means of tabular and graphical representation. We tested our method over different network sites and found it reduces the data transferred between the sites during the execution time, minimizes the communication cost needed for processing applications, and handles the database queries and meets their future needs.

A Review of Nonimaging Stand-Off Concealed Threat Detection with Millimeter-Wave Radar [Application Notes]

There is now, more than ever before, a need for technologies that enable the screening of people from a distance. A wide variety of weapons can be easily concealed under clothing and carried into crowded public sites to target national infrastructure, spread fear, and inflict mass murder and casualties. The most feared and devastating terrorist weapon is the suicide bomb or person borne improvised explosive device (PBIED). Such devices are relatively simple to conceal on the body, and successful detection is required at considerable distance or stand-off range before the bomber reaches the target area.

ON BODY CONCEALED WEAPON DETECTION USING A PHASED ANTENNA ARRAY

The detection and identiflcation of metal items and, in particular weapons, of linear size ‚ 10cm, concealed upon the human body, is demonstrated as being entirely feasible by using a phased array of suitably ultra wide band transceivers. The complex natural resonances and especially the fundamental resonance, are excited by ultra wide band, stepped frequency continuous wave illumination of the target, using a phased array of antennae to focus the radiation. Broadband illumination of the target with microwave radiation of suitable frequency range (Typically 0.3{3GHz for handgun sized objects) excites low order complex natural resonances and the late time response of the concealed item can be spatially located using phased array imaging techniques. Further processing of the late time response enables classiflcation of the concealed object, based on the complex natural resonant frequencies of the object, so that threat items such as handguns and knives can be difierentiated from benign items such as mobile phone handsets and cameras.

A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection

The incorporation of decision maker preferences is often neglected in the Evolutionary Multi-Objective Optimisation (EMO) literature. The majority of the research in the field and the development of EMO algorithms is primarily focussed on converging to a Pareto optimal approximation close to or along the true Pareto front of synthetic test problems. However, when EMO is applied to real-world optimisation problems there is often a decision maker who is only interested in a portion of the Pareto front (the Region of Interest) which is defined by their expressed preferences for the problem objectives. In this paper a novel preference articulation operator for EMO algorithms is introduced (named the Weighted Z-score Preference Articulation Operator) with the flexibility of being incorporated a priori, a posteriori or progressively, and as either a primary or auxiliary fitness operator. The Weighted Z-score Preference Articulation Operator is incorporated into an implementation of the Multi-Objective Evolutionary Algorithm Based on Decomposition (named WZ-MOEA/D) and benchmarked against MOEA/D-DRA on a number of bi-objective and five-objective test problems with test cases containing preference information. After promising results are obtained when comparing WZ-MOEA/D to MOEA/D-DRA in the presence of decision maker preferences, WZ-MOEA/D is successfully applied to a real-world optimisation problem to optimise a classifier for concealed weapon detection, producing better results than previously published classifier implementations.

A performance evaluation of statistical tests for edge detection in textured images

This work presents an objective performance analysis of statistical tests for edge detection which are suitable for textured or cluttered images. The tests are subdivided into two-sample parametric and non-parametric tests and are applied using a dual-region based edge detector which analyses local image texture difference. Through a series of experimental tests objective results are presented across a comprehensive dataset of images using a Pixel Correspondence Metric (PCM). The results show that statistical tests can in many cases, outperform the Canny edge detection method giving robust edge detection, accurate edge localisation and improved edge connectivity throughout. A visual comparison of the tests is also presented using representative images taken from typical textured histological data sets. The results conclude that the non-parametric Chi Square and Kolmogorov Smirnov statistical tests are the most robust edge detection tests where image statistical properties cannot be assumed a priori or where intensity changes in the image are nonuniform and that the parametric Difference of Boxes test and the Student’s t-test are the most suitable for intensity based edges. Conclusions and recommendations are finally presented contrasting the tests and giving guidelines for their practical use while finally confirming which situations improved edge detection can be expected.

Detection of concealed explosives at stand-off distances using wide band swept millimetre waves

Millimetre waves in the range 20 to 110 GHz have been used to detect the presence and thickness of dielectric materials, such as explosives, by measuring the frequency response of the return signal. Interference between the reflected signals from the front and back surfaces of the dielectric provides a characteristic frequency variation in the return signal, which may be processed to yield its optical depth [Bowring et al, Meas. Sci. Technol. 19, 024004 (2008)]. The depth resolution depends on the sweep bandwidth, which is typically 10 to 30 GHz. By using super-heterodyne detection the range of the object can also be determined, which enables a signal from a target, such as a suicide bomber to be extracted from background clutter. Using millimetre wave optics only a small area of the target is illuminated at a time, thus reducing interference from different parts of a human target. Results are presented for simulated explosive materials with water or human backing at stand-off distances. A method of data analysis that involves pattern recognition enables effective differentiation of target types.

A swept millimeter-wave technique for the detection of concealed weapons and thin layers of dielectric material with or without fragmentation

Active millimetre wave systems, operating at frequencies up to 110 GHz have been used to detect the presence of both concealed dielectric and metallic objects at standoff distances. Co- and cross-polarized superheterodyne or direct detectors are used to differentiate between metallic and purely dielectric objects. The technique determines the thickness of a dielectric target and detects the presence of concealed handguns or fragmentation by utilising the pattern of the responses from both the co- and cross-polarized detectors. The returned signals are processed and analysed by an artificial neural network, which classifies the responses according to their correspondence to previous training data.

A MICROWAVE MEASUREMENT SYSTEM FOR METALLIC OBJECT DETECTION USING SWEPT-FREQUENCY RADAR

Guns and knives have become a significant threat to public safety. Recently, a variety of techniques based on Electromagnetics (EM) have been used for their detection. For example, walk-through metal detection has been used in airports; X-ray and THz detection systems have been used for luggage screening. Different EM frequencies for metallic object detection have demonstrated different merits. This paper reports on a 1-14 GHz swept-frequency radar system for metallic object detection using reflection configuration. The swept frequency response and resonant frequency behaviour of a number of metallic objects, in terms of position, object shape, rotation and multiple objects have been tested and analysed. The system working from 1 to 14 GHz has been set up to implement sensing of metal items at a standoff distance of more than 1 meter. Through a series of experimental investigations, it can be found that the optical depths derived from the Fourier Transform of the power spectrum profile is in close relation with the relative location of the metallic object. The cross correlation between coherence-polarisation and cross-polarisation RF returns can be used to distinguish different objects. Therefore the optical depth and the cross correlation can be used as useful features for metallic object detection and characterisation in this portion of the microwave frequency spectrum.

Ultra wide band detection of on body concealed weapons using the out of plane polarized late time response

A method of detecting concealed handguns and knives, both on and off body, has been developed. The method utilizes aspect-independent natural, complex resonances (poles) excited by illuminating the target with frequency swept, ultrawide band microwaves in the range 0.5 - 18 GHz. These natural resonances manifest as a Late Time Response (LTR) that extends significantly (~ 5 ns) beyond the direct reflections from the human body (the Early Time Response) and are of the form of a superposition of exponentially decaying sinusoidal waveforms. Two handguns are examined, both on the human body and in isolation, by the established methodology of applying the Generalised-Pencil-Of-Function to the late time response data of the target. These poles allow the weapon to be effectively classified. Out of plane polarized (cross-polarized) scattered response is used here as this gives improved discrimination between the early and late time responses. Determination of the presence or absence of particular weapons concealed under clothing, on the human body, is demonstrated. A novel bow-tie slot antenna is described which has good pulse and frequency response over the range 0.3-1 GHz and which is suitable for excitation of the fundamental natural resonances.