These are challenges 26-30 identified by EPSRC and DSTL as part of their Signal Processing Call. The closing date is 29th October 2008.

CHALLENGE #26: To develop signal processing to optimise acoustic communications for mobile platforms in urban and littoral environments which acts as a limiter to high capacity communications.

How can high capacity acoustic communications be optimised in a limiting environment? The constraints lie with the limited and intermittent information that is made available in the environment and the connectivity between 'disadvantaged' users. Future joint capability depends crucially on reliable, secure and timely communications and the availability of high capacity communication networks. The requirement is for optimised communications between 'disadvantaged' users in challenging environments without compromising stealth. This include underwater communications for autonomous vehicles and submarines, and communications between underwater and air platforms.

CHALLENGE #27: To develop data processing to enhance target features in image recognition tasks.

How can object or event identification be optimised using feature enhancement? There is a known difficulty, beyond current feature enhancement capability, in selecting features that are not pre-defined in order to enhance only the recognition of the object. There is a requirement in sidescan sonar images and/or synthetic aperture sonar (SAS) images for increased resolution, region segmentation, sidelobe reduction and speckle suppression. Sensors are increasingly improving in resolution but the underwater environment still remains as hostile and the information content of the images remain poor. Ideally one would expect to employ autonomous vehicles armed with intelligent automatic target recognition algorithms to distinguish between potentially dangerous and non-threatening objects. Their success depends on how well sonar images exhibit certain features of the underlying scene and how those features are combined and presented to the classifier.

CHALLENGE #28: To develop locally invariant signal processing to discriminate between key man-made and natural features.

The difficulty in reliable identification of objects and discrimination between targets and nontargets and across classes of targets under different conditions is selecting features that are invariant (or tolerant) to identify-preserving changes such as position, size, background or viewing conditions. Most features fail to be both discriminatory and robust while the problem is faced routinely in minehunting and weapons sonar, and indeed in everyday life. Texture classification based on fractals could offer a means to discriminate between features such as edges and uniform regions which can be used at different resolutions to separate man-made objects from the natural environment. When there are limited features available or the background has time-varying characteristics it can be difficult to extract suitable features e.g. in noisy sidescan sonar images. Fractional calculus can be utilised to detect weak signals and it becomes a requirement to understand how different fractional orders could offer an optimal solution to extracting key features.

CHALLENGE #29: To analyse an element specific spectral profile and identify the most likely material present.

Neutron activation spectroscopy is used to provide non-invasive classification of materials through a range of barriers. A wide angle neutron beam is used to induce nuclear transitions in the materials present which give rise to element specific gamma-ray photons. Using timing information, elemental spectra are collected for each volume element (voxel) covering the target volume. Currently, these spectra are first matched to library profiles for each element (e.g. carbon, nitrogen, oxygen) to produce an elemental signature. This elemental signature is then classified against a second library to identify the material present. The challenge is to derive a more efficient and robust technique for material classification that will also provide confidence levels on the material predicted.

CHALLENGE #30: To automatically segment complex X-ray images into object outlines.

X-ray transmission imaging is used in aviation security to screen passenger baggage for threat objects. Many algorithms exist (Sobel, Canny, Phase Congruency) that will extract edges and other features from an image. Currently, segmented multi-view X-ray images are being investigated for 3D image reconstruction, where the accuracy of the reconstruction depends on the effectiveness of the edge detection algorithm. The challenge is to derive an alternative edge detection approach (not necessarily a single algorithm) that can improve the accuracy of 3D image reconstruction.