The device could identify veins that were invisible to the naked eye and too shallow for ultrasound detection. The V-V-P may help find feeder veins and may also help various types of vein treatments.

Waste management processes generally represent a significant loss of material, energy and economic resources, so legislation and financial incentives are being implemented to improve the recovery of these valuable resources whilst reducing contamination levels. Material recovery and waste derived fuels are potentially valuable options being pursued by industry, using mechanical and biological processes incorporating sensor and sorting technologies developed and optimised for recycling plants. In its current state, waste management presents similarities to other industries that could improve their efficiencies using process analytical technology tools. Existing sensor technologies could be used to measure critical waste characteristics, providing data required by existing legislation, potentially aiding waste treatment processes and assisting stakeholders in decision making. Optical technologies offer the most flexible solution to gather real-time information applicable to each of the waste mechanical and biological treatment processes used by industry. In particular, combinations of optical sensors in the visible and the near-infrared range from 800nm to 2500nm of the spectrum, and different mathematical techniques, are able to provide material information and fuel properties with typical performance levels between 80% and 90%. These sensors not only could be used to aid waste processes, but to provide most waste quality indicators required by existing legislation, whilst offering better tools to the stakeholders.

A vein contrast enhancer (VCE) has been constructed to make vein access easier by capturing an infrared image of veins, enhancing the contrast using software, and projecting the vein image back onto the skin. The VCE also uses software to align the projected image with the vein to 0.06 mm. Clinical evaluation of earlier monitor-based vein enhancement test systems has demonstrated the clinical utility of the infrared imaging technology used in the VCE.

Increasing the rate of material identification, separation and recovery is a priority in resource management and recovery, and rapid, low cost imaging and interpretation is key. This study uses different combinations of cameras, illuminations and data augmentation techniques to create databases of images to train deep neural networks for the recognition of fibre materials. Using a limited set of 24 material samples sized 1,200 cm 2 , it compares the outcome of reducing them to 30 cm 2. The best classification accuracies obtained range from 76.6% to 77.5% indicating it is possible to overcome problems such as limited available materials, time, or storage capabilities, by using a setup with 5 cameras, 5 lights and applying simple software image manipulation techniques. The same method can be used to create deep neural network training databases to recognise a wider range of materials typically found in solid waste streams, in real-time. Furthermore, it offers flexibility as the classification cameras could be deployed at different stages within solid waste processing plants, providing feedback for process control, with the potential of increasing plant efficiency and reducing costs.

A proof-of-principle prototype Vein Contrast Enhancer (VCE) has been designed and constructed. The VCE is an instrument that makes vein access easier by capturing an infrared image of peripheral veins, enhancing the vein-contrast using software image processing, and projecting the enhanced vein-image back onto the skin using a modified commercial projector. The prototype uses software alignment to achieve alignment accuracy between the captured infrared image and the projected visible image of better than 0.06 mm. Figure 1 shows the prototype demonstrated in our laboratory.