The purpose of this work is to investigate algorithms and hardware architectures for context-based statistical lossless compression of visual and data content using dynamically reconfigurable hardware to support optimal modelling strategies for each data and compression type. Entropy coding of the modelling output will be performed using a statically configured arithmetic coding engine. The current trend of network convergence where visual and data content are transmitted along the same physical channel suggests a technology capable of delivering optimal compression ratios and fast adaptation to the nature of the content will become increasingly important. These are the two key concepts that will drive this research effort. Context-based statistical compression differs fundamentally from dictionary-based compression as used in popular algorithms such as the ZIP family and it is recognised as being able to offer superior compression ratios to these. However, this has been only achieved with complex software algorithms that require considerable amounts of memory capacity and have very low throughputs in the range of thousands of CPU cycles per byte. This means that power-hungry Pentium 4 class microprocessors running at GHz rates are needed to provide the required computing power to run these advanced statistical algorithms and even these CPUs will find difficult to support applications such as telemedicine where still images, video and scientific data would require lossless real-time compression with high bandwidths. Other applications such as data, video and image transmission in space require the performance to be achieved in an energy and silicon efficient platform. To achieve the demands set by these applications we propose the first universal lossless compression hardware core combining context-based variable-order statistical modelling and arithmetic coding. At present, there are no practical hardware realisations of these techniques, since no satisfactory solutions have yet been proposed for a viable architecture.

The unique properties of light have made it central to our high-tech society. For example, our information-rich world is only enabled by the remarkable capacity of the fibre-optic network, where thin strands of glass are used to carry massive amounts of information around the globe as high-speed optical signals. Light also impacts areas of our society as diverse as laser-based manufacturing, solar energy, space-based remote sensing and even astronomy. One area where the properties of light open up otherwise-impossible capabilities is medicine. In ophthalmology for example, lasers are routinely used to perform surgery on the eye through corneal reshaping. This involves two different lasers. In the first step, a laser producing very short pulses of infrared light cuts a flap in the front surface of the eye to provide access. In the second step, another laser producing longer pulses of ultraviolet (UV) light sculpts the shape of the cornea and correct focusing errors. The flap is then folded back into place so that the cornea can heal. The two very-different laser systems in that example illustrate an important point: the effects of light on human tissues are highly-dependent on the specific properties of both the light and the tissues involved. To sculpt the cornea, the laser wavelength of 193 nm is in the deep UV region of the electromagnetic spectrum, much shorter than the visible range (380 - 740 nm) we are familiar with. This is because (unlike visible light) it is very efficiently absorbed by the cornea, so that essentially all the energy of the light is deposited at the surface. Thus only a very thin layer of tissue (a few microns thick) is removed, or "resected", with each pulse of light, facilitating very-precise shaping of the cornea and accurate adjustment of its focusing properties. 193 nm light can be generated by an ArF excimer gas laser, a >40 year-old technology producing a poor-quality low-brightness beam of light. This is suitable for corneal reshaping, but not for a range of other important therapies requiring higher-quality deep UV beams. Unfortunately, alternative ways to generate such short wavelengths are non-trivial, resulting in complex and expensive laser systems not suitable for widespread clinical uptake. U-care aims to address this gap by exploiting cutting-edge techniques in laser physics. We will develop new sources of deep UV light which will be highly compact, robust and low cost. We will develop ways to deliver this light precisely to tissues, and work to understand in detail the biophysical mechanisms involved. Our efforts will focus on new therapies that target some of the biggest challenges facing medicine: cellular-precision cancer surgery, and the emergence of drug-resistant "super-bugs". Importantly, U-care will involve engineers and physical scientists working in close collaboration with clinicians and biomedical scientists to verify that the therapies we develop are effective and safe. By doing so in an integrated manner, we will drive our deep-UV light therapies towards healthcare impact and widespread use in the clinic by 2050.

In the 1980s it began to be possible to produce potentially unlimited quantities of human proteins by placing the gene defining them in a simple organism such as yeast. From this grew a new kind of medicine capable of treating conditions such as severe arthritis, haemophilia, growth deficiency, and some cancers that previously had no satisfactory treatments. As well as having great clinical value the resulting technology has become the basis of a new and fastest growing part of the pharmaceutical industry, described as biopharmaceuticals. Because the molecules involved are proteins, they are orders of magnitude larger and more complex than conventional drugs such as aspirin and their processing is much more demanding. They are also so complex that they cannot in general be characterised with precision except in relation to the methods by which they are made. That means the capacity to precisely define such processes is critical to clinical safety and commercial success. Full scale trials of the processes are so costly they can only be conducted once clinical promise is established but, given the number of factors governing processing of even first generation products, there have often been hold-ups so extensive as to delay availability to patients. UCL has pioneered micro scale methods that are sufficiently good at predicting efficient conditions for large scale performance that far fewer and better focussed large scale trials suffice. That resolves part of the problem but an even greater challenge is now emerging. The early biopharmaceuticals were in general the easiest ones to produce. The final scales were also relatively modest. Now, the next generation of biopharmaceuticals are more complex materials and with rising demand the scales are far larger so that processes push the boundaries of the possible. The combined complexity of the product and the process with so many variables to consider means that the managers need better systematic means of supporting their decisions. Already the cost of developing a single biopharmaceutical can exceed 0.7 billion and take 10 years. With more advanced biopharmaceuticals these figures tend to rise and yet the world's governments are facing a healthcare cost crisis with more older people. They therefore exert pressure on companies to reduce prices. Because the public wishes to have medicines that do not pose risks, regulations become ever more stringent so they are a major factor in defining the bioprocess. This also adds to the need for managers to have sector-specific decisional-support aids well grounded in the detailed engineering of the processes. Finally, it is now possible to apply molecular engineering to proteins and vaccines to enhance their therapeutic properties but this can also cause serious bioprocessing problems. The research vision developed with detailed input from UK industry experts will apply these methods as the foundation for another step change whereby much faster and lower cost information can be gathered and integrated with advanced decisional techniques to give managers a better foundation on which to base their policies. The academic team from leading UK universities provides the necessary continuum of skills needed to assess the ease of manufacture of novel drugs, the costs of processing and of delivery to patients. We will work with companies to test the outcomes to ensure they are well proven prior to use on new biopharmaceuticals. This will cut costs so that all the patients who might benefit can receive them and at the earliest possible date achieved within the severely restricted budgets now available to the NHS.