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The present report is dedicated to a systematic comparison of crystal structures produced by the nanobiofilm template method and by the classical hanging-drop vapour-diffusion method. Crystals grown by the innovative nanostructured template method appear indeed radiation-resistant even in the presence of a third-generation highly focused beam at the European Synchrotron Radiation Facility. The implications of this finding for protein crystallography are discussed here in terms of water redistribution and of the detailed atomic resolution comparative studies of the two crystal structures with or without nanobiofilm template, as emerging also from circular-dichroism and thermal denaturation studies.

We present the DNA analyzer (DNASER), a novel bioinstrumentation for real-time acquisition and elaboration of images from fluorescent DNA microarrays. A white light beam illuminates the target sample allowing the images grabbing on a high sensibility and wide-band charge-coupled device camera (ORCA II-Hamamatsu). This high-performance device permits to acquire images faster and of higher quality than the traditional systems. The DNA microarrays images are processed to recognize the DNA chip spots, to analyze their superficial distribution on the glass slide and to evaluate their geometric and intensity properties. Differently form conventional techniques, the spots analysis is fully automated and the DNASER does not require any additional information about the DNA microarray geometry. The DNASER hardware and software architecture is illustrated. Preliminary results are shown from experiments performed on real DNA samples.

We describe a novel system for grading oranges into three quality bands, according to their surface characteristics. The system is designed to process fruit with a wide range of size (55–100 mm), shape (spherical to highly eccentric), surface coloration and defect markings. This application requires both high throughput (5–10 oranges per second) and complex pattern recognition. The grading is achieved by simultaneously imaging each item of fruit from six orthogonal directions as it is propelled through an inspection chamber. In order to achieve the required throughput, the system contains state-of-the-art processing hardware, a novel mechanical design, and three separate algorithmic components. One of the key improvements in this system is a method for recognising the point of stem attachment (the calyx) so that it can be distinguished from defects. A neural network classifier on rotation invariant transformations (Zernike moments) is used to recognise the radial colour variation that is shown to be a reliable signature of the stem region. The succession of oranges processed by the machine constitute a pipeline, so time saved in the processing of defect free oranges is used to provide additional time for other oranges. Initial results are presented from a performance analysis of this system.

X-ray radiation damage of lysozyme single crystals by an intense monochromatic beam from a focussed third-generation synchrotron radiation source has been studied. The preliminary results show a significantly higher resistance to synchrotron radiation of lysozyme microcrystals produced by means of nanotechnology-based template with respect to those prepared by classical methodology. The implications of this finding for protein crystallography are discussed.

We describe a novel system for grading oranges into three quality bands, according to their surface characteristics. This processing operation is currently the only non-automated step in citrus packing houses. The system must handle fruit with a wide range of size (55-100 mm), shape (spherical to highly eccentric), surface coloration and defect markings. Furthermore, the point of stem attachment (the calyx) must be recognised in order to distinguish it from defects. A neural network classifier on rotation invariant transformations (Zernike moments) is used to recognise radial colour variation, that is shown to be a reliable signature of the stem region. This application requires both high throughput (5-10 oranges per second) and complex pattern recognition. Three separate algorithmic components are used to achieve this, together with state-of-the-art processing hardware and novel mechanical design. The grading is achieved by simultaneously imaging the fruit from six orthogonal directions as they are propelled through an inspection chamber. In the first stage processing colour histograms from each view of an orange are analysed using a neural network based classifier. Views that may contain defects are further analysed in the second stage using five independent masks and a neural network classifier. The computationally expensive stem detection process is then applied to a small fraction of the collected images. The succession of oranges constitute a pipeline, and, time saved in the processing of defect free oranges is used to provide additional time for other oranges. Initial results are presented from a performance analysis of this system.

We describe the system control architecture of a large orange harvesting robot. This robot has two independent electrically driven telescopic arms mounted on a common platform which is itself held by a large hydraulic arm. This arm, in turn, is mounted on a tracked vehicle. The telescopic arms have cameras within the end-effecters, which are used to detect and measure the position and distance of the fruit within the canopy of a tree. Most of the development and control software was implemented using the matrix-based Virtual Machine Language (VML). This language was designed to implement neural networks, and has been extended and enhanced for robotic applications and the particular low level control requirements of the hardware. The device drivers provide the interface to frame grabbers, motor drivers, digital interface electronics, proximity detectors, and file handling. The same interface is used to implement interprocess communications with display and monitoring tools.