Mora Lopez, C. et al. (2017) A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS. IEEE Transactions on Biomedical Circuits and Systems, 11(3), pp. 510-522. (doi:10.1109/TBCAS.2016.2646901) This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/139992/

Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion. Currently, no real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. While marker-based methods exist for the high-grade glioma case, there is no convenient solution available for the low-grade case; thus, marker-free optical techniques represent an attractive option. Although RGB imaging is a standard tool in surgical microscopes, it does not contain sufficient information for tissue differentiation. We leverage the richer information from hyperspectral imaging (HSI), acquired with a snapscan camera in the 468 − 787 nm range, coupled to a surgical microscope, to build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance. However, the main limitation of the HSI snapscan camera is the image acquisition time, limiting its widespread deployment in the operation theater. Here, we investigate the effect of HSI channel reduction and pre-selection to scope the design space for the development of cheaper and faster sensors. Neural networks are used to identify the most important spectral channels for tumor tissue differentiation, optimizing the trade-off between the number of channels and precision to enable real-time intra-surgical application. We evaluate the performance of our method on a clinical dataset that was acquired during surgery on five patients. By demonstrating the possibility to efficiently detect low-grade glioma, these results can lead to better cancer resection demarcations, potentially improving treatment effectiveness and patient outcome.

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This paper presents a memory organization for SDR inner modem baseband processors that focus on exploiting ILP. This memory organization uses power-efficient, single-ported, interleaved scratch-pad memory banks to provide enough bandwidth to a high-ILP processors. A system of queues in the memory interface is used to resolve bank conflicts among the single-ported banks, and to spread long bursts of conflicting accesses to the same bank over time. Bank address rotation is used to spread long bursts of conflicting accesses over multiple banks. All proposed techniques have been implemented in hardware, and are evaluated for a number of different wireless communication standards. For the 11a|n benchmarks, the overhead of stall cycles resulting from unresolved bank conflicts can be reduced to below 2% with the proposed organization. For 3GPP-LTE, the most demanding wireless standard we evaluated, the overhead is reduced to less than 0.13%. This is achieved with little energy and area overhead, and without any bank-aware compiler support.

Multispectral imaging technology analyzes for each pixel a wide spectrum of light and provides more spectral information compared to traditional RGB images. Most current Unmanned Aerial Vehicles (UAV) camera systems are limited by the number of spectral bands (≤10 bands) and are usually not fully integrated with the ground controller to provide a live view of the spectral data.We have developed a compact multispectral camera system which has two CMV2K 4x4 snapshot mosaic sensors internally, providing 31 bands in total covering the visible and near-infrared spectral range (460-860nm). It is compatible with (but not limited to) the DJI M600 and can be easily mounted to the drone. Our system is fully integrated with the drone, providing stable and consistent communication between the flight controller, the drone/UAV, and our camera payload. With our camera control application on an Android tablet connected to the flight controller, users can easily control the camera system with a live view of the data and many useful information including histogram, sensor temperature, etc. The system acquires images at a maximum framerate of 2x20 fps and saves them on an internal storage of 1T Byte. The GPS data from the drone is logged with our system automatically. After the flight, data can be easily transferred to an external hard disk. Then the data can be visualized and processed using our software into single multispectral cubes and one stitched multispectral cube with a data quality report and a stitching report.