Several causes make brain cancer identification a challenging task for neurosurgeons during the surgical procedure. The surgeons’ naked eye sometimes is not enough to accurately delineate the brain tumor location and extension due to its diffuse nature that infiltrates in the surrounding healthy tissue. For this reason, a support system that provides accurate cancer delimitation is essential in order to improve the surgery outcomes and hence the patient’s quality of life. The brain cancer detection system developed as part of the “HypErspectraL Imaging Cancer Detection” (HELICoiD) European project meets this requirement exploiting a non-invasive technique suitable for medical diagnosis: the hyperspectral imaging (HSI). A crucial constraint that this system has to satisfy is providing a real-time response in order to not prolong the surgery. The large amount of data that characterizes the hyperspectral images, and the complex elaborations performed by the classification system make the High Performance Computing (HPC) systems essential to provide real-time processing. The most efficient implementation developed in this work, which exploits the Graphic Processing Unit (GPU) technology, is able to classify the biggest image of the database (worst case) in less than three seconds, largely satisfying the real-time constraint set to 1 minute for surgical procedures, becoming a potential solution to implement hyperspectral video processing in the near future.

The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~ $150\times$ with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system.

Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become necessary. This paper presents a comparison between the performances provided by five different HPC platforms while processing a spatial-spectral approach to classify HS images, assessing their main benefits and drawbacks. To provide a complete study, two different medical applications, with two different requirements, have been analyzed. The first application consists of HS images taken from neurosurgical operations; the second one presents HS images taken from dermatological interventions. While the main constraint for neurosurgical applications is the processing time, in other environments, as the dermatological one, other requirements can be considered. In that sense, energy efficiency is becoming a major challenge, since this kind of applications are usually developed as hand-held devices, thus depending on the battery capacity. These requirements have been considered to choose the target platforms: on the one hand, three of the most powerful Graphic Processing Units (GPUs) available in the market; and, on the other hand, a low-power GPU and a manycore architecture, both specifically thought for being used in battery-dependent environments.

Today, mechanical tracking systems are becoming increasingly compact, enabling a new range of civil and military applications. These include aerial laser scanning, for which Risley prisms are used. In Risley systems, the so-called inverse problem, which focuses on obtaining the angles of the prisms for a given target coordinate, has not yet been solved mathematically. As a consequence, approximate approaches have been used, but the solutions obtained have significant errors and a lack of precision. To improve accuracy, iterative methods, which are computationally intensive, have also been implemented. In this paper, an analytical process which we call the geometric method is presented, and we verified that this strategy highly improves accuracy and computational speed. Using this method in an iterative process gives accuracies of up to 1 pm in only three iterations. This high accuracy would allow the geometric method to be applied in fields such as lithography, stereolithography, or 3D printing.

This article presents a wide range inductance–capacitance voltage controlled oscillator (VCO) with a unit cells-based varactor. The unit cell represents the minimum possible integrated varactor based on p–n junction diodes, where N⁺ diffusions are central rectangles, surrounded by doughnut shaped P⁺ diffusions, with their respective contacts. The varactors are designed using the AMS 0.35 µm BiCMOS process. A physical model has been derived from the measurement of a set of eight fabricated varactors. Measurements indicate that the VCO, which is intended to be used in DVB-H, oscillates from 1.087 to 2.032 GHz, with a 61% tuning range. The phase noises of −124 dBc/Hz at 1 MHz offset and −108 dBc/Hz at 100 kHz offset are obtained.