2019 |
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Esper-Chaín Falcón, R., Medina Escuela, A., Sendra Sendra, J. R., & Fariña Santana, D. (2019). Subsampling techniques for gaussian laser beam radius estimation using a configurable quadrant photodiode (CQD). Optics & Laser Technology, 109, 412–417.
Abstract: Gaussian laser beam diameter can be estimated using quadrant photodiodes and x-y micropositioners. The resolution of the measurement reported depends on the resolution of the micropositioners. In this article, an improvement of the resolution using subsampling estimation is reported. This technique in combination with configurable quadrant photodiodes allow to avoid the use of micropositioners, easing the design of ultra-compact laser beam displacement measurement systems avoiding mechanical elements.
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Fabelo, H., Halicek, M., Ortega, S., Szolna, A., Morera, J., Sarmiento, R., et al. (2019). Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients. Proceedings Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 10951.
Abstract: Brain cancer surgery has the goal of performing an accurate resection of the tumor and preserving as much as possible the quality of life of the patient. There is a clinical need to develop non-invasive techniques that can provide reliable assistance for tumor resection in real-time during surgical procedures. Hyperspectral imaging (HSI) arises as a new, noninvasive and non-ionizing technique that can assist neurosurgeons during this difficult task. In this paper, we explore the use of deep learning (DL) techniques for processing hyperspectral (HS) images of in-vivo human brain tissue. We developed a surgical aid visualization system capable of offering guidance to the operating surgeon to achieve a successful and accurate tumor resection. The employed HS database is composed of 26 in-vivo hypercubes from 16 different human patients, among which 258,810 labelled pixels were used for evaluation. The proposed DL methods achieve an overall accuracy of 95% and 85% for binary and multiclass classifications, respectively. The proposed visualization system is able to generate a classification map that is formed by the combination of the DL map and an unsupervised clustering via a majority voting algorithm. This map can be adjusted by the operating surgeon to find the suitable configuration for the current situation during the surgical procedure.
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Fabelo, H., Ortega, S., Szolna, A., Bulters, D., Piñeiro, J. F., Kabwama, S., et al. (2019). In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection. IEEE Access, 7, 39098–39116.
Abstract: The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository.
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Fei, M. H., Himar Fabelo, Samuel Ortega, James V. Little, Xu Wang, Amy Y. Chen, et al. (2019). Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth.10951.
Abstract: Head and neck squamous cell carcinoma (SCCa) is primarily managed by surgical resection. Recurrence rates after surgery can be as high as 55% if residual cancer is present. In this study, hyperspectral imaging (HSI) is evaluated for detection of SCCa in ex-vivo surgical specimens. Several methods are investigated, including convolutional neural networks (CNNs) and a spectral-spatial variant of support vector machines. Quantitative results demonstrate that additional processing and unsupervised filtering can improve CNN results to achieve optimal performance. Classifying regions that include specular glare, the average AUC is increased from 0.73 [0.71, 0.75 (95% confidence interval)] to 0.81 [0.80, 0.83] through an unsupervised filtering and majority voting method described. The wavelengths of light used in HSI can penetrate different depths into biological tissue, while the cancer margin may change with depth and create uncertainty in the ground-truth. Through serial histological sectioning, the variance in cancer-margin with depth is also investigated and paired with qualitative classification heat maps using the methods proposed for the testing group SCC patients.
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Halicek, M., Fabelo, H., Ortega, S., Callico, G. M., & Fei, B. (2019). In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer. Cancers, 11(6).
Abstract: In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed. The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology.
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Lentaris, G., Maragos, K., Soudris, D., Capua, F. D., Campajola, L., Campajola, M., et al. (2019). TID Evaluation System With On-Chip Electron Source and Programmable Sensing Mechanisms on FPGA. IEEE Transactions on Nuclear Science, 66(1), 312–319.
Abstract: Commercial-off-the-shelf system-on-chip field-programmable gate arrays (FPGAs) are gaining momentum in space applications and their radiation characterization becomes crucial. This paper targets a cost-effective test setup and procedures for total ionization dose testing of miniaturized complex devices. We combine hardware and software techniques to perform on-chip irradiation via a 90Sr/90Y electron source and assess in detail the degradation of complex SRAM FPGAs. Our methodology combines multiple sensing mechanisms with correlation analysis to facilitate modeling of effects and tuning of a custom ring-oscillator network for future autonomous in-flight use. Focusing on 28-nm Zynq7000 devices, our results show Mrad total ionizing dose tolerance with parameters’ degradation in the area of 5%, zero functional errors, and nonuniform spatial response.
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Martos-Sitcha, J. A., Sosa, J., Ramos-Valido, D., Bravo, F. J., Carmona-Duarte, C., Gomes, H. L., et al. (2019). Ultra-Low Power Sensor Devices for Monitoring Physical Activity and Respiratory Frequency in Farmed Fish. Frontiers in Physiology, 10, 667.
Abstract: Integration of technological solutions aims to improve accuracy, precision and repeatability in farming operations, and biosensor devices are increasingly used for understanding basic biology during livestock production. The aim of this study was to design and validate a miniaturized tri-axial accelerometer for non-invasive monitoring of farmed fish with re-programmable schedule protocols. The current device (AE-FishBIT v.1s) is a small (14 x 7 x 7 mm), stand-alone system with a total mass of 600 mg, which allows monitoring animals from 30-35 g onwards. The device was attached to the operculum of gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax) juveniles for monitoring their physical activity by measurements of movement accelerations in x- and y-axes, while records of operculum beats (z-axis) served as a measurement of respiratory frequency. Data post-processing of exercised fish in swimming test chambers revealed an exponential increase of fish accelerations with the increase of fish speed from 1 body-length to 4 body-lengths per second, while a close relationship between oxygen consumption (MO2) and opercular frequency was consistently found. Preliminary tests in free-swimming fish kept in rearing tanks also showed that device data recording was able to detect changes in daily fish activity. The usefulness of low computational load for data pre-processing with on-board algorithms was verified from low to submaximal exercise, increasing this procedure the autonomy of the system up to 6 h of data recording with different programmable schedules. Visual observations regarding tissue damage, feeding behaviour and circulating levels of stress markers (cortisol, glucose, lactate) did not reveal at short term a negative impact of device tagging. Reduced plasma levels of triglycerides revealed a transient inhibition of feed intake in small fish (sea bream 50-90 g, sea bass 100-200 g), but this disturbance was not detected in larger fish. All this considered together is the proof of concept that miniaturized devices are suitable for non-invasive and reliable metabolic phenotyping of farmed fish to improve their overall performance and welfare. Further work is underway for improving the attachment procedure and the full device packaging.
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Ortega, S., Fabelo, H., Iakovidis, D. K., Koulaouzidis, A., & Callico, G. M. (2019). Use of Hyperspectral/Multispectral Imaging in Gastroenterology. Shedding Some–Different–Light into the Dark. Journal of Clinical Medicine, 8(1).
Abstract: Hyperspectral/Multispectral imaging (HSI/MSI) technologies are able to sample from tens to hundreds of spectral channels within the electromagnetic spectrum, exceeding the capabilities of human vision. These spectral techniques are based on the principle that every material has a different response (reflection and absorption) to different wavelengths. Thereby, this technology facilitates the discrimination between different materials. HSI has demonstrated good discrimination capabilities for materials in fields, for instance, remote sensing, pollution monitoring, field surveillance, food quality, agriculture, astronomy, geological mapping, and currently, also in medicine. HSI technology allows tissue observation beyond the limitations of the human eye. Moreover, many researchers are using HSI as a new diagnosis tool to analyze optical properties of tissue. Recently, HSI has shown good performance in identifying human diseases in a non-invasive manner. In this paper, we show the potential use of these technologies in the medical domain, with emphasis in the current advances in gastroenterology. The main aim of this review is to provide an overview of contemporary concepts regarding HSI technology together with state-of-art systems and applications in gastroenterology. Finally, we discuss the current limitations and upcoming trends of HSI in gastroenterology.
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Rodríguez, A., Santos, L., Sarmiento, R., & de la Torre, E. (2019). Scalable Hardware-Based On-Board Processing for Run-Time Adaptive Lossless Hyperspectral Compression. IEEE Access, 7, 10644–10652.
Abstract: Hyperspectral data processing is a computationally intensive task that is usually performed in high-performance computing clusters. However, in remote sensing scenarios, where communications are expensive, a compression stage is required at the edge of data acquisition before transmitting information to ground stations for further processing. Moreover, hyperspectral image compressors need to meet minimum performance and energy-efficiency levels to cope with the real-time requirements imposed by the sensors and the available power budget. Hence, they are usually implemented as dedicated hardware accelerators in expensive space-grade electronic devices. In recent years though, these devices have started to coexist with low-cost commercial alternatives in which unconventional techniques, such as run-time hardware reconfiguration are evaluated within research-oriented space missions (e.g., CubeSats). In this paper, a run-time reconfigurable implementation of a low-complexity lossless hyperspectral compressor (i.e., CCSDS 123) on a commercial off-the-shelf device is presented. The proposed approach leverages an FPGA-based on-board processing architecture with a data-parallel execution model to transparently manage a configurable number of resource-efficient hardware cores, dynamically adapting both throughput and energy efficiency. The experimental results show that this solution is competitive when compared with the current state-of-the-art hyperspectral compressors and that the impact of the parallelization scheme on the compression rate is acceptable when considering the improvements in terms of performance and energy consumption. Moreover, scalability tests prove that run-time adaptation of the compression throughput and energy efficiency can be achieved by modifying the number of hardware accelerators, a feature that can be useful in space scenarios, where requirements change over time (e.g., communication bandwidth or power budget).
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2018 |
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Bravhar, K., Martins, V., Santos, L., & Codinachs, D. M. (2018). BRAVE NG-MEDIUM FPGA reconfiguration through SpaceWire: example use case and performance analysis. In 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS) (pp. 135–141).
Abstract: On-board reconfiguration of FPGAs on a spacecraft offers the important advantage of allowing the reuse of the same hardware component for different functionalities during a mission. To be able to benefit from these features, a solution to cope with the challenges of using SRAMbased FPGAs in a radiation environment is required, and at the same time it is desired that the FPGA reconfiguration takes place at high speed. The BRAVE NG-MEDIUM FPGA offers a solution for this problem, by combining being radiation-hardened by design and reconfiguration of the programmable logic via a high-speed communication protocol, namely SpaceWire. In this paper we compare the performance of SpaceWire with Joint Test Action Group (JTAG) for NG-MEDIUM reconfiguration, in terms of the time elapsed when uploading a bitstream file from a Central Processing Unit (in our case from a computer) to the Field Programmable Gate Array (FPGA) and the time needed for uploading such bitstream and configuring the FPGA with the uploaded bitstream.
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