Horacio Lamela Rivera

CORDIC algorithms for SVM FPGA implementation

Support Vector Machines are currently one of the best classification algorithms used in a wide number of applications. The ability to extract a classification function from a limited number of learning examples keeping in the structural risk low has demonstrated to be a clear alternative to other neural networks. However, the calculations involved in computing the kernel and the repetition of the process for all support vectors in the classification problem are certainly intensive, requiring time or power consumption in order to function correctly. This problem could be a drawback in certain applications with limited resources or time. Therefore simple algorithms circumventing this problem are needed. In this paper we analyze an FPGA implementation of a SVM which uses a CORDIC algorithm for simplifying the calculation of as specific kernel greatly reducing the time and hardware requirements needed for the classification, allowing for powerful in-field portable applications. The algorithm is and its calculation capabilities are shown. The full SVM classifier using this algorithm is implemented in an FPGA and its in-field use assessed for high speed low power classification.

A compact multi-wavelength optoacoustic system based on high-power diode lasers for characterization of double-walled carbon nanotubes (DWCNTs) for biomedical applications

During the last decade, Optoacoustic Imaging (OAI), or Optoacoustic Tomography (OAT), has evolved as a novel imaging technique based on the generation of ultrasound waves with laser light. OAI may become a valid alternative to techniques currently used for the detection of diseases at their early stages. It has been shown that OAI combines the high contrast of optical imaging techniques with high spatial resolution of ultrasound systems in deep tissues. In this way, the use of nontoxic biodegradable contrast agents that mark the presence of diseases in near-infrared (NIR) wavelengths range (0.75–1.4 um) has been considered. The presence of carcinomas and harmful microorganisms can be revealed by means of the fluorescence effect exhibited by biopolymer nanoparticles. A different approach is to use carbon nanotubes (CNTs) which are a contrast agent in NIR range due to their absorption characteristics in the range between 800 to 1200 nm. We report a multi-wavelength (870 and 905 nm) laser diode–based optoacoustic (OA) system generating ultrasound signals from a double-walled carbon nanotubes (DWCNTs) solution arranged inside a tissue-like phantom, mimicking the scattering of a biological soft tissue. Optoacoustic signals obtained with DWCNTs inclusions within a tissue-like phantom are compared with the case of ink-filled inclusions, with the aim to assess their absorption. These measurements are done at both 870 and 905 nm, by using high power laser diodes as light sources. The results show that the absorption is relatively high when the inclusion is filled with ink and appreciable with DWCNTs.

Optimization of a hardware implementation for pulse coupled neural networks for image applications

Pulse Coupled Neural Networks are a very useful tool for image processing and visual applications, since it has the advantages of being invariant to image changes as rotation, scale, or certain distortion. Among other characteristics, the PCNN changes a given image input into a temporal representation which can be easily later analyzed for pattern recognition. The structure of a PCNN though, makes it necessary to determine all of its parameters very carefully in order to function optimally, so that the responses to the kind of inputs it will be subjected are clearly discriminated allowing for an easy and fast post-processing yielding useful results. This tweaking of the system is a taxing process. In this paper we analyze and compare two methods for modeling PCNNs. A purely mathematical model is programmed and a similar circuital model is also designed. Both are then used to determine the optimal values of the several parameters of a PCNN: gain, threshold, time constants for feed-in and threshold and linking leading to an optimal design for image recognition. The results are compared for usefulness, accuracy and speed, as well as the performance and time requirements for fast and easy design, thus providing a tool for future ease of management of a PCNN for different tasks.

Wavelength tuning of polymer optical fibre Bragg grating at longer wavelengths permanently

Permanent Bragg wavelength tuning of polymer optical fibre Bragg grating (POFBG) at longer wavelengths has been demonstrated for the first time utilising the thermal annealing process. In general, exposing the polymer material above its β-transition temperature, the fibre shrinks in length, the Bragg grating period becomes shorter and the Bragg wavelength shifts permanently to shorter wavelengths. In this work, a positive tuning of Bragg wavelength has been shown to be feasible when the polymer fibre is stretched during its thermal exposure. The results show that the degree of Bragg wavelength tuning strongly depends on the applying fibre strain or equivalently stress. The work presented in this paper can be used to multiplex POFBGs at any desirable wavelength.

Improvement of signal-to-noise ratio of optoacoustic signals from double-walled carbon nanotubes by using an array of dual-wavelength high-power diode lasers

Optoacoustic (OA) imaging is a rising biomedical technique that has attracted much interest over the last 15 years. This technique permits to visualize the internal soft tissues in depth by using short laser pulses, able to generate ultrasonic signals in a large frequency range. It combines the high contrast of optical imaging with the high resolution of ultrasound systems. The OA signals detected from the whole surface of the body serve to reconstruct in detail the image of the internal tissues, where the absorbed optical energy distribution outlines the regions of interest. In fact, the use of contrast agents could improve the detection of growing anomalies in soft tissues, such as carcinomas. This work proposes the use of double-walled carbon nanotubes (DWCNTs) as a potential nontoxic biodegradable contrast agent applicable in OA to reveal the presence of malignant in-depth tissues in near infrared (NIR) wavelength range (0.75–1.4 μm), where the biological tissues are fairly transparent to optical radiation. A dual-wavelength (870 and 905 nm) OA system is presented, based on arrays of high power diode lasers (HPDLs) that generate ultrasound signals from a DWCNT solution embedded within a biological phantom. The OA signals generated by DWCNTs are compared with those obtained using black ink, considered to be a very good absorber at these wavelengths. The experiments prove that DWCNTs are a potential contrast agent for optoacoustic spectroscopy (OAS).

Modelling and characterization of photothermal effects assisted with gold nanorods in ex vivo samples and in a murine model

We discuss in this article the implementation of a laser-tissue interaction and bioheat-transfer 2-D finite-element model for Photothermal Therapy assisted with Gold Nanorods. We have selected Gold Nanorods as absorbing nanostructures in order to improve the efficiency of using compact diode lasers because of their high opto-thermal conversion efficiency at 808 and 850 nm. The goal is to model the distribution of the optical energy among the tissue including the skin absorption effects and the tissue thermal response, with and without the presence of Gold Nanorods. The heat generation due to the optical energy absorption and the thermal propagation will be computationally modeled and optimized. The model has been evaluated and compared with experimental ex-vivo data in fresh chicken muscle samples and in-vivo BALB/c mice animal model.