Daniel Madroñal

Daniel Madroñal
Università degli Studi di Sassari | UNISS · Department of Chemistry and Pharmacy

PhD

About

36
Publications
6,073
Reads
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396
Citations
Introduction
Daniel Madroñal currently works at University of Sassari as a postdoc researcher
Additional affiliations
October 2020 - February 2021
Università degli Studi di Sassari
Position
  • PostDoc Position

Publications

Publications (36)
Book
Full-text available
Contents:::: Lech Jozwiak, Radovan Stojanovic, Introduction >>> Ioannis Pitas, Privacy Protection, Ethics, Robustness and Regulatory Issues in Autonomous Systems >>> Lech Jozwiak, Design of Green CPS and IoT>>> Mario Kovac, European Processor Initiative: Cornerstone of European HPC and eHPC strategy >>> Nicola Capodieci, Timing predictability in GP...
Preprint
In the era of Cyber Physical Systems, designers need to offer support for run-time adaptivity considering different constraints, including the internal status of the system. This work presents a run-time monitoring approach, based on the Performance Application Programming Interface, that offers a unified interface to transparently access both the...
Article
Full-text available
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) platfo...
Article
The widening of the complexity-productivity gap in application development witnessed in the last years is becoming an important issue for the developers. New design methods try to automate most designers tasks to bridge this gap. In addition, new Models of Computation (MoCs), as those dataflow-based, ease the expression of parallelism within applic...
Chapter
Cyber-Physical Systems (CPS) are interconnected devices, reactive and dynamic to sensed external and internal triggers. The H2020 CERBERO EU Project is developing a design environment composed by modelling, deployment and verification tools for adaptive CPS. This paper focuses on its efficient support for run-time self-adaptivity.
Article
Full-text available
This paper presents a study of the adaptation of a Non-Linear Iterative Partial Least Squares (NIPALS) algorithm applied to Hyperspectral Imaging to a Massively Parallel Processor Array manycore architecture, which assembles 256 cores distributed over 16 clusters. This work aims at optimizing the internal communications of the platform to achieve r...
Conference Paper
In the era of Cyber Physical Systems, designers need to offer support for run-time adaptivity considering different constraints, including the internal status of the system. This work proposes a run-time monitoring approach for hardware accelerators, based on the Performance Application Programming Interface.
Article
Full-text available
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, hyperspectral medical data. The work described in this paper was developed within the framew...
Article
Full-text available
The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest...
Conference Paper
Abstract—In this work, a standard and unified method formonitoring hardware accelerators in Reconfigurable ComputingArchitectures is proposed, based on a standard software moni-toring interface.The open source Performance Application Programming In-terface (PAPI) library is commonly used in the field of HighPerformance Computing and aims at providi...
Article
Full-text available
Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer fromtheir computationally demanding nature, becoming advisable for their implementati...
Conference Paper
The widening of the complexity-productivity gap witnessed in the last years is becoming unaffordable from the application development point of view. New design methods try to automate most designers tasks in order to bridge this gap. In addition, new Models of Computation (MoC), as those dataflow-based, ease the expression of parallelism within app...
Article
Full-text available
Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. Howe...
Data
Confusion matrix results of the SVM supervised classification with polynomial kernel applying the 10-fold cross validation method to each patient. (DOCX)
Data
Confusion matrix results of the SVM supervised classification with linear kernel applying the 10-fold cross validation method to each patient. (DOCX)
Data
Confusion matrix results of the SVM supervised classification with RBF kernel applying the 10-fold cross validation method to each patient. (DOCX)
Data
Confusion matrix results of the SVM supervised classification with sigmoid kernel applying the 10-fold cross validation method to each patient. (DOCX)
Article
Full-text available
Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substan...
Article
This paper presents a study of the design space of a Support Vector Machine (SVM) classifier with a linear kernel running on a manycore MPPA (Massively Parallel Processor Array) platform. This architecture gathers 256 cores distributed in 16 clusters working in parallel. This study aims at implementing a real-time hyperspectral SVM classifier, wher...
Conference Paper
The HELICoiD project is a European FP7 FET Open funded project. It is an interdisciplinary work at the edge of the biomedical domain, bringing together neurosurgeons, computer scientists and electronic engineers. The main target of the project was to provide a working demonstrator of an intraoperative image-guided surgery system for real-time brain...
Article
This paper presents a study of the parallelism of a Principal Component Analysis (PCA) algorithm and its adaptation to a manycore MPPA (Massively Parallel Processor Array) architecture, which gathers 256 cores distributed among 16 clusters. This study focuses on porting hyperspectral image processing into manycore platforms by optimizing their proc...
Article
Introduction: Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. In this research work, a multidisciplinary team, made up of pathologists and engineers, presents a proof of concept on the use of HSI analysis in order to automatically detect human brain tumour tissue from pathological slides. The samples were acquired from...
Conference Paper
Full-text available
In this paper, a demonstrator of three different elements of the EU FET HELICoiD project is introduced. The goal of this demonstration is to show how the combination of hyperspectral imaging and machine learning can be a potential solution to precise real-time detection of tumor tissues during surgical operations. The HELICoiD setup consists of two...
Conference Paper
Hyperspectral Imaging (HI) collects high resolution spectral information consisting of hundred of bands raging from the infrared to the ultraviolet wave lengths. In the medical field, specifically, in the cancer tissue identification at the operating room, the potential of HI is huge. However, given the data volume of HI and the computational compl...
Conference Paper
Hyperspectral imaging (HI) collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. Although this technology was initially developed for remote sensing and earth observation, its multiple advantages - such as high spectral resolution - led to its application in other fields, as cancer detection. However,...

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Projects

Projects (3)
Project
http://www.cerbero-h2020.eu/ The Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments (CERBERO) project aims at developing a design environment for CPS based of two pillars: a cross-layer model based approach to describe, optimize, and analyze the system and all its different views concurrently; an advanced adaptivity support based on a multi-layer autonomous engine. To overcome the limit of current tools, CERBERO provides: libraries of generic Key Performance Indicators for reconfigurable CPSs in hybrid/uncertain environments; novel formal and simulation-based methods; a continuous design environment guaranteeing early-stage analysis and optimization of functional and non-functional requirements, including energy, reliability and security.
Project
Detection of brain tumors invivo using hyperspectral imaging
Project
Main objective is to use hyperspectral imaging for real-time identification of tumour margins during surgery, helping surgeons to extract the entire tumour and to spare as much of the healthy tissue as possible. The main contribution from my group (GDEM-CITSEM-UPM) is the implementation of the different hyperspectral image processing and classification algorithms in high-performance many-core embedded computing systems for real-time detection of the cancerous tissue. http://helicoid.eu/