Real-time classification of coffee fruits using FPGA

The goal in this work was to design a circuit that could classify objects by color in real-time that can be used for quality improvement. A circuit that performs color analysis of an image and, according to that analysis, classifies the object was designed. A histogram of the Spherical Coordinate Transform of the image is computed and compared to histogram patterns to make a classification decision. The circuit was tested on the classification of coffee fruits in four maturity stages: immature, under-mature, mature and over-mature. The results showed that it is possible to build a system for color object classification that works in real-time and that can be affordable and portable. The designed circuit is implemented on a Field Programmable Gate Array (FPGA), acquires video at 64 frames per second, classifies the coffee fruits at a rate of 25 fruits per second and achieved an average efficacy of 75.7%Abstract : El objetivo de este trabajo fue diseñar un circuito que lograra clasificar objetos basado en su color funcionando en tiempo real y que pueda ser usado en aplicaciones de control de calidad. Se diseñó un circuito que toma una imagen, analiza sus características de color y, de acuerdo a este análisis, asigna el objeto a una categoría. La imagen se transforma del espacio RGB a coordenadas esféricas, se calcula el histograma de la imagen transformada y se compara con los patrones de cada categoría para realizar la asignación. El circuito fue usado para clasificar frutos de café en cuatro estados de maduración: inmaduro, pintón, maduro y sobremaduro. Los resultados mostraron que es posible construir un sistema que clasifique objetos basado en su color, que funcione en tiempo real y que además sea económico y portátil. El circuito diseñado adquiere video a una velocidad de 64 cuadros por segundo, clasifica frutos de café a una tasa de 25 frutos por segundo y obtuvo una eficacia promedio de 75.7%Doctorad

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

Heterogeneous computing systems for vision-based multi-robot tracking

Irwansyah A. Heterogeneous computing systems for vision-based multi-robot tracking. Bielefeld: Universität Bielefeld; 2017

Hardware acceleration using FPGAs for adaptive radiotherapy

Adaptive radiotherapy (ART) seeks to improve the accuracy of radiotherapy by adapting the treatment based on up-to-date images of the patient's anatomy captured at the time of treatment delivery. The amount of image data, combined with the clinical time requirements for ART, necessitates automatic image analysis to adapt the treatment plan. Currently, the computational effort of the image processing and plan adaptation means they cannot be completed in a clinically acceptable timeframe. This thesis aims to investigate the use of hardware acceleration on Field Programmable Gate Arrays (FPGAs) to accelerate algorithms for segmenting bony anatomy in Computed Tomography (CT) scans, to reduce the plan adaptation time for ART. An assessment was made of the overhead incurred by transferring image data to an FPGA-based hardware accelerator using the industry-standard DICOM protocol over an Ethernet connection. The rate was found to be likely to limit the performanceof hardware accelerators for ART, highlighting the need for an alternative method of integrating hardware accelerators with existing radiotherapy equipment. A clinically-validated segmentation algorithm was adapted for implementation in hardware. This was shown to process three-dimensional CT images up to 13.81 times faster than the original software implementation. The segmentations produced by the two implementations showed strong agreement. Modifications to the hardware implementation were proposed for segmenting fourdimensional CT scans. This was shown to process image volumes 14.96 times faster than the original software implementation, and the segmentations produced by the two implementations showed strong agreement in most cases.A second, novel, method for segmenting four-dimensional CT data was also proposed. The hardware implementation executed 1.95 times faster than the software implementation. However, the algorithm was found to be unsuitable for the global segmentation task examined here, although it may be suitable as a refining segmentation in the context of a larger ART algorithm.Adaptive radiotherapy (ART) seeks to improve the accuracy of radiotherapy by adapting the treatment based on up-to-date images of the patient's anatomy captured at the time of treatment delivery. The amount of image data, combined with the clinical time requirements for ART, necessitates automatic image analysis to adapt the treatment plan. Currently, the computational effort of the image processing and plan adaptation means they cannot be completed in a clinically acceptable timeframe. This thesis aims to investigate the use of hardware acceleration on Field Programmable Gate Arrays (FPGAs) to accelerate algorithms for segmenting bony anatomy in Computed Tomography (CT) scans, to reduce the plan adaptation time for ART. An assessment was made of the overhead incurred by transferring image data to an FPGA-based hardware accelerator using the industry-standard DICOM protocol over an Ethernet connection. The rate was found to be likely to limit the performanceof hardware accelerators for ART, highlighting the need for an alternative method of integrating hardware accelerators with existing radiotherapy equipment. A clinically-validated segmentation algorithm was adapted for implementation in hardware. This was shown to process three-dimensional CT images up to 13.81 times faster than the original software implementation. The segmentations produced by the two implementations showed strong agreement. Modifications to the hardware implementation were proposed for segmenting fourdimensional CT scans. This was shown to process image volumes 14.96 times faster than the original software implementation, and the segmentations produced by the two implementations showed strong agreement in most cases.A second, novel, method for segmenting four-dimensional CT data was also proposed. The hardware implementation executed 1.95 times faster than the software implementation. However, the algorithm was found to be unsuitable for the global segmentation task examined here, although it may be suitable as a refining segmentation in the context of a larger ART algorithm

Reducing the Complexity of Heterogeneous Computing: A Unified Approach for Application Development and Runtime Optimization

Heterogeneous systems with accelerators promise considerable performance improvements at a lower cost than homogeneous CPU-only systems. However, to benefit from this potential, considerable work is required from developers to integrate them efficiently in an application. This work contributes a new framework implemented with an online-learning runtime system that simplifies development and makes applications more portable, efficient and reliable across different systems

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Advanced statistical methods for prognostic biomarkers and disease incidence models

Due to their prognostic value, biomarkers can support physicians in making the appropriate choice of therapy for a patient. In this thesis, several advanced statistical methods and machine learning algorithms were considered and applied to projects in collaboration with departments of the University Hospital Augsburg. A machine learning algorithm capturing hidden structures in binary immunohistologically stained images of colon cancer was developed to identify patients with a high risk of occurrence of distant metastases. Further, generalized linear models were used to estimate the probability of the need for a permanent shunt in patients after an aneurysmatic subarachnoid hemorrhage. Patients with oligometastatic colon cancer were stratified by a score developed using approaches from survival analysis to investigate which groups might benefit from surgical removal of metastases with prolonged overall survival. Another important point is the selection of suitable statistical models dependent on the structure of the data. We found that a linear regression may only be suited with a transformation of the response variable in the context of association of a COVID-19 infection with lymphocyte subsets. In addition, modeling the course of daily reported new COVID-19 cases is a relevant task and requires suitable statistical models. We compared non-seasonal and seasonal ARIMA models and examined the performance of different log-linear autoregressive Poisson models. To add more structure and enable theoretical prognosis for the further course depending on nonpharmaceutical interventions, we fitted a Bayesian SEIR model with several change points and set the determined change points in context with the distribution of variants of the virus.Biomarker können Ärzte durch ihren prognostischen Wert bei der Auswahl geeigneter Therapieoptionen unterstützen. In dieser Arbeit wurden mehrere fortgeschrittene statistische Methoden sowie Algorithmen des maschinellen Lernens eingeführt und in Zusammenarbeit mit verschiedenen Abteilungen des Universitätsklinikums Augsburg angewendet. Mit Hilfe eines Algorithmus des maschinellen Lernens, der versteckte Strukturen in binären, immunhistologisch gefärbten Bildern von Darmkrebstumoren feststellen kann, wurden Patienten mit einem hohen Risiko für auftretende Fernmetastasen identifiziert. Ebenso wurden Generalisierte Lineare Modelle verwendet, um eine Vorhersage der Wahrscheinlichkeit für eine dauerhafte Shunt-Anlegung nach einer aneurysmatischen Subarachnoidalblutung zu treffen. Patienten mit oligometastastischen Darmkrebs wurden mittels eines Scores, der anhand von Methoden der Survival Analysis entwickelt wurde, stratifiziert, um eine Gruppe zu identifizieren, die von einer operativen Entfernung der Metastasen durch ein langes Gesamtüberleben profitieren kann. Ein weiterer wichtiger Punkt bei der Datenanalyse ist die geeignete Auswahl der statistischen Methode abhängig von der Datenstruktur. Es konnten am Beispiel der Assoziation einer Coronainfektion mit der Anzahl von Lymphozytensubpopulationen gezeigt werden, dass eine Transformation der Zielvariable notwendig sein kann, um die Voraussetzungen der linearen Regression zu erfüllen. Die Modellierung der Anzahl an täglichen Neuinfektionen stellt eine relevante Aufgabe dar und benötigt passende statistische Modelle. Ein non-seasonal und ein seasonal ARIMA-Model wurden ebenso wie mehrere log-linearen autoregressiven Poisson-Modellen verglichen. Zusätzlich wurde ein weiterer Modellierungsansatz untersucht, der die biologischen Mechanismen stärker einbezieht und eine theoretische Prognose für den weiteren Verlauf unter verschiedenen Szenarien ermöglicht. Der Verlauf wurde mittels eines bayesschen SEIR Modell mit mehreren Wendepunkten an die Daten angepasst. Die gefundenen Wendepunkte wurden in Kontext der Verteilung der Virusvarianten analysiert

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC