Brazil

The Neutrinos Angra Experiment aims at monitoring nuclear plants by detecting antineutrino particles coming out from its fuel burn-up process. The Neutrinos Angra Collaboration developed a fully-equipped detector to accomplish this task. It
is currently in operation on the surface and next to the dome of the Angra II nuclear reactor. Selecting antineutrinos events on the surface is a challenge task due to the high level of background noise produced by cosmic ray particles. One of the
main parameters used to select antineutrino events is the number of photons acquired by the detector’s sensors. This quantity is estimated based on the signals generated at the output of the readout electronics. If any of those signals is corrupted, the estimation fails, compromising the Experiment performance. This
work proposes a study of the performance of some classifier algorithms applied to identify corrupted signals for the Neutrinos Angra Experiment. If corrupted signals can be identified, they could go through a recovering process in order to improve the Experiment’s events-selection algorithms.

IWSSIP 2020

Identification and Classification of Corrupted Signals for the Neutrinos Angra Experiment

signal simulation

neutrino

electronics

machine learning

classification

The 27th International Conference on Systems, Signals and Image Processing.

technical paper

We discuss the use of Associative Rule Learning (ARL) in the interface design of a 3D interactive visualization of temporal medical data. Our results indicate that ARL can be a valuable resource to improve the design of Information Visualization interface tools as well as to customize visualizations according to the user´s preferences.

Machine Learning in Information Visualization: Using Rule-based Learning Algorithms to Personalize Interfaces

The Neutrinos Angra Experiment aims at developing a system capable of monitoring the fuel-burning process of nuclear reactors. This system is based on an antineutrino detector located a few meters far from the core of the Angra II nuclear reactor. One of the main parameters used to select antineutrino events is the released energy left by a particle when interacting with the detector medium which, in turn, is proportional to the amplitude and area of the signals generated by the detector’s transducers. However, the estimation of such quantities may lose accuracy
due to saturation effects caused by the experiment electronics, making it necessary to apply methods to compensate or correct such effects. This work proposes to implement and test machine learning algorithms designed to estimate the energy of saturated signals.

Amplitude Estimators Applied to Saturated Signals for the v-Angra Experiment

This work presents a technique of working together to accumulate learning between various artificial neural networks, the technique has been successfully adapted to a convolutional network and has shown promising results in the context of traffic light classification.

Optimizing CNN's using Cumulative Learning with Auxiliary Networks for Traffic Light Recognition

This presentation shows a study about the feasibility of combining sustained vowels for computer-based pathological voice characterization. Although recent work has shown that single sustained vowels enable accurate voice characterization, the literature lacks evidence about combining them. Here we propose a pipeline for pathological voice characterization and classification that encompasses a feature extraction method based on the Haar wavelet. We also evaluate the benefits of our pipeline when using an input signal that comprises three different sustained vowels instead of single ones. We conducted experiments on samples of sustained vowels /a/, /i/, and /u/ from two public datasets, exploring the wavelet decomposition levels in the range of 4 to 18.

Combined Sustained Vowels Improve the Performance of the Haar Wavelet for Pathological Voice Characterization

The skin is an essential system for the human body, also the most susceptible to diseases. The diagnosis of these diseases is made mainly through dermatoscopy images, a noninvasive procedure. This paper presents a semi-automatic method that can assist the physician in monitoring the skin lesion’s evolution. Our approach applies the SLIC0 algorithm for superpixels generation, and texture features extraction. Subsequently, the algorithm Seeded Fuzzy C-means clusters the superpixels based on marked regions made by a specialist. Finally, the images undergo a post-processing step, eliminating noise and smoothing edges. We used 1380 images from four public datasets: the PH2 and DermIs to settle the method parameters, and ISIC 2016 and ISIC 2017 to the performance evaluation. Our tests show that by marking 1% of the superpixels, our method achieves an accuracy of 96.16% in ISIC 2016 and 95.34% in ISIC 2017. According to the literature, the segmentation was considered excellent in 93.49% of the images in ISIC 2016 and on 79.44% of ISIC 2017 images.

A Skin Lesion Semi-Supervised Segmentation Method

The chest radiography is one of the most accessible radiological examinations for diagnosis of lung and heart diseases. Deep learning techniques have been increasingly used to provide more accurate detection of thorax lesions on Chest X-Ray (CXR) images. However, we observe that we can use the complementarity of dual asymmetric deep convolutional neural networks (DCNNs) to improve the ability of CXR image classification compared to the single network. In this paper, we propose a novel dual lesion attention network named DuaLAnet for the classification of 14 thorax diseases on chest radiography. The DuaLAnet consists of two asymmetric attention networks, DenseNet-169 and ResNet-152, to integrate the advantages into a wider architecture, thus extracting more discriminative features of different abnormalities from the raw CXRs. Moreover, a training strategy is designed to integrate the loss contribution of the involved classifiers into a unified loss.

DuaLAnet: Dual Lesion Attention Network for Thoracic Disease Classification in Chest X-Rays

In prostate analysis clinical routine, manual and semi-manual techniques are used to interpret MRI scans, which in addition to being subjective, are time-consuming and operator-dependent. Prostate segmentation is necessary for the diagnosis of a possible tumor, biopsy, staging, monitoring and treatment. Thus, an automatic segmentation can be crucial for fast and reliable diagnosis, but is challenging to accomplish due to the varying sizes, shapes and also unclear prostate boundaries that are blended with surrounding tissues. Thereupon, this work proposes an automatic method for prostate segmentation on 3D MRI scans based on superpixels, taxonomic indexes, probabilistic atlas and the XGBoost algorithm. In addition, the proposed method has been evaluated on the Prostate 3T and PROMISE12 databases, presenting a dice similarity coefficient of 84.03%, volumetric similarity of 95.42%, recall of 88.11%, specificity of 90.82%, and an accuracy of 90.06\%. Experimental results demonstrate the high potential of the proposed method comparable to those previously published.

Taxonomic Indexes for Automatic Prostate Segmentation on 3D MRI Scans Using Superpixels and Probabilistic Atlas

Precision agriculture (PA) has greatly benefited from new technologies over the years. The use of multispectral and hyperspectral sensors coupled to Unmanned Aerial Vehicles (UAV) has enabled farmers to monitor crops, improve the use of resources and reduce costs. Despite being widely used, multispectral images present a natural misalignment among the various spectra due to the use of different sensors. The variation of the analyzed spectrum also leads to a loss of characteristics among the bands which hinders the feature detection process among them, which makes the alignment process complex. In this paper, is proposed a new framework for the band co-registration process based on the premise that natural misalignment is an attribute of the camera, so it does not change during the acquisition process. The results were compared with the ground-truth generated by a specialist and with other methods present in the literature. The proposed framework had an average back-projection (BP) error of $0.425$ pixels, this result being $335\%$ better than the evaluated frameworks.

UAV-Multispectral Sensed Data Band Co-Registration Framework

One important strength of adaptive filtering algo-rithms is their ability to learn from their interactions with theenvironment. 
Such a flexibility comes with a price, since theirperformance strongly depends on adjustable parameters. 
In orderto provide to the designer guidelines and guaranties for bothperformance and stability, the open literature often relies onstochastic models that predict the learning capabilities of differentalgorithms.
Unfortunately, usually some statistical hypotheses are assumedto make the mathematics tractable. 
The most critical of themis the independence assumption, which states that the adaptivecoefficients are statistically independent from the excitation data.
Such an assumption hampers the accuracy of the theoreticalpredictions in the case of large step sizes, which is a commonchoice in practice. 
Fortunately, a technique that avoids theindependence assumption, respectively exact expectation analysis,has experienced some advances. 
In this paper, it is demonstratedthat such recent advances can be used to address a complexconfiguration of the least mean squares algorithm, where theadditive noise is colored and the ideal plant the adaptive schemeintends to estimate is nonlinear.

Recent Advances in Overcoming the Independence Assumption in Adaptive Filtering Analyses

The development of efficient tools for conducting measures to characterize the degeneration of brain structures is useful to facilitate early detection and monitoring of the evolution of neurodegenerative diseases such as dementia and Alzheimer's. It is well-known that, with the evolution of these diseases, certain brain structures especially the corpus callosum (CC), progressively diminish in dimension. This work considers using Magnetic Resonance Imaging (MRI) and delimiting the tissues of interest to calculate the volume of this structure. This delimitation or segmentation if done using computational techniques offers reproducibility, in addition to increasing the speed and more precision in calculating the volume variation. Therefore, a study of techniques of automatic segmentation of the CC is presented for the volumetric evaluation of MRI images weighted in T1, in the DICOM format, where the calculated volume is independent of the equipment, allowing the estimation of the progression of the loss of volume of such a structure.

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Machine Learning in Information Visualization: Using Rule-based Learning Algorithms to Personalize Interfaces

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