Brazil

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.

IWSSIP 2020

UAV-Multispectral Sensed Data Band Co-Registration Framework

multispectral registration

band co-registration

unmanned aerial vehicle

precision agriculture

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

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

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.

Comparing the Degeneration of the Corpus Callosum by Magnetic Resonance Images

This paper presents the interim results of the ongoing CORELA Erasmus+ project. The aim of the CORELA project is to increase the effectiveness of electrotechnical engineering education and higher the attraction of the specific engineering field. The purpose of the project is to develop a common platform, which can be used in the education process at different levels of higher school education. The developed platform has different operation modes and can be used as a tool for analytical calculation and theoretical exercise. The platform has integrated different models of electronic components, which can be used on a simulation of the real scenarios. The most important future of the platform is real-time operation mode with the engagement of real measurement types of equipment and electronic components. With the capability of internet connection and exchange the data with the Massive Open Online Course (MOOC) platform, the system can be used for Remote Virtual Laboratories (VRL) and collaborative learning. A common MOOC and CORELA platform enable pupils to interact with each other in a manner to share their suggestion and experiences to solve exercise efficiently.

CORELA Collaborative Learning Environment for Electrical Engineering Education

Image Registration is first step in analysis of the same point of the patient over some time. This work proposes a new approach to define the same anatomical point in a set of images without the intensity of the content or the constant intensity hypothesis. It considers Möbius transformation of matched points having affine and projective transformations as particular cases. The experiments consider breast infrared dynamic examination of 23 patients, each one with 20 images.
They are registered in two diverse ways in a total of 874 registrations. Results are evaluated qualitatively and quantitatively. The used examinations are public available and the achieved results are even compared with other well know approach resulting 1748 registration on real images. The proposed approach improves the image matching in 92%, 92%, 81%, 85%, and 81% according to Dice, Jaccard, Superposition of Reference, False Negative and False Positive respectively.

A Non Linear Registration Without the Use of the Brightness Constancy Hypothesis

Glaucoma is a group of eye diseases characterized by progressive degeneration of the optic nerve, which can lead the patient to blindness, however it can be prevented through diagnosis and treatment of the disease. It is the leading cause of irreversible blindness worldwide and is expected to affect 76 million by 2020. The cup-to-disc ratio technique is a structural examination of the optic nerve used to glaucoma diagnose, and requires an accurate segmentation of the optic disc and cup. Thus, this work proposes an automated method for cup segmentation in fundus images of DRISHTI-GS dataset using a combination of the green channel of RGB image and the provided optic disc mask as input to a modified U-Net convolutional neural network. The results achieved were promising, yielding a mean dice coefficient of 94%, much better than state-of-the-art algorithms. The method could be used for glaucoma diagnosis.

Mask Overlaying: A Deep Learning Approach for Individual Optic Cup Segmentation from Fundus Image

In this presentation, the comparison between deep learning methods and feature extraction algorithms is presented. The principle of Grey-Level Co-occurrence Matrix (GLCM) and its modifications are used for our research. The main idea was to design a method for the description of combined features and textures. The texture classification process is carried out with the robust support vector machine classifier (SVM). We compare these feature extraction methods with proposed Convolutional Neural Networks (CNN). This proposed network contains 25 layers. Finally, the all evaluation and comparison of color texture retrieval results for all used methods are presented. The all feature extraction algorithms and proposed CNN have been tested on two different color texture datasets (Outex and Vistex datasets).

The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods

Efficient management of cloud traffic leads to resource sharing among clients. To meet clients’ performance restrictions, however, cloud providers adopt strict resource allocation that implies idle resources as clients’ bandwidth thresholds are over-provisioned. We propose a dynamic mechanism to provide resources on-demand in a multitenant data center. The mechanism relies on Q-learning multi-agent for managing each client access to the cloud resources. The proposed mechanism either measure the throughput continuously or map the CPU usage into bandwidth usage. We assess our mechanism in an emulated environment, in which a network controller provisions bandwidth. Results show that the mechanism reduces cloud idleness, allowing low-priority clients to use bandwidth while there is idle capacity. Our mechanism incurs low processing overhead, even when the number of states and action space grows, while the memory cost per agent increases, peaking at 300 kB for 200 states and actions. When mapping CPU usage into bandwidth usage, the mechanism achieves fair bandwidth sharing at the cost of a small increase on the convergence time to an optimal policy.

A Lightweight Reinforcement-Learning-based Mechanism for Bandwidth Provisioning on Multitenant Data Center

Wind energy is promoting the disruption of the current energy model, as the world begins to reduce the use of non-renewable energy sources. At the core of this energetic revolution, the wind turbines are designed to capture the wind's kinetic energy to produce electricity. Wind turbines are complicated and expensive systems whose operating signals are converted into high dimensional data for health management. This signal-to-data conversion involves a variety of constraints that produces an imperfect dataset as an output.
In recent years, many semi-supervised learning approaches were developed to detect faults in a wind turbine preemptively. In this paper, we propose a workflow to detect faults based on two hot research topics in data science: (a) automatic feature selection; and (b) semi-supervised classification for fault detection. The multi-objective optimization framework Non-dominated Sorting Genetic Algorithm II (NSGA-II) was adopted to select a subset of features and hyperparameters to perform the semi-supervised fault detection using both a Soft-Label and a Binary support vector machine (SVM). Our experiments were carried out using a dataset containing unlabelled records of 5 2.0MW wind turbines. We discuss the results from NSGA-II based on the trade-off between the number of selected features and the detection performance. We also select the algorithm configuration that achieved the best savings by predicting a faulty system before its collapse.

Wind Turbine Fault Detection: A Semi-Supervised Learning Approach With Automatic Evolutionary Feature Selection

In order to minimize evasion losses, socioeconomic policies and programs were created to assist and support actions seeking to maximize the number of students that graduate in suitable program time. This work presents a methodology that aims to predict evasion by using machine learning. Our approach was able to classify student abandonment with an average f-score and accuracy results above 95%. Our approach combines a decision tree alongside a genetic algorithm and cluster stratified sampling.

EvolveDTree: Analyzing Student Dropout in Universities

This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.

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Recent Advances in Overcoming the Independence Assumption in Adaptive Filtering Analyses

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