Artigos - Ciência da Computação e Estatística - IBILCE

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An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection
(2023-06-01) Contreras, Rodrigo Colnago [UNESP]; Viana, Monique Simplicio; Fonseca, Everthon Silva; dos Santos, Francisco Lledo; Zanin, Rodrigo Bruno; Guido, Rodrigo Capobianco [UNESP]; Universidade Estadual Paulista (UNESP); Federal Institute of São Paulo; Mato Grosso State University
Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one’s own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice.
Optimization of SNP Search Based on Masks Using Graphics Processing Unit
(2023-01-01) da Cruz, Álvaro Magri Nogueira [UNESP]; Gomes, Vitoria Zanon [UNESP]; Andrade, Matheus Carreira [UNESP]; Rici Amorim, Anderson [UNESP]; Valêncio, Carlos Roberto [UNESP]; Vaughan, Gilberto; Donegá Zafalon, Geraldo Francisco [UNESP]; Universidade Estadual Paulista (UNESP); Centers for Diseases Control and Prevention
In the context of bioinformatics one of the most important problems to be solved is the search for simple nucleotide polymorphism (SNP). When we perform the analysis of the files from the next generation sequencing (NGS) the search task for SNPs becomes more prohibitive due to the millions of sequences present on them. CPU multithreaded approaches are not enough when millions of sequences as considered. Then, the use of graphics processing units (GPUs) is a better alternative, because it can operate with hundreds of arithmetic logic units while CPU with no more than tens. Thus, in this work we developed a method to detect SNPs using a mask approach under GPU architecture. In the tests, a speedup of up to 5175.86 was obtained when compared to the multithreaded CPU approach, evaluating from 100,000 to 800,000 sequences using five masks to detect the occurrence of SNPs.
A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection
(2022-01-01) Contreras, Rodrigo Colnago [UNESP]; Nonato, Luis Gustavo; Boaventura, Maurilio [UNESP]; Boaventura, Ines Aparecida Gasparotto [UNESP]; Santos, Francisco Lledo Dos; Zanin, Rodrigo Bruno; Viana, Monique Simplicio; Universidade de São Paulo (USP); Universidade Estadual Paulista (UNESP); Faculty of Architecture and Engineering; Universidade Federal de São Carlos (UFSCar)
The use of user recognition and authentication systems has become very common and is part of everyday routines for many people, guaranteeing access to the automatic teller machines, entrance to the gym or even to smartphones. Among all the biometrics that can be analyzed in this type of system, the fingerprint is the most considered due to the ease of collection, the uniqueness of each user, and the large amount of solid theories and computational libraries available in the scientific literature. However, in recent years, the falsification of these biometrics with synthetic materials, known as spoofing, has become a real threat to these systems. To circumvent these effects without the addition of hardware devices, techniques based on the analysis of texture pattern descriptors were developed. In this work, we propose a new framework based on steps of data augmentation, image processing and replication, and feature fusion and reduction. The method has as main objective to improve the ability of classifiers, or sets of classifiers, to recognize life in fingerprints. Furthermore, it is proposed a generalization of vector representation of patterns described in matrix form from the systematic use of sets of mapping functions. All the proposed material was analyzed on the well-established benchmark of the Liveness Detection competition of the 2009, 2011, 2013 and 2015 editions, presenting an average accuracy of 97.77% and being a competitive strategy in relation to the other techniques that make up the state of the art of specialized literature.
A Hybrid Approach using Progressive and Genetic Algorithms for Improvements in Multiple Sequence Alignments
(2021-01-01) Zafalon, Geraldo Francisco Donegá [UNESP]; Gomes, Vitoria Zanon [UNESP]; Amorim, Anderson Rici [UNESP]; Valêncio, Carlos Roberto [UNESP]; Universidade Estadual Paulista (UNESP); Universidade de São Paulo (USP); Universidade Paulista
The multiple sequence alignment is one of the main tasks in bioinformatics. It is used in different important biological analysis, such as function and structure prediction of unknown proteins. There are several approaches to perform multiple sequence alignment and the use of heuristics and meta-heuristics stands out because of the search ability of these methods, which generally leads to good results in a reasonable amount of time. The progressive alignment and genetic algorithm are among the most used heuristics and meta-heuristics to perform multiple sequence alignment. However, both methods have disadvantages, such as error propagation in the case of progressive alignment and local optima results in the case of genetics algorithm. Thus, this work proposes a new hybrid refinement phase using a progressive approach to locally realign the multiple sequence alignment produced by genetic algorithm based tools. Our results show that our method is able to improve the quality of the alignments of all families from BAliBase. Considering Q and TC quality measures from BaliBase, we have obtained the improvements of 55% for Q and 167% for TC. Then, with these results we can provide more biologically significant results.
An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19
(2023-01-01) Miguel, Pedro Lucas [UNESP]; Cansian, Adriano Mauro [UNESP]; Rozendo, Guilherme Botazzo [UNESP]; Medalha, Giuliano Cardozo; do Nascimento, Marcelo Zanchetta; Neves, Leandro Alves [UNESP]; Universidade Estadual Paulista (UNESP); WZTECH NETWORKS; Universidade Federal de Uberlândia (UFU)
In this proposal, a study based on deep-learned features via transfer learning was developed to obtain a set of features and techniques for pattern recognition in the context of COVID-19 images. The proposal was based on the ResNet-50, DenseNet-201 and EfficientNet-b0 deep-learning models. In this work, the chosen layer for analysis was the avg pool layer from each model, with 2048 features from the ResNet-50, 1920 features from the DenseNet0201 and 1280 obtained features from the EfficientNet-b0. The most relevant descriptors were defined for the classification process, applying the ReliefF algorithm and two classification strategies: individually applied classifiers and employed an ensemble of classifiers using the score-level fusion approach. Thus, the two best combinations were identified, both using the DenseNet-201 model with the same subset of features. The first combination was defined via the SMO classifier (accuracy of 98.38%) and the second via the ensemble strategy (accuracy of 97.89%). The feature subset was composed of only 210 descriptors, representing only 10% of the original set. The strategies and information presented here are relevant contributions for the specialists interested in the study and development of computer-aided diagnosis in COVID-19 images.
Classification of H&E Images via CNN Models with XAI Approaches, DeepDream Representations and Multiple Classifiers
(2023-01-01) Neves, Leandro Alves [UNESP]; Martinez, João Manuel Cardoso [UNESP]; da Costa Longo, Leonardo H. [UNESP]; Roberto, Guilherme Freire; Tosta, Thaína Aparecida Azevedo; de Faria, Paulo Rogério; Loyola, Adriano Mota; Cardoso, Sérgio Vitorino; Silva, Adriano Barbosa; do Nascimento, Marcelo Zanchetta; Rozendo, Guilherme Botazzo [UNESP]; Universidade Estadual Paulista (UNESP); Universidade de São Paulo (USP); Universidade Federal de Uberlândia (UFU)
The study of diseases via histological images with machine learning techniques has provided important advances for diagnostic support systems. In this project, a study was developed to classify patterns in histological images, based on the association of convolutional neural networks, explainable artificial intelligence techniques, DeepDream representations and multiple classifiers. The images under investigation were representatives of breast cancer, colorectal cancer, liver tissue, and oral dysplasia. The most relevant features were associated by applying the Relief algorithm. The classifiers used were Rotation Forest, Multilayer Perceptron, Logistic, Random Forest, Decorate, IBk, K*, and SVM. The main results were areas under the ROC curve ranging from 0.994 to 1, achieved with a maximum of 100 features. The collected information allows for expanding the use of consolidated techniques in the area of classification and pattern recognition, in addition to supporting future applications in computer-aided diagnosis.
A stain color normalization with robust dictionary learning for breast cancer histological images processing
(2023-08-01) Tosta, Thaína A. Azevedo; Freitas, André Dias; de Faria, Paulo Rogério; Neves, Leandro Alves [UNESP]; Martins, Alessandro Santana; do Nascimento, Marcelo Zanchetta; Universidade de São Paulo (USP); Universidade Federal de Uberlândia (UFU); Universidade Estadual Paulista (UNESP); Federal Institute of Triângulo Mineiro
Microscopic analyses of tissue samples are crucial for confirming the diagnosis of breast cancer. The digitization of these samples has led to the development of computational systems that can assist pathologists. However, these systems may face limitations owing to color variations in the images. Normalization studies have been widely conducted to address these issues, but there is still a need for new proposals that take into account the biological properties of dyes and tissues. This study presents a novel method for normalizing hematoxylin and eosin-stained histological images by estimating the color appearance matrices and density maps of the stain. The proposed method offers contributions in terms of pixel selection and weight definition to improve the color estimation of histological images. Besides, to the best of our knowledge, no previous studies have evaluated normalized images considering both handcrafted and learning features. Breast cancer images with significant color variations were used to evaluate this approach and the results demonstrated its effectiveness and efficiency. The average values of FSIM, NIQE, and QSSIM were up to 0.9866, 3.4298, and 0.9655, respectively. Compared with other normalization techniques, the proposed method showed an increase of up to 5.9261, with the largest difference observed in the amount of noise added, as indicated by the NIQE metric. To determine the impact of normalization on feature extraction, the evaluations included an analysis of both color and deep-learned features. These experiments showed that all evaluated methods harmed the separation of breast cancer samples by color features. In contrast, the deep-learned features resulted in less complex classification problems, especially with the proposed normalization. This technique also reached one of the lowest processing times, nearly 6 s with the largest image from the databases.
Short-term effects of α-melanocyte-stimulating hormone in three distinct melanin-pigmented cell types of Anura
(2023-01-01) Zieri, Rodrigo; Franco-Belussi, Lilian; DE OLIVEIRA, Classius [UNESP]; Laboratório de Zoologia e Anatomia Animal Comparada; Universidade Federal de Mato Grosso do Sul (UFMS); Universidade Estadual Paulista (UNESP)
Ectothermic animals present melanin-containing cells in their integument and viscera. Besides cutaneous melanophores, amphibians have melanomacrophages in the hepatic parenchyma and melanocytes in the viscera, which are also present in their testicular stroma. The native melanocyte stimulating hormone (α-MSH) is the main hormone that modulates the color change in melanophores. However, we still know too little about how the α-MSH acts in vivo on visceral melanin-containing cells. In this study, we collected 30 adult males of Physalaemus nattereri (Anura, Leptodactylidae) to evaluate the short-term effects of α-MSH on melanophores, melanocytes and melanomacrophages under light microscopy. For this, we injected 0.05 ml of a single intraperitoneal dose containing 2.5x10-7 mmol/10g of α-MSH, diluted in ringer solution, in five experimental groups with five individuals each one. The different groups were analyzed after 1, 3, 6, 12 and 24h. The control group with five other individuals received only 0.05 ml of ringer solution. The skin pigmentation increased quickly after animals received the hormone α-MSH with the consequent darkening of the body (body darkness). Melanophores, melanocytes and melanomacrophages responded similarly to the test, with an increase in the area containing melanin. However, melanophores and melanomacrophages reached their darkest pigmentation in a shorter period of time in comparison to the testicular melanocytes, probably due to specific metabolic characteristics of each organ. Thus, we verified that the three types of cells, although present in different organs, are responsive to the native hormone α-MSH, which enables us to treat them as a pigmentary system.
Video-article publication as strategy to enhance science consumption
(2022-01-01) Santos, Adriana Barbosa [UNESP]; Universidade Estadual Paulista (UNESP)
New technologies have promoted important changes in the social relations in recent years, stimulating the growth of scientific content production in audiovisual format, especially of video-articles. This article examines the applicability of the new trends of audiovisual publications as a strategy to enhance science consumption inside as well as outside the academic ecosystem. An exploratory-descriptive survey was performed with Brazilian researchers from the Health Sciences and Human Sciences areas, focusing on mitigating the lack of empirical evidence, regarding four points: their view of science consumption in Brazil; the lack of knowledge on the video-articles like scientific communication; the level of interest in scientific publications in audiovisual format; and feelings about the visibility and recognition of science in Brazil. Results reinforce the interest in audiovisual resource usage in scientific communication valorization, given that the researchers are interested in video-articles publishing for improving visibility, altmetrics, and expanding the dissemination of scientific culture, focusing to enhance science consumption.
Assessment of the association of deep features with a polynomial algorithm for automated oral epithelial dysplasia grading
(2022-01-01) Silva, Adriano B.; De Oliveira, Cleber I. [UNESP]; Pereira, Danilo C.; Tosta, Thaina A. A.; Martins, Alessandro S.; Loyola, Adriano M.; Cardoso, Sergio V.; De Faria, Paulo R.; Neves, Leandro A. [UNESP]; Do Nascimento, Marcelo Z.; Universidade Federal de Uberlândia (UFU); Universidade Estadual Paulista (UNESP); Universidade de São Paulo (USP); Federal Institute of Triângulo Mineiro (IFTM)
Oral epithelial dysplasia is a potentially malignant lesion that presents challenges for diagnosis. The use of digital systems in histological analysis can aid specialists to obtain data that allows a robust and fast grading process, but there are few methods in the literature proposing a grading system for this lesion. This study presents a method for oral epithelial dysplasia grading in histopathological images combining deep features and a polynomial classifier. The ResNet50 and AlexNet models were trained with the images and information was extracted from the convolutional layers, exploring convolutional neural networks via transfer learning. Then, the ReliefF algorithm was used to rank and select the most relevant features, which were given as an input to the polynomial classifier. The methodology was employed in a dataset with 296 regions of mice tongue images. The results were compared with the gold standard and other algorithms present in the literature. The classification stage presented AUC values ranging from 0.9663 to 0.9800. When compared to other algorithms present in the literature, our method provided relevant results regarding accuracy and AUC values. The proposed approach presented relevant results and can be used as a tool to aid pathologists in grading oral dysplastic lesions.
Classification of lymphomas images with polynomial strategy: An application with Ridge regularization
(2022-01-01) Pereira, Danilo C.; Longo, Leonardo C. [UNESP]; Tosta, Thaina A. A.; Martins, Alessandro S.; Silva, Adriano B.; Faria, Paulo R. De; Neves, Leandro A. [UNESP]; Do Nascimento, Marcelo Z.; Universidade Federal de Uberlândia (UFU); Universidade Estadual Paulista (UNESP); Universidade de São Paulo (USP); Federal Institute of Triângulo Mineiro (IFTM)
Histological image analysis through systems to aid diagnosis plays an important role in medicine with supplementary reading for the specialist's diagnosis. This work proposes a method based on the association of extracted features by fractal techniques, regularization and polynomial classifier. The feature vectors were classified by applying the cross-validation technique with 10 folds. The evaluation of the results occurred through metrics such as accuracy (ACC) and imbalance accuracy metric (IAM). The proposed approach achieved significant results for all metrics with non-Hodgkin lymphoma lesion sets. The proposed approach provided values around 0.97 of IAM and 99% of ACC for investigated groups. These results are considered relevant to studies in the literature and the association of Hermite polynomial and regularization can contribute to the detection of the lesions by supporting specialists in clinical practices.
New artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas
(2022-10-01) Almeida, Gildásio Castello; Guido, Rodrigo Capobianco [UNESP]; Balarin Silva, Henrique Monteiro; Brandão, Cinara Cássia; De Mattos, Luiz Carlos; Lopes, Bernardo T.; Machado, Aydano Pamponet; Ambrósio, Renato; Faculty of Medicine of São José do Rio Preto; Base Hospital of São José do Rio Preto; Visum Eye Center; Universidade Estadual Paulista (UNESP); Rio Claro Eye Institute; University of Liverpool; Universidade de São Paulo (USP); Federal University of Alagoas; Federal University the State of Rio de Janeiro
Purpose: To assess the efficiency of an index derived from multiple logistic regression analysis (MLRA) to measure differences in corneal tomography findings between subclinical keratoconus (KC) in 1 eye, corneal ectasia, and healthy corneas. Setting: 2 private Brazilian ophthalmological centers. Design: Multicenter case-control study. Methods: This study included 187 eyes with very asymmetric ectasia and with normal corneal topography and tomography (VAE-NTT) in the VAE-NTT group, 2296 eyes with healthy corneas in the control group (CG), and 410 eyes with ectasia in the ectasia group. An index, termed as Boosted Ectasia Susceptibility Tomography Index (BESTi), was derived using MLRA to identify a cutoff point to distinguish patients in the 3 groups. The groups were divided into 2 subgroups with an equal number of patients: validation set and external validation (EV) set. Results:2893 patients with 2893 eyes were included. BESTi had an area under the curve (AUC) of 0.91 with 86.02% sensitivity (Se) and 83.97% specificity (Sp) between CG and the VAE-NTT group in the EV set, which was significantly greater than those of the Belin-Ambrósio Deviation Index (BAD-D) (AUC: 0.81; Se: 66.67%; Sp: 82.67%; P <.0001) and Pentacam random forest index (PRFI) (AUC: 0.87; Se: 78.49%; Sp: 79.88%; P =.021). Conclusions: BESTi facilitated early detection of ectasia in subclinical KC and demonstrated higher Se and Sp than PRFI and BAD-D for detecting subclinical KC.
Virtual Laboratories Development Using 3D Environments
(Igi Global, 2016-01-01) Oliveira, Toni Amorim; Marranghello, Norian [UNESP]; Rodrigues Silva, Alexandre Cesar [UNESP]; Pereira, Aledir Silveira [UNESP]; Neto, FMM; DeSouza, R; Gomes, AS; Univ State Mato Grosso; Universidade Estadual Paulista (UNESP)
3D virtual worlds or metaverses are immersion environments that allow the simulation of some real environment characteristics such as sound and gravity. In this chapter we describe the development of a 3D virtual environment as well as its integration with Moodle platform using Sloodle plugin. We discuss related works dedicated to the development of virtual laboratories. We also describe the main software used and how to perform the settings required for the operation of the environment. To introduce the developed software utilization we present a laboratory built for teaching subjects based on multiple intelligences theory defined by Gardner.
Statistical Model Applied to NetFlow for Network Intrusion Detection
(Springer, 2010-01-01) Proto, Andre [UNESP]; Alexandre, Leandro A.; Batista, Maira L.; Oliveira, Isabela L.; Cansian, Adriano M.; Gavrilova, M. L.; Tan, CJK; Moreno, E. D.; Universidade Estadual Paulista (UNESP); ACME Comp Secur Res Lab
The computers and network services became presence guaranteed in several places. These characteristics resulted in the growth of illicit events and therefore the computers and networks security has become an essential point in any computing environment. Many methodologies were created to identify these events; however, with increasing of users and services on the Internet, many difficulties are found in trying to monitor a large network environment. This paper proposes a methodology for events detection in large-scale networks. The proposal approaches the anomaly detection using the NetFlow protocol, statistical methods and monitoring the environment in a best time for the application.
Video-article publication as strategy to enhance science consumption
(Pontificia Universidade Catolica Campinas, 2022-01-01) Santos, Adriana Barbosa [UNESP]; Universidade Estadual Paulista (UNESP)
New technologies have promoted important changes in the social relations in recent years, stimulating the growth of scientific content production in audiovisual format, especially of video-articles. This article examines the applicability of the new trends of audiovisual publications as a strategy to enhance science consumption inside as well as outside the academic ecosystem. An exploratory-descriptive survey was performed with Brazilian researchers from the Health Sciences and Human Sciences areas, focusing on mitigating the lack of empirical evidence, regarding four points: their view of science consumption in Brazil; the lack of knowledge on the video-articles like scientific communication; the level of interest in scientific publications in audiovisual format; and feelings about the visibility and recognition of science in Brazil. Results reinforce the interest in audiovisual resource usage in scientific communication valorization, given that the researchers are interested in video-articles publishing for improving visibility, altmetrics, and expanding the dissemination of scientific culture, focusing to enhance science consumption.
Training Transformers for Question Generation Task in Intelligent Tutoring Systems
(Ieee, 2022-01-01) Santi, Matheus [UNESP]; Manacero, Aleardo [UNESP]; Peronaglio, Fernanda F. [UNESP]; Lobato, Renata S. [UNESP]; Spolon, Roberta [UNESP]; Cavenaghi, Marcos Antonio; Rocha, A.; Bordel, B.; Penalvo, F. G.; Goncalves, R.; Universidade Estadual Paulista (UNESP); Humber Inst Technol & Adv Learning
Over the last few years, natural language processing (NLP) technologies have largely evolved, allowing their application in new scenarios with much more significant results. With the introduction of NLP into intelligent tutoring systems, several automation techniques could be used to improve the teaching process, among them, question generation, which allows the automated creation of interpretative questions from textual sources. This work explores the application of Transformers neural networks in the Question Generation task, developing several models and comparing their initial results.
Heuristics and meta-heuristics for lot sizing and scheduling in the soft drinks industry: a comparison study
(Springer, 2008-01-01) Ferreira, D.; Franca, P. M. [UNESP]; Kimms, A.; Morabito, R.; Rangel, S. [UNESP]; Toledo, C. F. M.; Xhafa, F; Abraham, A; Universidade Federal de São Carlos (UFSCar); Universidade Estadual Paulista (UNESP); Univ Duisburg Essen; Universidade Federal de Lavras (UFLA)
This chapter studies a two-level production planning problem where, on each level, a lot sizing and scheduling problem with parallel machines, capacity constraints and sequence-dependent setup costs and times must be solved. The problem can be found in soft drink companies where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. Models and solution approaches proposed so far are surveyed and conceptually compared. Two different approaches have been selected to perform a series of computational comparisons: an evolutionary technique comprising a genetic algorithm and its memetic version, and a decomposition and relaxation approach.
Corpus-based Methodology for an Online Multilingual Collocations Dictionary: First Steps
(2021-01-01) Orenha-Ottaiano, Adriane [UNESP]; Garcia, Marcos; de Oliveira Silva, Maria Eugênia Olímpio; L'Homme, Marie-Claude; Ramos, Margarita Alonso; Valêncio, Carlos Roberto [UNESP]; Tenório, William [UNESP]; Universidade Estadual Paulista (UNESP); Universidade de Santiago de Compostela; University of Alcalá; Université de Montréal; Universidade da Coruña
This paper describes the first steps of a corpus-based methodology for the development of an online Platform for Multilingual Collocations Dictionaries (PLATCOL). The platform is aimed to be customized for different target audiences according to their needs. It covers various syntactic structures of collocations that fit into the following taxonomy: verbal, adjectival, nominal, and adverbial. Part of its design, layout and methodological procedures are based on the Bilingual Online Collocations Dictionary Platform (Orenha-Ottaiano, 2017). The methodology also relies on the combination of automatic methods to extract candidate collocations (Garcia et al., 2019a) with careful post-editing performed by lexicographers. The automatic approaches take advantage of NLP tools to annotate large corpora with lemmas, PoS-tags and dependency relations in five languages (English, French, Portuguese, Spanish and Chinese). Using these data, we apply statistical measures (Evert et al., 2017; Garcia et al., 2019b) and distributional semantics strategies to select the candidates (Garcia et al., 2019c) and retrieve corpus-based examples (Kilgarriff et al., 2008). We also rely on automatic definition extraction (Bond & Foster, 2013) so that collocations can be more effectively organized according to their specific senses.
Wavelets behind the scenes: Practical aspects, insights, and perspectives
(2022-11-01) Guido, Rodrigo Capobianco [UNESP]; Universidade Estadual Paulista (UNESP)
Over the years, wavelet-based analyses have been responsible for remarkable achievements in physics and related sciences. Nevertheless, a deep inspection on wavelet-based strategies described in recent scientific papers, dissertations, and theses reveals that a significant number of authors, i.e., students and even researchers with a modest background on signal analysis, still misunderstand the fundamentals of wavelets. One classical source of confusion, for instance, involves two different but related approaches used to perform discrete-time wavelet transformations and their inverses: algebra- and filter-based. Although the latter is usually adopted in practice, the former reveals the beauty of multiresolution analysis over Heisenberg's uncertainty principle, showing what really happens behind the scenes. Thus, based on the solid and easy-to-follow explanations provided in this smoothly written tutorial-review article, interested readers will definitively comprehend the different types of wavelet transforms and their specific applications, getting hands-on experience and insights on how to extract the most of their research by using that powerful tool. Because its mission is clarification, example wavelet-related applications are provided in this document to stimulate state-of-the-art research in a diversity of branches in physics.
Interpretability, Reproducibility, and Replicability
(2022-07-01) Adali, Tulay; Guido, Rodrigo Capobianco [UNESP]; Ho, Tin Kam; Muller, Klaus-Robert; Strother, Stephen; University of Maryland; Universidade Estadual Paulista (UNESP); Senior Artificial Intelligence Scientist; Computer Science; Medical Biophysics
Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction. However, this is not necessarily the case when we choose to rely more heavily on the data and minimize modeling assumptions.

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