A study on compression techniques for off-the-person electrocardiogram signals

The compression of Electrocardiography (ECG) signals acquired in off-the-person scenarios requires methods that cope with noise and other impairments on the acquisition process. In this paper, after a brief review of common on-the-person ECG signal compression algorithms, we propose and evaluate techniques for this compression task with off-the-person acquired signals, in both lossy and lossless scenarios, evaluated with standard metrics. Our experimental results show that the joint use of Linear Predictive Coding and Lempel-Ziv-Welch is an adequate lossless approach, and the amplitude scaling followed by the Discrete Wavelet Transform achieves the best compression ratio, with a small distortion, among the lossy techniques.info:eu-repo/semantics/publishedVersio

Explainable machine learning for malware detection on Android applications

The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically developed apps. We find that the classifiers that achieve better performance for this task are support vector machines (SVM) and random forests (RF). We emphasize the use of feature selection (FS) techniques to reduce the data dimensionality and to identify the most relevant features in Android malware classification, leading to explainability on this task. Our approach can identify the most relevant features to classify an app as malware. Namely, we conclude that permissions play a prominent role in Android malware detection. The proposed approach reduces the data dimensionality while achieving high accuracy in identifying malware in Android apps.info:eu-repo/semantics/publishedVersio

Tuning iris recognition for noisy images

The use of iris recognition for human authentication has been spreading in the past years. Daugman has proposed a method for iris recognition, composed by four stages: segmentation, normalization, feature extraction, and matching. In this paper we propose some modifications and extensions to Daugman's method to cope with noisy images. These modifications are proposed after a study of images of CASIA and UBIRIS databases. The major modification is on the computationally demanding segmentation stage, for which we propose a faster and equally accurate template matching approach. The extensions on the algorithm address the important issue of pre-processing that depends on the image database, being mandatory when we have a non infra-red camera, like a typical WebCam. For this scenario, we propose methods for reflection removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images show that the template matching segmentation method is more accurate and faster than the previous one, for noisy images. The proposed algorithms are found to be efficient and necessary when we deal with non infra-red images and non uniform illumination

CETC2013 conference on electronics, telecommunications and computers

CETC 2013 is a premier conference in the broad field of Electronics, Telecommunications and Computers. The aim of the conference is to provide a platform for engineers to disseminate and discuss their current research findings and also to explore recent development, current practices and future trends in Electronics, Telecommunications and Computers. We also encourage the dissemination of R&D linked to the Industry. The conference program includes sessions with invited speakers and breakout sessions with oral and poster presentations in the fields of Electronics, Telecommunications, and Computers.info:eu-repo/semantics/publishedVersio

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster

Toward Light-Controlled Supramolecular Peptide Dimerization

The selective photodeprotection of the NVoc-modified FGG tripeptide yields the transformation of its 1:1 receptor−ligand complex with cucurbit[8]uril into a homoternary FGG2@CB8 assembly. The resulting lightinduced dimerization of the model peptide provides a tool for the implementation of stimuli-responsive supramolecular chemistry in biologically relevant contexts.The work was supported by the Associate Laboratory for Green ChemistryLAQV (UIDB/50006/2020) and by the Applied Molecular Biosciences UnitUCIBIO (UIDB/ 04378/2020), both financed by FCT. FCT/MCTES is also acknowledged for supporting the National Portuguese NMR Network (ROTEIRO/0031/2013-PINFRA/22161/2016, cofinanced by FEDER through COMPETE 2020, POCI, PORL, and FCT through PIDDAC) and for the grants PTDC/QUICOL/32351/2017, PTDC/QUI-QFI/30951/2017, and CEECIND/00466/2017 (N.B.). U.P. thanks the Spanish Ministry of Science, Innovation, and Universities (CTQ2017-89832-P). We are grateful to Dr. J.P. Da Silva for the mass spectrometry data (equipment financed by CRESC Algarve 2020 and COMPETE 2020; project EMBRC.PT ALG-01-0145-FEDER022121). Funding for open access charge: Universidad de Huelva / CBU

Litoestratigrafía del “Dominio Esquisto-Grauváquico” en Portugal: una reevaluación

A synthesis of the knowledge of the Schist-Greywacke Domain (SGD) in Portugal is here presented. Until recently, this sequence assumed the designation of Dúrico-Beirão Supergroup composed by the Douro Group (DG) and the Beiras Group (BG). The DG is considered of Neoproterozoic – Cambrian age and the BG is of Neoproterozoic age. The identification and mapping in the BG of an unconformity as the Cadomian unconformity identified in Spain, which splits the Neoproterozoic in “lower Alcudian” and “upper Alcudian”, is a turning point for the understanding and establishment of consistent stratigraphic sequences that now compose the Fróia and the Lousã groups assembled in the Beiras Supergroup. These new groups are correlated with the Neoproterozoic sequences currently recognized in Spain: the Lousã group is equivalent to the Ibor Group (upper Alcudian) and the Fróia Group is equivalent pro parte, to the Domo Extremeño Supergroup.  En este trabajo se presenta una síntesis del conocimiento actual del Dominio del Complejo Esquisto-Grauváquico en Portugal. Hasta al presente esta secuencia ha sido designada como Supergrupo Dúrico-Beirão, compuesto por el Grupo Douro (GD) y por el Grupo Beiras (GB). El GD se consideraba de edad Ediacárico superior – Cámbrico inferior y el GB era atribuido al Neoproterozoico. La identificación y cartografía en el GB de una discordancia correlacionable con la del Cadomiense identificada en España, que divide el Neoproterozoico en "Alcudiense inferior" y "Alcudiense superior", es un punto de inflexión para la comprensión y el establecimiento de secuencias estratigráficas consistentes, que ahora componen los grupos Fróia y Lousã reunidos en el Supergrupo Beiras. Estos nuevos grupos se correlacionan con las secuencias neoproterozoicas actualmente reconocidas en España: el Grupo Lousã es equivalente al Grupo Ibor (Alcudiense Superior) y el Grupo Fróia es equivalente, pro parte, al Supergrupo Domo Extremeño

Litoestratigrafía del “Dominio Esquisto-Grauváquico” en Portugal: una reevaluación

[Abstract] A synthesis of the knowledge of the Schist-Greywacke Domain (SGD) in Portugal is here presented. Until recently, this sequence assumed the designation of Dúrico-Beirão Supergroup composed by the Douro Group (DG) and the Beiras Group (BG). The DG is considered of Neoproterozoic – Cambrian age and the BG is of Neoproterozoic age. The identification and mapping in the BG of an unconformity as the Cadomian unconformity identified in Spain, which splits the Neoproterozoic in “lower Alcudian” and “upper Alcudian”, is a turning point for the understanding and establishment of consistent stratigraphic sequences that now compose the Fróia and the Lousã groups assembled in the Beiras Supergroup. These new groups are correlated with the Neoproterozoic sequences currently recognized in Spain: the Lousã group is equivalent to the Ibor Group (upper Alcudian) and the Fróia Group is equivalent pro parte, to the Domo Extremeño Supergroup.[Resumen] En este trabajo se presenta una síntesis del conocimiento actual del Dominio del Complejo Esquisto-Grauváquico en Portugal. Hasta al presente esta secuencia ha sido designada como Supergrupo Dúrico-Beirão, compuesto por el Grupo Douro (GD) y por el Grupo Beiras (GB). El GD se consideraba de edad Ediacárico superior – Cámbrico inferior y el GB era atribuido al Neoproterozoico. La identificación y cartografía en el GB de una discordancia correlacionable con la del Cadomiense identificada en España, que divide el Neoproterozoico en "Alcudiense inferior" y "Alcudiense superior", es un punto de inflexión para la comprensión y el establecimiento de secuencias estratigráficas consistentes, que ahora componen los grupos Fróia y Lousã reunidos en el Supergrupo Beiras. Estos nuevos grupos se correlacionan con las secuencias neoproterozoicas actualmente reconocidas en España: el Grupo Lousã es equivalente al Grupo Ibor (Alcudiense Superior) y el Grupo Fróia es equivalente, pro parte, al Supergrupo Domo Extremeño