Wrist vascular biometric recognition using a portable contactless system

Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR®. The results obtained by combining these three elements, TGS-CVBR®, PIS-CVBR®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system.Publicad

Vein biometric recognition on a smartphone

Topic: Intelligent Biometric Systems for Secure Societies.Human recognition on smartphone devices for unlocking, online payment, and bank account verification is one of the significant uses of biometrics. The exponential development and integration of this technology have been established since the introduction in 2013 of the fingerprint mounted sensor in the Apple iPhone 5s by Apple Inc.© (Motorola© Atrix was previously launched in 2011). Nowadays, in the commercial world, the main biometric variants integrated into mobile devices are fingerprint, facial, iris, and voice. In 2019, LG© Electronics announced the first mobile exhibiting vascular biometric recognition, integrated using the palm vein modality: LG© G8 ThinQ (hand ID). In this work, in an attempt to become the become the first research-embedded approach to smartphone vein identification, a novel wrist vascular biometric recognition is designed, implemented, and tested on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 devices. The near-infrared camera mounted for facial recognition on these devices accounts for the hardware employed. Two software algorithms, TGS-CVBR® and PIS-CVBR®, are designed and applied to a database generation and the identification task, respectively. The database, named UC3M-Contactless Version 2 (UC3M-CV2), consists of 2400 contactless infrared images from both wrists of 50 different subjects (25 females and 25 males, 100 individual wrists in total), collected in two separate sessions with different environmental light environmental light conditions. The vein biometric recognition, using PIS-CVBR®, is based on the SIFT®, SURF®, and ORB algorithms. The results, discussed according to the ISO/IEC 19795-1:2019 standard, are promising and pave the way for contactless real-time-processing wrist recognition on smartphone devices

Deep Learning for Vein Biometric Recognition on a Smartphone

The ongoing COVID-19 pandemic has pointed out, even more, the important need for hygiene contactless biometric recognition systems. Vein-based devices are great non-contact options although they have not been entirely well-integrated in daily life. In this work, in an attempt to contribute to the research and development of these devices, a contactless wrist vein recognition system with a real-life application is revealed. A Transfer Learning (TL) method, based on different Deep Convolutional Neural Networks architectures, for Vascular Biometric Recognition (VBR), has been designed and tested, for the first time in a research approach, on a smartphone. TL is a Deep Learning (DL) technique that could be divided into networks as feature extractor, i.e., using a pre-trained (different large-scale dataset) Convolutional Neural Network (CNN) to obtain unique features that then, are classified with a traditional Machine Learning algorithm, and fine-tuning, i.e., training a CNN that has been initialized with weights of a pre-trained (different large-scale dataset) CNN. In this study, a feature extractor base method has been employed. Several architecture networks have been tested on different wrist vein datasets: UC3M-CV1, UC3M-CV2, and PUT. The DL model has been integrated on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 smartphones obtaining high biometric performance, up to 98% of accuracy and less than 0.4% of EER with a 50–50% train-test on UC3M-CV2, and fast identification/verification time, less than 300 milliseconds. The results infer, high DL performance and integration reachable in VBR without direct user-device contact, for real-life applications nowadays

Modelo para la mejora de la calidad de la formación en ingeniería mecánica aplicado a la ineniería industrial

This doctoral thesis is structured in six chapters, of which the five first are dedicated to the theoretical part, and the sixth and last one to the practical part. In the chapter first is analyzed the present situation of the public university from the point of view of the factors that affect to its general environment, emphasizing quality like an exigency for the success, mainly in its external slope of clients satisfaction. In the chapter second is studied engineering education at general level and in relation to the degrees of industrial engineering. It's pointed out the importance of Higher Engineering for Education Europe (H3E) Agency standards, for the accreditation of the degrees in the New Higher Education European Space. The chapter third is centered in the industrial engineering education at particular level, for the degrees distributed by the Higher Polytechnical School of Algeciras (University of Cadiz, Spain). It's considered the formation contributed by the area of knowledge of mechanical engineering to each one of these degrees, and the necessity to reorient this university product to the necessities of the clients (employers) of the next surroundings. The chapter fourth is dedicated to justify the necessity and utility of designing the Model as improvement tool, protagonist of this thesis. For it are analyzed other management models recognized, in their aspect related to the reorientation to the clients necessities, making clear their inadecuidad for such aim. In the chapter fifth is shown the design of the Model at general level, with the Methodology of performance and the resources for the diagnosis, like tool for reorienting the formation distributed by an general educational unit to the employers necessities. In the chapter sixth is developed the application of the Model, designed to orient the formation distributed particularly by the area of mechanical engineering (treated in the chapter third) to the necessities of the companies of its surroundings, this is the main industrial enterprises placed at Field of Gibraltar (south of Spain). Finally are shown the results and the proposed improvement actions, like main conclusions of this thesis, for the improvement of the formation of each one of the degrees of the Higher Polytechnical School of Algeciras.352 página

Inspirational perspectives and principles on the use of catalysts to create sustainability

Producción CientíficaMost of the products on which our welfare state is based are composed of chemicals. The growth of the world's population, its ageing and the continuous improvement of welfare state aspirations augur an increase in the needs for all these everyday products in our lives. A high percentage of these chemicals are synthesised using catalysis. In this perspective paper, we highlight the importance of catalysis, which is at the heart of chemical processes, and therefore one of the tools for creating products that drive sustainability. We have compiled twelve methodological best practices in catalyst design and conception that can serve as inspiration for the creation and improvement of catalysts. We include some application examples to illustrate this.Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (project PID2019-105975GB-I00

Sistema de visión estereoscópica para navegación autónoma de vehículos no tripulados

La visión estereoscópica artificial es un campo muy amplio que forma parte de lo que se conoce como visión por computador. Técnicamente consiste en el procesamiento de dos imágenes obtenidas mediante sendas cámaras, a partir de una escena tridimensional 3D. Este procesamiento está orientado a reconstruir la escena en 3D a partir de las dos imágenes, izquierda y derecha. Un aspecto que cabe destacar es que las cámaras están desplazadas una cierta distancia, tal y como ocurre con nuestros ojos. El trabajo del computador consiste en identificar en ambas imágenes aquellos píxeles en las dos imágenes que se corresponden con la misma entidad física en la escena 3D, usando para ello algoritmos especializados. La distancia que separa estos píxeles se conoce como disparidad. La medida de la disparidad sirve para obtener la distancia a la que se sitúa físicamente ese objeto en la escena con respecto a las dos cámaras. La visión estereoscópica es un campo que tiene numerosas aplicaciones y en el que a día de hoy se están invirtiendo numerosos recursos en investigación. Concretamente, una de esas aplicaciones es la detección de obstáculos por parte de robots. Nuestro proyecto está orientado hacia esa aplicación práctica, si bien se centra exclusivamente en los aspectos relacionados con la correspondencia de los elementos homólogos en las imágenes. Para ello hemos implementado diversas técnicas y algoritmos de visión estereoscópica usando el lenguaje de programación C#. [ABSTRACT] Stereo vision is a broad eld that is part of computer vision. Technically, it consists of the processing of two images adquired by two cameras, from a given scenario. This processing is aimed to reconstruct the 3D scene from both images, namely left and right images. One thing that is worth mentioning is that the two cameras are shifted a certain distance, as it happens with our eyes. The computer basically identies in both images those pixels that match, using specialized algorithms. The distance that separates those pixels is known as disparity. Disparity is next used in the calculation of the distance between the object in the scene and the cameras. Stereo vision (also known as stereopsis) is a eld with multiple applications and in which it is invested many resources in research. One of those applications is the detection of obstacles by robots. Our project is oriented towards this practical application; although this work is focused only on the computation of the disparities, i.e. the correspondence between pixels in the images. We have implemented several stereo vision techniques and algorithms using the C# programming language

Gait-based Gender Classification Considering Resampling and Feature Selection

Two intrinsic data characteristics that arise in many domains are the class imbalance and the high dimensionality, which pose new challenges that should be addressed. When using gait for gender classification, benchmarking public databases and renowned gait representations lead to these two problems, but they have not been jointly studied in depth. This paper is a preliminary study that pursues to investigate the benefits of using several techniques to tackle the aforementioned problems either singly or in combination, and also to evaluate the order of application that leads to the best classification performance. Experimental results show the importance of jointly managing both problems for gait-based gender classification. In particular, it seems that the best strategy consists of applying resampling followed by feature selection

Introducing multi-energy ratios as an alternative to multi-energy calibration for Br determination: Via high-resolution continuum source graphite furnace molecular absorption spectrometry. A case study

This manuscript explores the advantages of using multi-signal calibration approaches for the determination of non-metals via high-resolution continuum source graphite furnace molecular absorption spectrometry (HR CS GFMAS), targeting Br as an example. Besides multi-energy calibration (MEC), a novel approach deriving from it, multi-energy ratios (MER), is introduced and compared under different conditions. This approach makes use of the same data but in a different way, such that no linear regression is performed; instead, ratios are calculated. This article investigates the potential errors deriving from the use of amounts of spike dissimilar from the sample content, leading to too high (close to 1) or too low (close to 0) slopes/ratios, setting the best conditions in terms of precision and accuracy for the intended determination in the range of approx. 0.5 to 0.6. Also, situations where the use of MER could be recommended over MEC are identified: namely when only a few transitions of sufficient sensitivity and free from overlaps are available or else, many transitions but of similar sensitivity, which may occur when HR CS GFMAS is deployed. Otherwise, for multiple transitions covering a wider sensitivity range, use of linear regression and thus, of MEC, seems favoured, as a better precision can be achieved. The calculation of limits of detection and quantification for both approaches is also discussed. It is finally further demonstrated that these multi-signal strategies help in solving chemical interferences, which very often hamper the determination of non-metals with HR CS GFMAS, and they do so in a simple way, without the need for laborious work or for the preparation of several standards and sample aliquots, therefore making them a very intriguing option when this technique is deployed

Embroidered textile frequency-splitting sensor based on stepped-impedance resonators

©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents an embroidered textile frequency-splitting microwave sensor based on a pair of identical stepped-impedance resonators (SIRs) loading a microstrip transmission line. The sensor is implemented by means of conductive threads. The sensing region of the proposed structure is the capacitive square patch of one of the SIRs. If such region is kept unaltered, the structure is symmetric, and the frequency response (transmission coefficient) exhibits a single transmission zero. However, if symmetry is broken (e.g., through liquid absorption in the sensing region), the frequency response of the proposed sensor exhibits two transmission zeros (frequency splitting). The difference (in frequency and magnitude) between such zeros (or notches) is intimately related to the dielectric properties of the absorbed liquids to be sensed / detected. The proposed sensing structure is applied to the detection of deionized (DI) water absorption, and to the quantification of the number of DI water drops. The maximum measured sensitivity is found to be 2.70 MHz /µl and 0.03 dB /µl for the incremental frequency and incremental magnitude of the notches.This work was supported by MICIIN-Spain (projects PID2019-103904RB-I00, TEC2016-79465-R, and PDC2021-121085-I00),Generalitat de Catalunya (project 2017SGR-1159), Institució Catalana de Recerca i Estudis Avançats (who awarded Ferran Martín), and by FEDER funds.Peer ReviewedPostprint (author's final draft

Efficient algorithms for constructing D- and I-optimal exact designs for linear and non-linear models in mixture experiments

The problem of finding optimal exact designs is more challenging than that of approximate optimal designs. In the present paper, we develop two efficient algorithms to numerically construct exact designs for mixture experiments. The first is a novel approach to the well-known multiplicative algorithm based on sets of permutation points, while the second uses genetic algorithms. Using (i) linear and non-linear models, (ii) D/- and I-optimality criteria, and (iii) constraints on the ingredients, both approaches are explored through several practical problems arising in the chemical, pharmaceutical and oil industry