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The objective of this article was to establish the first contextual approaches for the development of a framework focused on the design of automotive infotainment systems. Therefore, a method was developed to identify a group of characteristics of the infotainment system interfaces that ensure a good user experience by the user, according to previous studies. Besides, the establishment of usability is proposed as a basis for the development of the tool.

Diabetes is one of the four main non-communicable diseases worldwide. Despite not being a fatal disease, many complications derive from this illness that causes a drastic deterioration in the patient's health. Diabetes is a silent disease that, on many occasions, causes symptoms until the disease is already advanced, and due to the lack of education in health prevention, only a small part of the population undergoes routine laboratory studies. If this disease is detected on time, the quality of life could be improved. However, the simple facts of taking a blood sample, control studies are omitted. Besides, there is a need to sample the patient many times according to its severity and control. In the present work, we provide a novel technique based on the FTIR spectra of saliva samples to diagnose this disease. After analyzing the samples of 1,000 people, we found that it is possible to identify patients with this pathology through artificial neural networks and SVMr reliably. As it is not invasive and does not require reagents or complex processes, the proposed technique could be more agile and cheaper than traditional ones.

This paper presents a description of the first version of a set of guidelines that help in designing therapy-support virtual environments (VE) for patients on physical rehabilitation. The proposed guidelines were mainly identified by understanding the needs of the people involved in the physical therapy process through User-Centered Design (UCD) and based on Human-Computer Interaction (HCI). Initial user research was performed at facilities of the "Center for the Integral development of Families (DIF, in Spanish), a Mexican federal social assistance institution, and complemented by a literature review. Preliminary evaluation by panel of experts suggested good conceptual roots for this proposal but also several enhancements. The summary of these guidelines and the methodology to find them are presented below, ending with following actions for this work.

The objective of this paper was to propose a methodological process for the design of frameworks oriented to infotainment user interfaces. Four stages comprise the proposed process, conceptualization, structuring, documentation, and evaluation, in addition, these stages include activities, tasks, and deliverables to guide a work team during the design of a framework. To determine the stages and their components, an analysis of 42 papers was carried out through a systematic literature review in search of similarities during the design process of frameworks related to user interfaces. The evaluation method by a panel of experts was used to determine the validity of the proposal, the conceptual proposal was provided to a panel of 10 experts for their analysis and later a questionnaire in the form of a Likert scale was used to collect the information on the validation of the proposal. The results of the evaluation indicated that the methodological process is valid to meet the objective of designing a framework oriented to infotainment user interfaces.

The World Health Organization has declared that diabetes is one of the four leading causes of death attributable to non-communicable diseases. Currently, many devices allow monitoring blood glucose levels for diabetes control based mainly on blood tests. In this paper, we propose a novel methodology based on the analysis of the Fourier Transform Infrared (FTIR) spectra of saliva using machine learning techniques to characterize controlled and uncontrolled diabetic patients, clustering patients in groups of a low, medium, and high glucose levels, and finally performing the point estimation of a glucose value. After analyzing the obtained results with Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Linear Regression (LR), we found that using ANN, it is possible to carry out the characterizations mentioned above efficiently since it allowed us to identify correctly the 540 spectra that make up our database studying the region 4000-2000 cm−1.

Automated real-time tissue assessment using Raman spectroscopy for breast cancer detection is feasible; however, long-term specificity and sensitivity as reported so far in the literature can still be improved by a more reliable algorithm for breast cancer detection. Applying automatic reduction of background fluorescence to the Raman spectra and Bayesian classification on 18 discriminant Raman bands in the range of 1200–1800 cm−1, we achieved 100% accuracy in classifying breast biopsies of healthy and cancerous tissues, making it suitable for automated breast cancer diagnosis and appropriate for long-term use in real time in a surgery room or research scenarios. The long-term reliability of this approach was cross-validated using three methods: resubstitution, leave-one-out, and holdout. The holdout method has the potential of estimating the upper bound of classification error probability of the Bayesian classifier; the holdout method allowed us to perform 50,000 classification trials with only three mis...

En el presente artículo se da a conocer una alternativa algorítimica a los sistemas actuales de reconocimiento automático del habla (ASR), mediante una propuesta en la forma de realizar la caracterización de las palabras basada en una aproximación que usa la extracción de coeficientes de la codificación de predicción lineal (LPC) y la correlación cruzada. La implementación consiste en extraer las características fonéticas mediante los coeficientes LPC, después se forman vectores de patrones de la pronunciación conformados por el promedio de los coeficientes LPC de las muestras de las palabras obteniendo un vector característico de cada pronunciación mediante la autocorrelación de las secuencias de coeficientes LPC; estos vectores se utilizan para entrenar un clasificador de tipo perceptrón multicapa (MLP). Se realizaron pruebas de desempeño previo entrenamiento con los diferentes patrones de las palabras a reconocer. Se utilizó la fonética de los dígitos del cero al nueve como vocabulario objetivo, debido a su amplia aplicación, y para estimar el desempeño de este método se utilizaron dos corpus de pronunciaciones: el corpus UPA, que contempla en su base de datos la pronuncación de la región occidente de México, y el corpus Tlatoa, que hace lo propio para la región centro de México. Las señales en ambos corpus fueron adquiridas en el lenguaje español, y a una frecuencia de muestreo de 8kHz. Los porcentajes de reconocimiento obtenidos fueron del 96.7 y 93.3% para las modalidades de mono-locutor para el corpus UPA y múltiple-locutor para el corpus Tlatoa, respectivamente. Asimismo, se realizó una comparación contra dos métodos clásicos del reconocimiento de voz y del habla, Dynamic Time Warping (DTW) y Hidden Markov Models (HMM).

This work aims to design a filter to attenuate high- and medium- frequency noise in optical test images without changing the edges and original characteristics of the test image, generated by traditional filters (spatial or frequential). The noise produced by the LCD pixels (used as a diffraction grating in the Ronchi test) was analyzed. The diffraction is modulated by the spherical wavefront of the mirror under test, generating at least two frequency band noise levels. To reduce this bi-frequential noise, we propose to use an array of filters with the following structure: a low-pass frequential filter LPFF, a band- pass frequential filter BPFF and a circular mask spatial filter CMSF; thus obtaining the composed filter CF=LPFF-(BPFF)(CMSF). Various sizes of filters were used to compare their signal-to-noise ratio against simple filters (low-pass and band-stop).

In every pattern recognition problem there exist the need for variable and feature selection and, in many cases, feature generation. In pattern recognition, the term variable is usually understood as the raw measurements or raw values taken from the subjects to be classified, while the term feature is used to refer to the result of the transformations applied to the variables in order to transform them into another domain or space, in which a bigger discriminant capability of the new calculated features is expected; a very popular cases of feature generation are the use of principal component analysis (PCA), in which the variables are projected into a lower dimensional space in which the new features can be used to visualize the underlying class distributions in the original data [1], or the Fourier Transform, in which a few of its coefficients can represent new features [2], [3]. Sometimes, the literature does not make any distinction between variables and features, using them indistinctly [4], [5].

This article presents an innovative technique for solving the problem of finding the core within a fingerprint. The Radon transform and a tree clustering algorithm were key to locating the coordinates of the core. Binarization and high-pass filtering processes to improve the contrast in fingerprints are proposed. The core of a fingerprint is located in the geometric cross section of maxima and minima in the Radon transforms at 0° and 90°. The technique is very stable, since it only presents difficulties when the fingerprint core is located on the edges of the image or is nonexistent.

This paper presents an innovative approach to solve the on-line signature verification problem in the presence of skilled forgeries. Genetic algorithms (GA) and fuzzy reasoning are the core of our solution. A standard GA is used to find a near optimal representation of the features of a signature to minimize the risk of accepting skilled forgeries. Fuzzy reasoning here is carried out by Neural Networks. The method of a human expert examiner of questioned signatures is adopted here. The solution was tested in the presence of genuine, random and skilled forgeries, with high correct verification rates.

El presente artículo propone utilizar la convolución como método de reducción de ruido producido por la digitalización de señales electrocardiográ-ficas. Se implementan diferentes ventanas de convolución para obtener un perfil geométrico tipo pasa-bajos utilizando funciones con una estructura matemática básica (gaussiana, cuadrática, triangular y trigonométrica). Los resultados obtenidos muestran que con la aplicación del método propuesto se logra una mejor apreciación de las distintas variables que componen una señal electrocardiográfica. También se obtienen reducciones de ruido por debajo del 4% del ruido original como muestra del desempeño de las ventanas propuestas.