16 research outputs found
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Modelling global human systems using a large-scale economic model, with some novel approaches to the analysis and visualisation of its outputs
We present an international economic model which follows the input-output and computable general equilibrium (CGE) literatures in being based in large part on empirical observation. The model is a compromise between the mathematical elegance and linear simplicity of the first approach, and the theoretically rigorous, highly non-linear nature of the second. Like both of these data-driven approaches, our new model is large-scale: each of up to around 200 countries is modelled with 35 economic sectors. This leads to challenges of both analysis and visualisation. We present some novel approaches to handling large-scale economic models which apply equally to input-output and CGE as they do to the model on which they are demonstrated here. Additionally, the model presents a simple set of coefficient, which allow for it to be extended in ways which are explicable to non-specialists and policy-makers. We present the use of these coefficient sets to study four global human systems: trade, security, migration and international development. We make the case that these are too often modelled in the context of a single country or pair of countries. Our approach shows how placing these models in a wider context offers new, broader perspectives
Libro de actas. XXXV Congreso Anual de la Sociedad Española de IngenierÃa Biomédica
596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio cientÃfico de conocimiento, experiencias y promoción de la I D i en IngenierÃa Biomédica. Un punto de encuentro de cientÃficos, profesionales de la industria, ingenieros biomédicos y profesionales clÃnicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clÃnica de la ingenierÃa biomédica.
En la presente edición, más de 160 trabajos de alto nivel cientÃfico serán presentados en áreas relevantes de la ingenierÃa biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales.
Cabe destacar las sesiones dedicadas a la competición por el Premio José MarÃa Ferrero Corral, y la sesión de competición de alumnos de Grado en IngenierÃa biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores
Analyzing Granger causality in climate data with time series classification methods
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio