Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide

EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291

Cell Detection by Functional Inverse Diffusion and Non-negative Group Sparsity−-Part I: Modeling and Inverse Problems

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs' Green function to derive a novel parametrization of the acquired images. This parametrization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a non-negative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.Comment: published, 15 page

Multi-wavelength pyrometric systems for emissivity-independent non-contact temperature sensing

A Multi-Wavelength Imaging Pyrometer (M-WIP) for real-time remote sensing of temperature profiles of targets with unknown emissivity was developed and demonstrated. To measure the spectral radiance of a target at several distinct wavelengths an M-WIP system was implemented based on an 320x122-element PtSi IR-CCD imager with an assembly of 7 narrow-band 1k filters in the range from 1790nm to 4536nm. A real-time algorithm for simultaneous estimation of the temperature and model parameters of the target emissivity from the least-squares fit of the theoretical model of 1k camera output signal to the experimental spectral measurements was developed and implemented. This rea1-time least-squares minimization was accomplished by combination of Levenberg-Marquardt and simulated annealing algorithms. The least-squares-based calibration algorithm was developed for evaluation of effective values of peak transmissions and center wavelengths of M-WIP channels based on the detection of radiation from pre-calibrated blackbody source. To achieve high radiometric accuracy, compensation for the dark current charge as function of the detected signal level was implemented. The effect of the response non-linearity of IR imager was minimized by developing an algorithm for imager operation at fixed pre-selected signal level for each M-WIP spectral channel based on adaptive control of the duration of the optical integration time of the imager. Initial testing demonstrated an accuracy of ±l.0°C for real-time temperature measurements of the center of the blackbody aperture in the range from 500°C to 1000°C. Temperature resolution of ±3°C was demonstrated for the blackbody source viewed through a double side polished silicon wafer with unknown spectral transmissivity in the temperature range from 500°C to 900°C