Compressed sensing with continuous parametric reconstruction

This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted

The analysis of exponential signals by maximum likelihood estimation

Peer Reviewe

DYNAMIC DAC TESTING BY REGISTRATION OF THE INPUT CODE WORDIN EQUALITY WHEN THE DAC OUTPUT MATCHES A REFERENCE SIGNAL

ABSTRAC

MODEL FOR GENERATING SIMPLE SYNTHETIC ECG SIGNALS

This paper proposes a mathematical model for generating synthetic artificial ECG signal based on geometrical features of a real ECG signal. By variation of its parameters each particular wave of PQRST complex can be adjusted as needed allowing the generation of arbitrary ECG patterns typical for diseases and arrhythmia. The input parameters are treated to avoid mixing order of PQRST waves in case of automatic parameter variation and allow generating different patterns for each subsequent heartbeat independently. Each particular wave is modelled using an elementary trigonometric function or a Gaussian monopulse. Including possible addition of equipment noise as well as respiration frequency such an artificial signal can be used as a test signal for some signal processing methods. The model was tested by comparison of synthetized patterns against patterns generated by LabVIEW Biomedical Toolkit, while the parameters of model are found using the differential evolution algorithm

Full Information from Measured ADC Test Data using Maximum Likelihood Estimation

Abstract ADC testing is often done using sine wave excitation (see e.g. IEEE standard 1241). A sine wave is fitted to the measured data in least squares sense, and the residuals are analyzed further. In recent papers, it has been recognized that even more (and more precise) information can be extracted by the solution of the maximum likelihood equations. This is an improvement to the usual three-parameter and four-parameter fits. In this paper practical implementation of this algorithm is suggested. Then, theoretical background is overviewed. Further investigations lead to the statement that the same principle can be extended to any measurement which uses an excitation signal which can be described with a few parameters. A candidate for this is an exponential signal, with 3 parameters: e.g. start value, end (steady-state) value, and time constant. The maximum likelihood (ML) equations yield a solution for these too, more accurate than least squares (LS) fitting. Reasonable approximations make the ML problem solvable in practice

Optimization Paradigm in the Signal Recovery after Compressive Sensing

Compressive sensing is a processing approach aiming to reduce the data stream from the observed object with the inherent sparsity using the optimal signal models. The compression of the sparse input signal in time or in the transform domain is performed in the transmitter by the Analog to Information Converter (AIC). The recovery of the compressed signal using optimization based on the differential evolution algorithm is presented in the article as an alternative to the faster pseudoinverse algorithm. Pseudoinverse algorithm results in an unambiguous solution associated with lower compression efficiency. The selection of the mathematically appropriate signal model affects significantly the compression efficiency. On the other hand, the signal model influences the complexity of the algorithm in the receiving block. The suitability of both recovery methods is studied on examples of the signal compression from the passive infrared (PIR) motion sensors or the ECG bioelectric signals

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion. The signal is recovered with minimal information loss from the reduced data record via compressed sensing reconstruction. Several methods of analog to information conversion are described with focus on numerical complexity and implementation in existing embedded devices. Two novel analog to information conversion methods are proposed, distinctive by their computational simplicity - direct subsampling and subsampling with integration. Proposed sensing methods are intended for and evaluated with real water parameter signals measured by a wireless sensor network. Compressed sensing proves to reduce the data transfer rate by >80 % with very little signal processing performed at the sensing side and no appreciable distortion of the reconstructed signal