This article describes some filtering methods to remove artifacts from the EMG signal envelope. Diverse EMG waveforms are studied using the Kalman filter (KF) and unbiased finite impulse response (UFIR) filter. The filters are developed in discrete-time state-space for Gauss-Markov colored measurement noise (CMN) and termed as cKF and cUFIR. It is shown that a choice of a proper CMN factor allows extracting the EMG waveform envelope with a high robustness. Extensive investigation have shown that the cKF and cUFIR filter are most efficient when the density is low of the motor unit action potential (MUAP) of the EMG and the Hilbert transform is required. Otherwise, when the envelope is well-pronounced and well-shaped with sharp edges due to a high MUAP density, the filters can be applied directly without using the Hilbert transform.
In this paper, we proposed a system to integrate optical and electronic instrumentation devices to predict a mode-locking fiber laser response, using a remote data acquisition with processing through an artificial neural network (ANN). The system is made up of an optical spectrum analyzer (OSA), oscilloscope (OSC), polarimeter (PAX), and the data acquisition automation through transmission control protocol/internet protocol (TCP/IP). A graphic user interface (GUI) was developed for automated data acquisition with the purpose to study the operational characteristics and stability at the passively mode-locked fiber laser (figure-eight laser, F8L) output. Moreover, the evolution of the polarization state and the behavior of the pulses are analyzed when polarization is changed by proper control plate adjustments. The data is processed using deep learning techniques, which provide the characteristics of the pulse at the output. Therefore, the parameter classification-identification is in accordance with the input polarization tilt used for the laser optimization.
Environmental monitoring requires an analysis of large and reliable amount of data collected through node stations distributed over a very wide area. Equipments used in such stations are often expensive that limits the amount of sensing stations to be deployed. The technology known as Wireless Sensor Networks (WNS) is a viable option to deliver low-cost sensor information. However, electromagnetic interference, damaged sensors, and the landscape itself often cause the network to suffer from faulty links as well as missing and corrupted data. Therefore robust estimators are required to mitigate such effects. In this sense, the unbiased finite impulse response (UFIR) filter is used as a robust estimator for applications over WSN, especially when the process statistics are unknown. In this paper, we investigate the robustness of the distributed UFIR (dUFIR) filter with optimal consensus on estimates against missing and incorrect data. The dUFIR algorithm is tested in two different scenarios of very unstable WSN using real data. It is shown that the dUFIR filter is more suitable for real life applications requiring the robustness against missing and corrupted measurements under the unknown noise statistics.
Environmental monitoring requires an analysis of large and reliable amount of data collected through node stations distributed over a very wide area. Equipment used in such stations are often expensive that limits the amount of sensing stations to be deployed. The technology known as Wireless Sensor Networks (WNS) is a viable option to deliver low-cost sensor information. However, unpredictable issues such as interference from electromagnetic sources, damaged and unstable sensors and the landscape itself may cause the network to suffer from unstable links as well as missing and corrupted data. Therefore robust estimators are required to mitigate such effects. In this sense,the unbiased finite impulse response (UFIR) filter is used as a robust estimator for applications over WSN, especially when the process statistics are unknown. In this paper, we investigate the robustness of the distributed UFIR (dUFIR) filter with optimal consensus on estimates against missing and incorrect data. The dUFIR algorithm is tested in two different scenarios of very unstable WSN using real data. It is shown that the dUFIR filteris more suitable for real life applications requiring the robustness against missing and corrupted measurements under the unknown noise statistics.
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