Nonlinear inverse filtering technique for estimating the glottal‐area waveform

Development of technique for synthesizing multilevel sequences with good correlation properties is very useful for several radar applications where as a set of phase and amplitude coded sequences will be synthesized directly for compression technique. In reality, the signal processing also been significant to transmit or store signals, to enhance desired signal component and to extract useful information carried by signals. Consequently, this paper describes affectively methods to generating the finite length multilevel sequence of any length that have low side lobe energy (SLE) and improved energy ratio (ER) in their autocorrelation function (ACF). Testing for the stability and the analyzing of systems zero pattern using z-transform for the generating sequence indicates a possible position of roots in the radius of circle lies. This is illustrated by application of 13-element Huffman code as a starting sequence, this technique more low complexity and compatible compared inverse filtering technique.

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Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles: Fundamental Developments and Laboratory Verifications

Sensors ◽

10.3390/s20041190 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1190 ◽

Cited By ~ 2

Author(s):

Nima Shirzad-Ghaleroudkhani ◽

Mustafa Gül

Keyword(s):

Inverse Filtering ◽

Inverse Filter ◽

Vibration Data ◽

Collection Device ◽

Frequency Identification ◽

Suspension Stiffness ◽

Recorded Data ◽

Filtering Technique ◽

Data Source ◽

Acceleration Signals

This paper puts forward a novel methodology of employing inverse filtering technique to extract bridge features from acceleration signals recorded on passing vehicles using smartphones. Since the vibration of a vehicle moving on a bridge will be affected by various features related to the vehicle, such as suspension and speed, this study focuses on filtering out these effects to extract bridge frequencies. Hence, an inverse filter is designed by employing the spectrum of vibration data of the vehicle when moving off the bridge to form a filter that will remove the car-related frequency content. Later, when the same car is moving on the bridge, this filter is applied to the spectrum of recorded data to suppress the car-related frequencies and amplify the bridge-related frequencies. The effectiveness of the proposed methodology is evaluated with experiments using a custom-built robot car as the vehicle moving over a lab-scale simply supported bridge. Nine combinations of speed and suspension stiffness of the car have been considered to investigate the robustness of the proposed methodology against car features. The results demonstrate that the inverse filtering method offers significant promise for identifying the fundamental frequency of the bridge. Since this approach considers each data source separately and designs a unique filter for each data collection device within each car, it is robust against device and car features.

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