Estimating acoustic flow parameters for multiple echoes within a biologically motivated signal processing framework

1997 ◽  
Vol 102 (5) ◽  
pp. 3153-3153
Author(s):  
Rolf Müller ◽  
Hans‐Ulrich Schnitzler
Keyword(s):  
Signal Processing ◽  
Acoustic Flow ◽  
Flow Parameters ◽  
Processing Framework

scholarly journals Signal-Processing Framework for Ultrasound Compressed Sensing Data: Envelope Detection and Spectral Analysis

2020 ◽  
Vol 10 (19) ◽  
pp. 6956
Author(s):  
Yisak Kim ◽  
Juyoung Park ◽  
Hyungsuk Kim
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Frequency Domain ◽  
Time Domain ◽  
Sampling Rate ◽  
Processing Technique ◽  
Original Form ◽  
Basis Matrix ◽  
Full Reconstruction ◽  
Processing Framework

Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signals can be reconstructed using fewer data samples collected at frequencies below the Nyquist sampling rate. However, further analysis of CS data in both time and frequency domains requires the reconstruction of the original form of the time-domain data, as traditional signal-processing techniques are designed for uncompressed data. In this paper, we propose a signal-processing framework that extracts spectral properties for frequency-domain analysis directly from under-sampled ultrasound CS data, using an appropriate basis matrix, and efficiently converts this into the envelope of a time-domain signal, avoiding full reconstruction. The technique generates more accurate results than the traditional framework in both time- and frequency-domain analyses, and is simpler and faster in execution than full reconstruction, without any loss of information. Hence, the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems handling large datasets.

A Signal-Processing Framework for Occlusion of 3D Scene to Improve the Rendering Quality of Views

2020 ◽  
Vol 29 ◽  
pp. 8944-8959
Author(s):  
Changjian Zhu ◽  
Hong Zhang ◽  
Qiuming Liu ◽  
Zhixian Zhuang ◽  
Li Yu
Keyword(s):  
Signal Processing ◽  
3D Scene ◽  
Processing Framework

scholarly journals An Adaptive Signal Processing Framework for PV Power Maximization

2015 ◽  
Vol 34 (9) ◽  
pp. 2973-2992 ◽  
Author(s):  
André A. Vidal ◽  
Vítor Grade Tavares ◽  
José C. Príncipe
Keyword(s):  
Signal Processing ◽  
Adaptive Signal Processing ◽  
Power Maximization ◽  
Adaptive Signal ◽  
Processing Framework
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