Results 281 to 290 of about 887,103 (337)

Validation and comparative study of the Motus system for accurately identifying movement behaviours using different sampling frequencies. [PDF]

Sci Rep
Ludvigsen TP, Hørlück SAS, Larsen JR, Lund CB, Hettiarachchi P, Brandt M, Gupta N.   +6 more
europepmc   +1 more source

Design of a Modularized IoT Multi-Functional Sensing System and Data Pipeline for Digital Twin-Oriented Real-Time Aircraft Structural Health Monitoring. [PDF]

Sensors (Basel)
Guo S, West A, Papuga J, Theodossiades S, Jiang J.   +4 more
europepmc   +1 more source

An Improved Extensive Cancellation Method for Clutter Removal in Passive Bistatic Radar. [PDF]

Sensors (Basel)
Chen G, Su S, Zhang D, Wang S, Ping Y, Li F.   +5 more
europepmc   +1 more source

A rapid detection method for coal dust thickness in heavy-haul railway ballast based on time and frequency domain response characteristics from GPR signal. [PDF]

Sci Rep
Yang W, Gao J, Xu Z.
europepmc   +1 more source

High-dimensional solid-state NMR facilitated by transverse-mixing optimal control

Linser R, Klein A, Blahut J, Tošner Z, Blokhin D, Vasa S.   +5 more
europepmc   +1 more source

Similarity-aware VAE with wavelet-convolutional 1D-CNN for rolling bearing fault diagnosis. [PDF]

PLoS One
Xiong W, Xiao N, Wang R.
europepmc   +1 more source

Signal processing based on irregular sampling : reconstruction, compression, and signal transformation

openaire   +1 more source

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Sampling and processing of color signals

IEEE Transactions on Image Processing, 1996
Digital signal processing tools are used to determine the proper sampling of color spectra and the effect of sampling on the accuracy of derived properties such as CIE tristimulus values and color rendering indexes. It is found that 10 nm sampling is adequate for most applications, but not for more exacting textile and paint matching applications ...
H J, Trusseli, M S, Kulkarni
openaire   +2 more sources

Compressed Signal Processing on Nyquist-Sampled Signals

IEEE Transactions on Computers, 2016
Pattern-recognition algorithms from the domain of machine learning play a prominent role in embedded sensing systems, in order to derive inferences from sensor data. Very often, such systems face severe energy constraints. The focus of this work is to mitigate the computational energy by exploiting a form of compression which preserves a similarity ...
Jie Lu, Naveen Verma, Niraj K. Jha
openaire   +1 more source