Results 281 to 290 of about 784,273 (337)

Fibre Bragg Grating Wavelength Shift Demodulation with Filtering and Enhancement of Spectra by Simple Nonlinear Signal Processing

Sławomir Cięszczyk, Patryk Panas, Krzysztof Skorupski, Marek Kida   +3 more
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Editorial: Wearable biosensing devices. [PDF]

Front Bioeng Biotechnol
Zou F, Xu T.
europepmc   +1 more source

An FPGA-Based SiNW-FET Biosensing System for Real-Time Viral Detection: Hardware Amplification and 1D CNN for Adaptive Noise Reduction. [PDF]

Sensors (Basel)
Hadded A, Ben Ayed M, Alshaya SA.
europepmc   +1 more source

A Wavelet-based Filtering Algorithm for Enhancing Signal Processing in Coriolis Flow Meters

David Wee Yang Khoo, Zhi Qun Ng, Leong Keey Seah   +2 more
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ULTRAWX: A ubiquitous realtime acoustic gesture information interaction system based on Tiou DODA. [PDF]

Sci Rep
Zhang Z, Hao Z, Li M.
europepmc   +1 more source

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Microelectromechanical filters for signal processing

[1992] Proceedings IEEE Micro Electro Mechanical Systems, 1992
Microelectromechanical filters based on coupled lateral microresonators are demonstrated. This new class of microelectromechanical systems (MEMS) has potential signal-processing applications for filters which require narrow bandwidth (high Q), good signal-to-noise ratio, and stable temperature and aging characteristics.
Liwei Lin, Roger T. Howe, Clark T.-C. Nguyen, Albert P. Pisano   +3 more
openaire   +2 more sources

Analog signal processing/filtering

IEEE Photonic Society 24th Annual Meeting, 2011
Optics has the potential to solve some of the most exciting problems in information systems. It promises crosstalk-free interconnects with essentially unlimited bandwidth, long-distance data transmission without skew and without power- and time-consuming regeneration, miniaturization, parallelism, and efficient implementation of important algorithms ...
Steve Zamek, Dawn T. H. Tan, Yeshaiahu Fainman, Boris Slutsky, L. Fang, Maziar P. Nezhad, Andrew Grieco, Kazuhiro Ikeda   +7 more
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Kriging filters for multidimensional signal processing

Signal Processing, 2005
The Wiener filter is the well-known solution for linear minimum mean square error (LMMSE) signal estimation. This filter assumes the mean to be known and usually constant. On the other hand, the Kriging filter is an incremental theory, developed within the Geostatistical community, with respect to that of Wiener filters.
Carl-Fredrik Westin, Juan Ruiz-Alzola, Carlos Alberola-López   +2 more
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Quadratic filters for signal processing

Proceedings of the IEEE, 1992
Polynomial (or Volterra) filters are introduced, and the quadratic filters are presented as the simplest example of such filters. The principle aspects and properties of quadratic filters are derived in the framework of the discrete Volterra expansion. Fixed as well as adaptive filters are considered in one-dimensional and multidimensional environments.
openaire   +3 more sources