Detection algorithms for hyperspectral imaging applications: a signal processing perspective

<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>

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2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

10.1109/whispers47970.2019 ◽

2019 ◽

Keyword(s):

Remote Sensing ◽

Signal Processing ◽

Hyperspectral Imaging

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Enhanced signal processing for conventional and unconventional PD measuring methods: Wavelet de-noising, automatic detection algorithms and averaging for arrival time-based PD location in transformers and power cables

2008 International Conference on Condition Monitoring and Diagnosis ◽

10.1109/cmd.2008.4580479 ◽

2008 ◽

Cited By ~ 5

Author(s):

Sacha Markalous ◽

Thomas Strehl ◽

Christoph Herold ◽

Thomas Leibfried

Keyword(s):

Signal Processing ◽

Arrival Time ◽

Automatic Detection ◽

Power Cables ◽

Measuring Methods ◽

Detection Algorithms

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Classification and anomaly detection algorithms for weak hyperspectral signal processing

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) ◽

10.1109/whispers.2016.8071758 ◽

2016 ◽

Author(s):

Pierre Lahaie

Keyword(s):

Signal Processing ◽

Anomaly Detection ◽

Detection Algorithms

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Deeply learned broadband encoding stochastic hyperspectral imaging

Light Science & Applications ◽

10.1038/s41377-021-00545-2 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Wenyi Zhang ◽

Hongya Song ◽

Xin He ◽

Longqian Huang ◽

Xiyue Zhang ◽

...

Keyword(s):

Artificial Intelligence ◽

Signal Processing ◽

High Resolution ◽

Hyperspectral Imaging ◽

Spatial Information ◽

Filter Design ◽

Field Of View ◽

Noise Tolerance ◽

Limited Data ◽

Data Volume

AbstractMany applications requiring both spectral and spatial information at high resolution benefit from spectral imaging. Although different technical methods have been developed and commercially available, computational spectral cameras represent a compact, lightweight, and inexpensive solution. However, the tradeoff between spatial and spectral resolutions, dominated by the limited data volume and environmental noise, limits the potential of these cameras. In this study, we developed a deeply learned broadband encoding stochastic hyperspectral camera. In particular, using advanced artificial intelligence in filter design and spectrum reconstruction, we achieved 7000–11,000 times faster signal processing and ~10 times improvement regarding noise tolerance. These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view, previously unreachable with compact computational spectral cameras.

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