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Advances in Wearable Biosensors for Non-Invasive Biofluid Monitoring. [PDF]

Biosensors (Basel)
Mondal R, Saikia MJ.
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Signal denoising with average sampling

Digital Signal Processing, 2012
Based on the theory of average sampling, we present a new algorithm to reconstruct bandlimited signals from sampled values in the presence of zero mean, independent and identically distributed random noises. Numerical results show that our algorithm has good performance on denoising.
Wenchang Sun
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Sampling walls in signal detection of Bernoulli nonuniformly sampled signals

2013 IEEE International Conference on Communications (ICC), 2013
Best Paper Award for the Signal Processing for Communication Symposium, 2013 IEEE International Conference on Communications (ICC), atorgat per la IEEE Communications ...
Font Segura, Josep, Vázquez Grau, Gregorio, Riba Sagarra, Jaume   +2 more
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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
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Compressive sampling of correlated signals

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011
The recently developed theory of Compressive sensing (CS) has shown that sparse signals can be reconstructed from a much smaller number of measurements than their bandwidth suggests. In this paper we present a sampling scheme to acquire ensembles of correlated signals at a sub-Nyquist rate.
Ali Ahmed 0004, Justin K. Romberg
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Adaptive Sampling of Speech Signals

IEEE Transactions on Communications, 1974
The present work gives a proposed method of sampling speech signals with a nonuniform sampling rate according to the magnitude of the slope of the signal, provided that the average sampling rate satisfies Shannon's requirements. With this proposed method, the quality of the reconstructed speech signal is improved.
Abd El-Samie Mostafa, Mohsen M. T. El-Hagry   +1 more
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Robust Graph Signal Sampling

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
This paper considers the graph signal sampling problem when some of the selected samples are lost or unavailable due to sensor failures or adversarial erasures. We formulate a robust graph signal sampling problem where only a subset of selected samples are received, and the goal is to maximize the worst-case performance.
Basak Güler, Ajinkya Jayawant, Amir Salman Avestimehr, Antonio Ortega   +3 more
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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. Joel Trussell, Manish S. Kulkarni
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Ginibre sampling and signal reconstruction

2016 IEEE International Symposium on Information Theory (ISIT), 2016
The spatial distribution of sensing nodes plays a crucial role in signal sampling and reconstruction via wireless sensor networks. Although homogeneous Poisson point process (PPP) model is widely adopted for its analytical tractability, it cannot be considered a proper model for all experiencing nodes.
ZABINI, FLAVIO, Conti, Andrea
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Sampling theory for graph signals

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
We propose a sampling theory for finite-dimensional vectors with a generalized bandwidth restriction, which follows the same paradigm of the classical sampling theory. We use this general result to derive a sampling theorem for bandlimited graph signals in the framework of discrete signal processing on graphs.
Siheng Chen, Aliaksei Sandryhaila, Jelena Kovacevic   +2 more
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