Results 21 to 30 of about 1,805,271 (277)

Spike sorting at sub-Nyquist rates [PDF]

open access: yes2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
Spike sorting relies on the ability to establish the temporal occurrence of action potentials and their relation to specific neurons. Neural information is intrinsically compressible and as such suitable for sparse sampling. Potentially, this should allow for the use of multi-channel recordings, which is particularly advantageous to improve spike ...
Jose Caballero   +3 more
openaire   +1 more source

Wideband spectrum sensing based on advanced sub-Nyquist sampling structure

open access: yesEURASIP Journal on Advances in Signal Processing, 2022
As the bandwidth increases, the high-speed sampling rate becomes the bottleneck for the development of wideband spectrum sensing. Wideband spectrum sensing with sub-Nyquist sampling attracts more attention and modulated wideband converter (MWC) is an ...
Xue Wang, Qian Chen, Min Jia, Xuemai Gu
doaj   +1 more source

Sub-Nyquist Sampling OFDM Radar

open access: yesIEEE Transactions on Radar Systems, 2023
In this paper, we propose a sub-Nyquist sampling (SNS) orthogonal frequency-division multiplexing (OFDM) radar system capable of reducing the analog-to-digital converter (ADC) sampling rate in OFDM radar without any additional manipulations of its hardware and waveform.
Kawon Han   +2 more
openaire   +2 more sources

Directional DC Charge-Transfer Resistance on an Electrode–Electrolyte Interface in an AC Nyquist Curve on Lead-Acid Battery

open access: yesApplied Sciences, 2020
Both the frequency domain Nyquist curve of electrochemical impedance spectroscopy (EIS) and time domain simulation of DC equivalent first principle linear circuit (FPLCDCequ) are some of the fundamentals of lead-acid batteries management system design ...
Wubin Wang   +5 more
doaj   +1 more source

Towards sub-nyquist cognitive radar [PDF]

open access: yes2016 IEEE Radar Conference (RadarConf), 2016
Cognitive radar (CR) has recently been considered as a natural next step for traditional radar. The cognitive property assumes both transmitter and receiver to be able to dynamically adapt to environment changes. In this work, we propose to exploit sub-Nyquist sampling methods that have been originally proposed to reduce the sampling rate bottleneck at
Cohen, Deborah   +3 more
openaire   +1 more source

Wideband spectrum sensing based on modulated wideband converter with nested array

open access: yesIET Communications, 2021
Several spectrum sensing systems based on sub‐Nyquist sampling have been extensively studied to deal with difficulties of traditional wideband spectrum sensing in cognitive radio (CR) networks.
Qiuyue Li, Zhi Li, Jian Li
doaj   +1 more source

A Sub-Nyquist Uniform Linear Array Receiver Design

open access: yesIEEE Access, 2021
A design of sub-Nyquist uniform linear array receiver with different sampling rates is proposed, investigated, and reported in this paper. The proposed architecture increases functionality of conventional linear array receiver with modest modifications ...
Chi-Hao Cheng
doaj   +1 more source

Photonics-enabled sub-Nyquist radio frequency sensing based on temporal channelization and compressive sensing [PDF]

open access: yes, 2014
A novel approach to sensing broadband radio frequency (RF) spectrum beyond the Nyquist limit based on photonic temporal channelization and compressive sensing is proposed.
Wang, Chao   +3 more
core   +1 more source

Nyquist-Sampling and Degrees of Freedom of Electromagnetic Fields

open access: yes, 2021
A signal space approach is presented to study the Nyquist sampling, number of degrees of freedom and reconstruction of an electromagnetic field under arbitrary scattering conditions.
Thomas L. Marzetta (14356146)   +3 more
core   +1 more source

Deep Learning Based Sub-Nyquist Modulation Recognition [PDF]

open access: yes, 2023
In this paper, we designed a Convolutional Neural Network (CNN) for Sub-Nyquist modulation recognition and compare the performance Long Short-Term Memory (LSTM) network and Convolutional Long Short-term Deep Neural Network (CLDNN) respectively.
Li, S, Hu, S, Nilavalan, N
core  

Home - About - Disclaimer - Privacy