scholarly journals NMR Resolution Enhancement and Homonuclear Decoupling Using Non-Uniform Weighted Sampling

2020 ◽  
Author(s):  
Chris Waudby ◽  
John Christodoulou
Keyword(s):  
Linear Prediction ◽  
Resolution Enhancement ◽  
Simple Method ◽  
Uniform Sampling ◽  
Homonuclear Decoupling ◽  
Time Domain Signal ◽  
Window Functions ◽  
Uniform Noise ◽  
Increased Sensitivity ◽  
The Time Domain

Non-uniform weighted sampling (NUWS) is a simple method for multi-dimensional NMR spectroscopy in which window functions are applied during acquisition by sampling varying numbers of scans across indirect dimensions. While NUWS was previously shown to provide modest increases in sensitivity, here we describe a complementary application to enhance spectral resolution by increasing the sampling of later points of the time domain signal. Moreover, by combining NUWS with carefully constructed apodization functions signal envelopes can be modulated in an arbitrary manner while retaining a uniform noise level, permitting further signal manipulations such as linear prediction and non-uniform sampling (NUS). We leverage this to develop a combined NUWS-NUS scheme for broadband homonuclear decoupling, with substantially increased sensitivity in comparison to constant time experiments.

NMR Resolution Enhancement and Homonuclear Decoupling Using Non-Uniform Weighted Sampling

2020 ◽  
Author(s):  
Chris Waudby ◽  
John Christodoulou
Keyword(s):  
Linear Prediction ◽  
Resolution Enhancement ◽  
Simple Method ◽  
Uniform Sampling ◽  
Homonuclear Decoupling ◽  
Time Domain Signal ◽  
Window Functions ◽  
Uniform Noise ◽  
Increased Sensitivity ◽  
The Time Domain

Non-uniform weighted sampling (NUWS) is a simple method for multi-dimensional NMR spectroscopy in which window functions are applied during acquisition by sampling varying numbers of scans across indirect dimensions. While NUWS was previously shown to provide modest increases in sensitivity, here we describe a complementary application to enhance spectral resolution by increasing the sampling of later points of the time domain signal. Moreover, by combining NUWS with carefully constructed apodization functions signal envelopes can be modulated in an arbitrary manner while retaining a uniform noise level, permitting further signal manipulations such as linear prediction and non-uniform sampling (NUS). We leverage this to develop a combined NUWS-NUS scheme for broadband homonuclear decoupling, with substantially increased sensitivity in comparison to constant time experiments.

Advantageous Apodization Functions for Magnitude-Mode Fourier Transform Spectroscopy

1987 ◽  
Vol 41 (1) ◽  
pp. 93-98 ◽  
Author(s):  
Judy P. Lee ◽  
Melvin B. Comisarow
Keyword(s):  
Fourier Transform ◽  
Time Domain ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Time Domain Signal ◽  
Time Domain Data ◽  
Window Functions ◽  
Fourier Transform Spectra ◽  
The Time Domain ◽  
Ratio Reduction

A systematic examination of the efficacy of window functions for reducing the spectral skirt of magnitude-mode Fourier transform spectra is reported. The efficacy is examined for the general case of a damped time-domain signal, with specific cases ranging from undamped to essentially completely damped signals. The choice of the optimal window is dependent upon the required dynamic range and the amount of damping in the time-domain data. For a dynamic range of less than 100:1 and moderate damping, the Hamming window is the window of choice. For larger dynamic ranges or greater damping, the 3-term Blackman-Harris window and the Kaiser-Bessel window are the windows of choice. The 3-term Blackman-Harris window is preferred for a dynamic range of 1,000:1 and the Kaiser-Bessel window is preferred for a dynamic range of 10,000:1. The sensitivity (signal-to-noise ratio) reduction for windows is reported for a damping range from zero to essentially complete damping. All windows examined have the same sensitivity reduction within 25%.

scholarly journals Non-uniform sampling in pulse dipolar spectroscopy by EPR: the redistribution of noise and the optimization of data acquisition

2021 ◽  
Author(s):  
Anna G. Matveeva ◽  
Victoria N. Syryamina ◽  
Vyacheslav M. Nekrasov ◽  
Michael K. Bowman
Keyword(s):  
Data Acquisition ◽  
Spectroscopy Data ◽  
Distance Spectrum ◽  
Uniform Sampling ◽  
Increased Sensitivity ◽  
Non Uniform Sampling

Non-uniform schemes for collection of pulse dipole spectroscopy data can decrease and redistribute noise in the distance spectrum for increased sensitivity and throughput.

scholarly journals Is the time domain signal-averaged electrocardiogram helpful in patients with ventricular tachycardia without apparent structural heart disease?

1995 ◽  
Vol 18 (10) ◽  
pp. 568-572 ◽  
Author(s):  
Yelena S. K. Orlov ◽  
Michael A. Brodsky ◽  
Michael V. Orlov ◽  
Byron J. Allen ◽  
Rex J. Winters
Keyword(s):  
Heart Disease ◽  
Ventricular Tachycardia ◽  
Time Domain ◽  
Structural Heart Disease ◽  
Time Domain Signal ◽  
The Time Domain

Reversible Audio Data Hiding in Spectral and Time Domains

2013 ◽  
pp. 19-41 ◽  
Author(s):  
Akira Nishimura
Keyword(s):  
Data Hiding ◽  
Time Domain ◽  
Reversible Data Hiding ◽  
Linear Prediction ◽  
Data Consistency ◽  
Time Domains ◽  
Audio Data ◽  
Hidden Data ◽  
The Time Domain ◽  
Location Map

Reversible data hiding is a technique whereby hidden data are embedded in host data in such a way that the host data consistency is perfectly preserved and the host data are restored when extracting the hidden data. This chapter introduces basic algorithms for reversible data hiding, histogram shifting, histogram expansion, and compression. This chapter also proposes and evaluates two reversible data hiding methods, i.e., hiding data in the frequency-domain using integer Discrete Cosine Transform (DCT) and modified DCT and hiding in the time domain using linear prediction and error expansion. As no location map is required to prevent amplitude overflow, the proposed method in the time domain achieves a storage capacity of nearly 1 bit per sample of payload data. The proposed methods are evaluated by the payload amount, objective quality degradation of stego signal, and payload concealment.

Fault Diagnosis of Motor Rotor Based on Fuzzy C-Means Clustering Analysis

2013 ◽  
Vol 273 ◽  
pp. 409-413 ◽  
Author(s):  
Yu Xiang Cao ◽  
Xue Jun Li ◽  
Ling Li Jiang
Keyword(s):  
Time Domain ◽  
Clustering Analysis ◽  
Statistical Features ◽  
Fuzzy C Means ◽  
Time Domain Signal ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Fault Diagnostic ◽  
Characteristic Features ◽  
The Time Domain

For the fuzziness of the fault symptoms in motor rotor, this paper proposes a fault diagnostic method which based on the time-domain statistical features and the fuzzy c-means clustering analysis (FCM). This method is to extract the characteristic features of time-domain signal via time-domain statistics and to import the extracted characteristic vector to classifier. And then the fuzzy c-means realizes the classification by confirming the distance among samples, which is based on the degree of membership between the sample and the clustering center. The fault diagnostic cases of motor rotor show that the method which bases on the time-domain statistical features-FCM can detect the rotor fault effectively and distinguish the different types of fault correctly. Therefore, it can be used as an important means of rotor fault identification.

scholarly journals The Comparison of the Effect of Haimming Window and Blackman Window in the Time-Scaling and Pitch-Shifting Algorithms

2011 ◽  
Vol 1 ◽  
pp. 221-225
Author(s):  
Zhi Wei Lin ◽  
Li Da ◽  
Hao Wang ◽  
Wei Han ◽  
Fan Lin
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Method ◽  
Domain Type ◽  
Domain Method ◽  
Time Scaling ◽  
Window Functions ◽  
Inherent Frequency ◽  
The Time Domain

The real-time pitch shifting process is widely used in various types of music production. The pitch shifting technology can be divided into two major types, the time domain type and the frequency domain type. Compared with the time domain method, the frequency domain method has the advantage of large shifting scale, low total cost of computing and the more flexibility of the algorithm. However, the use of Fourier Transform in frequency domain processing leads to the inevitable inherent frequency leakage effects which decrease the accuracy of the pitch shifting effect. In order to restrain the side effect of Fourier Transform, window functions are used to fall down the spectrum-aliasing. In practical processing, Haimming Window and Blackman Window are frequently used. In this paper, we compare both the effect of the two window functions in the restraint of frequency leakage and the performance and accuracy in subjective based on the traditional phase vocoder[1]. Experiment shows that Haimming Window is generally better than Blackman Window in pitch shifting process.

scholarly journals Ultrawideband Impulse Radar Through-the-Wall Imaging with Compressive Sensing

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Wenji Zhang ◽  
Moeness G. Amin ◽  
Fauzia Ahmad ◽  
Ahmad Hoorfar ◽  
Graeme E. Smith
Keyword(s):  
Compressive Sensing ◽  
Time Domain ◽  
Measurement Data ◽  
Acquisition Time ◽  
Time Domain Signal ◽  
Spatially Extended ◽  
Data Volume ◽  
Order Of Magnitude ◽  
New Perspective ◽  
The Time Domain

Compressive Sensing (CS) provides a new perspective for addressing radar applications requiring large amount of measurements and long data acquisition time; both issues are inherent in through-the-wall radar imaging (TWRI). Most CS techniques applied to TWRI consider stepped-frequency radar platforms. In this paper, the impulse radar two-dimensional (2D) TWRI problem is cast within the framework of CS and solved by the sparse constraint optimization performed on time-domain samples. Instead of the direct sampling of the time domain signal at the Nyquist rate, the Random Modulation Preintegration architecture is employed for the CS projection measurement, which significantly reduces the amount of measurement data for TWRI. Numerical results for point-like and spatially extended targets show that high-quality reliable TWRI based on the CS imaging approach can be achieved with a number of data points with an order of magnitude less than that required by conventional beamforming using the entire data volume.

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