scholarly journals A new off-point-less observing method for millimeter and submillimeter spectroscopy with a frequency-modulating local oscillator

2019 ◽  
Vol 72 (1) ◽  
Author(s):  
Akio Taniguchi ◽  
Yoichi Tamura ◽  
Kotaro Kohno ◽  
Shigeru Takahashi ◽  
Osamu Horigome ◽  
...  
Keyword(s):  
Frequency Modulation ◽  
Principal Component ◽  
Local Oscillator ◽  
Spectral Lines ◽  
Heterodyne Receiver ◽  
Time Frequency ◽  
Fast Sweeping ◽  
Submillimeter Spectroscopy ◽  
Source Spectra ◽  
Galactic Sources

Abstract We propose a new observing method for single-dish millimeter and submillimeter spectroscopy using a heterodyne receiver equipped with a frequency-modulating local oscillator (FMLO). Unlike conventional switching methods, which extract astronomical signals by subtracting the reference spectra of off-sources from those of on-sources, the FMLO method does not need to obtain any off-source spectra; rather, it estimates them from the on-source spectra themselves. The principle uses high-dump-rate (10 Hz) spectroscopy with radio frequency modulation achieved by fast sweeping of a local oscillator of a heterodyne receiver. Because sky emission (i.e., off-source) fluctuates as $1/f$ and is spectrally correlated, it can be estimated and subtracted from time series spectra (a timestream) by principal component analysis. Meanwhile, astronomical signals remain in the timestream since they are modulated to a higher time-frequency domain. The FMLO method therefore achieves (1) a remarkably high observation efficiency, (2) reduced spectral baseline wiggles, and (3) software-based sideband separation. We developed an FMLO system for the Nobeyama $45\:$m telescope and a data reduction procedure for it. Frequency modulation was realized by a tunable and programmable first local oscillator. With observations of Galactic sources, we demonstrate that the observation efficiency of the FMLO method is dramatically improved compared to conventional switching methods. Specifically, we find that the time to achieve the same noise level is reduced by a factor of 3.0 in single-pointed observations and by a factor of 1.2 in mapping observations. The FMLO method can be applied to observations of fainter ($\sim$mK) spectral lines and larger ($\sim$deg$^{2}$) mapping. It offers much more efficient and baseline-stable observations compared to conventional switching methods.

Identification of cavitation in centrifugal pump by artificial immune network

2021 ◽  
pp. 095440892110284
Author(s):  
Seyed M Matloobi ◽  
Mohammad Riahi
Keyword(s):  
Immune System ◽  
Production Systems ◽  
Principal Component ◽  
Immune Network ◽  
Centrifugal Pumps ◽  
Artificial Immune ◽  
Human Immune System ◽  
Time Frequency ◽  
Artificial Immune Network ◽  
Damage Condition

Reducing the cost of unscheduled shutdowns and enhancing the reliability of production systems is an important goal for various industries; this could be achieved by condition monitoring and artificial intelligence. Cavitation is a common undesired phenomenon in centrifugal pumps, which causes damage and its detection in the preliminary stage is very important. In this paper, cavitation is identified by use of vibration and current signal and artificial immune network that is modeled on the base of the human immune system. For this purpose, first data collection were done by a laboratory setup in health and five stages damage condition; then various features in time, frequency, and time–frequency were extracted from vibration and current signals in addition to pressure and flow rate; next feature selection and dimensions reduction were done by artificial immune method to use for classification; finally, they were used by artificial immune network and some other methods to identify the system condition and classification. The results of this study showed that this method is more accurate in the detection of cavitation in the initial stage compared to methods such as non-linear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means with the same data. Also, selected features with artificial immune system were better than principal component analysis results.

scholarly journals Complex Principal Component Analysis of Antarctic Ice Sheet Mass Balance

2021 ◽  
Vol 13 (3) ◽  
pp. 480
Author(s):  
Jingang Zhan ◽  
Hongling Shi ◽  
Yong Wang ◽  
Yixin Yao
Keyword(s):  
Principal Component Analysis ◽  
Ice Sheet ◽  
Low Frequency ◽  
Principal Component ◽  
Equatorial Pacific ◽  
Mass Change ◽  
Frequency Variation ◽  
Periodic Signal ◽  
Time Frequency ◽  
The Antarctic

Ice sheet changes of the Antarctic are the result of interactions among the ocean, atmosphere, and ice sheet. Studying the ice sheet mass variations helps us to understand the possible reasons for these changes. We used 164 months of Gravity Recovery and Climate Experiment (GRACE) satellite time-varying solutions to study the principal components (PCs) of the Antarctic ice sheet mass change and their time-frequency variation. This assessment was based on complex principal component analysis (CPCA) and the wavelet amplitude-period spectrum (WAPS) method to study the PCs and their time-frequency information. The CPCA results revealed the PCs that affect the ice sheet balance, and the wavelet analysis exposed the time-frequency variation of the quasi-periodic signal in each component. The results show that the first PC, which has a linear term and low-frequency signals with periods greater than five years, dominates the variation trend of ice sheet in the Antarctic. The ratio of its variance to the total variance shows that the first PC explains 83.73% of the mass change in the ice sheet. Similar low-frequency signals are also found in the meridional wind at 700 hPa in the South Pacific and the sea surface temperature anomaly (SSTA) in the equatorial Pacific, with the correlation between the low-frequency periodic signal of SSTA in the equatorial Pacific and the first PC of the ice sheet mass change in Antarctica found to be 0.73. The phase signals in the mass change of West Antarctica indicate the upstream propagation of mass loss information over time from the ocean–ice interface to the southward upslope, which mainly reflects ocean-driven factors such as enhanced ice–ocean interaction and the intrusion of warm saline water into the cavities under ice shelves associated with ice sheets which sit on retrograde slopes. Meanwhile, the phase signals in the mass change of East Antarctica indicate the downstream propagation of mass increase information from the South Pole toward Dronning Maud Land, which mainly reflects atmospheric factors such as precipitation accumulation.

scholarly journals UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

2021 ◽  
Vol 13 (6) ◽  
pp. 1205
Author(s):  
Caidan Zhao ◽  
Gege Luo ◽  
Yilin Wang ◽  
Caiyun Chen ◽  
Zhiqiang Wu
Keyword(s):  
Recognition Performance ◽  
Principal Component ◽  
Texture Features ◽  
Training Model ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Dynamic Feature ◽  
Complex Environments ◽  
Time Frequency ◽  
Model Training

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

scholarly journals Designing a waveform using LFM, NLFM for MIMO radar

2017 ◽  
Vol 7 (1.5) ◽  
pp. 84
Author(s):  
G S Krishnam Naidu Yedla ◽  
D. Siva Sankar Prasad ◽  
P. Raghavendra Rao ◽  
M Siva Kumar ◽  
M VenuGopala Rao
Keyword(s):  
Frequency Modulation ◽  
Frequency Component ◽  
Side Lobe ◽  
Mimo Radar ◽  
Linear Frequency Modulation ◽  
Wave Form ◽  
Boundary Segment ◽  
Middle Segment ◽  
Time Frequency ◽  
Linear Frequency

We propose a waveform that includes Linear frequency modulation and non linear frequency modulation wave applicable for MIMO radar. The wave form consists of three segments where the boundary segment consists of LFM content and the middle segment consists of NLFM. The time frequency component in the middle segment is controlled. The range and Doppler side lobe suppression is improved. The genetic algorithm is implemented to suppress the side lobes in the auto correlation and cross correlation functions. The performance is analysed by using ambiguity function.

scholarly journals Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Timur Düzenli ◽  
Nalan Özkurt
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Vanishing Moments ◽  
Zero Crossings ◽  
Time Frequency ◽  
Common Time ◽  
Classification Tool

The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.

scholarly journals Analytic wavelet packets for robust ultrasonic flow measurement

2020 ◽  
Vol 87 (1) ◽  
pp. 45-54
Author(s):  
Matthias Bächle ◽  
Daniel Alexander Schwär ◽  
Fernando Puente León
Keyword(s):  
Transit Time ◽  
Flow Measurement ◽  
Principal Component ◽  
Time Difference ◽  
Frequency Resolution ◽  
Wavelet Packets ◽  
Accurate Estimation ◽  
Time Frequency ◽  
Analytic Wavelet ◽  
Conventional Methods

AbstractA key element in robust transit-time ultrasonic flow measurement is the accurate estimation of the transit-time difference. Conventional methods, such as cross-correlation or the estimation in the phase domain, are limited in their robustness against signal distortions, interfering signals or noise. In this work, we present a novel method to estimate the transit-time difference through the fusion of selected analytic wavelet packet coefficients. The combination of the complex coefficients, which represent a projection of the signal on analytic wavelets, with a configurable time-frequency resolution allows a sub-sample estimation at the frequency of interest. After giving an introduction into the fundamentals of analytic wavelet packets based on multi-scale filtering, we introduce two features that correlate strongly with the transit-time difference. The selection and fusion of these features is done by using correlation coefficients with a calibration set and principal component analysis. Finally, using a clamp-on flow measurement system, the robustness against temperature variation and measurement noise is shown and compared with conventional methods.

A Direction of Arrival Estimation Method for Wideband LFM Signals Based on RWT and ESPRIT Algorithm

2014 ◽  
Vol 1044-1045 ◽  
pp. 976-981
Author(s):  
Jian Zhong Xu ◽  
Fu Qiang Yu ◽  
Ping Guang Duan ◽  
Shu Hua Li
Keyword(s):  
Frequency Modulation ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Wigner Transform ◽  
Signal Parameter ◽  
Time Frequency ◽  
Cross Terms ◽  
Stationary Signals ◽  
Simulation Results ◽  
Esprit Algorithm

In this paper, we proposed a new algorithm to estimate the direction of arrival (DOA) for wideband linear frequency modulation (LFM) signals, using Radon-Wigner transform (RWT) and estimation of signal parameter via rotational invariance techniques (ESPRIT). To eliminate the cross-terms, we first utilize the RWT with its excellent time-frequency concentration performance. Then, through peak searching, the number of targets, the initial interference and the frequency modulation slope are estimated. On the this base, the array signals are reconstructed. Finally, we adopt the ESPRIT algorithm to estimate the DOA of the array signals. The simulation results show that the proposed algorithm can estimate the DOA of non-stationary signals with high precision.

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