The low-frequency noise spectrum analysis of the reliability of the InGaN LED

2013 ◽  
Author(s):  
Tzung-Te Chen ◽  
Chun-Fan Dai ◽  
Chun-Wen Chu ◽  
Han-Kuei Fu ◽  
Chien-Ping Wang ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Low Frequency ◽  
Noise Spectrum ◽  
Frequency Noise ◽  
Low Frequency Noise

scholarly journals Portable Low-Frequency Noise Reduction Device for Both Small Open and Closed Spaces

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Chao Wang ◽  
Haiyang Gao ◽  
Liyao Yu ◽  
Tiantian Yu ◽  
Wenhui Yan ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Open Space ◽  
Active Noise Control ◽  
Low Frequency ◽  
Noise Pollution ◽  
Noise Spectrum ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Pollution Problem ◽  
Negative Impacts

Noise pollution has been given more attention due to its negative impacts on human health and disease. The portable low-frequency noise reduction device we developed in this research can provide an effective way for solving low-frequency noise pollution problem in the small space. This work describes the design principle and the prototype structures for two versions of V1.5 and V2.0 and builds the noise test systems for small spaces, respectively. These devices, installed on the outer surface of the small spaces, can automatically identify the noise spectrum and implement noise reduction by means of the active noise control (ANC) technology. The testing results indicate that the noise can be reduced 12 dB in the range of 250 Hz~400 Hz for the small closed space while, for the small open space, the best effect of 5.88 dB occurs in the optimal frequency of 450 Hz. These effects will be weakened with the increasing distance away from the source and show the obvious axisymmetric distribution in the inverted cone space.

scholarly journals Generation Recombination Noise in GaN Photoconducting Detectors

1999 ◽  
Vol 4 (S1) ◽  
pp. 817-822
Author(s):  
M. Misra ◽  
D. Doppalapudi ◽  
A.V. Sampath ◽  
T.D. Moustakas ◽  
P.H. McDonald
Keyword(s):  
Room Temperature ◽  
Low Frequency ◽  
Optical Excitation ◽  
Noise Spectrum ◽  
High Resistivity ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Noise Characteristics ◽  
Deep Traps ◽  
Noise Measurements

Low frequency noise measurements are a powerful tool for detecting deep traps in semiconductor devices and investigating trapping-recombination mechanisms. We have performed low frequency noise measurements on a number of photoconducting detectors fabricated on autodoped n-GaN films grown by ECR-MBE. At room temperature, the noise spectrum is dominated by 1/f noise and thermal noise for low resistivity material and by generationrecombination (G-R) noise for high resistivity material. Noise characteristics were measured as a function of temperature in the 80K to 300K range. At temperatures below 150K, 1/f noise is dominant and at temperatures above 150K, G-R noise is dominant. Optical excitation revealed the presence of traps not observed in the dark, at room temperature.

Vibration feature extraction based on the improved variational mode decomposition and singular spectrum analysis combination algorithm

2018 ◽  
Vol 22 (7) ◽  
pp. 1519-1530 ◽  
Author(s):  
Hui Li ◽  
Tengfei Bao ◽  
Chongshi Gu ◽  
Bo Chen
Keyword(s):  
Spectrum Analysis ◽  
Damage Identification ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Low Frequency Noise ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Discharge Structure

Extraction of the vibration characteristics of a flood discharge structure under the influence of intensive background noise is one of the main challenges in vibration-based damage identification. A novel algorithm called normalized central frequency difference spectrum is proposed to improve the variational mode decomposition algorithm for high-frequency noise filtering. To eliminate the errors caused by end effect, the waveform matching extension algorithm is used to further improve the variational mode decomposition. However, the vibration signal is still coupled in low-frequency noise. Thereupon, the singular spectrum analysis algorithm is applied to filter the low-frequency noise. In this article, a simulated signal and the measured signals from a dam model are analyzed by the proposed algorithm. The results indicate that the proposed algorithm is robust to noise and has high denoising precision. In addition, this algorithm can offer clues for damage identification and localization of a flood discharge structure.

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