Topic
Noise measurement
About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.
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TL;DR: The noise level of a thin-film ME sensor was measured, and suitable types of low-noise amplifiers were investigated in order to improve the sensitivity of the sensor.
Abstract: Sensors based on materials with a giant magnetoelectric (ME) effect may be used to measure biomagnetic fields at room temperature. It is necessary to know the noise behavior of the whole detection unit. The noise level of a thin-film ME sensor was measured at room temperature, and suitable types of low-noise amplifiers were investigated. Noise measurements were carried out at room temperature. Results show a sensitivity value of 5.4 pT/ √Hz at a resonance frequency of 330 Hz. Furthermore, the signal-to-noise ratio was investigated in order to improve the sensitivity of the sensor.
78 citations
TL;DR: In this article, the effects of measurement noise can be alleviated by filtering the measurement signal and the design of the filter is then a trade-off; heavy filtering reduces the undesired control activity but performance is degraded.
78 citations
TL;DR: A deep stage convolutional neural network with trainable nonlinearity functions is applied for the first time to remove noise in HSIs and the experimental results confirm that the proposed method can obtain a more effective and efficient performance.
Abstract: Hyperspectral images (HSIs) can describe subtle differences in the spectral signatures of objects, and thus they are effective in a wide array of applications. However, an HSI is inevitably contaminated with some unwanted components like noise resulting in spectral distortion, which significantly decreases the performance of postprocessing. In this letter, a deep stage convolutional neural network (CNN) with trainable nonlinearity functions is applied for the first time to remove noise in HSIs. Besides the fact that the weight and bias matrices are learned from cubic training clean-noisy HSI patches, the nonlinearity functions in each stage are also trainable, which differ from the conventional CNN with a fixed nonlinearity function. Compared with the state-of-the-art HSI denoising methods, the experimental results on both synthetic and real HSIs confirm that the proposed method can obtain a more effective and efficient performance.
78 citations
TL;DR: It is concluded that the bispectrum can be used effectively to detect non-Gaussian signals in the presence of interfering noise and that it may perform better, depending on the degree of non- Gaussianity, than energy detection.
Abstract: The problem of detecting a non-Gaussian time series in the presence of additive Gaussian or non-Gaussian noise is cast into a classical hypothesis testing framework, using the sample bispectrum as the test statistic The power of the test is demonstrated as a function of signal-to-noise ratio, the degree of skewness of the signal, and processing parameters The results are compared to the power of a classical energy detection test It is concluded that the bispectrum can be used effectively to detect non-Gaussian signals in the presence of interfering noise and that it may perform better, depending on the degree of non-Gaussianity, than energy detection >
78 citations