Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.

Design and performance of blocked-impurity-band detector focal plane arrays

Abstract: Blocked-Impurity-Band (BIB) extrinsic silicon (Si:As) detectors have demonstrated high sensitivity and quantum efficiency in the long wavelength infrared (LWIR) spectral region (to 28 microns) as well as wide frequency response, low optical crosstalk, nuclear radiation hardness, and stable, predictable performance. Furthermore, it has been demonstrated that SWItched mosFET (SWIFET) multiplexers provide a low noise readout approach for use with BIB detectors. This paper describes the state-of-the-art of multiplexed BIB detector hybrid focal plane arrays (HFPAs). The principle of operation and performance of optimized BIB and Back Illuminated BIB (BIBIB) detectors are presented. The SWIFET multiplexer, including necessary circuitry for BIBIB detector readout, has been designed and fabricated using a newly developed process for cryogenic (<20K) MOS electronics which avoids anomalies (lack of device isolation, excess noise, and long time constants) associated with conventional silicon processes. A description of the design and operation of this multiplexer is given. A number of uniform, highly responsive, 500 element HFPAs have been fabricated and their performance evaluated. The characterization measurements, to be described in the paper, include evaluation of detector dark current, responsivity, noise for various operating conditions, and uniformity of array characteristics. The results obtained and presented demonstrate that focal plane arrays with excellent, unprecedented, LWIR performance have been realized with Blocked-Impurity-Band detector technology.

Imaging with ambient noise

Abstract: Whether noise is a nuisance or a signal depends on how it’s processed. By cross-correlating noise recorded at two sensors, researchers can retrieve the waves that propagate between them and extract details about the intervening medium.

Circularity of the STFT and Spectral Kurtosis for Time-Frequency Segmentation in Gaussian Environment

Abstract: This paper investigates the circularity of short time Fourier transform (STFT) coefficients noise only, and proposes a modified STFT such that all coefficients coming from white Gaussian noise are circular. In order to use the spectral kurtosis (SK) as a Gaussianity test to check if signal points are present in a set of STFT points, we consider the SK of complex circular random variables, and its link with the kurtosis of the real and imaginary parts. We show that the variance of the SK is smaller than the variance of the kurtosis estimated from both real and imaginary parts. The effect of the noncircularity of Gaussian variables upon the spectral kurtosis of STFT coefficients is studied, as well as the effect of signal presence. Finally, a time-frequency segmentation algorithm based on successive iterations of noise variance estimation and time-frequency coefficients detection is proposed. The iterations are stopped when the spectral kurtosis on nondetected points reaches zero. Examples of segmented time-frequency space are presented on a dolphin whistle and on a simulated signal in nonwhite and nonstationary Gaussian noise.

Recognition of speech under stress and in noise

Abstract: Speech recognizers trained in one condition but operating in a different condition degrade in performance. Typical of this situation is when the recognizer is trained under normal conditions but operated in a stressful and noisy environment as in military applications. This paper reports on recognition experiments conducted with a "simulated stress" data base using a baseline algorithm and its modifications. These algorithms perform acceptably well (1 % substitution rate) for a vocabulary of 105 words under normal conditions, but degrade by an order of magnitude under the "stress" conditions. The experiments also show that the speech production variation caused by noise exposure at the ear is far more deleterious than ambient acoustic noise with a noise cancelling microphone.

Multireference Alignment Is Easier With an Aperiodic Translation Distribution

Abstract: In the multireference alignment model, a signal is observed by the action of a random circular translation and the addition of Gaussian noise. The goal is to recover the signal’s orbit by accessing multiple independent observations. Of particular interest is the sample complexity, i.e., the number of observations/samples needed in terms of the signal-to-noise ratio (SNR) (the signal energy divided by the noise variance) in order to drive the mean-square error to zero. Previous work showed that if the translations are drawn from the uniform distribution, then, in the low SNR regime, the sample complexity of the problem scales as $\omega (1/ \mathrm {SNR}^{3})$ . In this paper, using a generalization of the Chapman–Robbins bound for orbits and expansions of the $\chi ^{2}$ divergence at low SNR, we show that in the same regime the sample complexity for any aperiodic translation distribution scales as $\omega (1/ \mathrm {SNR}^{2})$ . This rate is achieved by a simple spectral algorithm. We propose two additional algorithms based on non-convex optimization and expectation–maximization. We also draw a connection between the multireference alignment problem and the spiked covariance model.