Search results for "noise measurement"

Three Different Methods for Determining the Microwave Noise Parameters of HEMT's at Decreasing Temperatures

1998

The noise characteristics of any transistor are usually represented by means of four parameters which are frequency-, bias- and temperature-dependent, similarly to the scattering parameters. The noise parameters are determined by a standard indirect procedure based on multiple noise figure measurements and appropriate data processing techniques requiring a complex instrumentation set-up and skilled operators. As an alterative way, we have shown that the noise parameters of packaged HEAMT's can be computed with very good accuracy from the analysis of a noisy circuit model derived from the scattering parameters plus a single noise figure measurement. A third way exists for the determination o…

Comparison between two measuring methods for complete characterization of low-noise HEMTs at microwaves

1996

The good performances of a set-up for the complete characterization of HEMTs up to 40 Ghz in terms of noise and scattering parameters through noise figure measurements only are shown by many experimental results. Because of some inconveniences in practice the use of the method is suggested for research laboratories only. For industrial applications an alternative symplified method is proposed whose performances are shown to be in surprising agreement with the ones of the standard method.

Complete characterization of low-noise devices at microwave frequencies: two alternative procedures for HEMTs

1995

Computer-Aided Simultaneous Determination of Noise and Gain Parameters of Microwave Transistors

1979

A new method for the determination of noise and gain parameters of microwave linear two-ports (transistors) is presented. The method allows the simultaneous determination of the two parameter sets through a proper computer-aided procedure which processes the experimental data obtained from a measuring system employing noise meters and generators only. Experimental verifications carried-out on a microwave low noise transistor in S-band are reported.

Up to Date Version of a Computer-Driven Noise Figure Measuring System for the Simultaneous Determination of Noise, Gain and Scattering Parameters of …

1995

The complete characterization of microwave transistors in terms of (four) noise, (four) gain and scattering parameters sets ({N), {G) and [SI, respectively) vs. frequency and bias conditions (and also vs. decreasing temperature, if required) is the first and most important step to design low noise amplifiers (LNAs). The characterization of the device under test (DUT) in terms of [SI is friendly by means of commercial Automatic Network Analyzer; then the { G) set may be determined by computation.

Effect of the non-gaussianity on the measurement error for the filtered 1/f noise intensity

1999

To study the nature of the 1/f noise phenomenon in conductors, we seek a tool for testing different hypotheses of 1/f noise origin. The method analyzing the noise intensity at the output of a bandpass filter is discussed for the case of non-Gaussian processes. Data on measurement error are presented for the 1/f noise intensity in GaAs films and the Gaussian white noise emulated by a computer. A numerical model of 1/f noise as the superposition of telegraph random processes has been created. This method requires further improvement to check the noise for stationarity. Some ideas of how to do that are proposed.

Cross-Spectrum PM Noise Measurement, Thermal Energy, and Metamaterial Filters.

2017

International audience; Virtually all commercial instruments for the measurement of the oscillator PM noise make use of the crossspectrum method (arXiv:1004.5539 [physics.ins-det], 2010). High sensitivity is achieved by correlation and averaging on two equal channels, which measure the same input, and reject the background of the instrument.We show that a systematic error is always present if the thermal energy of the input power splitter is not accounted for. Such error can result in noise underestimation up to a few decibels in the lowest-noise quartz oscillators, and in an invalid measurement in the case of cryogenic oscillators. As another alarming fact, the presence of metamaterial com…

Multiframe image restoration in the presence of noisy blur kernel

2009

We wish to recover an original image u from several blurry-noisy versions f k , called frames. We assume a more severe degradation model, in which the image u has been blurred by a noisy (stochastic) point spread function. We consider the problem of restoring the degraded image in a variational framework. Since the recovery of u from one single frame f is a highly ill-posed problem, we formulate two minimization problems based on the multiframe approach proposed for image super-resolution by Marquina-Osher [13]. Several experimental results for image restoration are shown, illustrating that the proposed models give visually satisfactory results.

Design of Low-Cost Noise Measurement Sensor Network: Sensor Function Design

2010

In this paper, we report the sensor function design and implementation of a wireless sensor network application for measuring environmental acoustic noise. The system is built on ATmega128 and CC2420 platform. The protocol stack is based on CiNet stack with a global synchronization scheme and supports multi-hop communications. Strict filtering function specified by ITU-R 468 (namely A-weighting) is followed. Both the indoor and outdoor test results were compared with standard sound level meters (CESVA SC-20c and Pulsar94) and showed a less than ±2dB error in both short-term and longterm measurement. Power consumption has been measured that a single AA-type battery can sustain the applicatio…

On the Robustness of Deep Features for Audio Event Classification in Adverse Environments

2018

Deep features, responses to complex input patterns learned within deep neural networks, have recently shown great performance in image recognition tasks, motivating their use for audio analysis tasks as well. These features provide multiple levels of abstraction which permit to select a sufficiently generalized layer to identify classes not seen during training. The generalization capability of such features is very useful due to the lack of complete labeled audio datasets. However, as opposed to classical hand-crafted features such as Mel-frequency cepstral coefficients (MFCCs), the performance impact of having an acoustically adverse environment has not been evaluated in detail. In this p…