Noise power

The detectability of critical defects with CT depends on the final image quality and the skill of the operator, see figure 2. The basic concepts of image quality are resolution, contrast, and noise. Image quality are generally described by the signal-to-noise ratio SNR), the modulation transfer function (MTF) and the noise power spectrum (NFS). SNR is the quotient of a signal and its variance, MTF describes the contrast as a function of spatial frequency and NFS in turn describes the noise power at various spatial frequencies [1, 3]. 

Semiconductor devices ate affected by three kinds of noise. Thermal or Johnson noise is a consequence of the equihbtium between a resistance and its surrounding radiation field. It results in a mean-square noise voltage which is proportional to resistance and temperature. Shot noise, which is the principal noise component in most semiconductor devices, is caused by the random passage of individual electrons through a semiconductor junction. Thermal and shot noise ate both called white noise since their noise power is frequency-independent at low and intermediate frequencies. This is unlike flicker or ///noise which is most troublesome at lower frequencies because its noise power is approximately proportional to /// In MOSFETs there is a strong correlation between ///noise and the charging and discharging of surface states or traps. Nevertheless, the universal nature of ///noise in various materials and at phase transitions is not well understood. 

We now apply Eqs. (4-194) to (4-201) to the frequency limited, power limited, additive white gaussian noise channel. If N is the block length of a code in samples, then T = N/2W is the block length in time. Furthermore if is the available signal power and if N0 is the noise power per unit bandwidth, then the signal to noise ratio, A, is 8/N0W. Finally we let JRT> the rate in nats per second, be 2 WB. Substituting these relations into Eqs. (4-194) and (4-197), we get... 

A noise power equivalent to one photon generates an interference signal which has an amplitude equals to twice the rms photon noise of the source. But as only the in-phase components of the source generates an interference with the local oscillator, the result is that the spectral Noise Equivalent Power of the heterodyne receiver is hv. 

The noise power is normalized to unity one needs N f obtain a signal equal to the noise... 

Noise is characterized by the time dependence of noise amplitude A. The measured value of A (the instantaneous value of potential or current) depends to some extent on the time resolution of the measuring device (its frequency bandwidth A/). Since noise always is a signal of alternating sign, its intensity is characterized in terms of the mean square of amplitude, A, over the frequency range A/, and is called (somewhat unfortunately) noise power. The Fourier transform of the experimental time dependence of noise intensity leads to the frequency dependence of noise intensity. In the literature these curves became known as PSD (power spectral density) plots. 

Ultimately, the power supply is only part of a larger system. Therefore, besides being concerned about the effect of noise and ripple on the converter itself, we need to worry about its effect on the rest of the system. The good news is that if the system were excessively noise sensitive, no one would have touched switchers with a ten-foot pole (or a lOdB zero) in the first place. They would have been using those low-noise, power-guzzling LDOs (linear regulators) instead ... 

We run Monte Carlo simulations to examine the performance of the sensor selection algorithm based on the maximization of mutual information for the distributed data fusion architecture. We examine two scenarios first is the sparser one, which consists of 50 sensors which are randomly deployed in the 200 m x 200 m area. The second is a denser scenario in which 100 sensors are deployed in the same area. All data points in the graphs represent the means of ten runs. A target moves in the area according to the process model described in Section 4. We utilize the Neyman-Pearson detector [20, 30] with a = 0.05, L = 100, r) = 2, 2-dB antenna gain, -30-dB sensor transmission power and -6-dB noise power. 

To detect the reflected signal in the presence of thermal white noise, the correlation process (matched filtering) is used according to the fundamentals given above. The received signal will be detected when its power is higher than the thermal noise power P/v multiplied by detectability factor D, e.g. 

Figure 12 shows the appropriately calculated detection threshold in the pure noise situation (12 a). This homogeneous noise model with unknown noise power was the motivation to develop CA-CFAR. The... 

In case of the GTC attack with a certain constraint on the attack distortion, the parameters a and A are obtained from those for an equivalent effective AWGN attack with noise power. ... 

If the input of an amplifier is connected to a device with a correctly matched electrical impedance, then even in the absence of any useful signal from the device there will be noise in the output of the amplifier equivalent to a noise power input (Robinson 1974)... 

For a broadband r.f. amplifier of bandwidth Afi sending a signal to a square-law diode detector and thence to a low-frequency video amplifier of bandwidth A/j the noise power is (Dicke 1946 Robinson 1974)... 

This would give the noise power for a continuous wave (c.w.) microscope such as the transmission microscope described in 2.2. However, for a pulsed instrument with heterodyne detection the bandwidth is defined in the intermediate frequency (i.f.) stage, and the i.f. bandwidth A/ maybe used in (3.5). 

This function (Fig. 3.3) has its inflection point at spectral density function. The nucleus picks up the needed to frequency for its relaxation. The probability of this to occur depends on the spectral density (i.e. on the value of function (3.2)) at that frequency. 

Summary. We discuss the concept of the Berry phase in a dissipative system. We show that one can identify a Berry phase in a weakly-dissipative system and find the respective correction to this quantity, induced by the environment. This correction is expressed in terms of the symmetrized noise power and is therefore insensitive to the nature of the noise representing the environment, namely whether it is classical or quantum mechanical. It is only the spectrum of the noise which counts. We analyze a model of a spin-half (qubit) anisotropically coupled to its environment and explicitly show the coincidence between the effect of a quantum environment and a classical one. 

As we have demonstrated above, in the quantum problem the results for the corrections to the phase and dephasing, associated with the controlled dynamics of the magnetic field, involve only the symmetric part of the noise correlator, one expects that the results for these quantities in the classical problem, expressed in terms of the noise power, would coincide with the quantum results. Indeed, we find this relation below. 

In this paper we have derived expressions for the environment-induced correction to the Berry phase, for a spin coupled to an environment. On one hand, we presented a simple quantum-mechanical derivation for the case when the environment is treated as a separate quantum system. On the other hand, we analyzed the case of a spin subject to a random classical field. The quantum-mechanical derivation provides a result which is insensitive to the antisymmetric part of the random-field correlations. In other words, the results for the Lamb shift and the Berry phase are insensitive to whether the different-time values of the random-field operator commute with each other or not. This observation gives rise to the expectation that for a random classical field, with the same noise power, one should obtain the same result. For the quantities at hand, our analysis outlined above involving classical randomly fluctuating fields has confirmed this expectation. 

At subgap voltage, eV < A,p, when the charge transport at T = 0 is only due to the Andreev reflection, the current I, the shot noise power Pi, and the third cumulant 63 read... 

Fig. 2. Shot noise power and third cumulant vs superconducting phase (a, b) at different voltages and T = 0, and vs voltage at different temperatures (c, d). Dashed lines denote voltage dependencies in the normal state at T = 0. In the panel (d), zero-bias slopes of the normalized Cz(V) are indicated.

At arbitrary temperature, the cumulants can be found analytically by asymptotic expansion in Eqs. (41), (43) over small rj and y. In particular, the noise power,... 

The second method, the coherence function, was developed by Bell Northern Research (Canada). It uses the coherent (signal) and non-coherent (noise) powers to derive a quality measure [CCITT86sgl2con46,1986],... 

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