Fluctuation and Noise Letters

Johnson Noise Thermometry (JNT) can be regarded as a quite well established technique for the evaluation of the temperature. It relays on the accurate estimation of the voltage noise across a resistor, that acts as a sensor, and on the knowledge of the resistance of the sensor itself. In order to perform a temperature measurement, therefore, the resistance of the sensor has to be measured with a high accuracy multimeter and afterwards, the voltage noise across its ends has to be estimated in the assumption that the resistance does not change with time and is independent of frequency. In this paper we present a new approach for JNT that is based on a four channel cross correlation technique that allows, at least in principle, to estimate the temperature of a passive bipole from noise measurements only, as the power spectra of the current noise of the bipole and the real and imaginary part of its admittance can all be obtained from a proper elaboration of the acquired noise spectra. In particular, as the real part of the bipole admittance can be estimated as a function of the frequency, the limitation of resorting to purely resistive bipoles is also removed. Preliminary results demonstrating the effectiveness of the approach we propose are reported in this paper. The most important technical limitations that, at present, prevent the new approach to compete with the most advanced technique developed for the case of the conventional JNT are also discussed.

Parrondo's Paradox has pointed out that combined losing strategies can win. In Parrondo's Paradox games, the design of game B is critical. Currently there are mainly three versions, which depend on capital, history and space. We devise a new version of game B which depends on the capital parity and draw on the structure of the benefit matrix in the Prisoner's Dilemma game model. Theoretical analysis and computer simulation results both demonstrate the establishment of this paradox version.

A successful pre-processing stage based on wavelet denoising algorithm for electromyography (EMG) signal recognition is proposed. From the limitation of traditional universal wavelet denoising, the optimal weighted parameter is assigned for universal thresholding method. The optimal weight for increasing EMG recognition accuracy is 50–60% of traditional universal threshold with hard transformation. Experimental results show that it improved approximately from 2 to 50% of recognition accuracy for EMG with signal-to-noise ratio (SNR) in the range of 20 to 0 dB compared to a baseline system (without pre-processing stage) and traditional universal wavelet denoising. The results are evaluated through a large EMG dataset with seven kinds of hand movements and eight types of muscle positions.

The heart beat RR intervals extracted from the electrocardiogram recorded during the stress test show a non stationary profile consisting of a decreasing trend during the exercise phase, an increasing trend during the recovery and a global minimum (acme). In addition this time series exhibits a time-varying variance. We decompose the series into a deterministic trend and random fluctuation. The trend is obtained as an exponential fit of the data; the fluctuation is modeled as a mean reverting process driven by the trend, in which the random innovation has a time-varying variance. Data analysis, performed on ambulatory recorded electrocardiograms of 10 healthy subjects, shows that the model describes correctly the data series on a scale of at least 300 beats.

The work introduces a new 1/f noise theory that focuses on limited signals. Usually, 1/f noise represents drift, because 1/f noise is the spectral power density of the drift. The subjects of the new theory are signals that have limited value and duration. Therefore the basis of the new theory of 1/f noise corresponds to the real properties of any device and signal comprising 1/f noise. On this basis, the standard deviation of 1/f noise was derived, which is the most important parameter of 1/f noise. This standard deviation has good consistency with (a) the widely used Hurst approximation, (b) the square root of time dependence of Brownian motion displacement, and (c) theoretically derived Brownian displacement. Comparison of an existing 1/f noise theory and the new one shows that both theories are incompatible because subjects of the existing 1/f noise theory are limitless signals, which do not exist.

The Detrended Fluctuation Analysis (DFA) and its extensions (MF-DFA) have been proposed as robust techniques to determine possible long-range correlations in self-affine signals. However, many studies have reported the susceptibility of DFA to trends which give rise to spurious crossovers and prevent reliable estimations of the scaling exponents. Lately, several modifications of the DFA method have been reported with many different techniques for eliminating the monotonous and periodic trends. In this study, a smoothing algorithm based on the Orthogonal V-system (OVS) is proposed to minimize the effect of power-law trends, periodic trends, assembled trends and piecewise function trends. The effectiveness of the new method is demonstrated on monofractal data and multifractal data corrupted with different trends.

Starting from the definition of the stochastic differential equation for amplitude and phase fluctuations of an oscillator described by an ordinary differential equation, we study the associated Fokker–Planck equation by using tools from stochastic integral calculus, harmonic analysis and Floquet theory. We provide an asymptotic characterization of the relevant correlation functions, showing that within the assumption of a linear perturbative analysis for the amplitude fluctuations phase noise and orbital fluctuations at the same time are asymptotically statistically independent, and therefore the nonlinear perturbative analysis of phase noise recently derived still exactly holds even if orbital noise is taken into account.

We investigate the effect of Lévy noise on the noise-induced residual aperiodic stochastic resonance (ASR). It is shown that, the residual ASR still exists when the noise is Lévy distributed. However, it is reduced by Lévy noise compared to that in the Gaussian noise with the same intensity. It is also shown that, the minimum bit error rate (BER) of the Lévy-noise-induced ASR is slightly higher than that of the Gaussian-noise-induced ASR, which indicates that tuning system parameters is a better approach to get the optimal performance.

Noise-based logic, by utilizing its multidimensional logic hyperspace, has significant potential for parallel operations in beyond-Moore-chips. However universal gates for Boolean logic thus far had to rely on either time averaging to distinguish signals from each other or, alternatively, on squeezed logic signals, where the logic-high was represented by a random process and the logic-low was a zero signal. A major setback is that squeezed logic variables are unable to work in the hyperspace, because the logic-low zero value sets the hyperspace product vector to zero. This paper proposes Boolean universal logic gates that alleviate such shortcomings. They are able to work with non-squeezed logic values where both the high and low values are encoded into nonzero, bipolar, independent random telegraph waves. Non-squeezed universal Boolean logic gates for spike-based brain logic are also shown. The advantages versus disadvantages of the two logic types are compared.