Digital Demodulator for BFSK waveform based upon Correlator and Differentiator Systems

The present article relates in general to digital demodulation of Binary Frequency Shift Keying (BFSK waveform) . New processing methods for demodulating the BFSK-signals are proposed here. Based on Sampler Correlator, the hardware consumption for the proposed techniques is reduced in comparison with other reported. Theoretical details concerning limits of applicability are also given by closed-form expressions. Simulation experiments are illustrated to validate the overall performance

Fractional Newton-Raphson Method Accelerated with Aitken's Method

The Newton-Raphson (N-R) method is characterized by the fact that generating a divergent sequence can lead to the creation of a fractal, on the other hand the order of the fractional derivatives seems to be closely related to the fractal dimension, based on the above, a method was developed that makes use of the N-R method and the fractional derivative of Riemann-Liouville (R-L) that has been named as the Fractional Newton-Raphson (F N-R) method. In the following work we present a way to obtain the convergence of the F N-R method, which seems to be at least linearly convergent for the case where the order $\alpha$ of the derivative is different from one, a simplified way to construct the fractional derivative and fractional integral operators of R-L is presented, an introduction to the Aitken's method is made and it is explained why it has the capacity to accelerate the convergence of iterative methods to finally present the results that were obtained when implementing the Aitken's method in F N-R method.Comment: Newton-Raphson Method, Fractional Calculus, Fractional Derivative of Riemann-Liouville, Method of Aitken. arXiv admin note: substantial text overlap with arXiv:1710.0763

Short-term synaptic facilitation improves information retrieval in noisy neural networks

Short-term synaptic depression and facilitation have been found to greatly influence the performance of autoassociative neural networks. However, only partial results, focused for instance on the computation of the maximum storage capacity at zero temperature, have been obtained to date. In this work, we extended the study of the effect of these synaptic mechanisms on autoassociative neural networks to more realistic and general conditions, including the presence of noise in the system. In particular, we characterized the behavior of the system by means of its phase diagrams, and we concluded that synaptic facilitation significantly enlarges the region of good retrieval performance of the network. We also found that networks with facilitating synapses may have critical temperatures substantially higher than those of standard autoassociative networks, thus allowing neural networks to perform better under high-noise conditions.Comment: 6 pages, 3 figures, to appear in EP

Tunneling Splittings in Mn12-Acetate Single Crystals

A Landau-Zener multi-crossing method has been used to investigate the tunnel splittings in high quality Mn$_{12}$-acetate single crystals in the pure quantum relaxation regime and for fields applied parallel to the magnetic easy axis. With this method several individual tunneling resonances have been studied over a broad range of time scales. The relaxation is found to be non-exponential and a distribution of tunnel splittings is inferred from the data. The distributions suggest that the inhomogeneity in the tunneling rates is due to disorder that produces a non-zero mean value of the average transverse anisotropy, such as in a solvent disorder model. Further, the effect of intermolecular dipolar interaction on the magnetic relaxation has been studied.Comment: Europhysics Letters (in press). 7 pages, including 3 figure