All data sent to and from IOT devices, (as well as any data held), must be encrypted in order to enhance the security of IOT devices. At the moment, elliptic curve cryptosystems (ECCs) are used as fundamental building...
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We develop quantum corrections to the Wilson line-based action which we recently derived through a transformation that eliminates triple gluon vertices from the Yang-Mills action on the light-cone. The action efficien...
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In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted, efficient, and interpretable ML algo...
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This work presents an implementation of a reference optical cavity based on parasitic cavities on a low coherence interferometric system. This method allows a maximization of the number of sensors to be implemented wi...
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The work is devoted to the development of semi-empirical models for describing the depth-dose distribution in the layer depending on the angle of incidence of the electron beam. A geometric model of the region of ener...
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Nanodevices that show the potential for non-linear transformation of electrical signals and various forms of memory can be successfully used in new computational paradigms, such as neuromorphic or reservoir computing ...
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Nanodevices that show the potential for non-linear transformation of electrical signals and various forms of memory can be successfully used in new computational paradigms, such as neuromorphic or reservoir computing (RC). Dedicated hardware implementations based on functional neuromorphic structures significantly reduce energy consumption and/or increase computational capabilities of a given artificial neural network system. Concepts of RC, which as a flexible computational paradigm can be highly inclusive, are often used as a model to describe computations performed in materia. With mostly fixed internal structure, solid-state devices, especially memristors, are studied as computational substrates in various RC systems. In this work, we present single-node Echo State Machine (SNESM) RC system based on bridge synapse as a computational substrate (consisting of 4 memristors and a differential amplifier) used for epileptic seizure detection. KNOWM memristors were posed as ideal candidates because of their easy prototyping and reliability of operation. In this account, we present an application of commercially available KNOWM memristors in various neuromorphic applications, from simple analysis of switching and internal dynamics (elucidated form noise spectroscopy and total harmonic distortion analysis) to the classification and recognition of complex time series: epilepsy seizure recognition using a wrist-worn triaxial accelerometer. The results show that the evolution of the signal in a feedback loop helps improve the classification accuracy of the system for that task. The transformation in SNESM changes the correlation and distribution of the complexity parameters of the input signal. In general, there are more differences in the correlation of complexity parameters between the transformed signal and the input signal, which may explain the improvement in the classification scores. SNESM could prove to be a useful time series signal processing system designed to im
Superconductors without inversion symmetry in their crystal structure are known to exhibit unconventional properties. Recently, based on the measured temperature dependence of the magnetic field penetration depth, sup...
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Superconductors without inversion symmetry in their crystal structure are known to exhibit unconventional properties. Recently, based on the measured temperature dependence of the magnetic field penetration depth, superconductivity in noncentrosymmetric ThCoC2 was proposed to be a nodal d wave and mediated by the spin fluctuations. Moreover, a non-BCS behavior of the temperature dependence of the electronic specific heat and the magnetic upper critical field were reported. In this work, the electronic structure, phonons, and electron-phonon coupling are studied in ThCoC2 on the basis of ab initio computations. The effect of the spin-orbit coupling on the electronic structure and electron-phonon interaction is analyzed, and a large splitting of the electronic band structure is found. The calculated electron-phonon coupling constant λ=0.59 remains in decent agreement with the experimental estimates, suggesting that the electron-phonon interaction is strong enough to explain superconductivity with Tc≃2.5 K. Nevertheless, we show that the conventional isotropic Eliashberg formalism is unable to describe the thermodynamic properties of the superconducting state, as calculated temperature dependence of the electronic specific heat and magnetic penetration depth deviate from experiments, which is likely driven by the strong spin-orbit coupling and inversion symmetry breaking. In addition, to shed more light on the pairing mechanism, we propose to measure the carbon isotope effect, as our calculations based on the electron-phonon coupling predict the observation of the isotope effect with an exponent α≃0.15.
Quantum catalysis, the ability to enable previously impossible transformations by using auxiliary systems without degrading them, has emerged as a powerful tool in various resource theories. Although catalytically ena...
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Positronium is a suitable leptonic system to test Charge-Parity (CP) discrete symmetry involving the correlations of photons momenta originating from ortho-positronium (o-Ps) annihilation. The photon–photon interacti...
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This article presents a description of research that has led to the development of a new design permanent magnet machine. The machine is based on the operational principle of the transverse-flux machine (TFM). In the ...
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ISBN:
(数字)9798350370607
ISBN:
(纸本)9798350370614
This article presents a description of research that has led to the development of a new design permanent magnet machine. The machine is based on the operational principle of the transverse-flux machine (TFM). In the proposed solution, a core commonly used in radial flux machines was employed, significantly simplifying the construction of the TFM machine. This simultaneously reduces flux leakage and enhances the value of the developed electromagnetic torque. The article sequentially outlines the stages of research on the new design, ranging from analytical calculations through Finite Element Method (FEM) simulation to the construction of a machine prototype.
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