Exponentiation conversion circuit capable of changing the power exponent to any value

We proposed an exponentiation conversion circuit which can change its power exponent to any value to compensate the nonlinearity of electronic devices. The proposed circuit is a small scale circuit utilizing the exponential characteristic in the subthreshold operation of MOSFET. In the proposed circuit, the new exponential conversion circuit converts signal multiplied logarithmically transformed input signal by the power exponent value, thereby obtaining the exponential power raised power function characteristic. The proposed circuit is suitable to integrate on a microcomputer chip used for IoT. The performance of the circuit was evaluated by a prototype IC made by 0.6 μm CMOS process. In measured results, the exponential conversion characteristics as set were obtained, the exponent value was set to 0.50, 1.00 and 2.00. By using the cascode exponential conversion circuit, the signal dynamic range was expanded by 5.2 dB when the exponent value was set to 2.00.

Optimized Design of OTA-Based Gyrator Realizing Fractional-Order Inductance Simulator: A Comprehensive Analysis

A detailed analysis of an operational transconductance amplifier based gyrator implementing a fractional-order inductance simulator is presented. The influence of active element non-ideal properties on the gyrator operation is investigated and demonstrated by admittance characteristics and formulas for important values and cut-off frequencies in these characteristics. Recommendations to optimize the performance of the gyrator in terms of operation bandwidth, the range of obtainable admittance magnitude, and signal dynamic range are proposed. The theoretical observations are verified by PSpice simulations of the gyrator with LT1228 integrated circuit.

Dynamic Range Processing and Its Influence on Perceived Timing in Electronic Dance Music

In this article, we explore the extent to which dynamic range processing (such as compression and sidechain compression) influences our perception of a sound signal’s temporal placement in music. Because compression reshapes the sound signal’s envelope, scholars have previously noted that certain uses of sidechain compression can produce peculiar rhythmic effects. In this article, we have tried to interrogate and complicate this notion by linking a description of the workings and effects of dynamic range processing to empirical findings on the interaction between sound and perceived timing, and by analyzing multitracks and DAW project files, as well as released audio files, of selected EDM tracks. The analyses of the different EDM tracks demonstrated that sidechain compression affects the music in many possible ways, depending on the settings of the compressors’ parameters, as well as the rhythmic pattern and the sonic complexity of both the trigger signal and the sidechained signal. Dynamic range processing’s impact on groove and perceived timing indicates, in line with previous findings, that sound and timing interact in fundamental ways. Because of this interaction, then, we cannot limit ourselves to technical terms that describe how particular effects are achieved if we want to fully understand the grooves that are characteristic of EDM or other music. We must also consider how listeners experience these effects.

Statistic Model of Homodyne Acousto-Optic Spectrum Analyzer

Introduction. Acousto-optic spectrum analyzers interferometric schemes have been developed to increase dynamic range. It was assumed that dynamic range, expressed in dB, would double. An expected increase was not achieved yet.Aim. To analyze the homodyne acousto-optic spectrum analyzer noise characteristics, to estimate the signal-tonoise ratio and the dynamic range.Materials and methods. A mathematical model was compiled which took into account the need to form quadrature components to obtain an amplitude spectrum of an input signal, shot noise and readout noise.Results. An interferometric scheme did not allow to achieve dynamic range doubling compared to an acoustooptical power spectrum analyzer. The dynamic range increase was less than 1.35 dB. Constant illumination led to a significant increase of the spectrum analyzer self-noise due to shot noise, compared to which thermal noise and readout noise became insignificant. The spurious-free dynamic range estimation expression was obtained. It was prior determined by acousto-optic interaction nonlinearity. With typical analyzer blocks parameters the spurious-free dynamic range covered a single-signal dynamic range. Signal-to-noise ratio estimation expression was presented.Conclusion. The homodyne acousto-optic spectrum analyzer single-signal dynamic range is determined primarily by the photosensor saturation charge. One needs to optimize their relation by taking into account light source power, acousto-optical modulator diffraction efficiency and photosensor saturation charge. Presented noise model gives more accurate estimation of the dynamic range with an error of 1 dB.

Multi-sensor measurements of mixed-phase clouds above Greenland

Liquid-only and mixed-phase clouds in the Arctic strongly affect the regional surface energy and ice mass budgets, yet much remains unknown about the nature of these clouds due to the lack of intensive measurements. Lidar measurements of these clouds are challenged by very large signal dynamic range, which makes even seemingly simple tasks, such as thermodynamic phase classification, difficult. This work focuses on a set of measurements made by the Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland and its retrieval algorithms, which use both analog and photon counting as well as orthogonal and non-orthogonal polarization retrievals to extend dynamic range and improve overall measurement quality and quantity. Presented here is an algorithm for cloud parameter retrievals that leverages enhanced dynamic range retrievals to classify mixed-phase clouds. This best guess retrieval is compared to co-located instruments for validation.

Improved Cloud Phase Determination of Low Level Liquid and Mixed Phase Clouds by Enhanced Polarimetric Lidar

The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes affect polarization measurements. A systematic bias of the traditional lidar depolarization ratio can occur due to a lidar system's inability to accurately measure the entire backscattered signal dynamic range, and these biases are not always identifiable in traditional polarimetric lidar systems. This results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. Using novel measurements of diattenuation that are sensitive to both horizontally oriented ice crystals and counting system non-linear effects, unambiguous measurements are possible by over constraining polarization measurements. This overdetermined capability for cloud phase determination allows for system errors to be identified and quantified in terms of their impact on cloud properties. It is shown that lidar system dynamic range effects can cause errors in cloud phase fractional occurrence estimates on the order of 30% causing errors in attribution of cloud radiative effects on the order of 10%-30%. This paper presents a method to identify and remove lidar system effects from atmospheric polarization measurements and uses co-located sensors at Summit to validate this method.