Error Signal Power Research Articles

A Psychoacoustic Quality Criterion for Path-Traced Sound Propagation.

In developing virtual acoustic environments, it is important to understand the relationship between the computation cost and the perceptual significance of the resultant numerical error. In this paper, we propose a quality criterion that evaluates the error significance of path-tracing-based sound propagation simulators. We present an analytical formula that estimates the error signal power spectrum. With this spectrum estimation, we can use a modified Zwicker's loudness model to calculate the relative loudness of the error signal masked by the ideal output. Our experimental results show that the proposed criterion can explain the human perception of simulation error in a variety of cases.

Linien: A versatile, user-friendly, open-source FPGA-based tool for frequency stabilization and spectroscopy parameter optimization.

We present a user-friendly and versatile tool for laser frequency stabilization. Its main focus is spectroscopy locking, but the software is suitable for lock-in techniques in general as well as bare proportional-integral-derivative (PID) operation. Besides allowing for sinusoidal modulation (up to 50MHz), triangular ramp scanning, in-phase and quadrature demodulation (1-5 f), infinite impulse response, and PID filtering, Linien features two different algorithms for automatic lock point selection; one of them performs time-critical tasks completely on field-programmable gate arrays. Linien is capable of autonomously optimizing spectroscopy parameters by means of machine learning and can measure the error signal's power spectral density. The software is built in a modular way, providing both a graphical user interface as well as a Python scripting interface. It is based on the RedPitaya STEMLab platform but may be ported to different systems.

필터뱅크 시스템을 이용한 전력신호 품질 해석

In power signal quality analyses, accurate detection of harmonics, inter harmonics, triggering points and flicker is important. This paper presents a technique which employ filter bank system, adaptive predictor and recursive algorithm for detecting harmonics, inter harmonics, triggering points and flicker. Filter bank system decompose input signal to sub bands, and then each sub band signals are processed by adaptive predictor. Magnitudes and frequencies of harmonics are estimated at predictor outputs by recursive algorithm. And inter harmonics are detected by the same processing at predictor error terminal. To detect triggering points of power signal event, error signal power change of adaptive predictor is used. In detecting flicker, filter bank composed by three filters is used. The magnitudes and frequencies of each filter outputs are obtained by recursive algorithm, and from these data flicker’s magnitude and frequency are calculated. To asses validities of the method, simulations were performed by synthesized input signals based on IEC standards. And the detecting results were compared with conventional methods.

Design and implementation of adaptive FxLMS on FPGA for online active noise cancellation

This paper presents an online secondary path modeling method in active noise cancellation based on an enhanced Filtered-X Least Mean Squares (FxLMS) algorithm, implemented on an Altera Cyclone II board. The main specifications of the proposed algorithm are: (1) it avoids overdriving noise in ear by applying a limiter block, (2) it updates online secondary path modeling filter coefficients, utilizes error signal power, and minimizes system error by injecting random white noise at an appropriate time, which can modify the system changes, and (3) it synthesizes the hardware architecture using VHDL Hardware Description language for an Altera Cyclone II board. In this architecture, only 22 of 300 embedded multipliers and 6% of the logical elements in the FxLMS algorithm have been used. The proposed method has used less than 1% of the Field-programmable Gate Arrays’ board memory. The experiments illustrate convincing results in terms of accuracy, convergence speed, and functionality of the designed and implemented system.

Nonparametric VSS-APA based on precise background noise power estimate

The adaptive algorithm used for echo cancellation (EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm (APA) is a better alternative than normalized least mean square (NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA (VSS-APA) provides a more reliable solution. A nonparametric VSS-APA (NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power (BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.

A Study on Multi-Level PWM and Asynchronous /spl Sigma/ /spl Delta/ Modulations for Enhanced Bandlimited Signal Tracking in Switching Power Amplifiers

This work deals with multi-level switching amplifiers, in the context of high-efficiency power amplification for signal tracking applications. In particular, this paper evaluates the reduction in the error signal's power due to multi-level power amplification (compared to conventional two-level amplifiers) and compares the performance of two multi-level pulse modulations: PWM and Asynchronous ΣΔ Modulation. First the intrinsic bandwidth limits of multi-level switching amplifiers are inferred, to clearly state the advantages and limitations of multi-level power amplification. From the existing analyses of Pulse Width Modulation already reported in the literature, PWM is herein extended to multiple levels based on an equivalent representation, which allows to derive a closed expression for the power spectrum of multi-level PWM in bandlimited signal tracking. The Asynchronous ΣΔ Modulation is extended to multiple levels and the resulting multi-level encoding algorithm is analyzed in both time and frequency domains. The performance of both modulations is characterized and compared at different operating frequencies and using different number of levels. The main outcomes of this in-depth characterization show that, if the switching frequency is high enough, the tracking error is independent of the modulation and the switching frequency, i.e., it only depends upon the number of levels, which points out the suitability of asynchronous modulations for relatively low switching frequencies (compared to the number of levels).

A Novel Variable Step-Size NLMS Algorithm and Its Analysis

By building the generalized Sigmoid function relationship between normalized step-size and the power of error signal, a novel variable step-size NLMS algorithm is proposed. It is proved that the step-size of NPVSS-NLMS changes as the new algorithm does when A=σv -m and B=2. The physical meanings of the parameters in this algorithm are explored. The theoretical analysis illustrate that this algorithm combine the virtues of NPVSS-NLMS and Sigmoid function, and it leads to faster convergence rate and lower final misalignment. The computer simulation results support the theoretical analysis.

Extraction Technique of Differential Second Harmonic Output in CMOS $LC$ VCO

A technique to extract differential second harmonic output signals in a CMOS LC voltage-controlled oscillator (VCO) is introduced. In a cross-coupled n-type field effect transistor (NFET) and p-channel field effect transistor (PFET) VCO topology, the upper and lower common source nodes of the FET pairs can provide well-balanced differential second harmonic output by resonating the impedances at the common source nodes and operating the VCO in the voltage-limited regime. The idea is verified experimentally by implementing a 5.6-GHz CMOS VCO having a tunable impedance element at the common source node. The error signal power between the differential signals is measured to be -70dBm when properly tuned, which indicates almost perfect differentiality of the second harmonic output signals

Adaptive threshold nonlinear algorithm for adaptive filters with robustness against impulse noise

A new adaptation algorithm for filters is proposed, introducing adaptive in the nonlinear correlation function. The threshold controlled by the long-term average of the error signal power makes the filtering system highly robust against impulse noise, which is demonstrated by the results of simulation and theoretical calculation.

Joint recursive optimality—a non-probabilistic approach to adaptive transversal filter design

It is common practice to design and interpret adaptive filter algorithms in terms of some optimality principle. The predominant approach purports to miminize a quadratic measure of the filter's error signal. This approach is, however, tied to the assumption of a time-invariant applications environment so that operational algorithm structures (such as LMS) are only obtained from ad hoc modifications aiming at continuous tracking. Joint recursive optimality is a unified framework for the “optimal” design of adaptive transversal filters. It covers a wide class of adaptation algorithms including standards like least mean squares and recursive least squares algorithms. Contrary to existing optimality principles, a deterministic criterion is proposed which operational algorithms meet exactly for each time step. The criterion features a trade-off between time variance of the filter coefficients and error signal power. The paper discusses implications for the interpretation of a variety of algorithms and concludes with guidelines for the incorporation of prior statistical or coefficient modeling information into the adaptation algorithm.

Noise reduction in biological step signals: application to saccadic EOG

A weighted filter for noise reduction in nonrecurrent step signals where adaptive filtering cannot be applied is described. An optimal correction of a conventional finite impulse response (FIR) filter is achieved by using a priori knowledge of noise variance and a continuous estimation of the error signal's power. The weighted filter provides an optimal compromise between noise filtering and distortionless tracking. The prior knowledge required is that of the noise power and the lowest frequency in the noise spectrum. Application of the weighted filter to the saccadic electro-oculogram (EOG) results in better estimations of saccade duration and velocity.