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The human central nervous system is the integrative basis for the functioning of the organism. The basis of such integration is provided by the fact that the same neurons are involved in various sets of sensory, cognitive, and motor functions. Therefore, the analysis of one set of integrative system components makes it possible to draw conclusions about the state and efficiency of the other components. Thus, to evaluate a person’s cognitive properties, we can assess their involuntary motor acts, i.e., a person’s subsensory reactions. To measure the parameters of involuntary motor acts, we have developed a strain gauge measuring system. This system provides measurement and estimation of the parameters of involuntary movements against the background of voluntary isometric efforts. The article presents the architecture of the system and shows the organization of the primary signal processing in analog form, in particular the separation of the signal taken from the strain-gauge sensor into frequency and smoothly varying components by averaging and subtracting the analog signals. This transfer to analog form simplifies the implementation of the digital part of the measuring system and allowed for minimizing the response time of the system while displaying the isometric forces in the visual feedback channel. The article describes the realization of the system elements and shows the results of its experimental research.

The noise performance of digital correlated double sampling (DCDS) for readout of charge-coupled devices (CCDs) with dominant white noise is presented. The trade-offs between analogue and digital signal filtering and the impact on the sampling rate are investigated and numerically simulated for realistic systems. The results can be used to select the signal bandwidth, the settling accuracy and the ADC sampling rate for optimal DCDS noise performance.

Distortions of electrocardiograms (ECGs) caused by mandatory high-pass and low-pass analog RC filters in ECG devices are always present. The fidelity of the ECG waveform requires limiting the RC cutoff frequencies of the diagnostic (0.05–150 Hz) and monitoring systems (0.5–40 Hz). However, the use of fixed frequency bands is a compromise between enhanced noise immunity and ECG distortions. This study aims to propose active inverse high-pass and low-pass filters which are able to compensate for distortions in digital recordings of RC-filtered ECGs, thereby overcoming the limitations imposed by analog filtering. A new straightforward design of an inverse high-pass filter (IHPF) uses an integrator as the forward-path gain block, with a feedback loop containing an active digital filter equivalent to the analog RC high-pass filter. In contrast, the inverse low-pass filter (ILPF) employs a constant-gain block in the forward path to ensure stability and prevent phase delay, while its feedback path features an active digital counterpart of the RC low-pass filter. Second-order inverse filters are created by cascading two first-order stages. The proposed filters were validated according to essential performance requirements for electrocardiographs. The low-frequency (impulse) responses of IHPFs with cutoff frequencies of 0.05–5 Hz exhibit no overshoot and undershoot by magnitudes of 0.1–25 µV, well within the ±100 µV compliance limit defined for a test rectangular pulse (3 mV, 100 ms). The high-frequency responses of ILPFs with cutoff frequencies of 10–150 Hz present a relative amplitude drop of only 0.2–2.5%, far below the 10% limit for peak amplitude reduction of a triangular pulse (1.5 mV) with 20 ms vs. 200 ms widths. For any of the eight ECG leads (I, II, and V1–V6) available in the standard signal (ANE20000), the IHPF (0.05–5 Hz) presents ST-segment deviations <5 μV (within the ±25 μV limit) and R- and S-peak deviations <±3.5% (within the ±5% limit). The ILPF (10–150 Hz) preserves R- and S-peak amplitudes with deviations less than −1%. Diagnostic-level recovery of ECG waveforms distorted by first- and second-order analog RC filters in ECG devices is possible with the innovative and comprehensive inverse filter design presented in this study. This approach offers a significant advancement in ECG signal processing, effectively restoring essential waveform components even after aggressive, noise-robust analog filtering in ECG acquisition circuits. Although validated for ECG signals, the proposed inverse filters are also applicable to other biosignal front-end circuits employing RC coupling.

The design process of an integrated bandpass filter targeted for the noise filtering stage of the synchronous demodulation unit of an electric field mill sensor interface is presented. The purpose of this study of filter integration techniques is to avoid the challenging and, in some cases, impossible passive element integration process and to incorporate the final filter design in an entirely integrated field mill sensing system with superior performance and an optimized silicon-to-cost ratio. Four different CMOS filter implementations in the 0.18 μm process of XFAB, using OTA (Operational Transconductance Amplifier)-based configurations for passive element replacement in cascaded filter topologies and leapfrog techniques, are compared in terms of noise performance, total harmonic distortion, dynamic range, and power consumption, as well as in terms of integrability, silicon area, and performance degradation at process corners/mismatches. The optimum filter design performance-wise and process-wise is included in the final design of the integrated analog readout of a field mill sensor, and post-layout simulation results of the total circuit are presented.

This work investigates the role of the filters implemented on analog-to-digital converters for the reconstruction of magnetic resonance images. We analyze the effects of these filters both from a theoretical and an experimental point of view and demonstrate how it may lead to severe degradation of the reconstructed images when the distance between consecutive samples is larger than Shannon’s limit. Based on these findings, we propose a mathematical model and a numerical algorithm that allow to mitigate such filtering effects both for linear and nonlinear reconstructions. Experiments on simulated and real data on a 7 Tesla scanner show that the proposed ideas allow to significantly improve the overall image quality. These findings are particularly relevant for high resolution imaging and for recent sampling schemes saturating the maximum gradient amplitude. They also open new challenges in sampling theory.

In digital filters theory, filtering techniques generally deal with pole-zero structures. In this context, filtering schemes, such as infinite impulse response (IIR) filters, are described by linear differential equations or linear transformations, in which the impulse response of each filter provides its complete characterization, under filter design specifications. On the other hand, finite impulse response (FIR) digital filters are more flexible than the analog ones, yielding higher quality factors. Since many approaches to the circuit synthesis using the wavelet transform have been recently proposed, here we present a digital filter design algorithm, based on signal wavelet decomposition, which explores the energy partitioning among frequency sub-bands. Exploring such motivation, the method involves the design of a perfect reconstruction wavelet filter bank, of a suitable choice of roots in the Z-plane, through a spectral factorization, exploring the orthogonality and localization property of the wavelet functions. This approach resulted in an energy partitioning across scales of the wavelet transform that enabled a superior filtering performance, in terms of its behavior on the pass and stop bands. This algorithm presented superior results when compared to windowed FIR digital filter design, in terms of the intended behavior in its transition band. Simulations of the filter impulse response for the proposed method are presented, displaying the good behavior of the method with respect to the transition bandwidth of the involved filters.

In order to capture the large-scale interferometric signal generated by the space-borne interferometric infrared Fourier spectrometer (IRIFS) in real time, and overcome the limitations of insufficient sampling rate, transmission rate, and significant signal noise in current equipment, a multi-channel high-speed acquisition system for large-scale interferometric signals is designed. A high-performance analog-to-digital converter (ADC) oversampling scheme is designed, which can realize up to 8 synchronous acquisition channels and has a maximum sampling rate of 125 Msps/Ch to ensure the acquisition of interferometric signals. The scheme of jesd204b inter-board transmission and optical fiber terminal transmission is designed. The inter-board transmission rate is 12.5 Gbps, and the terminal transmission rate is 10 GB/s to ensure high-speed data transmission. A hardware filter is designed to realize spatial noise processing of interference signals and ensure the accuracy of acquisition results. The dynamic performance of the data acquisition (DAQ) card is analyzed using discrete Fourier transform in the frequency domain. The spurious free dynamic range (SFDR) is 84 dB, the signal-to-noise ratio (SNR) is 72.7 dB, and the cross-talk is −81.6 dB, which verifies the dynamic stability of the DAQ card. Finally, the infrared radiation in real space is measured. The average ΔNESR of long wave reaches 48 mW∗m−2∗sr−1, and the average ΔNESR of medium wave reaches 12.3 mW∗m−2∗sr−1, which verifies the reliability of the system performance. The system is of great significance for large-scale infrared interferometric signal acquisition, and has strong practical application value in multi-channel synchronization, real-time high-speed acquisition, and high-speed data transmission.

In an all-digital, class-D audio amplifier, pulse-width-modulation (PWM) of a digital signal source is usually followed by a low-order analog low-pass filter to construct the analog audio waveform. This study shows how to remove the non-linear distortion usually associated with PWM, by prefiltering the digital signal prior to the PWM mapping in such a manner that the overall result is distortion-free. The prefiltering is done using computationally effective infinite-impulse-response filters combined with short-kernel anticausal finite-impulse-response filters, and relies on the interpretation of PWM as a Volterra filter. A case study is presented where a second-order Butterworth analog low-pass filter is used for reconstruction of the analog audio signal. A complete amplifier system is modelled, including upsampling, Volterra prefiltering and noise feedback coding. Computer simulations on CD music signals were performed. Using a third-order prefilter, a signal-to-noise ratio of 97–102 dB was obtained for the music signals tested. All necessary filter data needed for realisation of the prefilter are given in the Appendix.

Through this paper, by using the subordination definition, the ℘ ‐analogues Cătaş operator , complex order, and biunivalent functions with coefficients introduced by Faber polynomial expansion, we introduced the new class . A Faber polynomial is known as a sequence of polynomials that are used to approximate an analytic function on a compact set. This new class provides a framework for exploring various properties of biunivalent functions. We obtained new subclasses from the class . In addition, we generalized and improved many previous classes. We obtained estimates for the bounds of the coefficients for functions belonging to the class We estimate the initial coefficients of the functions from the indicated class and determine In addition, since Faber polynomials are closely related to approximation and filtering, the results may also be applied in areas such as signal recovery and problems involving Gaussian weights.

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system.