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Filter synthesis is an inverse problem that is traditionally approached rationally by engineering the coupling between selected pairs of lumped resonators. The implicit restriction to spatially disjoint resonators strongly limits the design space, making it challenging to build extremely tunable filters. Here, agile free‐form signal filtering and routing are demonstrated with an alternative purely‐optimization‐based approach leveraging a multi‐parameter programmable system with many spatially overlapping modes. The approach is largely insensitive to system details other than the programmable system configuration. In the fabricated prototype, all ports and tunable meta‐elements are strongly coupled via a quasi‐2D chaotic cavity such that the meta‐elements’ configuration efficiently controls the transfer function between the ports. The all‐metallic device enables low‐loss and ultra‐wideband (UWB) tunability (7.5–13.5 GHz) and guarantees signal‐strength‐independent linearity. First, theoretical predictions about reflectionless and transmissionless scattering modes (including transmissionless exceptional points) are experimentally confirmed. Second, these transfer function zeros are imposed at desired frequencies within an UWB range. Third, low‐loss reflectionless programmable signal routing is achieved. Fourth, the trade‐off between routing fidelity and bandwidth is investigated, achieving 20 dB discrimination over 10 MHz bandwidth. Fifth, UWB‐tunable multi‐band filtering is demonstrated that rejects (< –24 dB) or passes (≥ –1 dB) signals in specified bands whose centers, widths and number are reprogrammable. A multi‐parameter‐tunable, all‐metallic, low‐loss, wave‐chaotic cavity enables a frequency‐insensitive (over a 7 GHz bandwidth) technique to synthesize desired transfer functions within smaller windows to high accuracy and with complete reconfigurability of shapes and center frequencies. Conventional rational approaches limited to sparsely coupled spatially disjoint resonators cannot achieve this. Moreover, the presented approach is extraordinarily robust to system design details.

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.

Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.

Analyses of joint moments are important in the study of human motion, and are crucial for our understanding of e.g. how and why ACL injuries occur. Such analyses may be affected by artifacts due to inconsistencies in the equations of motion when force and movement data are filtered with different cut-off frequencies. The purpose of this study was to quantify the effect of these artifacts, and compare joint moments calculated with the same or different cut-off frequency for the filtering of force and movement data. 123 elite handball players performed sidestep cutting while the movement was recorded by eight 240Hz cameras and the ground reaction forces were recorded by a 960Hz force plate. Knee and hip joint moments were calculated through inverse dynamics, with four different combinations of cut-off frequencies for signal filtering: movement 10Hz, force 10Hz, (10–10); movement 15Hz, force 15Hz; movement 10Hz, force 50Hz (10–50); movement 15Hz, force 50Hz. The results revealed significant differences, especially between conditions with different filtering of force and movement. Mean (SD) peak knee abduction moment for the 10–10 and 10–50 condition were 1.27 (0.53) and 1.64 (0.68) Nm/kg, respectively. Ranking of players based on knee abduction moments were affected by filtering condition. Out of 20 players with peak knee abduction moment higher than mean+1SD with the 10–50 condition, only 11 were still above mean+1SD when the 10–10 condition was applied. Hip moments were very sensitive to filtering cut-off. Mean (SD) peak hip flexion moment was 3.64 (0.75) and 5.92 (1.80) under the 10–10 and 10–50 conditions, respectively. Based on these findings, force and movement data should be processed with the same filter. Conclusions from previous inverse dynamics studies, where this was not the case, should be treated with caution.

In equine surface electromyography (sEMG), challenges related to the reliability and interpretability of data arise, among other factors, from methodological differences, including signal processing and analysis. The aim of this study is to demonstrate the filtering–induced changes in basic signal features in relation to the balance between signal loss and noise attenuation. Raw sEMG signals were collected from the quadriceps muscle of six horses during walk, trot, and canter and then filtered using eight filtering methods with varying cut–off frequencies (low–pass at 10 Hz, high–pass at 20 Hz and 40 Hz, and bandpass at 20–450 Hz, 40–450 Hz, 7–200 Hz, 15–500 Hz, and 30–500 Hz). For each signal variation, signal features—such as amplitude, root mean square (RMS), integrated electromyography (iEMG), median frequency (MF), and signal–to–noise ratio (SNR)—along with signal loss metrics and power spectral density (PSD), were calculated. High–pass filtering at 40 Hz and bandpass filtering at 40–450 Hz introduced significant filtering–induced changes in signal features while providing full attenuation of low–frequency noise contamination, with no observed differences in signal loss between these two methods. Other filtering methods led to only partial attenuation of low–frequency noise, resulting in lower signal loss and less consistent changes across gaits in signal features. Therefore, filtering–induced changes should be carefully considered when comparing signal features from studies using different filtering approaches. These findings may support cross-referencing in equine sEMG research related to training, rehabilitation programs, and the diagnosis of musculoskeletal diseases, and emphasize the importance of applying standardized filtering methods, particularly with a high–pass cut–off frequency set at 40 Hz.

In the case of SARS-CoV-2 pandemic management, wastewater-based epidemiology aims to derive information on the infection dynamics by monitoring virus concentrations in the wastewater. However, due to the intrinsic random fluctuations of the viral signal in wastewater caused by several influencing factors that cannot be determined in detail (e.g. dilutions; number of people discharging; variations in virus excretion; water consumption per day; transport and fate processes in sewer system), the subsequent prevalence analysis may result in misleading conclusions. It is thus helpful to apply data filtering techniques to reduce the noise in the signal. In this paper we investigate 13 smoothing algorithms applied to the virus signals monitored in four wastewater treatment plants in Austria. The parameters of the algorithms have been defined by an optimization procedure aiming for performance metrics. The results are further investigated by means of a cluster analysis. While all algorithms are in principle applicable, SPLINE, Generalized Additive Model and Friedman's Super Smoother are recognized as superior methods in this context (with the latter two having a tendency to over-smoothing). A first analysis of the resulting datasets indicates the positive effect of filtering to the correlation of the viral signal to monitored incidence values.

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

The mixed signal of respiratory waveform and heartbeat waveform detected by the Laser-Doppler system is processed with an intermediate-frequency (IF) interference filtering method, an enhanced extraction method and a waveform-fixing method. To filter the IF interference signals and the noise scatters in the time-frequency graph, the filtering method based on coefficient of variation (CoV) values and the enhanced curve extraction method based on noise-scatter theory are utilized in vital signal analysis. To decouple the respiratory signal and the heartbeat signal in time domain, the waveform-fixing method based on second-order difference theory is utilized in signal decoupling. This method as an algorithm is applied in the computer simulation and laboratory environments. The results show that the above methods can extract the mixed waveforms and identify the respiratory rates and heart rates in real experimental data. The IF interference signal can be filtered adaptively, and the accuracy of the analyzed rates can be improved to about 95%.

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.