Low-pass Research Articles

In-Situ Nanoindentation Surface Topography of Lead-Free Piezoelectric Thin Films

Surface roughness significantly affects the performance of microelectromechanical systems (MEMS) and piezoelectric films. This study investigates the impact of surface roughness on the mechanical properties of thin piezoelectric films using nanoindentation and scanning probe microscopy (SPM). Four piezoelectric films with different thicknesses (220, 350, and 450 nm) and substrate configurations (LNO/SiO2/Si or LNO/Si) were analyzed. A discriminant analysis revealed that the fractal dimension is more effective than the arithmetic mean height (Sa) for distinguishing surfaces, with only 2% misclassification versus 25% for Sa. A multiscale analysis identified the Smr2 parameter with low-pass filtering at 140 nm as highly effective for surface discrimination, achieving only 0.1% misclassification. The analysis of the roughness parameter Sa at various scales showed that band-pass filtering at 500 nm yielded a 0.7% misclassification rate, indicating its relevance for fractal roughness characterization. Most relevant roughness parameters for mechanical property correlation were found: Smr2 with low-pass filtering at 500 nm correlated best with hardness (R2 = 0.82), and Vvc with low-pass filtering at 2 nm correlated best with reduced elastic modulus (R2 = 0.84). These results demonstrate that surface roughness features like valley volume and voids significantly impact the apparent mechanical properties of piezoelectric films.

AI-Based Analysis of Archery Shooting Time from Anchoring to Release Using Pose Estimation and Computer Vision

This study presents a novel method for automatically analyzing archery shooting time using AI and computer vision technologies, with a particular focus on the critical anchoring to release phase, which directly influences performance. The proposed approach detects the start of the anchoring phase using pose estimation and accurately measures the shooting time by detecting the bowstring within the athlete’s facial bounding box, utilizing Canny edge detection and the probabilistic Hough transform. To ensure stability, low-pass filtering was applied to both the facial bounding box and pose estimation results, and an algorithm was implemented to handle intermittent bowstring detection due to various external factors. The proposed method was validated by comparing its results with expert manual measurements obtained using Dartfish software v2022 achieving a mean absolute error (MAE) of 0.34 s and an R2 score of 0.95. This demonstrates a significant improvement compared to the bowstring-only method, which resulted in an MAE of 1.4 s and an R2 score of 0.89. Previous research has demonstrated a correlation between shooting time and arrow accuracy. Therefore, this method can provide real-time feedback to athletes, overcoming the limitations of traditional manual measurement techniques. It enables immediate technical adjustments during training, which can contribute to overall performance improvement.

What does ‘quality teaching and learning’ mean in TVET contexts?

Over the years, technical and vocational education and training (TVET) colleges in South Africa have been subject to criticism for their low pass rates, raising concerns about the quality of vocational education. In contrast to this, successes at certain colleges, as documented in official reports, are less frequently acknowledged. Our research has shifted the focus to these successes in order to understand the factors that are driving positive student outcomes and how they align with quality TVET teaching and learning. In this study, we aimed to identify the elements of success and propose models of good practice. A meta-review of the scholarly literature identified five themes critical to quality in vocational education: student-centred methodologies, technology-integrated learning, skill development, innovative pedagogies, and collaboration. Guided by these themes, we conducted empirical research across five South African provinces using questionnaires, interviews and focus groups. Key findings on successful outcomes highlighted the importance of student support services (financial, psychosocial, academic), empathetic teaching and effective learning environments, including work-based learning (WBL) and work-integrated learning (WIL).

Modified Bott-Parker Method for Gravimetric Moho Modeling

Determining the detailed Moho topography is crucial for understanding the Earth's geodynamic processes. Inversion of the Moho depth in the frequency-domain from gravity observations is an effective tool for this purpose. However, existing inversion methods such as the Parker-Oldenburg (P-O) and Bott-Parker (B-P) methods face challenges including non-convergence and noise amplification. We have developed a modified B-P (mB-P) method to estimate the Moho depth with variable density contrast from gravity data. The proposed method casts exponential density-based Parker’s formula into the iterative continuation theoretical framework, allowing the use of an adaptive filter that has been proven effective in iterative continuation studies. This modification retains the efficiency of the B-P method while offering improved control over solution stability. Gauss-Fast Fourier Transform was adopted to improve the precision of the forward and inverse Fourier transforms in the inversion process. Furthermore, the mathematical connection between the P-O and B-P methods was established. Specifically, the B-P method acts as a low-pass filter to reduce the downward continuation effect in the P-O method and recovers the nonlinear topography through the nonlinearity of the fitting function. Synthetic inversion tests demonstrate that the proposed method achieves superior inversion accuracy compared to the P-O and B-P methods under 3% gravity observations noise and significantly outperforms the P-O method in terms of computational efficiency. We applied the mB-P method to estimate the Moho depth beneath the Tibetan Plateau using satellite gravity data and seismic data, achieving reasonable agreement between the gravimetric model and seismic data.

A 35 nV/√Hz Analog Front-End Circuit with Adjustable Bandwidth and Gain in UMC 40 nm CMOS for Biopotential Signal Acquisition

This paper presents a 35 nV/√Hz analog front-end (AFE) circuitdesigned in the UMC 40 nm CMOS technology for the acquisition of biopotential signal. The proposed AFE consists of a capacitive-coupled instrumentation amplifier (CCIA) and a combination of a programmable gain amplifier (PGA) and a low-pass filter (LPF). The CCIA includes a DC servo loop (DSL) to eliminate electrode DC offset (EDO) and a ripple rejection loop (RRL) with self-zeroing technology to suppress high-frequency ripples caused by the chopper. The PGA-LPF is realized using switched-capacitor circuits, enabling adjustable gain and bandwidth. Implemented in theUMC 40 nm CMOS process, the AFE achieves an input impedance of 368 MΩ at 50 Hz, a common-mode rejection ratio (CMRR) of 111 dB, an equivalent input noise of 1.04 μVrms over the 0.5–1 kHz range, and a maximum elimination of 50 mV electrode DC offset voltage. It occupies an area of only 0.39 × 0.47 mm2 on the chip, with a power consumption of 8.96 μW.

Order Reduction Adaptive Current Tracking Control with Proportional–Integral-Type Filter for MAGLEV Applications

The proposed adaptive controller robustly tracks the coil current to its desired trajectory, regulating the target position of the magnetic levitation (MAGLEV) systems under the modeling errors and load variations. This paper makes the following two contributions. First, the order reduction proportional–integral filter replaces the conventional low-pass filter for the current measurement without the signal distortions in the high-frequency operation modes. Second, the proposed technique consists of the proportional-type feedback control and parameter adaptation mechanism estimating the DC component of the uncertain disturbances, involving only one design parameter. The practical advantages of the proposed technique are validated by the numerical simulations based on MATLAB/Simulink (ver. 2017b) considering the nonlinear dynamics of the MAGLEV systems and modeling errors.

The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain

Abstract The ability to detect fast responses with functional MRI depends on the speed of hemodynamic responses to neural activity, because hemodynamic responses act as a temporal low-pass filter which blurs rapid changes. However, the shape and timing of hemodynamic responses are highly variable across the brain and across stimuli. This heterogeneity of responses implies that the temporal specificity of fMRI signals, or the ability of fMRI to preserve fast information, could also vary substantially across the cortex. In this work we investigated how local differences in hemodynamic response timing affect the temporal specificity of fMRI. We used ultra-high field (7T) fMRI at high spatiotemporal resolution, studying the primary visual cortex (V1) as a model area for investigation. We used visual stimuli oscillating at slow and fast frequencies to probe the temporal specificity of individual voxels. As expected, we identified substantial variability in temporal specificity, with some voxels preserving their responses to fast neural activity more effectively than others. We investigated which voxels had the highest temporal specificity, and tested whether voxel timing was related to anatomical and vascular features. We found that low temporal specificity is only weakly explained by the presence of large veins or cerebral cortical depth. Notably, however, temporal specificity depended strongly on a voxel’s position along the anterior-posterior anatomical axis of V1, with voxels within the calcarine sulcus being capable of preserving close to 25% of their amplitude as the frequency of stimulation increased from 0.05Hz to 0.20Hz, and voxels nearest to the occipital pole preserving less than 18%. These results indicate that detection biases in high-resolution fMRI will depend on the anatomical and vascular features of the area being imaged, and that these biases will differ depending on the timing of the underlying neuronal activity. While we attribute this variance primarily to hemodynamic effects, neuronal nonlinearities may also influence response timing. Importantly, this spatial heterogeneity of temporal specificity suggests that it could be exploited to achieve higher specificity in some locations, and that tailored data analysis strategies may help improve the detection and interpretation of fast fMRI responses.

Design and experimental verification of programmable metastructures based on constant force cells

Abstract Mechanical metastructures consisting of periodic cells with adjustable output force charactersitics and ranges have received increasing attention in recent years owing to its unique capability to tune mechanical properties such as stiffness and Poisson’s ratio etc. In this paper, we present the design, simulation, and experimental characterization of a mechanical metastructure that realizes customized constant force output. The metastructure consists of periodic constant force units that are formed by combining a positive and negative stiffness element. Notably, the force unit also contains a unique flexure design with solid and hollow pins to reduce the lateral stress by 50%, which allows for precise control of the output force. By using a programmable design method, the force unit forms 2D and 3D metastructures via parallel and tendem stacking. Simulations were performed to optimize the design and predict the device performance. Finally, experiments were devised and performed to verify the simulation results of the metastructures. The promising results warrant the wide application of the new mechanical metastructure as well as the programmable design method, such as low-pass mechanical filters, noise and vibration cancellation devices etc.

Active primitive reflexes obstruct tactical and technical skills in football players

ABSTRACT Primitive reflexes (PRs) are involuntary motor responses present at birth, typically replaced by voluntary motor control during development. If PRs remain active beyond infancy, they can impair motor and cognitive abilities. This study investigated the correlation between active PRs and Tactical (TaS) and Technical Skills (TeS) in professional football matches. Fifty-eight male football players (17.5 ± 1.4 years) from a French academy were assessed for PR activity, categorized into inactive or active PR groups. Assessed PRs included asymmetrical tonic neck reflex (ATNR), symmetrical tonic neck reflex (STNR), tonic labyrinthine reflex (TLR), and Moro reflex (MR). Analysis of twenty matches showed players with active PRs had significantly lower success rates in TaS and TeS, including 15.5% and 31.8% lower pass success in imbalance and finishing zones (p < 0.01). Players with active PRs also performed worse in defensive actions and duels (p < 0.01). The findings highlight that active PRs hinder football performance, especially in challenging match situations like duels and crowded zones. This suggests the need for training programs to address PR effects and enhance player performance. Further research should refine methods for integrating these RPs.

Low Pass Filter Based Lithium‐ion Battery Equivalent Circuit Model With High Accuracy

AbstractThe equivalent circuit model (ECM) of lithium batteries provides a simplified way to describe their output behaviors. In this paper, a low pass filter‐based ECM of lithium battery is proposed with high accuracy. A voltage source is employed to represent the capability of the lithium battery to store energy chemically, a RC branch paralleled with the voltage source represents the charge transfer process. This RC branch acts as a low pass filter, and the capacitance buffers the charges during charging or discharging process, and this explains the dynamic voltage recovery process. However, with the traditional RC branches‐based ECM, the dynamic voltage is realized by the resistance in parallel with the capacitor, which discharges the capacitor and the RC branch is series‐connected with the voltage source. The proposed ECM is compared with the 1‐RC Thevenin model, which has the same number of model parameters with the proposed model. The parameters are obtained by the hybrid pulse power characterization (HPPC) test. The output voltage accuracy of the 1‐RC Thevenin model and the proposed model with LFP battery and NCM battery is verified with different test conditions. The experimental results show that the proposed ECM has higher accuracy, this validates the correctness of the proposed model.

Frequency-Aware Feature Fusion for Dense Image Prediction.

Dense image prediction tasks demand features with strong category information and precise spatial boundary details at high resolution. To achieve this, modern hierarchical models often utilize feature fusion, directly adding upsampled coarse features from deep layers and high-resolution features from lower levels. In this paper, we observe rapid variations in fused feature values within objects, resulting in intra-category inconsistency due to disturbed high-frequency features. Additionally, blurred boundaries in fused features lack accurate high frequency, leading to boundary displacement. Building upon these observations, we propose Frequency-Aware Feature Fusion (FreqFusion), integrating an Adaptive Low-Pass Filter (ALPF) generator, an offset generator, and an Adaptive High-Pass Filter (AHPF) generator. The ALPF generator predicts spatially-variant low-pass filters to attenuate high-frequency components within objects, reducing intra-class inconsistency during upsampling. The offset generator refines large inconsistent features and thin boundaries by replacing inconsistent features with more consistent ones through resampling, while the AHPF generator enhances high-frequency detailed boundary information lost during downsampling. Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries. Extensive experiments across various dense prediction tasks confirm its effectiveness.

Noise as a Factor of Useful Signal Amplification Based on the Effect of Stochastic Resonance

Abstract The effect of stochastic resonance is investigated. The principles of the effect are considered on the basis of a Brownian particle moving in a system with a symmetric bistable potential. Different from the widely existing noise elimination methods, stochastic resonance enables a new method to utilize the energy of noises, nonlinear system, and signal frequency to reach certain weak signal enhancement effects that noise elimination cannot perform well. The presence of noise at the input of nonlinear systems possessing the effect of the stochastic resonance allows to stand out a weak signal from an additive mixture with white Gaussian noise. The equation and structural scheme of stochastic resonance are considered The conditions for the occurrence of stochastic resonance are formulated and the determining role of noise variance in the realization of this effect is shown. For the first time, a numerical calculation of standing out of harmonic oscillation from an additive mixture with white Gaussian noise based on the stochastic resonance effect is carried out. The output signal-to-noise ratio dependence of the stochastic resonator on the intensity of input noise and the input signal frequency is investigated. The frequency dependencies of the output signal are investigated and the components of this signal are analysed. It is shown that the stochastic resonator acts as a low-pass filter and reduces the output noise level. Numerical modelling of the stochastic resonance effect also shows that the output signal of the stochastic resonator contains odd harmonics of the input oscillation, which is characteristic of nonlinear systems. It is shown that the stochastic resonance effect can be widely used in radio engineering and telecommunications to amplify the useful signal.

A deep learning approach to real-time Markov modeling of ion channel gating

The patch-clamp technique allows us to eavesdrop the gating behavior of individual ion channels with unprecedented temporal resolution. The signals arise from conformational changes of the channel protein as it makes rapid transitions between conducting and non-conducting states. However, unambiguous analysis of single-channel datasets is challenging given the inadvertently low signal-to-noise ratio as well as signal distortions caused by low-pass filtering. Ion channel kinetics are typically described using hidden Markov models (HMM), which allow conclusions on the inner workings of the protein. In this study, we present a Deep Learning approach for extracting models from single-channel recordings. Two-dimensional dwell-time histograms are computed from the idealized time series and are subsequently analyzed by two neural networks, that have been trained on simulated datasets, to determine the topology and the transition rates of the HMM. We show that this method is robust regarding noise and gating events beyond the corner frequency of the low-pass filter. In addition, we propose a method to evaluate the goodness of a predicted model by re-simulating the prediction. Finally, we tested the algorithm with data recorded on a patch-clamp setup. In principle, it meets the requirements for model extraction during an ongoing recording session in real-time.

Eliminating noise from a speech signal based on a pair of filters

Today, the increase in the number of devices working based on speech interfaces increases the importance of speech quality. However, even a minimal noise level can seriously affect the accuracy of speech signal recognition and processing. Therefore, denoising speech signals is an important task in signal processing, and it also serves to improve speech quality in telecommunications, speech recognition systems, and other speech-related applications. Applying existing noise reduction filters separately may not always be effective. Therefore, in this research, a noise reduction approach based on the sequential application of filters is proposed. Based on literature analysis, filters such as low-pass, band-pass, Kalman, Butterworth, and elliptic filters were selected, and pairs were formed based on them. Pairs of filters were applied to speech signals with different levels of noise, and the resulting filtered speech signals were evaluated based on the PESQ evaluation criterion. The purpose of this study is to determine the optimal pair of filters that can minimize the impact of noise on the quality of speech signals using the PESQ evaluation criterion. The results of the experiments showed that it is optimal to use a pair of band-pass and Butterworth filters at a low level of noise, a pair of low-pass and elliptic filters at a medium level, and a pair of band-pass and elliptic filters at a high level of noise. The results obtained are important and practical in the development of other hybrid methods of noise reduction.

Basement Configuration and Tectonic Evolution Beneath Zallah Trough, Western Sirt Basin, Libya, Obtained from Integrated Gravity, and Aeromagnetic Data

In this study, gravity and aeromagnetic datasets are analysed to map fault systems and the depth of the basement, which has hitherto been unknown. The gravity and magnetic data were provided by the Libyan Petroleum Institute. Low-pass filters separated local and regional components of gravity and aeromagnetic datasets, as well as the CET grid analysis filter, Euler deconvolution, Source Parameter Imaging (SPI) estimate of crystalline basement depth, Werner deconvolution, and 2D forward modelling. The results show the fault patterns, primarily trends, are NE-SW, N-S, and NW-SE, which define and constrain the trough. Some structural characteristics in the centre and southern trough have an NNE-SSW orientation with estimated depth ranging from (400 to 5350) m as resulted by Euler deconvolution. The SPI technique estimates the crystalline basement at about 5200 m. The Werner solution showed volcanic bodies in the form of sills; in addition, it approved that these volcanic bodies have depths of 4366m and 5067 m, respectively. Thle observed sill bodies may have been the result of the Sirt Basin rifting, causing a weakening of the crust to allow mantle flow. The 2D modelling showed that the depth of the basement ranges between (5100 and 5400) m beneath the depocenter of the study area.

Simultaneous bright-field and fluorescence lensless imaging with high excitation light extinction for microfluidics applications

Lensless imaging fluorescence applications are limited by the unavoidable trade-off between the object-sensor distance and the excitation light attenuation at the detection plane. Indeed, a shorter object-sensor distance reduces the spreading of fluorescent signals but at the cost of losing excitation light attenuation provided by filter thickness. Accordingly, the combination of four synergistic pathways to attain further attenuation of excitation light is proposed: the optimization of total internal reflection by microfluidic chip geometry modification, cross-polarization extinction, interchangeable absorption longpass filters, and the CMOS sensor built-in bandpass Bayer filters. The advantages of such a combined setup are demonstrated with a microfluidic application, as we managed to keep the object-sensor distance as short as 600 µm and still distinguish the fluorescence of individual droplets, flowing as close as 650 µm to each other, while attenuating excitation light to an OD 5.9 level. This approach also allowed simultaneous fluorescence and bright-field observation of on-chip droplet fluorescence during flow at a rate of 30 fps. The enhanced capabilities in our scheme pave the way to further lensless fluorescence applications requiring parallelism, lower detection thresholds, and real-time acquisition, such as fluorescence biosensing.

Semi-supervised anomaly traffic detection via multi-frequency reconstruction

Anomaly traffic detection is a crucial issue in cyber-security field. Traditionally, many researchers have approached anomaly traffic detection as a supervised classification problem. However, in real-world scenarios, anomaly network traffic is unpredictable, dynamic, and difficult to collect. To address these challenges, we adopt an anomaly detection setting that trains using only normal traffic data and propose a novel semi-supervised method: Multi-Frequency Reconstruction (MFR). Our approach is driven by a key observation: traffic images often lack explicit patterns and texture features, making accurate modeling of normal traffic difficult. To overcome this limitation, we employ low-pass filters to extract multi-scale low-frequency information, offering a more effective multi-view perspective to identify anomaly patterns. Additionally, we introduce a channel-spatial attention mechanism within reconstruction models to enhance the network’s ability to capture the spatio-temporal features of traffic flows. Ultimately, to adequately leverage multi-view information, we integrate anomaly scores from multi-frequency branches, achieving a more comprehensive anomaly detection. Extensive experiments on three public anomaly traffic detection datasets demonstrate the superior performance of our method, outperforming state-of-the-art approaches by 4.6% and 10.5% in AUROC on the DataCon2020 and CIC-IDS2017 datasets, respectively.

An improved digital predistortion scheme for nonlinear transmitters with limited bandwidth

In modern wireless communication systems, wide signal bandwidth is the most straightforward approach to accommodate high data rates. Wide signal bandwidth, on the other hand, introduces severe challenges to the power amplifier (PA) and digital predistortion (DPD) design in both performance and cost. Conventional DPD systems usually ignore the impact of the transmit low-pass filter (Tx LPF) bandwidth and assume the transmit bandwidth is sufficiently large. In wideband signal transmissions, the bandwidth of Tx LPF can become the system bottleneck, limiting DPDs compensation effects. Existing DPD studies mostly investigate the DPD with reduced feedback bandwidth. In this paper, we study the impact of Tx LPF bandwidth on the DPD performance. A full-band error minimization DPD based on direct learning structure is proposed. The DPD coefficients are estimated by minimizing the full-band error between the input signal and PA output signal in the frequency domain. Furthermore, we propose a weighted DPD with improved performance by introducing a weighting diagonal matrix to the error function. Compared to existing solutions, the weighted DPD achieves a good trade-off between the in-band distortion compensation and out-of-band spectral regrowth suppression. Simulations and experiments validate the effectiveness of the proposed DPD schemes.

Realizing high-performance four active plasmonic filters using a single structure

This research aims to contribute significantly to the field of plasmonic filtering technology within modern optical communication systems. By focusing on the development of a high-performance, more compact, and efficient design, this study explores the potential of hybrid plasmonic filters to revolutionize optical filtering applications. The approach leverages an innovative active material with electrically tunable permittivity, allowing for dynamic control over the filter’s optical properties. The research specifically examines four types of filters: low-pass filters (LPF), high-pass filters (HPF), band-pass filters (BPF), and band-reject filters (BRF). These filters are designed to operate effectively across a broad wavelength range of 1200–1800 nm, achieving a transmittance exceeding 98% at the output port, while maintaining isolation with transmittance below 2% at the isolated ports. The structure demonstrates a FWHM of approximately 216 nm for the band-pass filter and approximately 223 nm for the band-reject filter, which are considered moderate values, ensuring the versatility and multifunctionality of the design. The ultra-compact size, with a footprint of just 21 µm2, makes these filters particularly advantageous for integration into space-constrained optical communication systems.

A Q‐Learning and Fuzzy Logic Control of Hybrid Energy Storage System Using Two Stage Low‐Pass Filter to Smooth Power Fluctuations in Microgrid

ABSTRACTThe intermittent and fluctuating nature of wind turbine output power is increasingly being recognized as a significant issue adversely impacting the power quality and stability of electrical grids. With the increasing integration of wind power, this challenge cannot be underestimated. However, a potential solution for mitigating the adverse effects of such fluctuations lies in the Hybrid Energy Storage System (HESS), which encompasses battery energy storage systems (BESS) and supercapacitors (SC). The HESS is equipped with flexible operational modes for charging and discharging, thus enabling grid‐connected microgrids to possess the ability to counteract these oscillations. In this article, a control strategy based on the combination of Q‐learning and fuzzy logic control approaches is presented for tuning the parameters of a utilized two‐stage variable time constant low‐pass filter (LPF) in a grid‐connected microgrid. The proposed strategy adaptively tunes the time constants of LPFs to mitigate wind power fluctuations. Furthermore, practical constraints for the energy storage systems and their interfaced converters, such as preventing overcharge/discharge, ramp rate requirements, and certain maximum power conversion ranges, have been taken into account. Numerical simulation results verify the effectiveness of the proposed two‐stage variable time constant LPF for output wind power fluctuation reduction considering practical constraints of HESS.