Low-frequency Filter Research Articles

Open-source synthetic photoplethysmographic signal generator with analog output.

The photoplethysmographic (PPG) signal of the finger is being used to create embedded devices that estimate physiological variables. This project outlines an innovative method for developing a synthetic PPG generator that produces both actual reference digital signals and their equivalent analog signals using open-source technology. A series of PPG profiles is synthesized using three variant Gaussian functions. A low-frequency trend induced by respiratory frequency and background noise are then added. To generate a diverse range of continuously variable PPG profiles within specified boundaries and customizable levels of interference, all parameters undergo random fluctuations on a cycle-by-cycle basis, as per user-defined constraints. The generated signal is then converted into its equivalent analog form through the use of an RC filter that low-frequency filters a Pulse-Width Modulation square wave that is modulated directly by the generated signal. The software returns different PPG profiles and allows the signal comparison before vs after the addition of different-intensity modulated respiratory trends and background noise. The digital signal is faithfully converted into an equivalent analog voltage signal capable of reproducing not only the waveform profile but also the respiratory trend and various levels of noise.

Low-frequency Acoustic Collimation Modulation Based on Monopole Mie Resonance Acoustic Metamaterials

Acoustic metamaterials show great flexibility in manipulating acoustic waves and exhibit important applications. However, an increasing number of applications are suitable for the low-frequency range, which makes it challenging to design acoustic metamaterials for low-frequency modulation. Here, we propose a subwavelength monopole Mie resonance acoustic metamaterial that can flexibly control the propagation route of acoustic waves. Compared to the background medium, the designed Mie unit has a higher refractive index and can achieve directional manipulation of low-frequency acoustic waves based on monopole Mie resonance. Furthermore, an array formed by multiple Mie units in a specific arrangement can achieve the acoustic collimation phenomenon. The designed monopole Mie resonance acoustic metamaterial has potential application prospects in fields such as low-frequency filtering, noise suppression, and nondestructive testing.

CNTFET VDGA tabanlı ultra düşük güç düşük voltaj yüksek frekanslı filtre uygulamaları

MOSFET boyutlarının ölçeklendirilmesinde sınır değere yaklaşılmasıyla birlikte birçok problem ortaya çıkmıştır. MOSFET’in boyutlarının küçültülmesiyle ortaya çıkan kısa kanal etkileri ve azaltılmış geçit kontrolü gibi birçok problemler için alternatif çözüm yolları araştırılmıştır. Karbon Nanotüp Alan Etkili Transistör (CNTFET- Carbon Nanotubes Field Effect Transistors) üstün elektriksel ve mekaniksel özellikleri ile MOSFET’ in yerini alacak en uygun alternatif olarak düşünülmektedir. Bu çalışmada, 32 nm CNTFET kullanılarak Voltaj Farkı Alan Kazanç Kuvvetlendirici (VDGA-Voltage Differencing Gain Amplifier) yapısının benzetim çalışmaları yapılmıştır. Buna ek olarak CNTFET VDGA yapısı ile üç girişli tek çıkışlı filtre yapısı sunulmuştur. ±0.3V düşük besleme voltajında ve 1µA düşük kutuplama akımında gerçekleştirilen filtre yapısı, CMOS VDGA ile gerçekleştirilen filtre yapısına kıyasla 22,909 MHz gibi daha yüksek merkez frekansına sahiptir. Ayrıca CNTFET VDGA yapısıyla gerçekleştirilen filtre 8,3412 µW düşük güç tüketimine sahiptir.

Spatial release from masking with simulated hybrid cochlear implant speech

Spatial release from masking (SRM) is the improvement in speech intelligibility when the masking signals are spatially separated from the target signal. Cochlear implant (CI) users utilizing electric-only (E-Only) stimulation had poorer speech recognition than CI users utilizing electric-acoustic stimulation (EAS). Previous research investigating SRM on hybrid CI users big spatial separations (±90°) between the target and the maskers which were unrealistic in conversational settings. Here, we present SRM data using natural speech, simulated CI speech, and simulated EAS speech from young, normal hearing listeners for smaller, yet realistic, spatial separations between the target and the maskers. An eight-channel noise-excited vocoder was used to simulate cochlear implant processing and low-frequency filtering was used to simulate residual low-frequency hearing. Five spatial configurations were used: (colocated (target and the two maskers presented from 0° azimuth) and one of four spatially separated conditions (target at 0°, symmetrical maskers at ± 5°, ± 10°, ±15°, or ±30°). Initial analysis of the data revealed that the listeners had significantly poorer speech identification thresholds for the simulated CI speech when compared to EAS speech. The individual variability and the relationship between the speech identification thresholds in the three conditions will be discussed.

A novel low light object detection method based on the YOLOv5 fusion feature enhancement

Low-light object detection is an important research area in computer vision, but it is also a difficult issue. This research offers a low-light target detection network, NLE-YOLO, based on YOLOV5, to address the issues of insufficient illumination and noise interference experienced by target detection tasks in low-light environments. The network initially preprocesses the input image with an improvement technique before suppressing high-frequency noise and enhancing essential information with C2fLEFEM, a unique feature extraction module. We also created a multi-scale feature extraction module, AMC2fLEFEM, and an attention mechanism receptive field module, AMRFB, which are utilized to extract features of multiple scales and enhance the receptive field. The C2fLEFEM module, in particular, merges the LEF and FEM modules on top of the C2f module. The LEF module employs a low-frequency filter to remove high-frequency noise; the FEM module employs dual inputs to fuse low-frequency enhanced and original features; and the C2f module employs a gradient retention method to minimize information loss. The AMC2fLEFEM module combines the SimAM attention mechanism and uses the pixel relationship to obtain features of different receptive fields, adapt to brightness changes, capture the difference between the target and the background, improve the network's feature extraction capability, and effectively reduce the impact of noise. The AMRFB module employs atrous convolution to enlarge the receptive field, maintain global information, and adjust to targets of various scales. Finally, for low-light settings, we replaced the original YOLOv5 detection head with a decoupled head. The Exdark dataset experiments show that our method outperforms previous methods in terms of detection accuracy and performance.

Investigations into Higher-Frequency Hysteresis Current Controller for Supraharmonic Hybrid Active Filters

Hysteresis current controllers (HCCs) are extensively used in low-frequency power inverters and active power filters. Very little is known about their strengths and weaknesses when applied at supraharmonic frequencies. The major concern regarding the use of HCCs is their variable and uncontrollable switching frequencies. This results in difficulties in filter design, high switching losses, and the possibility of resonance conditions with power system elements. In this article, investigations are conducted on the application of HCCs in a hybrid filter for a 6 kHz matrix converter (MC)-coupled supraharmonic load. The effects of the MC-coupled load and VSC switches on the HCC’s switching frequency are analyzed. A novel mathematical model for obtaining a fixed-frequency HCC is presented. The model is verified in open- and closed-loop HCC control configurations for a three-phase hybrid active power filter (HAPF). Fast Fourier transform (FFT) analysis of the supply current after the implementation of the novel HCC-HAPF showed 2.31% of the 50 Hz fundamental. The analysis is verified in a MATLAB/Simulink environment.

A Large Frequency Ratio Dual-Band Integrated Bandpass Filter Based on MCSICL Structure

In this paper, a dual-band integrated bandpass filter (DI-BPF) based on a mode composite substrate integrated coaxial line (MCSICL) is proposed for a large frequency ratio. The low-frequency bandpass filter is formed by incorporating an SICL line and a gap into the MCSICL, operating in the fundamental mode of the MCSICL. The high-frequency bandpass filter is formed by introducing grounded vias into the MCSICL, operating in the first high-order mode of the MCSICL. To guide the design, the equivalent circuit models of the low- and high-frequency bandpass filters are built. Based on the equivalent circuit models, the DI-BPF is synthesized for a large frequency ratio. The transitions from the DI-BPF to ground coplanar waveguides (GCPWs) are designed for the low- and high-frequency bandpass filters. The DI-BPF with the transitions is fabricated by the printed circuit board (PCB) process. Measurement results indicate a large frequency ratio of 23.16, with the isolation between the low- and high-frequency bandpass filters exceeding 30 dB from dc to 50 GHz.

A System for Collecting and Evaluating Load Information about Bioelectric Signals of the Muscular Apparatus of the Foot of a Person with Cerebral Palsy

The purpose of the research is to develop a system of biomechanical investigation of the volume of movement in the foot and ankle joint for the diagnosis of patients with cerebral palsy in assessing the quality of rehabilitation measures. The process of creating a computer diagnostic system for patients with impaired motor functions of the lower extremities, which is equipped with special bio-measuring devices provided with specialized algorithms for processing the information obtained, is investigated due to the need for scientific research aimed at developing methods and tools for a comprehensive assessment of the results of physical rehabilitation using biomedical indicators.Methods of kinematic analysis of movements in the ankle joint were used, dynamic and static analyses of the forces of the supporting reactions of the foot of the lower extremities were carried out.Results. A motion detection system based on an electronic goniometer implemented on the basis of a B10K resistive potentiometer has been developed. The determination of the absolute rotation angles of the links of the diagnostic system was carried out using the MMA7361 acceleration sensor, which contains three micromachine capacitors mounted on one silicon plate. In the course of research, changing the capacitances of micromachine capacitors makes it possible to fix the recorded value in each direction. Signals passing through low-frequency filters and temperature compensation cascades are sent to the corresponding outputs X, Y and Z. The obtained angulograms allow us to evaluate the gait of the studied patient in two classes ‒ normal and in the presence of pathology.Conclusion. A system for the formation and evaluation of bioelectric signals obtained using the muscular apparatus of the foot of patients with cerebral palsy, assembled on the platform of the Arduino UNO control board and a generalpurpose personal computer, has been developed. The developed system makes it possible to register bioelectric signals with their subsequent processing using a personal computer and making diagnostic decisions.

Randomness-restricted Diffusion Model for Ocular Surface Structure Segmentation.

Ocular surface diseases affect a significant portion of the population worldwide. Accurate segmentation and quantification of different ocular surface structures are crucial for the understanding of these diseases and clinical decision-making. However, the automated segmentation of the ocular surface structure is relatively unexplored and faces several challenges. Ocular surface structure boundaries are often inconspicuous and obscured by glare from reflections. In addition, the segmentation of different ocular structures always requires training of multiple individual models. Thus, developing a one-model-fits-all segmentation approach is desirable. In this paper, we introduce a randomness-restricted diffusion model for multiple ocular surface structure segmentation. First, a time-controlled fusion-attention module (TFM) is proposed to dynamically adjust the information flow within the diffusion model, based on the temporal relationships between the network's input and time. TFM enables the network to effectively utilize image features to constrain the randomness of the generation process. We further propose a low-frequency consistency filter and a new loss to alleviate model uncertainty and error accumulation caused by the multi-step denoising process. Extensive experiments have shown that our approach can segment seven different ocular surface structures. Our method performs better than both dedicated ocular surface segmentation methods and general medical image segmentation methods. We further validated the proposed method over two clinical datasets, and the results demonstrated that it is beneficial to clinical applications, such as the meibomian gland dysfunction grading and aqueous deficient dry eye diagnosis.

FEASIBILITY AND VALIDITY OF WEARABLE TECHNOLOGY FOR ASSESSING MOBILITY AMONG HOSPITALIZED OLDER ADULTS

Abstract The need for strategies to promote mobility during acute care hospital stays among older adults is increasingly recognized. Wearable technologies show great potential for this purpose. The objective of this study was to determine the most accurate wearable sensor device and location for capturing in-hospital mobility in older patients. Twenty-five older adults (79.6±8.1 years) admitted to a hospital medical care unit volunteered to test the feasibility and concurrent validity of the ActiGraph wGT3X-BT, Mox1, MetaMotionC and Fitbit Versa. All 25 enrolled patients wore the devices simultaneously on the wrist, hip, and ankle while undertaking a mobility activity protocol consisting of supine lying, sitting, and standing tasks. Participants also performed the Timed Up and Go (TUG) and 10-meter walk test (10MWT) wearing the devices. A trained physiotherapist supervised the performance of the tasks. Most participants (58%) preferred to wear the device on the ankle. Overall, the ActiGraph and Mox1 were the easiest to set up and download. The ActiGraph was the most reliable device, retrieving 100% of the collected data. Regarding body posture, the thigh-worn ActiGraph algorithm accurately classified 78% of sitting and lying postures, as well as 84% of standing postures, indicating high overall accuracy. After implementing the recommended low-frequency filter extension for slower gait speeds, the ankle-worn ActiGraph showed the narrowest limits of agreement and observations closer to zero for step count during the TUG and the 10MWT. These findings lay the foundation for subsequent work to validate wearable devices for monitoring “free-living” mobility in this population.

Remote monitoring of implantable cardiac monitors in patients with unexplained syncope: Predictors of false-positive alert episodes.

Remote monitoring is recommended for patients with implantable cardiac monitors (ICMs), but compared to other cardiac implantable devices, ICMs are less accurate and transmit a higher number of alerts. The aim of this study was to investigate the predictors of false-positive (FP) arrhythmic alerts in patients with unexplained syncope who were implanted with ICM and followed by an automatic remote monitoring system. We retrospectively evaluated all consecutive patients who received a long-sensing vector ICM for unexplained syncope between January 2019 to September 2021 at our Syncope Unit. The primary endpoint was the incidence of the first FP episode. The secondary endpoints included assessing the incidence of FP episodes for all types of algorhythms and indentifying the reasons for the misdetection of these episodes. Among 105 patients (44.8% males, median age 51 years), 51 (48.6%) transmitted at least one FP alert during a median follow-up of 301 days. The presence of pre-ventricular complexes (PVCs) on the resting electrocardiogram was the only clinical characteristic associated with an increased risk of FP alerts (adjusted Hazard ratio [HR] 5.76 [2.66-12.4], p=0.010). The other significant device-related variables were a low-frequency filter at 0.05Hz versus the default 0.5Hz (adjusted HR 3.82 [1.38-10.5], p=0.010) and the R-wave amplitude (adjusted HR 0.35 [0.13-0.99], p=0.049). Patients who have PVCs are at higher risk of inappropriate ICM activations. To reduce the occurrence of FP alerts, it may be beneficial to target a large R-wave amplitude during device insertion and avoid programming a low-frequency filter at 0.05Hz.

Instagram use and negative and positive body image: the relationship with following accounts and content and filter use among female students

Exposure to Instagram content and photo manipulation behaviors may negatively impact women’s body image. However, some studies did not find support for this assumption. The current study aimed to explore this issue further. The first objective was to investigate associations between self-reported frequency of following Instagram beauty-related and body positive-related accounts and content and body image dimensions (i.e., Body Dysmorphic Disorder [BDD] symptoms, body appreciation, and body functionality appreciation). To achieve this, we controlled for the role of Instagram appearance comparison and internalization of general attractiveness ideal. As for the second objective, the influence of Instagram filter use was assessed: two groups of Instagram filter users (high-frequency and low-frequency users) and non-users were compared on dimensions of Instagram use and body image. Findings from a sample of 149 female undergraduates demonstrated that self-reported frequency of following body positive Instagram content was associated with higher levels of BDD symptoms even after controlling for appearance comparison and internalization of general attractiveness ideal. Conversely, the self-reported frequency of following beauty content was not. Moreover, positive body image dimensions were not associated with self-reported frequency of following any Instagram content. High-frequency Instagram filter users reported a higher frequency of following beauty content and higher levels of appearance comparison and internalization of general attractiveness ideal compared to non-users. However, these two groups did not differ on negative and positive body image dimensions. Finally, low-frequency Instagram filter users did not differ on any investigated dimensions from the other two groups.

Ultra-broad low-frequency forbidden bands in 1D solid–fluid acoustic superlattices with multiple solid constituents

In this paper, a theoretical model of underwater acoustic wave propagation, based on the transfer matrix method, is employed to calculate acoustic wave transmission through a 1D solid–fluid superlattice (SFSL) with multiple solid constituents. The band structure of infinite multi-element SFSL and the transmission coefficient of finite one are obtained, both of which reveal the existence of multiple separated ultra-broad low-frequency forbidden bands, even in the case of solid constituents with very close acoustic parameters. Simulations about the finite multi-element SFSL are carried out to verify the correctness of these findings. In addition, the influence of the acoustic parameters and the structural parameters on the low-frequency forbidden bands (LFBs) of SFSL with two solid constituents (m = 2) is also investigated. It is found that the required two separated LFBs can be achieved by choosing appropriate material parameters. For the situation of m ≥ 2, it is expected to provide more possibilities to engineer low-frequency bandgaps. Our results infer that multiple separated LFBs in multi-element SFSL can be quite useful for making highly performing low-frequency acoustic filters in selective angle ranges, which are useful for the elimination of the side lobes of low-frequency acoustic transducers.

A Compact and Ultra-Low-Power Low-Pass Filter Based on Band-to-Band Tunneling Effect

Continuous-time filters with low-frequency cutoff are crucial building blocks in analog signal processing circuits for speech processing and biomedical applications. The design of an integrated continuous-time filter with a cutoff frequency spanning from sub-Hz to a few kHz is constrained by the ultra-low power and area minimization requirement. In this article, we have experimentally demonstrated a low pass filter response using only a single partially depleted (PD) silicon on insulator (SOI) transistor. The proposed novel filter is based on band to band tunnelling (BTBT). This low pass filter is on-chip tunable to provide a wide cutoff frequency ranging from 2 Hz to 20 kHz, with a silicon footprint of 9 μm2 and the maximum power consumption of 0.6 nW in Global Foundries’ (GF) 45 nm RFSOI technology. The proposed tunneling-based filter is less prone to process variations and mismatches as compared to traditional integrated analog filters. The proposed BTBT-based passive RC filter with spurious free dynamic range (SFDR) of 29 dbc, outperforms previously reported filters in the literature in terms of power consumption and area. The ultra-low power consumption and area efficiency make this proposed low pass filter suitable for the multichannel analog front-end low-frequency filters for noise-tolerant neural network-based applications such as speech recognition and electrophysiological signal recognition.

Ultra-High-Resistance Pseudo-Resistors With Small Variations in a Wide Symmetrical Input Voltage Swing

This paper presents a new strategy and circuit configuration composed of serially-connected PMOS devices operating in the subthreshold region for implementing ultra-highvalue resistors required in very low-frequency active-RC filters and bio-amplifiers. Depending on the application, signal bandwidth for instance in bio-amplifiers may vary from a few mHz up to a maximum of 10 kHz. Three different resistor structures are proposed to achieve ultra-high resistance. While ranging in the order of several TY, the proposed ultra-high-resistance pseudoresistors occupy a small on-chip silicon area, which is one of the main issues in the design of analog front-end circuits in ultra-low power implantable biomedical microsystems. In addition, these ultra-high-value resistors lead to the use of a small capacitance to create a very small cut-off frequency. Therefore, the large area to implement capacitances is also considerably reduced. The proposed resistor structures have very small variations about 7% and 12% in a wide input voltage range (-0.5 V +0.5 V), thus significantly improving the total harmonic distortion of bioamplifiers and the analog front-end of the system. Simulation results of different circuits designed in a 180nm CMOS technology, are shown to demonstrate the advantages of the proposed ultra-high-resistance pseudo-resistors.

Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis.

The classification of limb movements can provide with control commands in non-invasive brain-computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control commands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the-art method in the multi-class classification of limb movements. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, similarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correlation measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the essential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used models is evaluated using two datasets. mFBTRCA achieved a classification accuracy of 0.4193 ± 0.0780 (7 classes) and 0.4032 ± 0.0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0.3590 ± 0.0645 and 0.3159 ± 0.0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.

Modeling mental peculiarities of a decision maker by a Fourier-holography technique

A task of modeling individual mental features of a decision-maker using a Fourier holography setup is considered. The problem is considered for a situation when current conditions of decision-making contradict to the previously learned rule of decision-making logic modeled by the non-cooperative game "Prisoner's Dilemma". The approach to the problem is based on a hypothesis of the correlation between mental features and the properties of the neural network as a material carrier of intelligence. The 6f Fourier holography scheme of the resonant architecture is considered as a three-layer neural network implementing a neuro-physiologically motivated concept of the "excitation ring" proposed by A.M. Ivanitsky. We analytically assess the dependence of the validity limits of the classical total probability formula for a disjunction of incompatible events on the characteristics of low-frequency filters in holograms and the correlation radii of the training image of the basic decision rule. Analytical results are confirmed by results of the numerical simulation.

МЕТОД ИЗМЕРЕНИЯ ЧАСТОТЫ ПО ТРЕМ ОТСЧЕТАМ ВХОДНОГО СИГНАЛА

The paper deals with the method of signal frequency measurement based on the analysis of three consecutive samples of the input signal. The ad-vantages and disadvantages of the method are considered. The method of error re-duction, based on the use of a low-frequency filter, is proposed. The simulation model is built with the help of Simulink package. An estimate of the error for the case of sinusoidal, polyharmonic and sinusoidal distorted by noise signals is obtained.

Bandgap prediction for a beam containing membrane-arch-mass resonators

This work aims to propose a promising locally resonating system consisting of a tensioned elastic membrane and two-arch masses attached on the membrane surface. Traditional membrane-type resonators, which usually create one obvious attenuation zone at low frequencies, might not be efficient in multi-frequency vibration suppression. The proposed structure can produce an extra clear flexural attenuation region and shift bandgap frequencies below 300 Hz. By adjusting geometric parameters (thickness, width, and location) of the arch mass, the bandgap region can be tuned. Introducing a feasible analytical model for accurately predicting the first and second initial frequencies of the bandgaps for a beam structure containing membrane-arch-mass resonators is another focus of this study. The proposed theoretical framework can be used to tune the bandgap to different target frequency ranges without knowing the actual width of the bandgap. Finite-element analysis and experiments are conducted to verify the theoretical predictions. A good agreement is seen among the theoretical, finite-element analysis, and experimental results. In addition, adjacent cells with different arch-mass distributions can generate two pairs of flexural bandgaps, increasing the practicality in engineering applications. The proposed structure might be used in low-frequency vibration isolation and filters.

Current-Feedback Instrumentation Amplifier Using Dual-Chopper Fill-In Technique

In this study, we describe a dual-chopper glitch-reduction current-feedback instrumentation amplifier (CFIA) with a ripple reduction loop. The amplifier employs the chopping technique to reduce low-frequency noise, such as 1/f noise. A glitch caused by chopping occurs at each chopper clock edge and results in intermodulation distortion (IMD). Owing to the input offset, the chopping technique also produces ripples. In this study, the glitch-induced IMD was reduced using a fill-in technique whereby only neat signals were alternately used as outputs by avoiding the glitch section with dual-chopping channel CFIA. To avoid using a high-order, low-frequency filter, a ripple reduction loop was implemented to reduce the ripple generated by chopping. The CFIA is based on a low-noise chopper fully differential difference amplifier with a cascode stage and a Monticelli-class AB output stage, which can drive a larger load and increase power efficiency. The proposed dual-chopper CFIA was fabricated using a 0.18 µm CMOS standard process, and its current consumption with a 1.8-V power supply is 29.5 μA. The proposed CFIA has a gain of 51 V/V, input referred noise of 53.3 nV/√Hz at 1 Hz, and a noise efficiency factor of 4.48.