Frequency Interference Research Articles

On the RFI Detection in Differential Interferometric Synthetic Aperture Radar

Abstract. Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites.

Modes of the Dark Ages 21 cm field accessible to a lunar radio interferometer.

At redshifts beyond [Formula: see text], the 21 cm line from neutral hydrogen is expected to be essentially the only viable probe of the three-dimensional matter distribution. The lunar far-side is an extremely appealing site for future radio arrays that target this signal, as it is protected from terrestrial radio frequency interference, and has no ionosphere to attenuate and absorb radio emission at low frequencies (tens of MHz and below). We forecast the sensitivity of low-frequency lunar radio arrays to the bispectrum of the 21 cm brightness temperature field, which can in turn be used to probe primordial non-Gaussianity generated by particular early universe models. We account for the loss of particular regions of Fourier space due to instrumental limitations and systematic effects, and predict the sensitivity of different representative array designs to local-type non-Gaussianity in the bispectrum, parametrized by [Formula: see text]. Under the most optimistic assumption of sample variance-limited observations, we find that [Formula: see text] could be achieved for several broad redshift bins at [Formula: see text] if foregrounds can be removed effectively. These values degrade to between [Formula: see text] and 0.7 for [Formula: see text] to [Formula: see text], respectively, when a large foreground wedge region is excluded. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'.

A Multicenter Randomized Controlled Trial Of Microbiome-Based Artifıcial Intelligence-Assisted Personalized Diet Vs Low Fodmap Diet: A Novel Approach for the Management of Irritable Bowel Syndrome.

Personalized management strategies are pivotal in addressing Irritable Bowel Syndrome (IBS). This multicenter randomized controlled trial focuses on comparing the efficacy of a microbiome-based artificial intelligence (AI)-assisted personalized diet (PD) with a low FODMAP diet (LFD) for IBS management. One hundred and twenty-one patients participated, with 70 assigned to the PD group and 51 to the LFD group. IBS subtypes, demographics, symptom severity (IBS-SSS), anxiety, depression, and quality of life (IBS-QOL) were evaluated. Both interventions spanned 6 weeks. The trial's primary outcome was the within-individual difference in IBS-SSS compared between intervention groups. For the primary outcome, there was a change in IBS-SSS of -112.7 for those in the PD group vs -99.9 for those in the LFD group (p: 0.29). Significant improvement occurred in IBS-SSS scores (p<.001), frequency (p<.001), abdominal distension (p<.001), and life interference (p<.001) in both groups. Additionally, there were significant improvements in anxiety levels and IBS-QOL scores for both groups (p<.001). Importantly, PD was effective in reducing IBS SSS scores across all IBS subtypes IBS-C (p<.001), IBS-D (p=.01), and IBS-M (p<.001) subtypes, while LFD exhibited comparable improvements in IBS-C (p=.004) and IBS-M (p<.001). PD intervention significantly improved IBS-QOL scores for all subtypes (IBS-C (p<.001), IBS-D (p<.001), and IBS-M (p=.008)), while the LFD did so for the IBS-C (p=.004) and IBS-D (p=.022). Notably, PD intervention led to significant microbiome diversity shifts (p<0.05) and taxa alterations compared to LFD. The AI-assisted personalized diet emerges as a promising approach for comprehensive IBS management. With its ability to address individual variation, the PD approach demonstrates significant symptom relief, enhanced quality of life, and notable diversity shifts in the gut microbiome, making it a valuable strategy in the evolving landscape of IBS care.

Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment.

Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.

Polarization and frequency multiplexed multi-channel focusing terahertz vortex with a birefringent dielectric metasurface

We propose an approach to generate polarization and frequency multiplexed focusing vortex beams using a birefringent dielectric metasurface in terahertz (THz) range. The designed metadevice consists of Si nanopillars with different sizes. The transmission efficiency of each unit cell under orthogonal polarized illumination can exceed 70% at both operating frequencies 1 THz and 1.2 THz. Combining the functions of beam deflection, focusing, and vortex beam generation in the interference holography design strategy, polarization and frequency multiplexed multi-channel focusing THz vortex beams with different topological charges and generation positions can be achieved, which can greatly improve the transmission capacity of THz communications. Combining its compact and efficient features with multiplexing methods, our designed metadevice has enormous potential for application in THz vortex generation and information processing.

Remove First, Detect Later: A Counter-intuitive Approach For Detecting Radio Frequency Interference in Radio Sky Imagery

Abstract The modern era has witnessed a rapid uptake of technological use - from air travel to mobile cellphones. Technological advancement has however come at the cost of radio spectrum crowding and as such the efficient detection of radio frequency interference (RFI) from radio sky images has become more paramount. Detecting RFI is a complex task that blends semantic segmentation and anomaly detection, further complicated by the limited availability of public datasets with accurate ground truth labels. Recent studies show that deep learning models improve RFI detection compared to current state-of-the-art tools. However, many astronomers are hesitant to adopt these models, possibly due to the dependence of these models on noisy labels from existing tools when accurate ground truth labels are largely unavailable in the public domain. This study argues that utilizing large weakly labelled training datasets yields lower performance than appropriately employing a modest set of expertly annotated samples. Further, RFDL (Remove First Detect Later), an augmented deep learning framework, is proposed. By first, counter-intuitively, removing RFI with inpainting, RFDL feeds the difference between the original and inpainted images into existing detection models. RFDL’s performance is benchmarked against current state-of-the-art deep learning methods and the prevelant AOFlagger pipeline, using AUROC, AUPRC, and F1 score metrics. It is shown that RFDL significantly outperforms the state-of-the-art whilst only necessitating the use of 20 expertly labelled images.

Design and Optimization of MEMS Resonant Pressure Sensors with Wide Range and High Sensitivity Based on BP and NSGA-II.

With the continuous progress of aerospace, military technology, and marine development, the MEMS resonance pressure sensor puts forward the requirements of not only a wide range but also high sensitivity. However, traditional resonators are hardly compatible with both. In response, we propose a new sensor structure. By arranging the resonant beam and the sensitive diaphragm vertically in space, the new structure improves the rigidity of the diaphragm without changing the thickness of the diaphragm and achieves the purpose of increasing the range without affecting the sensitivity. To find the optimal structural parameters for the sensor sensitivity and range, and to prevent the effects of modal disturbances, we propose a multi-objective optimization design scheme based on the BP and NSGA-II algorithms. The optimization of the structure parameters not only improved the sensitivity but also increased the interference frequency to solve the issue of mode interference. The optimized structure achieves a sensitivity and range of 4.23 Hz/kPa and 1-10 MPa, respectively. Its linear influence factor is 38.07, significantly higher than that of most resonant pressure sensors. The structural and algorithmic optimizations proposed in this paper provide a new method for designing resonant pressure sensors compatible with a wide range and high sensitivity.

A Picometre-Level Resolution Test Method without Nonlinearity for Heterodyne Interferometer Measurement Electronics

The wide application of displacement measurement in high-precision equipment production and high-precision metrology is placing increasing pressure on the resolution of heterodyne interferometers. However, as the core component of an interferometer, since measurement electronics includes the cross-physical process of photoelectric conversion, its resolution is rarely evaluated, either on an individual level or as a whole. Therefore, in this paper, we propose a picometer resolution test method for measurement electronics, that uses intensity modulation signals based on an AOM to replace the beat frequency interference signals, and an ordinary commercial guide rail to equivalently generate the pm-level displacement of the heterodyne interferometer under laboratory conditions. Based on the detailed analysis of the type of noise in the test device, the correlation between the light intensity and the nonlinear error was established, and nonlinearity was suppressed to 10% of the original level. Furthermore, this test method allows one to perform a 0.1 mrad phase step test at 1 MHz signal frequency, equivalent to a 2.5 pm resolution test in a double-pass heterodyne interferometer. Simultaneously, it can be directly applied to the resolution test for measurement electronics with a center frequency in the range of 1 MHz to 20 MHz.

Real-Time Detection Method for Saturation Speed of Air-Core Coil Current Transformer

Traditional methods generally rely on a fixed threshold to determine whether the current transformer is saturated. However, this threshold based judgment method is difficult to adapt to different working conditions and changes in current waveforms. In view of the different characteristics of current transformer and the diversity of working environment, it may not be accurate to judge its saturation state only by relying on a fixed threshold. In order to improve the efficiency of current transformer speed saturation detection and reduce the error, a new type of air core coil current transformer speed saturation real-time detection method is proposed. Based on the working and current characteristics of the fast saturation current transformer, the discrete Fourier algorithm is used to suppress power frequency interference signals, with the fundamental frequencies set to 45 Hz and 55 Hz respectively, in order to separate the signal from power frequency interference and reduce the interference intensity of the power frequency interference signal; Identify all faults and saturation points, and extract effective data segments. The wavelet transform characteristics of the secondary current waveform of current transformer in saturated and unsaturated states are deeply studied. By analyzing the changes of different operating conditions and current waveforms, more discriminant features are extracted by using the characteristics of wavelet transform, so as to realize the real-time and accurate detection of saturation state. The results indicate that after adopting the proposed method, the highest accuracy of data segment extraction reached 97%; The maximum detection error is only 4%. The detection time is only 1.31 s. This indicates that the proposed method improves the detection performance and is practical.

Vehicle to Vehicle Communication Using Li-Fi Technology

Abstract: Vehicle-to-vehicle (V2V) communication has emerged as a pivotal technology in the automotive industry, enabling enhanced safety, efficiency, and convenience on the roads. This abstract explores the integration of Light Fidelity (LiFi) technology in V2V communication systems. LiFi, utilizing visible light communication (VLC), offers high-speed wireless connectivity by using light-emitting diodes (LEDs) to transmit data. By harnessing LiFi's unique advantages such as increased bandwidth, low latency, and immunity to radio frequency interference, V2V communication systems can facilitate real-time exchange of critical information between vehicles, including traffic conditions, collision warnings, and cooperative driving maneuvers. This paper delves into the technical aspects, challenges, and potential applications of LiFi-enabled V2V communication, shedding light on its promising role in revolutionizing the future of smart and connected transportation systems

Study of Sky Radio Frequency Interference Background Level Behavior During the Partial Solar Eclips on 25 Oct. 2022 at The Baghdad University Location

Radiofrequency interference (RFI) describes the weakening of radio astronomy transmissions caused by man-made radio signal noise in radio telescope observation indicates the level of radio frequency noise in the sky at all times, regardless of any astronomical sources. This noise is affected by various sources, including Earth's atmosphere or solar activity and all communications equipment operating near the radio telescope. In this study, the background noise which includes RFI was measured using HSA2000 series receiver spectrum analyzer, which connected with two dipole antennas in different frequency bands (15-80 MHz) low band antenna, and (110-240 MHz) high band antenna during the partial solar eclipse in Baghdad on October 25, 2022, at (12:00-15:00), as a simple radio telescope to observe the radio astronomical background level in the sky. It is clear that the average amount of RFI level during a partial solar eclipse (-83.249 dBm for LBA decreased by about (0.93) from the value on a normal day (-77.495 dBm). And For HBA -84.711 dBm decreased by about (0.85) from the value on a normal day (-72.784 dBm). Also, it was noticed that these amounts are approximately equal to the RFI level at night (-82.352 dBm for LBA and -82.739 dBm for HBA). It is (0.98) for LBA and (0.97) for HBA, respectively. This research confirms that despite the short duration of the partial eclipse, the average value during the partial eclipse period is close to the average values during the night period due to the decrease in the electronic density of the ionosphere, especially at low frequencies, and the FRI of ground disturbances as a result of the activities of human from the surrounding the monitoring area.  also, found that at the monitoring point, there is a good relationship between the time of the maximum partial eclipse and the peaks of signal gain strength.

A comparison framework for deep learning RFI detection algorithms

Abstract In the realm of radio astronomy, the detection of Radio Frequency Interference (RFI) stands as a pivotal pursuit. This study performs a novel comparative analysis of deep learning methodologies, and introduces a novel transfer learning method, called fine-tuning. We compare various aspects and factors relating to this problem, including supervised Fully Convolutional Network (FCN) architectures used within the literature, loss functions, regularization techniques, and training methodologies, to establish the most effective strategies for RFI detection. Moreover, the relationship between parameters, FLOPS, and inference times are examined. Fine-tuning involves pre-training models with low-quality AOFlagger reference outputs, a very popular and accessible RFI flagging software package, and thereafter re-training the models with high-quality reference outputs. We utilize two datasets: real observations from LOFAR and simulated data from HERA. The Mean Squared Error (MSE) loss function emerges as a robust performer if a high recall is desired. In contrast, the Binary Cross-Entropy (BCE) loss function excels in generalization but falls short in classification performance. The Dice loss function emerges as the top performer, maximizing the F1 score and thereby serving as the choice for our further investigations. Notably, we highlight the important role of data quality and model capacity. In particular, we find that low-capacity models exhibit resilience when trained with low-quality flags from AOFlagger, showcasing their ability to mitigate overfitting and overflagging tendencies. In contrast, high-capacity models excel when trained with high-quality flags. Fine-tuning proved to be an effective method to unlearn the overflagging tendencies of AOFlagger, whilst requiring very little data.

Radio frequency interference detection using efficient multiscale convolutional attention UNet

ABSTRACT Studying the Universe through radio telescope observation is crucial. However, radio telescopes capture not only signals from the universe but also various interfering signals, known as radio frequency interference (RFI). The presence of RFI can significantly impact data analysis. Ensuring the accuracy, reliability, and scientific integrity of research findings by detecting and mitigating or eliminating RFI in observational data, presents a persistent challenge in radio astronomy. In this study, we proposed a novel deep learning model called EMSCA-UNet for RFI detection. The model employs multiscale convolutional operations to extract RFI features of various scale sizes. Additionally, an attention mechanism is utilized to assign different weights to the extracted RFI feature maps, enabling the model to focus on vital features for RFI detection. We evaluated the performance of the model using real data observed from the 40 m radio telescope at Yunnan Observatory. Furthermore, we compared our results to other models, including U-Net, RFI-Net, and R-Net, using four commonly employed evaluation metrics: precision, recall, F1 score, and IoU. The results demonstrate that our model outperforms the other models on all evaluation metrics, achieving an average improvement of approximately 5 per cent compared to U-Net. Our model not only enhances the accuracy and comprehensiveness of RFI detection but also provides more detailed edge detection while minimizing the loss of useful signals.

Quantifying the impact of sample, instrument, and data processing on biological signatures in modern and fossil tissues detected with Raman spectroscopy

AbstractRaman spectroscopy is a popular tool for characterizing complex biological materials and their geological remains. Ordination methods, such as principal component analysis (PCA), use spectral variance to create a compositional space, the ChemoSpace, grouping samples based on spectroscopic manifestations reflecting different biological properties or geological processes. PCA allows to reduce the dimensionality of complex spectroscopic data and facilitates the extraction of informative features into formats suitable for downstream statistical analyses, thus representing a first step in the development of diagnostic biosignatures from complex modern and fossil tissues. For such samples, however, there is presently no systematic and accessible survey of the impact of sample, instrument, and spectral processing on the occupation of the ChemoSpace. Here, the influence of sample count, unwanted signals and different signal‐to‐noise ratios, spectrometer decalibration, baseline subtraction, and spectral normalization on ChemoSpace grouping is investigated and exemplified using synthetic spectra. Increase in sample size improves the dissociation of groups in the ChemoSpace, and our sample yields a representative and mostly stable pattern in occupation with less than 10 samples per group. The impact of systemic interference of different amplitude and frequency, periodical or random features that can be introduced by instrument or sample, on compositional biological signatures is reduced by PCA and allows to extract biological information even when spectra of differing signal‐to‐noise ratios are compared. Routine offsets ( 1 cm−1) in spectrometer calibration contribute in our sample to less than 0.1% of the total spectral variance captured in the ChemoSpace and do not obscure biological information. Standard adaptive baselining, together with normalization, increases spectral comparability and facilitates the extraction of informative features. The ChemoSpace approach to biosignatures represents a powerful tool for exploring, denoising, and integrating molecular information from modern and ancient organismal samples.

Synthetic Aperture Radar Radio Frequency Interference Suppression Method Based on Fusing Segmentation and Inpainting Networks

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR.

Structural design and dynamic characteristics analysis of braided composite two-stage gear transmission system

In order to realize the lightweight design of the transmission system, the braided composite material is applied to the two-stage gear transmission system. According to the structural characteristics of the two-stage gear reducer box, the whole box is designed to be assembled with the braided base and the box wall. Woven composite materials are applied to the web parts of mixed metal composite gears to realize the design goal of lightweight gears. Then, under the assumption of ignoring the influence of friction, bearings and other factors on the system, the dynamic model of the two-stage gear transmission system considering the box is established. By normalizing and dimensionless processing of the equations, the dimensionless differential equations of motion are obtained. The fourth-order Runge–Kutta method is used to analyze the relationship between the connection parameters and the dynamic characteristics of the system under the two working conditions of rigid and flexible connection between the composite base and the box wall.Through the analytical analysis of vibration displacement of two-stage gear reducer and box, the theoretical basis is found for the numerical analysis results. Finally, the dynamic characteristics of the transmission system are studied by vibration resonance analysis through high and low frequency interference. It is found that in a certain frequency range, with the decrease of the mass and moment of inertia of the transmission parts corresponding to the mixed metal composite gear, the amplitude-frequency characteristic Q of the lightweight gear and gearbox transmission system is slightly lower than that of the common gear and gearbox system, and the stability of the system is increased, and the dynamic characteristics of the system are improved.

A Cryogenic Wideband (2.5–14 GHz) Receiver System for the Arecibo Observatory 12 m Telescope

AbstractIn this paper we present details of the construction of a wideband, cryogenic receiver and its successful commissioning on the Arecibo Observatory 12 m telescope. The cryogenic receiver works in the 2.5–14 GHz frequency range. We upgraded the current narrow band, room temperature receivers of the telescope with the new wideband receiver. The current receiver is built around a Quadruple‐Ridged Flared Horn (QRFH) developed by Akgiray et al. (2013, https://doi.org/10.1109/tap.2012.2229953). To mitigate strong radio frequency interference (RFI) below 2.7 GHz, we installed a highpass filter before the first stage low noise amplifier (LNA). The QRFH, highpass filter, noise coupler and LNA are located inside a cryostat and are cooled to 15 K. The measured receiver temperature is 25 K (median value) over 2.5–14 GHz. The system temperature measured at zenith is about 40 K near 3.1 and 8.6 GHz and the zenith antenna gains are 0.025 and 0.018 K/Jy at the two frequencies respectively. We recommend the following improvements to the telescope system: (a) Upgrade the highpass filter to achieve better RFI rejection near 2.5 GHz; (b) Improve aperture efficiency at 8.6 GHz; (c) Upgrade the intermediate frequency system to increase the upper frequency of operation from 12 to 14 GHz.

RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference—Application to pulsar observations

Radio frequency interference (RFI) has been an enduring concern in radio astronomy, particularly for the observations of pulsars which require high timing precision and data sensitivity. In most works of the literature, RFI mitigation has been formulated as a detection task that consists of localizing possible RFI in dynamic spectra. This strategy inevitably leads to a potential loss of information since parts of the signal identified as possibly RFI-corrupted are generally not considered in the subsequent data processing pipeline. Conversely, this work proposes to tackle RFI mitigation as a joint detection and restoration that allows parts of the dynamic spectrum affected by RFI to be not only identified but also recovered. The proposed supervised method relies on a deep convolutional network whose architecture inherits the performance reached by a recent yet popular image-denoising network. To train this network, a whole simulation framework is built to generate large data sets according to physics-inspired and statistical models of the pulsar signals and of the RFI. The relevance of the proposed approach is quantitatively assessed by conducting extensive experiments. In particular, the results show that the restored dynamic spectra are sufficiently reliable to estimate pulsar times-of-arrivals with an accuracy close to the one that would be obtained from RFI-free signals.

Optimal Incoherent Reception Noise Immunity of Binary Signals with Differential Phase Manipulation in the Presence of Several Harmonic Interferences

This article proposes the calculation of the probabilities of incoherent reception of signals with binary relative phase manipulation (OFM–2) in the presence of several harmonic interference. Examples of calculating the error probability per bit of receiving signals from OFM – 2 according to the presented method in the presence of several harmonic interference are shown. The obtained graphs depend on the magnitude of the signal at different values of interference and the same magnitude of the frequency shift, as well as on the frequency shift of interference at different levels of signals and interference. The results of the study using this technique allow us to calculate with high accuracy and reliable forecasting the reliability of transmitted information in radio communication channels under the influence of several harmonic interference during processing on two clock cycles. And also to predict the maximum damage to the process of transmitting information in a radio communication channel under various conditions: the coincidence of interference frequencies with the signal frequency, the offset of interference frequencies relative to the signal frequency and the spread of interference frequencies with the signal frequency.

Design method for VDCC-based analog comb filter for power line interference cancellation

An analog comb filter is implemented by linking multiple VDCC-based notch filters in a cascading fashion (N in total), eliminating N different pole frequencies. This study focuses on suppressing a fundamental frequency of power-line interference of 50 Hz and its consecutive three odd harmonics at 150 Hz, 250 Hz, and 350 Hz. One significant advantage of this comb filter is the independent control over filters' parameters like quality factor and pole frequency. Additionally, these filters can be electronically tuned by adjusting the transconductance gain of VDCC. The suggested notch filter configuration involves 2 capacitors, 2 resistors, and 1 VDCC element. Extensive simulations were conducted using PSPICE simulator software to validate the effectiveness of these filters. The basic building block, VDCC, is designed and implemented in the simulation using integrated circuits MAX435 and AD844.•Design uses a VDCC-based high Q notch filter as the active building block.•The filter employs fewer active and passive components.•Simulated results using commercially available ICs, MAX435 and AD844, confirm the filter's practical utility.