Low-pass Research Articles

Frequency control enhancement for hybrid microgrid using multi-terminal multi-function inverter

Renewable energy sources (RESs) are considered a crucial energy transformation to reduce carbon emissions, so more RESs are being integrated into contemporary power systems. Power electronic converters are extensively utilized to connect power grids with renewable generators to manage the fluctuations and unpredictability of these renewable energy sources. This paper introduces a multi-terminal multi-function inverter (MT-MF) designed for a battery energy storage system (BESS) to maintain the frequency stability of a hybrid microgrid (MG). The MG comprises a photovoltaic generation system, a diesel generator, BESS, and two loads: one constant load and the other variable, fed through a medium-voltage radial feeding system. An introduced approach involves utilizing a model predictive control controlled virtual synchronous generator (MPC-VSG) for BESS. This method offers inertia support during transient states and improves the dynamic characteristics of system frequency. In addition, it enables the connection of multiple batteries, provides individualized control for each, and supports the injection of reactive power into the MG. The required power from the BESS is shared between the two batteries using the low pass filter technique. The simulation outcomes affirm the proposed control strategy’s effectiveness and underscore the MT-MF inverter approach’s potential in integrating extensive RESs. This paper also explores how the proposed technique outperforms other methods in improving frequency stability.

Graph Aggregating-Repelling Network: Do Not Trust All Neighbors in Heterophilic Graphs

Graph neural networks (GNNs) have demonstrated exceptional performance in processing various types of graph data, such as citation networks and social networks, etc. Although many of these GNNs prove their superiority in handling homophilic graphs, they often overlook the other kind of widespread heterophilic graphs, in which adjacent nodes tend to have different classes or dissimilar features. Recent methods attempt to address heterophilic graphs from the graph spatial domain, which try to aggregate more similar nodes or prevent dissimilar nodes with negative weights. However, they may neglect valuable heterophilic information or extract heterophilic information ineffectively, which could cause poor performance of downstream tasks on heterophilic graphs, including node classification and graph classification, etc. Hence, a novel framework named GARN is proposed to effectively extract both homophilic and heterophilic information. First, we analyze the shortcomings of most GNNs in tackling heterophilic graphs from the perspective of graph spectral and spatial theory. Then, motivated by these analyses, a Graph Aggregating-Repelling Convolution (GARC) mechanism is designed with the objective of fusing both low-pass and high-pass graph filters. Technically, it learns positive attention weights as a low-pass filter to aggregate similar adjacent nodes, and learns negative attention weights as a high-pass filter to repel dissimilar adjacent nodes. A learnable integration weight is used to adaptively fuse these two filters and balance the proportion of the learned positive and negative weights, which could control our GARC to evolve into different types of graph filters and prevent it from over-relying on high intra-class similarity. Finally, a framework named GARN is established by simply stacking several layers of GARC to evaluate its graph representation learning ability on both the node classification and image-converted graph classification tasks. Extensive experiments conducted on multiple homophilic and heterophilic graphs and complex real-world image-converted graphs indicate the effectiveness of our proposed framework and mechanism over several representative GNN baselines.

Simulating a numerical UV completion of quartic Galileons

The Galileon theory is a prototypical effective field theory that incorporates the Vainshtein screening mechanism—a feature that arises in some extensions of general relativity, such as massive gravity. The Vainshtein effect requires that the theory contain higher order derivative interactions, which results in Galileons, and theories like them, failing to be technically well posed. While this is not a fundamental issue when the theory is correctly treated as an effective field theory, it nevertheless poses significant practical problems when numerically simulating this model. These problems can be tamed using a number of different approaches: introducing an active low-pass filter and/or constructing a UV completion at the level of the equations of motion, which controls the high momentum modes. These methods have been tested on cubic Galileon interactions, and have been shown to reproduce the correct low-energy behavior. Here we show how the numerical UV-completion method can be applied to quartic Galileon interactions, and present the first simulations of the quartic Galileon model using this technique. We demonstrate that our approach can probe physics in the regime of the effective field theory in which the quartic term dominates, while successfully reproducing the known results for cubic interactions. Published by the American Physical Society 2024

A broadband diplexer-integrated double-pole single-throw switch for FDD/TDD operations

ABSTRACT A broadband diplexer-integrated double-pole single-throw (DPST) switch is proposed to realize frequency division duplexing (FDD) and time division duplexing (TDD). It consists of a lowpass filter (LPF) and a bandpass filter (BPF). The series LC resonators are used in both channels to create transmission zeros (TZs) in the stopband to ensure high selectivity. The diplexer is realized by inter-digital capacitors and microstrip section inductors, which can achieve low cost and compact size. Besides, this diplexer does not require a T-junction, achieving a compact size. For demonstration, a diplexer operating in DC-1.78/3.29–4.42 GHz has been designed, fabricated, and measured. The diplexer has four operating modes. And it has a compact size of 24.67 mm × 21.4 mm. The measured results show that the diplexer exhibits broadband, low insertion losses, high isolation, and high selectivity.

Analysis of Moiré pattern for parallel binary gratings based on AND and OR operators

Abstract A method for analyzing the Moiré pattern of a parallel binary grating based on the high-frequency component of the omitted pattern is proposed. The analysis aims to investigate the signal components that influence the dark-light band formation of the Moiré pattern. The analytical technique begins with the consideration that both grating patterns are a set of binary rectangular signals with two signal level values, “0” and “1.” Subsequently, Fourier series expansion of these two sets of signals was performed considering the frequency, initial phase, and duty cycle of the signals. The superposition of both input signals relies on algebraic equations equivalent to AND and OR operators. The final step was to eliminate the effect of the high-frequency component, which is analogous to the low-pass filter of the electronic circuit (based on averaging the signal intensity per image length). If the frequencies of the two gratings are equal, the analysis based on both operators provides an output signal depending on the initial phase (placement position) and duty cycle values. If the frequencies of the two gratings are not equal, the analysis results show that the output signal has a frequency equal to the frequency difference between the two signals. Moreover, if the two input gratings had a duty cycle of 0.5, the overlapping resulted in a triangular shape. The results from the computer program to simulate the AND and OR operators were consistently compared to the plots of the analytical equations. In addition, they are compatible with image processing results.

Torque Ripple Suppression in the 6/4 Variable Flux Reluctance Machine with Open Winding Configuration by Using Harmonic Injection

High torque ripple can be observed with a 6/4 variable flux reluctance machine (VFRM). In order to minimize the torque ripple in VFRMs, this paper presents a harmonic injection method for 6/4 VFRMs with an open-winding configuration. By analyzing the impact of harmonics on VFRMs, the method involves detecting the third harmonic using a first-order low-pass filter (FLPF). Subsequently, the extracted harmonics are controlled and shifted to counteract the voltage harmonics in both inverters without inducing phase imbalance or overvoltage. With the proposed method, the torque ripple can be significantly reduced by about 50% under load conditions. The effectiveness of the harmonic injection method is validated through a prototype VFRM.

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

Hanning FIR window filtering analysis for EEG signals

The window function is a mathematical function in signal processing that is multiplied by a signal to lessen or eliminate the impact of undesired characteristics such as spectral leakage and edge effects that might arise while analyzing finite-duration signals. The Hanning window is just one of several window functionalities available. It is designed to gradually taper the endpoints of a signal to zero, reducing spectral leakage in the frequency domain. Filtering is commonly employed in Electroencephalography (EEG) signal processing to remove noise, artifacts, or undesired frequency components while retaining the desired information. Filtering techniques include band pass, low pass, and high pass and band reject filtering. Hanning window technique using low pass, high pass, band pass, and band reject is used for filtering of EEG signal. The response time analysis for the Hanning window is calculated using low, high, band pass, and band-reject filters for different types of EEG waves alpha, beta gamma, theta, and delta using the signal processing simulation tool MATLAB 2023. The response time for delta wave using a low pass filter has a low response time of 14.125 sec which is comparatively less than another filter. For theta wave high pass filter has a low response time of 11.965 seconds. For alpha wave low pass filter has a low response time of 16.706 seconds whereas for beta wave low pass filter has a low response time of 15.202 seconds. For gamma waves high pass filter has a low response time of 11.349 seconds. Hanning band-reject filter has proven low response time for delta, theta, and gamma waveforms of 16.475 seconds, 16.182 seconds, and 17.743 seconds respectively. The novelty of the work is that the Hanning window improves frequency resolution by balancing the main lobe width with side lobe attenuation Hanning band-reject filter is suited best to figure out the widest main-lobe peak and highest peak for different EEG waveforms.

Combining optimal SASS (Sparsity Assisted Signal Smoothing) and notch filters for transient measurement signals denoising of large power generating unit

Denoising and harmonic interference removal from the measurement signal is crucial for accurate parameters calculation of a generating unit model. Determining the actual parameters values requires various unit transient signals data recorded during a well-defined testing procedure. However, the recorded signals usually contain noise and harmonic interferences which are difficult to remove due to a presence of different kinds of discontinuities.The paper presents a proposal of optimal denoising and harmonic interference removal method from transient measurement signals of 200 MW power generating unit. The method combines notch filters for harmonic rejection followed by SASS (Sparsity Assisted Signal Smoothing) algorithm for wideband noise rejection. Parameters of the notch filters as well as the SASS are determined using popular optimization algorithms (simulated annealing, Nelder-Mead simplex and gradient algorithm).Performance of the proposed method is compared to its direct Linear Time-Invariant (LTI) competitors such as low-pass and notch filters alone, notch and low-pass filters combined and to the optimal SASS method alone. The performance evaluation is based on a set of specially generated test signals which are using the developed mathematical model of the signals (to set the level of noise and interferences) as well as the mathematical model of the power generating unit (to provide the reference clean signals). The obtained results confirm that the optimal notch-SASS method might be a valuable noise removal tool for considered class of signals and leads to superior results than the LTI filters or the SASS method alone.

Novel epigenomic liquid biopsy assay to predict estrogen receptor (ER) status and to infer ER pathway activation in breast cancer.

1066 Background: Endocrine therapy (ET) is the cornerstone of treatment for ER-positive (ER+) breast cancer (BC). However, BC may lose ER expression over the disease course, and ER+ tumors may become resistant to ET regardless of ER expression. Blood-based biomarkers to infer ER expression and to predict ET sensitivity are lacking. Methods: We identified 44 patients (pts) with metastatic BC seen at Dana-Farber Cancer Institute from 2012-2023, who had blood samples drawn in Streck tubes within 6 weeks of tumor biopsy with ER scored via immunohistochemistry (IHC). We applied a novel, multi-analyte, liquid biopsy assay to capture genome-wide epigenomic signals, mapping enhancers, promoters, and DNA methylation data from 1mL of plasma. First, we applied a previously developed, IHC-benchmarked, regularized logistic regression model to infer ER status from plasma-based epigenomic profiles (Parsons et al., SABCS 2023). Second, we applied a novel ER pathway activity score involving promoters and enhancers of certain genes previously associated with ER activity (Guan et al., Cell 2019). The ER activity score was corrected for the circulating tumor DNA (ctDNA) fraction as assessed by low pass whole genome sequencing (ichorCNA algorithm). We evaluated the association between the ER activity score and the ER status by IHC using the Wilcoxon rank-sum test. ER expression ≥1% was considered positive. Results: The ER classifier and the ER activity score have been applied to 19 of 44 samples (43%) all of which had detectable ctDNA. Eleven out of 19 (58%) pts had ER+ BC by IHC (ER IHC range 80-95%). Overall, 18/19 (95%) were correctly classified as ER+ or ER- by the ER classifier (AUC=1). Three of 19 (16%) samples were collected from pts with ER+ BC at primary diagnosis and ER- BC at time of tissue sampling. For them, the ER classifier correctly predicted ER- status. We observed a significantly higher ER activity score in samples from pts with ER+ BC than pts with ER- BC as measured by IHC (p= 0.0012). For pts with ER+ BC, we observed an association between lower ER activity scores and a higher number of lines of ET prior to blood sampling, with lower scores for pts who received >4 prior lines. We will present full results from this cohort and an expanded cohort and correlate the ER score with response to ET. Conclusions: We applied a novel blood-based epigenomic classifier able to infer ER status and a novel epigenomic activity score to predict ER signaling from a minimal plasma volume. We plan to validate our findings on a larger cohort and correlate with response to ET. If confirmed, this assay may represent a valuable, minimally invasive tool to help guide treatment selection for patients with ER+ BC and predict response to novel endocrine agents.

Frequency importance functions in simulated bimodal cochlear-implant users with spectral holes.

Frequency importance functions (FIFs) for simulated bimodal hearing were derived using sentence perception scores measured in quiet and noise. Acoustic hearing was simulated using low-pass filtering. Electric hearing was simulated using a six-channel vocoder with three input frequency ranges, resulting in overlap, meet, and gap maps, relative to the acoustic cutoff frequency. Spectral holes present in the speech spectra were created within electric stimulation by setting amplitude(s) of channels to zero. FIFs were significantly different between frequency maps. In quiet, the three FIFs were similar with gradually increasing weights with channels 5 and 6 compared to the first three channels. However, the most and least weighted channels slightly varied depending on the maps. In noise, the patterns of the three FIFs were similar to those in quiet, with steeper increasing weights with channels 5 and 6 compared to the first four channels. Thus, channels 5 and 6 contributed to speech perception the most, while channels 1 and 2 contributed the least, regardless of frequency maps. Results suggest that the contribution of cochlear implant frequency bands for bimodal speech perception depends on the degree of frequency overlap between acoustic and electric stimulation and if noise is absent or present.

Design and Implementation of Digital Down Converter for WiFi Network

This paper introduces a field-programmable gate array (FPGA)-based digital down converter (DDC) processing a sampling frequency of about 3.64 GHz to a down-converted frequency of 28.4375 MHz to match the IEEE 802.11ah Wi-Fi HaLow standard. The proposed DDC uses a polyphase mixer(PM) and a resampling filter. The PM adopts parallel coordinate rotation digital computer (CORDIC) processors and lowpass filter arrays to reduce high-speed data rates with minimum resource utilization. Again, the resampling filter employs a cascaded integrator comb (CIC) filter associated with a parallel prefixed adder (PPA) and a multi-channel systolic finite impulse response (FIR) filter implemented with canonical expression, attaining optimum hardware cost. Converting floating-point to fixed-point data types provides significant resource savings. Finally, the improved design is coded in the Xilinx Vivado synthesis tool and successfully tested on the FPGA Kintex-7 device. In contrast to other recent architectures, the proposed design substantially reduces area requirements and power utilization. The Matlab tool verifies the design to achieve an acceptable spurious-free dynamic range (SFDR) of 115 dB.

Comparative Analysis of Bipolar and Unipolar SPWM Techniques in PIC-Based Pure Sine Wave Single-Phase Inverters

This paper provides a comparative analysis of bipolar versus unipolar Sinusoidal Pulse Width Modulation (SPWM) in DC-AC inverters, focusing on Total Harmonic Distortion (THD) across modulation indices and the latter’s effects on the R-L loads. Using the PIC18F2431 microcontroller for its efficiency, a single-phase inverter accomplished to deliver a high-fidelity sine wave. This study discovered that while both SPWM methods reduce harmonics, the unipolar approach yields more uniform THD reduction and superior performance, particularly noticeable in RL load conditions, where minimal harmonic distortion is crucial. The bipolar inverter, despite a higher initial THD, shows a considerable improvement at higher indices, significantly enhanced by an LC low-pass filter. This filter is a key component in achieving sub-1% THD levels at full modulation, ensuring optimal sine wave quality. The findings highlight the operational differences between the SPWM techniques and the importance of the LC filters in ameliorating the inverter output for various power applications.

Variable time constant filtering control for MMC-HESS considering wind power fluctuations

Wind power exhibits frequent fluctuations, which can impact the stability of the power grid. The impact of wind power on grid stability can be mitigated by using energy storage system whose battery lifespan can be extended by control strategies. This study focuses on the design of a modular multilevel hybrid energy storage system that incorporates both battery packs and supercapacitors. This system aims to effectively smooth the fluctuation of wind power and prolong the lifespan of battery packs. The power distribution between the battery packs and the supercapacitors is achieved through a first-order low-pass filter. The low-frequency power variations are smoothed using the battery packs, while the time constant of the filter is adjusted based on changes in the state of charge of battery packs. This approach prevents overcharging and overdischarging of the battery packs, thereby increasing their lifespan. The remaining high-frequency power variations are handled using the supercapacitors. Simulation results and a comparison with fixed time constant low-pass filtering demonstrate that the grid-connected power meets national standards following the implementation of the energy storage configuration. The proposed control strategy effectively allocates high- and low-frequency energy storage power, resulting in an 18.34% increase in battery pack lifespan.

Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis.

Most existing techniques consider hyperspectral anomaly detection (HAD) as background modeling and anomaly search problems in the spatial domain. In this article, we model the background in the frequency domain and treat anomaly detection as a frequency-domain analysis problem. We illustrate that spikes in the amplitude spectrum correspond to the background, and a Gaussian low-pass filter performing on the amplitude spectrum is equivalent to an anomaly detector. The initial anomaly detection map is obtained by the reconstruction with the filtered amplitude and the raw phase spectrum. To further suppress the nonanomaly high-frequency detailed information, we illustrate that the phase spectrum is critical information to perceive the spatial saliency of anomalies. The saliency-aware map obtained by phase-only reconstruction (POR) is used to enhance the initial anomaly map, which realizes a significant improvement in background suppression. In addition to the standard Fourier transform (FT), we adopt the quaternion FT (QFT) for conducting multiscale and multifeature processing in a parallel way, to obtain the frequency domain representation of the hyperspectral images (HSIs). This helps with robust detection performance. Experimental results on four real HSIs validate the remarkable detection performance and excellent time efficiency of our proposed approach when compared to some state-of-the-art anomaly detection methods.

Минимизация вероятности ошибки при взвешенном приеме сообщения с двухпозиционной импульсно-кодовой манипуляцией в условиях межсимвольной интерференции

In order to increase the noise immunity of data transmission systems, it is proposed to reduce the level of intersymbol interference by means of orthogonalizing the channel symbols. Orthogonalization is done through determining the orthogonality weight. Presented below is a calculation of the E-criterion for assessing the level of intersymbol interference for the proposed data transmission systems using the pulse-code modulation. For both systems, it was assumed that normalized Butterworth low-pass filters would be used as the shaping filter. It is shown that when signal-to-noise ratios exceed a certain critical value, weighted signal processing provides a lower bit error probability compared to the conventional processing technique. A general analytical expression for the orthogonality weight of channel symbols is given, which ensures a minimum bit error probability.

Evaluation of cost-effective multiple cancer early detection with extremely low coverage whole genome sequencing from plasma.

10538 Background: Cancer continues to be a significant global health concern, with approximately 19.3 million new cases diagnosed and 10.0 million cancer-related deaths reported in 2020. Multi-cancer early detection (MCED) test using the peripheral blood offers a great opportunity to improve the current cancer screening tests with better performance and benefit-to-harm balance. Methods: Here we introduce a novel MCED test called HIFI-PROF, which utilizes extremely low pass whole-genome sequencing (median coverage of 0.6X) to create a multi-dimensional fragmentation signatures model through machine learning approach. HIFI-PROF was developed to detect and localize six types of cancer (colorectal, pancreas, liver, lung, esophagus and gastric cancer). Results: The study included a total of 1,487 participants, comprising 235 colorectal cancers, 227 pancreatic cancers, 150 liver cancers, 125 lung cancers, 107 esophageal cancers, 106 gastric cancers and 537 healthy volunteers. Most of the cancer cases were I/II stage patients (stage I: 38%, stage II: 35%, stage III: 23%, stage IV: 4%). The participants, stratified by age and clinical status, were divided into two sets: a training set (n=1,079) and a validation set (n=408).HIFI-PROF exhibited an excellent performance in the early detection of multiple cancer types, achieving a sensitivity of 87.58% at a specificity of 99.09% and identifying the tissue of origin (TOO) with an accuracy of 81.99%. The sensitivity rates for detecting cancer at different stages were also promising, with a sensitivity of 84.62% for stage I, 85.59% for stage II, 89.61% for stage III and 92.86% for stage IV. Conclusions: These results highlight the high sensitivity and specificity of HIFI-PROF in identifying cancer cases while maintaining a low rate of false-positive results. The use of HIFI-PROF offers a cost-effective MCED test with excellent performance. Such advancements will contribute to improve strategies for multi-cancer early detection, leading to more effective clinical interventions and better outcomes.

Online estimation of vertical velocity for deep-sea carriers based on depth data

Seabed residential stations are essential for the future exploration of the deep-sea exploration. Deep-sea carriers accompany transport equipment for underwater resident stations. To accommodate the low-energy consumption and low-cost nature of deep-sea carriers, an online vertical velocity estimation method based on depth data is proposed. This method serves as a cost-effective alternative to the expensive acoustic and inertial velocity measurement equipment conventionally used in underwater navigation systems. To meet the demand for low-cost online velocity measurements, a dynamic model was created for buoyancy-driven vehicles such as deep-sea carriers during unpowered ascent. Two distinct methods were used to process depth sensor data: one involves central differencing followed by low-pass filtering, and the other entails the differentiation of the data after fitting it to dynamic equations. This approach yields two different types of estimated velocities. A sliding window was defined and used the variance of the velocity and fitting coefficient within the window as indicators to assess the uncertainty of the velocity data. Based on this evaluation, an adaptive weight allocation rule for the fusion process was formulated. This approach allows us to derive the weighted estimated velocity. Through lake trials, the weighted estimated velocity was validated to closely match the ideal velocity data and showed a substantial reduction in velocity variance compared to the differenced estimated velocity that was not fused. This indicates that the fluctuations in the velocity data were significantly reduced. In addition, the integrated displacement showed an average absolute error of 0.1769 m compared with the measured displacement. This indicates that the weighted estimated velocity effectively reflects the true state of motion of the deep-sea carrier. Furthermore, when unexpected motion occurs in deep-sea carriers, the weighted estimated velocity can prevent falling into the "smoothness trap.” This demonstrated the robustness of the proposed algorithm.

Design of Low Pass Filters Using a Symmetrical T-Shaped Defected Microstrip Structure

Design of Low Pass Filters Using a Symmetrical T-Shaped Defected Microstrip Structure

Faulty feeder detection based on the fluctuation characteristics of inflection point dense area for DC distribution network

A flexible direct current (DC) distribution network based on modular multilevel converter (MMC), with the advantages of no phase change failure and fewer current conversion links. It has gradually become the development trend of future medium voltage distribution networks. However, when a single-pole ground fault occurs in the feeder, the converter's low inertia and weak damping characteristics lead to a fast-rising fault current with a high peak value, which is harmful to the system. In this paper, for the difficulty of accurately selecting the faulty feeder when a single-pole ground fault occurs in a radial DC distribution network, a detection method based on the zero-mode current within the inflection point dense area (IPDA) for the fluctuation characteristics of concave and convexity (FCCC) is proposed. Firstly, the zero-mode current of each feeder is processed using a low-pass filter to select the eigen-components; Then, taking second order derivatives of the eigen-components, and the IPDA is established, to find a period in micro measurement to achieve accurate direction correspondence with opposite directions; Again, the data within IPDA is normalized; the criterion is constructed by determining each feeder's FCCC. Finally, the simulation model is built by Power Systems Computer Aided Design to verify that the method can identify the faulty feeder in various situations. Compared with existing methods, the fault detection method proposed in this paper has good stability and can withstand large grounding resistances.