High-pass Filter Research Articles

A large-aperture, high-power ultrawideband radiation system with beam broadening capacity.

Due to the limited maximum output power of the pulsers based on avalanche transistors, high-power ultrawideband (UWB) radiation systems usually synthesize plenty of modules simultaneously to achieve a high peak effective potential (rEp). However, this would lead to an increased aperture size as well as a narrower beam, which would limit their applications in intentional electromagnetic interference fields. In this paper, a high-power UWB radiation system with beam broadening capacity is developed. To achieve beam broadening in the time domain, a power-law time delay distribution method is proposed and studied by simulation, and then the relative excitation time delays of the modules are optimized to achieve higher rEp and avoid beam splitting in the beam broadening mode. In order to avoid false triggering of the pulser elements when implementing the beam broadening, the mutual coupling effect in the system is analyzed and suppressed by employing onboard high-pass filters, since the mutual coupling effect is much more severe in the low-frequency range. Finally, a radiation system with 36 modules is developed. Measuring results indicate that in the high-rEp mode, the developed system could achieve a maximum effective potential rEp of 313.6kV and a maximum pulse-repetition-rate of 20kHz. In the beam broadening mode, its half-peak-power beam width in the H-plane is broadened from the original value of 3.9° to 7.9°, with a maximum rEp of 272.9kV. The polarization direction of the system could be flexibly adjusted by a built-in motor.

Development of a procedure for fragmenting astronomical frames to accelerate high frequency filtering

The object of this study is the process of filtering astronomical frames that contain images of potential objects in the Solar System. To contour the image of each such object and recognize it in contrast with the background of the frame, it is necessary to filter the image. Most often, a variety of high-pass filters are used to determine the high-frequency component of the image, which can be removed as a coarse-grained component. Any image filtering is aimed at increasing the signal-to-noise ratio and reducing the dynamic range of the background image. However, the filtering process is quite resource- and time-consuming. This is especially true for systems for parallel processing of series of astronomical frames in real time (online). Therefore, to solve the problem of lack of frame fragmentation, which leads to high consumption of RAM, a procedure for fragmenting astronomical frames has been proposed. Owing to the introduction of a formal connection between the values of frame pixels and fragments, as well as determining their number, it was possible to reduce RAM utilization. Testing was carried out using the following high-pass filters ‒ ideal filter, Butterworth filter, and Gaussian filter. Using the devised procedure for fragmenting astronomical frames has made it possible to reduce the utilization of RAM during filtering. As a result, with parallel processing, this has also made it possible to speed up the high-frequency filtering procedure itself. The procedure devised for fragmenting astronomical frames was tested in practice within the framework of the CoLiTec project. It was implemented in the On-Line Data Analysis System (OLDAS) of the Lemur software. The study showed that when using the devised procedure, RAM utilization was reduced by 7–10 times. And the speed of filtration itself increased by 2–3 times. Accordingly, the processing time for each astronomical frame was reduced by 2–3 times

Nanoscale morphology imaging for arbitrary surfaces by optical coherence tomography

The imaging of nanoscale morphology using optical methods on arbitrary surfaces poses challenges, particularly in the presence of large axial distances and irregular shapes. In this work, we introduce a novel nano-resolution imaging method along the longitudinal direction, utilizing optical coherence tomography (OCT) to measure arbitrary surface morphology comprehensively. Based on the sparsity of the primary profile, our approach allows the adjustment of parameters such as the asymmetric penalty function ϕ, the regularization parameters αi and a high-pass filter to accommodate the micro-structure of any arbitrary surface. Remarkably, our method achieves an exceptional spatial resolution of 3.04 nm. The technique has been validated by observing the surface morphology of spherical and aspherical lenses with nanometer resolution in the longitudinal direction. With the ability to detect impurities and scratches with a height of tens of nanometers, this method can be used to study the fine structure of complex surfaces. This makes it an ideal choice for experimental requirements demanding high-resolution imaging and a broad en-face perspective.

Deep Learning-Assisted High-Pass-Filter-Based Fixed-Threshold Decision for Free-Space Optical Communications

This paper proposes a deep learning (DL)-assisted high-pass-filter (HPF)-based fixed-threshold decision (FTD) for free-space optical (FSO) communication. HPF is applied to reduce the scintillation effect by filtering out the low-frequency components of the received signal. However, the performance is limited owing to the signal distortion from HPF and remnant scintillation effect due to insufficient filtering. Therefore, the DL model is adopted to improve the performance of HPF-based scintillation effect compensation. The multilayer perceptron (MLP) model is used to adaptively select the peak frequency component of the received signal as the optimized cutoff frequency of HPF. Furthermore, recurrent neural network (RNN) and long short-term memory (LSTM) models are cascaded after HPF to compensate for the remnant scintillation effect and recover the signal distortion without the optimization of HPF cutoff frequency. The simulation was conducted under different turbulence channels and data rates. Simulation results showed that MLP-assisted adaptive optimized cutoff frequency and cascaded LSTM and HPF methods were close to the adaptive-threshold decision with precise channel state information under various turbulence channel degrees.

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.

RANCANG BANGUN PENGENDALI LAMPU DENGAN KOMUNIKASI SERIAL BERBASIS MIKROKONTROLLER PIC16F877 DAN 16F876 MELALUI JARINGAN LISTRIK RUMAH

Serial data transmission can be applied by the use of the grid as a carrier is one alternative that has some advantages, such as reducing the use of cables In this design project created a special application of serial data communication via the electricity network that uses circuit PIC16F877di Microcontroller Master controller as the main controller to control ON / OFF to 5 pieces Slave controller circuit using the PIC16F876 microcontroller to control the lights as the electrical load. Research conducted using a modulation method Frequency Shift Keying (FSK) as modulator and demodulator, also using a series transformer coupling and high pass filter circuit. The main component Frequency Shift Keying (FSK) the Integrated Circuit (IC) XR2206 as a Modulator and Demodulator IC XR2211 as, as well as using a passive filter that serves to pass a predetermined frequency is 70KHz - 80KHz. The frequency value to be achieved by calculating the value of an external capacitor and resistor. The results of the calculations at the design stage modulator, with a value of C = 10nF obtained value R1 = 1428Ω, R2 = 1250Ω. Keywords: PIC16F877, PIC16F876, XR2206, XR2211

Infrared and Visible Image Fusion Algorithm Based on Double-Domain Transform Filter and Contrast Transform Feature Extraction

Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the problem of incomplete detail extraction that exists in the traditional transform domain image decomposition, an adaptive high-pass filter is proposed to decompose images into high-frequency and low-frequency portions. Second, in order to address the issue of fuzzy fusion target caused by contrast loss during the fusion process, a novel feature extraction algorithm is devised based on a novel nonlinear transform function. Finally, the fusion results are optimized and color-corrected by our proposed spatial-domain logical filter, in order to solve the color loss and edge blur generated in the fusion process. To validate the benefits of the proposed algorithm, nine classical algorithms are compared on the LLVIP, MSRS, INO, and Roadscene datasets. The results of these experiments indicate that the proposed fusion algorithm exhibits distinct targets, provides comprehensive scene information, and offers significant image contrast.

A Fault Diagnosis Method for Analog Circuits Based on Improved TQWT and Inception Model

A soft fault in an analog circuit is a symptom where the parameter range of a component exists symmetrically to the left and right of its nominal value and exceeds a specific range. The proposed method uses the Grey Wolf Optimization (GWO) optimized tunable Q-factor wavelet transform (TQWT) algorithm for feature refinement, the Inception model for feature extraction, and an SVM for fault diagnosis. First, the Q-factor is optimized to make it more compatible with the signal. Second, the signal is decomposed, and a single-branch reconstruction is performed using the TQWT to extract features adequately. Then, fault feature extraction is conducted using the Inception model to obtain multiscale features. Finally, a Support Vector Machine (SVM) is used to complete the entire fault diagnosis process. The proposed method is comprehensively evaluated using the Sallen–Key bandpass filter circuit and the four-op-amp biquad high-pass filter circuit widely used in electronic systems. The experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.

On Constant Resistance Filter Design with Dissipative Elements

The advantage of constant resistance filters, as compared to active filters, is that any number of such filters can be cascaded - either to sharpen or broaden the response or for designing other kinds of filters, without being sensitive to temperature or other environmental effects. Also, although using lossy elements involves a larger number of components as compared to that using lossless components, the former is a practical design, while the latter is an ideal one and cannot be realized in practice. It is proved here that the circuit for constant resistance filter designed with dissipative elements, proposed earlier in the literature, is a versatile one, in that by properly designing or reorienting, it can perform the functions of a band-pass, band-stop, low-pass as well as high-pass filters.

Augmenting Aquaculture Efficiency through Involutional Neural Networks and Self-Attention for Oplegnathus Punctatus Feeding Intensity Classification from Log Mel Spectrograms.

Understanding the feeding dynamics of aquatic animals is crucial for aquaculture optimization and ecosystem management. This paper proposes a novel framework for analyzing fish feeding behavior based on a fusion of spectrogram-extracted features and deep learning architecture. Raw audio waveforms are first transformed into Log Mel Spectrograms, and a fusion of features such as the Discrete Wavelet Transform, the Gabor filter, the Local Binary Pattern, and the Laplacian High Pass Filter, followed by a well-adapted deep model, is proposed to capture crucial spectral and spectral information that can help distinguish between the various forms of fish feeding behavior. The Involutional Neural Network (INN)-based deep learning model is used for classification, achieving an accuracy of up to 97% across various temporal segments. The proposed methodology is shown to be effective in accurately classifying the feeding intensities of Oplegnathus punctatus, enabling insights pertinent to aquaculture enhancement and ecosystem management. Future work may include additional feature extraction modalities and multi-modal data integration to further our understanding and contribute towards the sustainable management of marine resources.

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.

Use of GIS tools, enhanced by FFT filtering methods, to detect blurred craters in Synthetic Digital Elevation Models, to improve their location and morphological characterisation

Filtered Geomorphic Reference Models (FGRMs), extracted from High-Pass Filters based on the Fast Fourier Transform (HPF-FFT), provide a semi-automated and objective detection of a wide range of geoforms and geomorphic situations (flat areas, slopes, depressions, or valley bottoms). The method is sensitive enough to reveal numerous inflection points that enable the identification of subtle changes in relief polarity, resulting in accurate representations of real land depressions. The effectiveness of this method has been demonstrated in lunar landscapes, utilizing altimetry extracted from high-precision Planetary Digital Elevation Models (PDEMs) to construct crater inventories with well-defined FGRMs. However, when analysing lunar environments with closely-spaced geomorphic features, the FGRMs display blurring and generalization effects, reducing their capability to identify all existing crater depressions. This limitation controls the ability of FGRMs, to generate reliable crater inventories. This study examines such effects in three experiments. Using Synthetic Digital Relief Models (SDRMs) constructed by means of a MATLAB script two experiments are performed. In Experiment_1 different scenarios of proximity between aligned craters are analysed. The marks, or footprints, formed by craters of identical size and shape (i.e., circular bowl-shaped depressions with central peaks) are considered, resulting in simplified PDEMs. Various scenarios of crater centre separation (proximity) and the presence of internal features within craters are also analysed. Experiment_2 validates the method with different proximities, locations and crater sizes. Experiment_3 validates the approach in a sampled area of LRO lunar PDEM corresponding to Zelinskiy crater (Mare Ingenii area). To address these blurring effects, a hybrid methodology is employed, generating FGRMs extracted from HPF-FFT, in conjunction with other GIS and remote sensing tools, such as singular point zoning, aspect-slope zoning, and edge-limits zoning. As a result, the correspondence between the FGRMs and the synthetic forms is examined, aiding the comprehension of the graphical response provided by filtering in these cratered areas. The methodology demonstrates that FGRMs derived from HPF-FFT are sensitive to capturing connections between shapes as soon as the crater features start to merge, while blurring becomes evident when the craters are located at a proximity of <20 model units. Additionally, the results highlight the method's utility in accurately locating crater footprints and morphologically characterizing inner crater features, even when crater centres are closely spaced. This approach effectively mitigates blurring effects and enables the precise localization of crater marks and their internal features. The validations carried out in complex relief and high precise DEMs provide a use in real situation.

Anatomic-guided ablation of the right ganglionated plexi validated by the automatic fractionation map

Abstract Background and aim Cardiac autonomic modulation through endocardial ablation of atrial ganglionated plexi (GP) is an alternative strategy in selected patients with severe vagal induced bradyarrhythmias. A strictly anatomical approach targeting the right GPs has demonstrated good success rates but is an empirical approach and has yet to be validated. The presence of fractionated electrograms has been associated with the areas of GPs and can be automatically mapped (fractionation map). The aim of this study was to compare the empirical ablated area, to the automatically assessed area of GPs based on the presence of fractionated electrograms (FEs). Methods We studied eight consecutive patients referred for parasympathetic modulation. A high-density map of the left and right atria (LA, RA) in sinus rhythm was performed. The settings for the automatic map were as follows: bipolar high pass filter of 500 Hz, a fractionation threshold of 4, and a width of 5 ms. The ablation was empirically performed by an operator blinded to the results of the fractionation map, as per site protocol. The ablation was aimed at the anatomic locations of the right GPs in the LA and RA. At the anterior and inferior aspect of the right superior pulmonary vein and inferior aspect of the right inferior pulmonary vein on the LA, and posterior RA opposite to the LA RF applications and around the coronary sinus ostium on the RA (Figure). Before ablation, 2 mg of atropine was administered in bolus, and the increase in heart rate was registered and repeated at the end of the procedure. After the operator completed the ablation, access to the fractionation map was available, and additional radiofrequency (RF) applications were applied as needed (Figure). The number of additional RF applications to cover all the areas with FEs was assessed. Success was defined as an absence of response to atropine after the procedure. Results Three patients underwent autonomic system modulation only, and five underwent additional pulmonary vein isolation (PVI). The mean age was 66 (50-77) years, five males. The median mapping time of the LA and RA was 11 (17-7) and 14 (9-18) min. All patients underwent successful parasympathetic modulation. The initial median (Q1-Q3) increase in heart rate with atropine was 34 (23-39)% versus 0 (0-2)% after ablation, p=0.012. The differences between empiric and FEs map-guided ablation are depicted in the Table. The additional number of points was low. However, a median of 9 (5-15) additional points were added. Conclusions A purely anatomic-guided procedure directed only at the atrial right GPs is usually enough as a therapeutic approach for cardiac autonomic modulation, however, the fractionation map tool helped validate the method and ensure success at the first pass.

Localization of ganglionated plexi by high density mapping: an atropine-response analysis

Abstract Introduction Growing interest has emerged around cardiac autonomic nervous system driven by ablation approaches for the treatment of neurally-mediated syncope (NMS). Ganglionated-plexi (GPs) located in the epicardial fat pads are the main target of ablative therapies, but little is known about the finest method to track their location from the endocardium. Most current techniques rely on empirical research of signal fragmentation and/or vagal response at anatomical landmarks. A poor accuracy in targeting GPs may lead to the risk of incomplete or over-extensive ablation. Purpose We analyzed the atrial electrograms (EGMs) in healty subjects and their response to atropine to describe physiological fragmentation and the relationship with vagal output. Methods Study partecipants were selected from young patients without structural heart desease who underwent electrophysiological study (EPS) for standard indications. High-density mapping of right atrium was performed before and after administration of 2 mg atropine at the end of the planned procedure. Offline analyses were performed comparing the two maps of each patient focusing on the fragmentation of the atrial EGMs. Results The study cohort comprised 10 patients (mean age of 39±11 years, 6 males). The indication for EPS was: supraventricular tachycardia (SVT) ablation in 9 patients, syncope in 1 patient. High density three-dimensional electro-anatomic maps (EAM) were all obtained with a multipolar catheter during sinus rhythm (number of points: 8794±937 in basal maps and 9007±1296 after atropine, p=0,87). The response to atropine was documented by an increase in heart rate (mean increase 59 ± 19% of basal cycle length). All basal maps showed a similar pattern of fragmentation in the posterior-septal wall reflecting the anatomical distribution of right GPs. In particular, three main stations were identified in the supero-, mid- and infero-posterior regions. Fragmentation of EGMs was found to progress from the edge to the core of each area. After administration of atropine, a reduction in fractionated area was observed in all patients. Filtering the maps according to a cut-off of fragmentation ≥ 6 peaks allowed identification of the nucleus with the highest degree of variation after atropine injection (mean basal area = 0,89 ± 0,81 cm2 ; mean atropine area = 0,44 ± 0,56 cm2 p&amp;lt;0,001). Conclusion Fragmentation of atrial electrograms is a dynamic property related to vagal output. High density mapping with a high pass filter of 6 peaks allows to identify the smallest vagal sensitive area in the anatomical stations of the GPs.

Detecting Fake Images Using Convolutional Neutral Networks - A Deep Learning Approach

One popular generative model with many uses is the Generative Adversarial Network (GAN). Based on this unique concept, recent investigations have shown that it is feasible to produce high-quality fake face photos. The misuse of those fictitious faces in picture manipulation might lead to moral, ethical, and legal issues. To identify fake face images produced by the best method available at the moment, we first propose a Convolutional Neural Network (CNN) based method in this paper [20]. We also present experimental evidence demonstrating that the proposed method can achieve satisfactory results with an average accuracy over 99.4%. To further bolster the logic of our approach, we also offer comparison findings based on a few variations of the suggested CNN design, such as the high pass filter, the quantity of layer groups, and the activation function.

Stratified water columns: homogenization and interface evolution

Stratified water columns are often found in lakes and oceans. Stratifications result from differences in density due to salt concentration, temperature, solid content and oxygenation. The stability of stratifications affects bioactivity, sedimentation, contaminant transport and environmental remediation. This study investigates the evolution of 6 stratified water columns created by differences in salinity, suspended minerals and the presence of a bottom heat source. We use acoustic wave reflection, photography, and both electrical conductivity and temperature profiles to track changes in stratification. Results show that multiple concurrent processes emerge across layers in otherwise quiescent water bodies. Dissimilar chemo-thermo conditions give rise to chemical and thermal diffusion, convection, and double-diffusion convection. When stratification involves suspended particles, interlayer processes include diffusiophoresis, flocculation/aggregation, sedimentation, osmosis, and chemo-consolidation; in this case, the specific surface and surface charge of suspended particles, and the salt concentration in contiguous layers determine aggregation-sedimentation-consolidation patterns. The interlayer transition zone acts as a high-pass filter that preferentially reflects low-frequency long-wavelength P-waves; invasive thermal and electrical conductivity probes provide complementary information and may identify stratification even when it is undetected by acoustic signals.

A two stage pipeline architecture for hardware implementation of multi-level decomposition of 1-D framelet transform

In this paper a two stage pipeline architecture for computation of multilevel decomposition of framelet transform is proposed. To handle the problem of perfect reconstruction, an area efficient symmetric extension router is used that duplicates the appropriate number of data samples of input signal at the boundary followed by reflection about the symmetry axis. In addition, to reduce the period and number of clock cycles required for computing the framelet transform, the inter-stage and intrastage pipeline of the computational units is maximized. The inter-stage pipelining is obtained by distributing the various levels of decomposition among the computational units of two stages, and a synchronization mechanism is adopted to reduce the total number of clock cycles. Similarly, the intrastage pipelining is achieved by using the pipeline registers such that the clock period is limited to the delay of multiplier and accumulator (MAC) circuit of the finite-impulse response (FIR) filter. To validate the feasibility and functionality of the proposed hardware architecture, the design is implemented on Artix7 XC7A100TCSG324-1 field-programmable gate array (FPGA) for the case of framelet transform with one low-pass and two high-pass filters. The proposed architecture is able to operate at a maximum clock frequency of 112 MHz.

Preserving Global Information for Graph Clustering with Masked Autoencoders

Graph clustering aims to divide nodes into different clusters without labels and has attracted great attention due to the success of graph neural networks (GNNs). Traditional GNN-based clustering methods are based on the homophilic assumption, i.e., connected nodes belong to the same clusters. However, this assumption is not always true, as heterophilic graphs are also ubiquitous in the real world, which limits the application of GNNs. Furthermore, these methods overlook global positions, which can result in erroneous clustering. To solve the aforementioned problems, we propose a novel model called Preserving Global Information for Graph Clustering with Masked Autoencoders (GCMA). We first propose a low–high-pass filter to capture meaningful low- and high-frequency information. Then, we propose a graph diffusion method to obtain the global position. Specifically, a parameterized Laplacian matrix is proposed to better control the global direction. To further enhance the learning ability of the autoencoders, we design a model with a masking strategy that enhances the learning ability. Extensive experiments on both homophilic and heterophilic graphs demonstrate GCMA’s advantages over state-of-the-art baselines.

A Precursor-Derived Ultramicroporous Carbon for Printing Iontronic Logic Gates and Super-Varactors.

A liquid precursor for 3D printing ultramicroporous carbons (pore width <0.7nm) to create a novel in-plane capacitive-analog of semiconductor-based diodes (CAPodes) is presented. This proof-of-concept integrates functional EDLCs into microstructured iontronic devices. The working principle is based on selective ion-sieving, controlling the size of the electrolyte ions, and the nanoporous sieving carbon's pore size. By blocking bulky electrolyte ions from entering the sub-nanometer pores, a unidirectional charging characteristic with controllable ion flux is achieved, leading to diodic U-I characteristics with a high rectification ratio. The liquid precursor approach enables successful printing of miniaturized in-plane CAPodes. A combination of inkjet and extrusion printing techniques with suitable inks is explored to fabricate electrode materials with engineered porosity. Deliberate fine-tuning of the ultramicroporous carbon's porosity and surface area is achieved using a customized carbon precursor and CO2 etching techniques. Electrochemical evaluation of the printed CAPodes demonstrates successful miniaturization compared with macroscopic film assembly. 3D manufacturing and miniaturization allow for the integration of CAPodes into logic gate circuits (OR, AND). For the first time, these switchable devices are used as variable capacitors in a high-pass filter application, adjusting the cut-off frequency of applied alternating voltage analogous to an I-MOS varactor.