Mode Filter Research Articles

Signal Integrity Analysis of Modal Filters Formed by Modification of Microstrip Lines and Coplanar Waveguides

Signal Integrity Analysis of Modal Filters Formed by Modification of Microstrip Lines and Coplanar Waveguides

Mitigating Camera Interference Arising From RF High Transmit Power: The Role of Common Mode Filters and Receiver Compensation Techniques

Mitigating Camera Interference Arising From RF High Transmit Power: The Role of Common Mode Filters and Receiver Compensation Techniques

A Novel AC Green Plug Switched Filter Scheme for Low Impact Efficient V2G Battery Charging Stations

In this paper, a novel switched/modulated capacitor filter scheme is proposed for enhancing vehicle‐to‐house (V2G) battery‐charging stations utilized in electric vehicles (EVs). The novel approach is tested on two controllers with a classical optimized PID controller. The technique, which employs modified multimode weighted charging modes for fast charging, improved power quality, and minimal inrush currents, results in reduced voltage transients on the DC side and less harmonics on the AC side. An intercoupled DC‐AC capacitor interface that features dual complementary switching modes is used by the switched modulated filter as a way to provide optimal pulsing in both the tuned‐arm filter and capacitive compensator modes of operations. This switched intercoupled AC‐DC filter compensation approach leads to enhanced power usage in EVs, along with lower AC‐DC voltage transients and inrush currents.

Measures for attenuation of interference at the level of design of the measuring device of spacecraft

When developing temperature control interface modules used as part of spacecraft onboard equipment, it is important to correctly substantiate the set of measures to ensure their noise immunity, taken at the design levels of the measuring system, measuring device and temperature control interface module. Such a justification involves an assessment of the effectiveness of possible interference mitigation techniques at each design level. The article is devoted to the consideration of interference mitigation measures at the design level of the measuring device, which should be taken to ensure the required noise immunity of the temperature control interface module in combination with measures taken at other design levels. It is shown that in order to ensure the required noise immunity of temperature control interface modules, it is necessary at the design level of the measuring device to provide for measures to mitigate interference in the power supply module, the central instrument module and inter-module interfaces, as well as measures to eliminate gross measurement errors (misses) when processing digital data in the central instrument module. In the power supply module, it is possible to attenuate interference by introducing differential and common mode filters at the input and switching filters at the output. In the central instrument module and inter-module interfaces of the secondary power buses, noise mitigation is possible by dividing the common power bus into analog and digital buses and combining them at an equipotential point. In the central instrument module, it is possible to eliminate misses by using various statistical methods for processing digital data.

SDSNet: Building Extraction in High-Resolution Remote Sensing Images Using a Deep Convolutional Network with Cross-Layer Feature Information Interaction Filtering

Building extraction refers to the automatic identification and separation of buildings from the background in remote sensing images. It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring.

A new efficient grain growth model using a random Gaussian-sampled mode filter

This paper presents the use of a Gaussian neighborhood mode filter for predicting grain growth in a manner similar to the solutions obtained by a Monte Carlo Potts model. This flexible grain growth model can quickly utilize modern, computationally optimized data science strategies on graphics processing units to simulate grain growth up to 100 times faster than the state-of-the-art, publicly available Monte Carlo Potts model. We show that, given the correct neighborhood, the mode filter can replicate normal grain growth in two or three dimensions. In addition, the paper briefly demonstrates the ability to model limited anisotropic in grain boundary energy and mobility. Anisotropic grain boundary energy is modeled by defining a weighted mode filter operation. Anisotropic grain boundary mobility is modeled by scaling and orienting the Gaussian neighborhood in a particular direction.

Multi-pulse random fiber laser with wide tuning range in L-band based on tunable band-pass filter and electro-optic modulator

In this paper, a L-band random fiber laser (RFL) with wide tuning range based on tunable band-pass filter (TBF) and electro-optic modulator (EOM) is proposed and studied. The L-band erbium-doped fiber amplifier (L-EDFA) is added to output L-band laser, and a wavelength tunable band-pass filter is used to generate tunable wavelength output. When the pump power is increased from 50mW to 350mW, the output spectra are all chaotic multi-wavelengths. After the filter being added, a stable narrow-band single-wavelength laser can be output in the range of 1564–1618 nm. An EOM is added to introduce actively Q-switched. When the pump power is 50mW, a stable single pulse output can be observed on the oscilloscope. When the pump power is 100mW, the output pulse is double pulse. When the pump power exceeds 100mW, three pulses or four pulses phenomenon appears. The adjustable multi-pulse laser has a wide application prospect in pulse coding, secure communication and other fields.

Combine model fine-tuning freezing layers and adaptive filter modulation to implement transfer learning for GANs

Generative Adversarial Network (GAN) requires more resources to train than other deep learning models and its loss function converges more slowly. For this reason, scholars at home and abroad have proposed a GANS algorithm based on transfer learning, which is applied to fewer samples, thus improving the training effect of GANS algorithm. In this paper, we provide a new way to perform the transfer of genetic algorithms and combine the two ways. On this basis, we will compare and analyze a variety of transfer learning algorithms to verify the feasibility and effectiveness of the joint application.

MCFilter: feature filter based on motion-correlation for LiDAR SLAM

PurposeThis study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.Design/methodology/approachDistinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.FindingsBased on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.Originality/valueIn this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

Polarization-stable single-mode multi-junction VCSELs with integrated anti-phase mode filter

In this paper, polarization-stable and spectrally single-mode operation of oxide-confined multi-junction vertical-cavity surface-emitting lasers (VCSELs) are achieved by using a non-circular integrated anti-phase transverse mode filter. A record-high single-mode output power is obtained by radially reducing effective mirror reflectivity at the surface areas where the higher order modes are expected to lase, leading to an increase in the threshold gain for these transverse modes, all the while minimal losses are introduced to the fundamental mode. The polarization control of such devices is achieved by using a non-circular mode filter in order to perturb the cylindrical symmetry of the cavity. An elliptically etched surface relief is shown to be effective in realizing a single-mode single-polarization device over a wide range of operating bias currents and temperatures. The continuous-wave light–current–voltage characteristics of 8-junction 940 nm VCSELs show single-mode output power >13 mW at room temperature, spectrally measured with a side-mode suppression ratio of >30 dB. This single-mode performance can be achieved depending on the ratio between the relief opening and the oxide aperture size. Additionally, the single-polarization behavior of such devices (with an extinction ratio of >15 dB) is shown to be dependent on the orientation of the elliptical mode filter, where the light is mainly polarized along the minor axis of the elliptical feature. Overall, combined with a multi-junction active region, the elliptical model filter is able to achieve highly efficient single-mode linearly polarized VCSELs.

Analytical Models for the Time Response of a Microstrip Line with Two Additional Symmetrical Conductors on Top under Different Boundary Conditions at Their Ends

Introduction. Protection of radio electronic equipment (REA) against various electromagnetic interferences is an important aspect of electromagnetic capability. Among interferences for REA, ultra-short pulses of picosecond and nanosecond ranges represent the highest danger due to their high voltage, short duration, and wide spectrum. One effective protection measure consists in the use of bandpass devices based on modal decomposition, such as modal filters (MF). This requires an analysis of distortion of the temporal response of bandpass devices, which is usually carried out numerically. However, even for simple configurations, this approach is associated with high computational costs. Yet simple analytical time-response models are acceptable in some cases. In the initial design stages, such models are extremely useful in providing a preliminary solution and a rapid assessment of response distortions. Therefore, comparison of time responses obtained by numerical methods and analytical models appears an important research task. Aim. To compare the time responses obtained by quasi-static analysis and analytical models. Materials and methods. Analytical models for computing time responses based on a modal analysis technique were considered. A quasi-static modeling of a microstrip line (MSL) with two additional symmetrical conductors on top in the TALGAT system was carried out. Results. Analytical models are proposed for an MSL with two additional symmetrical conductors on top taking different boundary conditions at their ends into account. The accuracy and reliability of the proposed models are verified by comparing the time responses obtained by quasi-static analysis and the proposed models. The results obtained showed good agreement. Conclusion. It is shown that an MSL with two additional symmetrical conductors on top can be used as an MF under different boundary conditions at the ends of these conductors. The proposed models allow the shape and amplitude of the response to be estimated with acceptable accuracy, reducing time and computational costs.

A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection

While existing reconstruction-based multivariate time series (MTS) anomaly detection methods demonstrate advanced performance on many challenging real-world datasets, they generally assume the data only consists of normal samples when training models. However, real-world MTS data may contain significant noise and even be contaminated by anomalies. As a result, most existing approaches easily capture the pattern of the contaminated data, making identifying anomalies more difficult. Although a few studies have aimed to mitigate the interference of the noise and anomalies by introducing various regularizations, they still employ the objective of fully reconstructing the input data, impeding the model from learning an accurate profile of the MTS’s normal pattern. Moreover, it is difficult for existing methods to apply the most appropriate normalization schemes for each dataset in various complex scenarios, particularly for mixed-feature MTS. This paper proposes a filter-augmented auto-encoder with learnable normalization (NormFAAE) for robust MTS anomaly detection. Firstly, NormFAAE designs a deep hybrid normalization module. It is trained with the backbone end-to-end in the current training task to perform the optimal normalization scheme. Meanwhile, it integrates two learnable normalization sub-modules to deal with the mixed-feature MTS effectively. Secondly, NormFAAE proposes a filter-augmented auto-encoder with a dual-phase task. It separates the noise and anomalies from the input data by a deep filter module, which facilitates the model to only reconstruct the normal data, achieving a more robust latent representation of MTS. Experimental results demonstrate that NormFAAE outperforms 17 typical baselines on five real-world industrial datasets from diverse fields.

Performance analysis of a nanofluid spectral splitting concentrating PV/T system with triangular receiver based on MCRT-FVM coupled method

Optimizing system structure is one promising way to enhance the spectral beam splitting concentrating photovoltaic/thermal (SBS CPV/T) system performance. In this study, a linear Fresnel CPV/T system incorporated with triangular cooling duct and Ag@SiO2/ethylene glycol (EG) nanofluid filter is designed to enhance the overall performance of the system. To quantitatively reflect the heat generation of Ag@SiO2/EG nanofluid filter and silicon PV modules under inhomogeneous solar flux distributions, a coupled method combining Monte Carlo ray tracing (MCRT) method with Finite Volume Method (FVM) is employed. After validating the coupled method, the performance of the system with triangular receiver is discussed under various operating conditions. The results demonstrate that the nanofluid outlet temperature and PV module temperature present 316.3 K and 306.8 K at nanofluid inlet temperature of 298 K, respectively. The high mass flow rate and low inlet temperature allow high thermal and total efficiencies, while they present slight effect on electrical efficiency. To quantify the achievable enhancements with the triangular receiver, a SBS CPV/T system with flat-plate receiver is also discussed. It is found that the total efficiency of the triangular receiver is higher than that of the flat-plate receiver, with a maximum enhancement of 7.9%.

Co-current filtrate flow in TFF perfusion processes: Decoupling transmembrane pressure from crossflow to improve product sieving.

Hollow fiber-based membrane filtration has emerged as the dominant technology for cell retention in perfusion processes yet significant challenges in alleviating filter fouling remain unsolved. In this work, the benefits of co-current filtrate flow applied to a tangential flow filtration (TFF) module to reduce or even completely remove Starling recirculation caused by the axial pressure drop within the module was studied by pressure characterization experiments and perfusion cell culture runs. Additionally, a novel concept to achieve alternating Starling flow within unidirectional TFF was investigated. Pressure profiles demonstrated that precise flow control can be achieved with both lab-scale and manufacturing-scale filters. TFF systems with co-current flow showed up to 40% higher product sieving compared to standard TFF. The decoupling of transmembrane pressure from crossflow velocity and filter characteristics in co-current TFF alleviates common challenges for hollow fiber-based systems such as limited crossflow rates and relatively short filter module lengths, both of which are currently used to avoid extensive pressure drop along the filtration module. Therefore, co-current filtrate flow in unidirectional TFF systems represents an interesting and scalable alternative to standard TFF or alternating TFF operation with additional possibilities to control Starling recirculation flow.

RETRACTED: Vision-based tracking method of nighttime construction workers by integrating YOLOv5 and Deepsort

Due to poor visibility and fatigue factors, although nighttime construction has been widely used, its safety problems like struck-by accidents have also become increasingly prominent. Most of the current tracking methods of workers are not suitable for direct application in nighttime construction scenarios, so this research proposes a vision-based method, which integrates low-light image enhancement technology, YOLOv5 and Deepsort to track nighttime workers. The proposed method is mainly composed of four modules, including illumination enhancement module, detection module, the Kalman filter and matching module. In the experiment based on nine test videos, the method achieved the average multiple-object tracking accuracy (MOTA) of 89.93% and multiple-object tracking precision (MOTP) of 97.07%. At the same time, the experimental results also show that the method is robust to the common tracking challenges of occlusions, scale variations and posture variations. The proposed method has practical application potential in the monitoring task in nighttime construction, which makes the nighttime construction activities safer and more efficient.

RETRACTED: Vision-based tracking method of nighttime construction workers by integrating YOLOv5 and Deepsort (REDIRECT)

Due to poor visibility and fatigue factors, although nighttime construction has been widely used, its safety problems like struck-by accidents have also become increasingly prominent. Most of the current tracking methods of workers are not suitable for direct application in nighttime construction scenarios, so this research proposes a vision-based method, which integrates low-light image enhancement technology, YOLOv5 and Deepsort to track nighttime workers. The proposed method is mainly composed of four modules, including illumination enhancement module, detection module, the Kalman filter and matching module. In the experiment based on nine test videos, the method achieved the average multiple-object tracking accuracy (MOTA) of 89.93% and multiple-object tracking precision (MOTP) of 97.07%. At the same time, the experimental results also show that the method is robust to the common tracking challenges of occlusions, scale variations and posture variations. The proposed method has practical application potential in the monitoring task in nighttime construction, which makes the nighttime construction activities safer and more efficient.

F-SCP: An automatic prompt generation method for specific classes based on visual language pre-training models

The zero-shot classification performance of large-scale vision-language pre-training models (e.g., CLIP, BLIP and ALIGN) can be enhanced by incorporating a prompt (e.g., “a photo of a [CLASS]”) before the class words. Modifying the prompt slightly can have significant effect on the classification outcomes of these models. Thus, it is crucial to include an appropriate prompt tailored to the classes. However, manual prompt design is labor-intensive and necessitates domain-specific expertise. The CoOp (Context Optimization) converts hand-crafted prompt templates into learnable word vectors to automatically generate prompts, resulting in substantial improvements for CLIP. However, CoOp exhibited significant variation in classification performance across different classes. Although CoOp-CSC (Class-Specific Context) has a separate prompt for each class, only shows some advantages on fine-grained datasets. In this paper, we propose a novel automatic prompt generation method called F-SCP (Filter-based Specific Class Prompt), which distinguishes itself from the CoOp-UC (Unified Context) model and the CoOp-CSC model. Our approach focuses on prompt generation for low-accuracy classes and similar classes. We add the Filter and SCP modules to the prompt generation architecture. The Filter module selects the poorly classified classes, and then reproduce the prompts through the SCP (Specific Class Prompt) module to replace the prompts of specific classes. Experimental results on six multi-domain datasets shows the superiority of our approach over the state-of-the-art methods. Particularly, the improvement in accuracy for the specific classes mentioned above is significant. For instance, compared with CoOp-UC on the OxfordPets dataset, the low-accuracy classes, such as, Class21 and Class26, are improved by 18% and 12%, respectively.

Comparison of a Genetic Algorithm and Evolutionary Strategies in Optimization of Strip-Structure Modal Filters

The features arising from the optimization of strip modal filters (MFs) using a genetic algorithm (GA) and evolutionary strategies (ESs) are considered. Sequential optimization of microstrip MFs and MFs with broad-side coupling was performed according to the criterion of minimizing the maximum amplitude of the output voltage. The results of such optimization with different numbers of calculations are presented, including the values of the optimized parameters, the objective function, the time spent on the calculation, stopping criteria (when optimized by ESs) and the voltage waveforms at the output of the studied MFs. A detailed analysis of the results of the two algorithms is presented. The advantages and disadvantages, as well as differences in the operation of each algorithm, are determined using the example of optimization of two MFs.

Evaluating the impact of low traffic neighbourhoods in areas with low car ownership: A natural experimental evaluation

BackgroundImproving the environment, including the streetscape, can enable walking and cycling, which impacts health. The aim of this study was to measure the impact of modal filters implemented as part of Low Traffic Neighbourhoods (LTNs) in Southwark, a borough with low car ownership located in Inner London, on driving, walking, and cycling levels. MethodologyThree neighbourhoods (Brunswick Park, North Peckham, East Faraday) introduced modal filters which restricted motorised traffic through-movement but not walking and cycling. Intervention areas were matched to control neighbourhoods with no street changes. Daily traffic, walking, and cycling counts and traffic speeds were collected using automatic counters and video monitors in all neighbourhoods at baseline and one year. In each neighbourhood we delineated ‘project’ and ‘boundary’ streets to capture potential displacement of traffic. We used a difference-in-differences analysis to estimate the effects of the LTNs by calculating the change in travel behaviours in intervention areas relative to control areas. ResultsDaily traffic counts decreased on ‘project’ streets in the intervention areas relative to controls by 860 (95% CI 409, 1312) in Brunswick Park, representing a 56% decrease (IRR 0.44, 95% CI 0.28, 0.68), and by 937 counts (95% CI 328, 1546), representing a 61% decrease (IRR 0.39, 95% CI 0.26, 0.71) in North Peckham. Traffic volumes did not significantly change in East Faraday. Across all three intervention project areas, traffic speeds decreased. Traffic levels remained unchanged on ‘boundary’ streets, with the exception of Brunswick Park, where there was an uplift in traffic levels during weekdays and peak hours. In general, there were no significant changes in daily walking and cycling counts. DiscussionFindings from this study demonstrate that modal filters can reduce traffic speeds and volumes on ‘project’ streets. Although these measures can offer a safer, less polluted, and more inviting environment for pedestrians and cyclists, areas with low car ownership may need further initiatives to boost walking and cycling.

ECG Signal Acquisition and Processing System

This paper mainly designs an ECG signal acquisition and processing acquisition system, which mainly includes two parts: an ECG acquisition front-end circuit module and a LabVIEW processing and display module. The front-end circuit of ECG acquisition mainly consists of amplifying and filtering modules, which are used to collect and process human signals in real time. In addition, a charging box circuit is designed at the same time, which can charge the ECG monitoring equipment while storing energy and increase the standby time of the equipment. At the same time, the acquired signal is filtered in LabVIEW, and the baseline drift is eliminated to obtain a cleaner and clearer ECG waveform.