A data science project's primary objective is to analyze and train the data in preparation for the relevant machine learning project. Gathering the necessary data from the beauty domain is a crucial step to provide accurate results for the machine learning project. To ensure that the data gathered is sufficient and relevant, it is vital to identify the appropriate data sources and analyze them. Homemade remedy recipes are becoming increasingly popular around the world. There are numerous remedy recipe videos available on YouTube and Google. The information provided above is required to recommend a remedy based on the conditions. The data set contains 18 different types of skin conditions that were identified by the user through surveys.

Contains scans of a bin filled with different parts ( screws, nuts, rods, spheres, sprockets). For each part type, RGB image and organized 3D point cloud obtained with structured light sensor are provided. In addition, unorganized 3D point cloud representing an empty bin and a small Matlab script to read the files is also provided. 3D data contain a lot of outliers and the data were used to demonstrate a new filtering technique.

Esri's ArcGIS Online tools provide three methods of filtering larger datasets using attribute or geospatial information that are a part of each individual dataset. These instructions provide a basic overview of the step a GeoHub end user can take to filter out unnecessary data or to specifically hone in a particular location to find data related to this location and download the specific information filtered through the search bar, as seen on the map or using the attribute filters in the Data tab.

This dataset comprises sea surface height (SSH) and velocity data at the ocean surface in two small regions near the Agulhas retroflection. The unfiltered SSH and a horizontal velocity field are provided, along with the same fields after various kinds of filtering, as described in the accompanying manuscript, Using Lagrangian filtering to remove waves from the ocean surface velocity field (https://doi.org/10.31223/X5D352). The code repository for this work is https://github.com/cspencerjones/separating-balanced .

Two time-resolutions are provided: two weeks of hourly data and 70 days of daily data.

Seventy_daysA.nc contains daily data for region A and Seventy_daysB.nc contains daily data for region B, including unfiltered, lagrangian filtered and omega-filtered velocity and sea-surface height.

two_weeksA.nc contains hourly data for region A and two_weeksB.nc contains hourly data for region B, including unfiltered and lagrangian filtered velocity and sea-surface height.

Note that region A has been moved in version 2 of this dataset.

See the manuscript and code repository for more information.

This work was supported by NASA award 80NSSC20K1142.

Dataset

This dataset was created by Sanjana Murthy

Released under CC BY-NC-SA 4.0

Contents

This data contains Sort & Filter functions

The algorithms in this data release implement a State-Space Model (SSM) of vertical infiltration through the unsaturated zone and recharge to the water table. These algorithms build on previous investigations available at https://doi.org/10.1029/2020WR029110 and https://doi.org/10.1111/gwat.13206. The SSM is defined by observed states (i.e., the water-table altitude) and unobserved states (i.e., fluxes through the unsaturated zone and recharge to the water table)and interprets time-series data for observations of water-table altitude and meteorological inputs (i.e., the liquid precipitation rate and the Potential Evapotranspiration (PET) rate). The algorithms first perform the estimation of the SSM parameters from the time-series data over a Parameter-Estimation Window (PEW). The estimated model parameters are then used in a subsequent State-Estimation Window (SEW) to estimate the observed and unobserved systems states of the SSM using the Kalman Filter (KF). The application of th ...

In the streaming data setting, where data arrive continuously or in frequent batches and there is no pre-determined amount of total data, Bayesian models can employ recursive updates, incorporating each new batch of data into the model parameters’ posterior distribution. Filtering methods are currently used to perform these updates efficiently, however, they suffer from eventual degradation as the number of unique values within the filtered samples decreases. We propose Generative Filtering, a method for efficiently performing recursive Bayesian updates in the streaming setting. Generative Filtering retains the speed of a filtering method while using parallel updates to avoid degenerate distributions after repeated applications. We derive rates of convergence for Generative Filtering and conditions for the use of sufficient statistics instead of fully storing all past data. We investigate the alleviation of filtering degradation through simulation and an ecological time series of counts. Supplementary materials for this article are available online.

These MATLAB files accompany the following publication:

Kulikova M.V., Tsyganova J.V. (2015) "Constructing numerically stable Kalman filter-based algorithms for gradient-based adaptive filtering", International Journal of Adaptive Control and Signal Processing, 29(11):1411-1426. DOI http://dx.doi.org/10.1002/acs.2552

The paper addresses the numerical aspects of adaptive filtering (AF) techniques for simultaneous state and parameters estimation (e.g. by the method of maximum likelihood). Here, we show that various square-root AF schemes can be derived from only two main theoretical results. These elegant and simple computational techniques replace the standard methodology based on direct differentiation of the conventional KF equations (with their inherent numerical instability) by advanced square-root filters (and its derivatives as well).

The codes have been presented here for their instructional value only. They have been tested with care but are not guaranteed to be free of error and, hence, they should not be relied on as the sole basis to solve problems.

If you use these codes in your research, please, cite to the corresponding article.

This dataset provides detailed information on the performance and efficiency of air filters installed in various locations such as shopping malls and hospital ventilation systems. It captures critical parameters like filter type, age, load, pressure drop, and efficiency over time. The dataset also includes measurements of particulate matter (PM2.5 and PM10) concentrations at both the inlet and outlet of the filters, offering insights into how effectively each filter is removing harmful particles from the air. Additionally, it tracks whether the filter requires replacement and flags any anomalies in its performance. This data is valuable for monitoring air quality, optimizing filter maintenance schedules, and ensuring optimal air filtration across different environments.

The computer programs implement the adaptive algorithms for real-time ECG signal filtering and numerical simulation for evaluation of filter effectiveness. The adaptive algorithms with complex use Hampel identifiers and Z-parameter are an author’s development. To launch a program, enter the name of the program and the ECG model signal in the command line. For example: ahzmthp.exe clean.txt. The test signal parameters (its length) and parameters of the filtering algorithms are read from the text file named as “filters.txt”. The program requests the additive and multiplicative noise variance, the probability and the amplitude of the spikes, and the number of realizations for statistical averaging of the calculated filter performance indicators. For example, via the “space” key enter: 0.0001 0 0 0 200, then press “Enter”. To apply filtering to a test signal which is read from a text file, select the menu item "Load from file" by pressing the key "6". The filter results are put in the “RESULT” subfolder. The filter efficiency estimates are written to the "MSE.res" and "SNR.res" output text files. The input signal has an extension “.x” (no noise), “.xn” (with simulated noise), “.xns” (with noise and spikes). The signals from filter algorithms outputs have the extension “.yf”. Also, files with functions of identifiers used to adapt the algorithm parameters to the local signal behavior and to the changes in the noise level and with adaptable filter parameters, and other intermediate signals are put to the “RESULT” subfolder. The program was compiled by Free Pascal.

Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction uncertainty is modeled via a rescaled Epanechnikov kernel and is assisted with resampling techniques and regularization algorithms. Uncertainty management is accomplished through parametric adjustments in a feedback correction loop of the state model and its noise distributions. The correction loop provides the mechanism to incorporate information that can improve solution accuracy and reduce uncertainty bounds. In addition, this approach results in reduction in computational burden. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.

Phylogenetic inference is generally performed on the basis of multiple sequence alignments (MSA). Because errors in an alignment can lead to errors in tree estimation, there is a strong interest in identifying and removing unreliable parts of the alignment. In recent years several automated filtering approaches have been proposed, but despite their popularity, a systematic and comprehensive comparison of different alignment filtering methods on real data has been lacking. Here, we extend and apply recently introduced phylogenetic tests of alignment accuracy on a large number of gene families and contrast the performance of unfiltered versus filtered alignments in the context of single-gene phylogeny reconstruction. Based on multiple genome-wide empirical and simulated data sets, we show that the trees obtained from filtered MSAs are on average worse than those obtained from unfiltered MSAs. Furthermore, alignment filtering often leads to an increase in the proportion of well-supported branches that are actually wrong. We confirm that our findings hold for a wide range of parameters and methods. Although our results suggest that light filtering (up to 20% of alignment positions) has little impact on tree accuracy and may save some computation time, contrary to widespread practice, we do not generally recommend the use of current alignment filtering methods for phylogenetic inference. By providing a way to rigorously and systematically measure the impact of filtering on alignments, the methodology set forth here will guide the development of better filtering algorithms.

The Product Recommendation System market is experiencing robust growth, projected to reach $6.88 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 33.06%. This expansion is fueled by the increasing adoption of e-commerce, the need for personalized customer experiences, and the rising availability of sophisticated data analytics tools. Key drivers include the growing preference for online shopping, the need to enhance customer engagement and loyalty, and the ability of recommendation systems to improve conversion rates and average order values. The market is segmented by deployment mode (on-premise and cloud), filtering techniques (collaborative, content-based, hybrid), and end-user industry (IT & Telecom, BFSI, Retail, Media & Entertainment, Healthcare). The cloud deployment model is gaining significant traction due to its scalability, flexibility, and cost-effectiveness. Hybrid recommendation systems, combining collaborative and content-based approaches, are also witnessing increased adoption for achieving a balance between personalization and efficiency. Major players like Amazon, Netflix, Salesforce, and Google are driving innovation and market competition, constantly improving algorithm accuracy and integrating AI-powered features. The competitive landscape is characterized by both established technology giants and specialized recommendation engine providers. Future growth will likely be driven by advancements in artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) technologies, enabling more accurate and personalized recommendations. The North American market currently holds a significant share, followed by Europe and Asia Pacific. However, the Asia Pacific region is anticipated to witness the fastest growth rate due to increasing internet penetration, rising smartphone usage, and a burgeoning e-commerce sector. While data privacy regulations and the potential for biased recommendations pose challenges, the overall market outlook remains highly positive, driven by ongoing technological advancements and the growing demand for personalized experiences across diverse industries. The market's growth trajectory signifies the crucial role product recommendation systems play in optimizing online retail experiences and enhancing customer satisfaction across multiple sectors. This ongoing expansion highlights the importance of continuous innovation and adaptation within this dynamic landscape. Recent developments include: January 2023 - Coveo Solutions Inc. opened a new office in London, England, to assist growth in Europe. The new office will serve clients in Europe, such as Philips, SWIFT, Vestas, Nestlé, Kurt Geiger, River Island, MandM Direct, Halfords, and Healthspan, which have chosen Coveo AI to improve the experiences of their customers, employees, and workplace. Coveo also collaborated with system integrators, referral partners, and strategic partners in other regions to offer search, personalization, recommendations, and merchandising to major corporations that want to significantly raise customer satisfaction, employee productivity, and overall profitability., August 2022 - Google announced plans to open three new Google Cloud regions in Malaysia, Thailand, and New Zealand, in addition to the six previously announced regions in Berlin, Dammam, Doha, Mexico, Tel Aviv, and Turin.. Key drivers for this market are: Increasing Demand for the Customization of Digital Commerce Experience Across Mobile and Web, Growing Adoption by Retailers for Controlling Merchandising and Inventory Rules. Potential restraints include: Complexity Regarding Incorrect Labeling Due to Changing User Preferences. Notable trends are: Increasing Demand for Customization of Digital Commerce Experience Across Mobile and Web Drives the Market's Growth.

In recent years, various real-time processing methods have been developed for Satellite Laser Ranging (SLR) data. However, the recognition rate of the single-stage Graz filtering algorithm for high-orbit satellites is less than 1%, and traditional two-stage filtering algorithms, such as polynomial fitting and iterative filtering techniques, exhibit high false and missed detection rates. These issues compromise the accuracy of laser positioning and real-time adjustments during observations. To address these problems, we propose a new, efficient real-time SLR data processing method. This algorithm combines single-stage filtering with a histogram-based approach and incorporates polynomial fitting to establish a predictive model. This offers the advantage of fast and efficient real-tim e signal recognition. The experimental results demonstrate that the proposed algorithm compensates for the limitations of single-stage filtering algorithms and performs better than traditional two-stage filtering algorithms in identifying medium- and high-orbit satellite signals. The false detection rate was reduced to below 15%, while achieving faster computation speeds. This method is convenience for researchers in their observations and offers new insights and directions for further research and application in the real-time identification of satellite laser ranging echo signals.

In recent years, various real-time processing methods have been developed for Satellite Laser Ranging (SLR) data. However, the recognition rate of the single-stage Graz filtering algorithm for high-orbit satellites is less than 1%, and traditional two-stage filtering algorithms, such as polynomial fitting and iterative filtering techniques, exhibit high false and missed detection rates. These issues compromise the accuracy of laser positioning and real-time adjustments during observations. To address these problems, we propose a new, efficient real-time SLR data processing method. This algorithm combines single-stage filtering with a histogram-based approach and incorporates polynomial fitting to establish a predictive model. This offers the advantage of fast and efficient real-tim e signal recognition. The experimental results demonstrate that the proposed algorithm compensates for the limitations of single-stage filtering algorithms and performs better than traditional two-stage filtering algorithms in identifying medium- and high-orbit satellite signals. The false detection rate was reduced to below 15%, while achieving faster computation speeds. This method is convenience for researchers in their observations and offers new insights and directions for further research and application in the real-time identification of satellite laser ranging echo signals.

This paper presents a sliding window constrained fault-tolerant filtering method for sampling data in petrochemical instrumentation. The method requires the design of an appropriate sliding window width based on the time series, as well as the expansion of both ends of the series. By utilizing a sliding window constraint function, the method produces a smoothed estimate for the current moment within the window. As the window advances, a series of smoothed estimates of the original sampled data is generated. Subsequently, the original series is subtracted from this smoothed estimate to create a new series that represents the differences between the two. This difference series is then subjected to an additional smoothing estimation process, and the resulting smoothed estimates are employed to compensate for the smoothed estimates of original sampled series. The experimental results indicate that, compared with sliding mean filtering, sliding median filtering, and Savitzky-Golay filtering,..., , , # Sliding window constrained fault-tolerant filtering of compressor vibration data

https://doi.org/10.5061/dryad.pc866t20z

Description of the data and file structure

Data type

Files containing ‘fdata1case1’ in the file represents the case "1" of the location of the outlier in the measured data "1", and so on;

Files containing ‘fwavedata’ in the file name are wave signals with outliers;

Files containing ‘fwave2data’ in the file name are polynomial signals with outliers;

Files containing ‘normaldata’ in the file name are normal measured data;

Files containing ‘normalwavedata’ in the file name are normal wave signals;

Files containing ‘normalwave2data’ in the file name are normal polynomial signals;

Files containing ‘ftffiltered’ in the file name indicate that the data have been processed by sliding-window constrained error-tolerant filtering;

Files containing ‘sgfiltered’ in the file name indicate data after Savitzky-Golay filtering...

Bandpass Filter Market Outlook

As per our latest research, the global Bandpass Filter market size reached USD 3.21 billion in 2024, demonstrating robust expansion driven by the surging adoption across telecommunications, consumer electronics, and industrial automation sectors. The market is poised to grow at a CAGR of 6.4% during the forecast period, propelling the market value to approximately USD 5.57 billion by 2033. This growth is primarily fueled by the escalating demand for high-performance signal processing components, the proliferation of wireless communication infrastructure, and advancements in filter technologies that cater to emerging applications across various industries.

The primary growth factor for the Bandpass Filter market is the exponential rise in wireless communication networks globally. With the deployment of 5G infrastructure and the ongoing expansion of LTE and IoT networks, there is a significant surge in the need for precise and efficient frequency selection components. Bandpass filters are crucial in these applications, ensuring signal integrity, minimizing interference, and optimizing data transmission. Furthermore, the increasing integration of bandpass filters in smartphones, base stations, and wearable devices is further augmenting market growth, as manufacturers seek to deliver enhanced connectivity and performance. The market is also witnessing increased investments in R&D, leading to the development of miniaturized and high-selectivity filters to meet the evolving requirements of advanced communication systems.

Another substantial driver is the growing adoption of bandpass filters in the automotive and aerospace & defense sectors. In automotive applications, the proliferation of advanced driver-assistance systems (ADAS), infotainment, and vehicular communication modules has created a strong demand for reliable signal filtering solutions. Similarly, in aerospace and defense, bandpass filters play a pivotal role in radar, satellite communication, and electronic warfare systems, where signal accuracy and reliability are paramount. The integration of these filters into next-generation vehicles and defense electronics is expected to further stimulate market expansion. Additionally, the increasing penetration of electronics in industrial automation, healthcare diagnostics, and medical imaging is contributing to the sustained demand for bandpass filters with high precision and stability.

Technological advancements are also shaping the trajectory of the Bandpass Filter market. Innovations such as the development of optical bandpass filters for photonic applications, the use of novel materials for improved performance, and the emergence of software-defined filtering techniques are transforming the competitive landscape. These advancements are enabling manufacturers to offer customized solutions tailored to specific frequency ranges and application requirements. Moreover, the trend towards miniaturization and integration of filters into compact electronic devices is opening new avenues for market players. The ability to deliver high-performance filtering in increasingly smaller footprints is a key differentiator, particularly in consumer electronics and portable communication devices.

Regionally, Asia Pacific dominates the Bandpass Filter market, accounting for the largest share in 2024, driven by the rapid expansion of electronics manufacturing, telecommunications infrastructure, and automotive production. North America follows closely, fueled by technological innovation, strong defense spending, and the presence of leading market players. Europe also represents a significant market, supported by advancements in industrial automation and healthcare technology. The Middle East & Africa and Latin America are emerging as promising regions, with increasing investments in communication networks and industrial modernization. The regional outlook remains positive, with Asia Pacific expected to maintain its lead through 2033, supported by favorable government initiatives and growing end-user industries.

The data include atmospheric-loading frequency response functions (table 1) and filtered detrended and reconstructed (trends restored) groundwater levels (tables 2–4) computed for selected, parsed time series for three USGS monitoring wells [BR–1 (USGS site 410233104093203); LN–1 (USGS site 410233104093202); and FH–1 (USGS site 410233104093201)], and the associated hourly resampled water-level and barometric-pressure time-series "pieces" (tables 2–4) used to create the parsed series. Table headings are defined in the Data Dictionary. Digital filters were developed based on the computed water-level response to Earth tides and barometric pressure in all three wells, and these filters were used to compute the filtered water-level time series in tables 2, 3 and 4 for wells BR–1, LN–1, and FH–1, respectively. The content of tables (1–4) and the development of the digital filters and filtering techniques are described in the associated publication, https://doi.org/10.3133/sir20215020.

The data include atmospheric-loading frequency response functions (table 1) and filtered detrended and reconstructed (trends restored) groundwater-levels (tables 2 to 4) computed for selected, parsed time series for three USGS monitoring wells [28A-25-1 (373904118570701); 28A-25-2 (373904118570702); and 14A-25-1 (373927118571701)], and the associated hourly resampled depth-to-water-level and barometric-pressure time-series "pieces" (tables 2 to 4) used to create the parsed series. Digital filters were developed based on the computed water-level response to Earth tides in well 14A-25-1 and barometric pressure in all three wells, and these filters were used to compute the filtered water-level time series in tables 2 and 3 for wells 28A-25-1 and 28A-25-2, respectively, and in table 4 for 14A-25-1. The tables (1-4) and the development of the digital filters and filtering techniques are described in Appendix 2 of the associated publication (https://doi.org/10.3133/ofr20191063).

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Radio Frequency Filters Market Size 2025-2029

The radio frequency filters market size is forecast to increase by USD 16.55 billion, at a CAGR of 16.8% between 2024 and 2029.

The market is driven by the high proliferation of mobile computing devices and the increasing deployment of fifth-generation (5G) technology. The widespread use of mobile devices has led to a significant increase in demand for radio frequency filters to ensure optimal signal quality and reduce interference. Similarly, the rollout of 5G networks necessitates the adoption of advanced filtering solutions to support higher frequencies and data transfer rates. Apart from this, the proliferation of mobile computing devices and the cyclical nature of the semiconductor industry is fueling the exansion. However, the market faces challenges due to the cyclical nature of the semiconductor industry. The industry's inherent volatility can lead to fluctuations in demand and pricing for radio frequency filters.
Additionally, the intense competition and rapid technological advancements can put pressure on companies to innovate and differentiate their offerings to stay competitive. Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on developing advanced filtering solutions tailored to the specific requirements of mobile devices and 5G networks while maintaining a flexible and responsive business strategy to address industry volatility. The market is also witnessing trends in millimeter wave and Extremely High-Frequency applications, including space-based Wi-Fi and SATCOM applications in the aerospace and defense sector.

What will be the Size of the Radio Frequency Filters Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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Radio frequency filters are essential components in various electronic applications, including wireless devices and electronic circuits, and are used to ensure efficient signal transmission and reception. Filter production is a significant market trend, driven by the increasing demand for advanced RF systems in telecommunications, defense, and satellite communications. Filter reuse and recycling are gaining traction due to the environmental impact of filter disposal. Filter analysis software, filter modeling tools, and filter synthesis techniques facilitate efficient filter design and optimization. Impedance matching and antenna matching are essential aspects of RF system integration. 
Filter validation testing, filter reliability testing, and filter assembly ensure the quality of filter components. Filter measurement instruments and filter characterization software enable precise filter testing and evaluation. Filter disassembly and filter supply chain optimization are key considerations for filter manufacturers and users. Signal conditioning is another critical application area for filters, particularly in industrial automation and medical equipment. Besides, these filters are crucial in ensuring reliable communication by selectively allowing desired signals and blocking unwanted radio frequencies.

How is this Radio Frequency Filters Industry segmented?

The radio frequency filters industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Cellular devices
  GPS devices
  Tablets
  Others


Technology

  SAW
  BAW


Frequency Range

  1-6 GHz
  Sub-1 GHz
  Above 6 GHz


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The cellular devices segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth due to the increasing demand for advanced filter technologies in various industries, including telecommunications and consumer electronics. Crystal filters, cavity filters, and ceramic filters are commonly used for filtering applications, each offering unique advantages in terms of size, weight, and frequency response. Filter manufacturers are investing heavily in research and development to improve filter performance, with a focus on reducing passband ripple, increasing stopband rejection, and enhancing filter reliability. Filter simulation software is essential for designing and optimizing filter specifications, allowing for precise control over pole frequency, passband width, and return loss.

Electromagnetic interference (EMI) is a significant challenge in many applications, leading to the development of high-performance filters for harmonic suppre