Filter Model Research Articles

Influence of X-ray spectrum and bowtie filter characterisation on the accuracy of Monte Carlo simulated organ doses: Validation in a whole-body CT scanning mode

PurposeFor patient-specific CT dosimetry, Monte Carlo dose simulations require an accurate description of the CT scanner. However, quantitative spectral information and information on the bowtie filter material and shape from the manufacturer is often not available. In this study, the influence of different X-ray spectra and bowtie filter characterisation methods on simulated CT organ doses is studied. MethodsUsing ImpactMC, organ doses of whole-body CTs were simulated in twenty adult whole-body voxel models, generated from PET/CT examinations previously conducted in these patients. Simulated CT organ doses based on the manufacturer X-ray spectra and bowtie filter data were compared with those obtained using alternative characterisation models, including spectrum generators and experimentally measured dose data. A total of four different X-ray spectra and one bowtie filter model were defined based on these data. ResultsFor all X-ray spectra and bowtie filter combinations, estimated CT organ doses are within 6% from those resulting from simulations with the CT characterisation models provided by the manufacturer. While varying the bowtie filter model results in CT organ dose differences smaller than 1%, dose differences up to 6% are observed when X-ray spectra are not based on the quantitative data from the manufacturer. ConclusionsEstimated organ doses slightly depend on the applied CT characterisation model. When manufacturer’s data are not available, half-value layer and dose measurements provide sufficient input to obtain equivalent X-ray spectra and bowtie filter profiles, respectively.

Enhanced Collaborative Filtering: Combining Autoencoder and Opposite User Inference to Solve Sparsity and Gray Sheep Issues

In recent years, the study of recommendation systems has become crucial, capturing the interest of scientists and academics worldwide. Music, books, movies, news, conferences, courses, and learning materials are some examples of using the recommender system. Among the various strategies employed, collaborative filtering stands out as one of the most common and effective approaches. This method identifies similar active users to make item recommendations. However, collaborative filtering has two major challenges: sparsity and gray sheep. Inspired by the remarkable success of deep learning across a multitude of application areas, we have integrated deep learning techniques into our proposed method to effectively address the aforementioned challenges. In this paper, we present a new method called Enriched_AE, focused on autoencoder, a well-regarded unsupervised deep learning technique renowned for its superior ability in data dimensionality reduction, feature extraction, and data reconstruction, with an augmented rating matrix. This matrix not only includes real users but also incorporates virtual users inferred from opposing ratings given by real users. By doing so, we aim to enhance the accuracy of predictions, thus enabling more effective recommendation generation. Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.

Receiving Paths Improvement of Digital Phased Array Antennas Using Adaptive Dynamic Range

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models.

Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization Algorithm.

Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally completed physically by testing embryos in a microscope. The traditional morphological calculation of embryos shows predictable disadvantages, including effort- and time-consuming and expected risks of bias related to individual estimations completed by specific embryologists. Different computer vision (CV) and artificial intelligence (AI) techniques and devices have been recently applied in fertility hospitals to improve efficacy. AI addresses the imitation of intellectual performance and the capability of technologies to simulate cognitive learning, thinking, and problem-solving typically related to humans. Deep learning (DL) and machine learning (ML) are advanced AI algorithms in various fields and are considered the main algorithms for future human assistant technology. This study presents an Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. The EDMCV-STBDTO technique aims to accurately and efficiently detect embryo development, which is critical for improving fertility treatments and advancing developmental biology using medical CV techniques. Primarily, the EDMCV-STBDTO method performs image preprocessing using a bilateral filter (BF) model to remove the noise. Next, the swin transformer method is implemented for the feature extraction technique. The EDMCV-STBDTO model employs the variational autoencoder (VAE) method to classify human embryo development. Finally, the hyperparameter selection of the VAE method is implemented using the boosted dipper-throated optimization (BDTO) technique. The efficiency of the EDMCV-STBDTO method is validated by comprehensive studies using a benchmark dataset. The experimental result shows that the EDMCV-STBDTO method performs better than the recent techniques.

Conceptual model of electrostatic non-ionizing air filter

Abstract Many infectious diseases such as SARS-CoV-2, rhinovirus, adenovirus, and influenza are transmitted through small respiratory droplets and various filtration systems are used to reduce the risk of airborne infection. Here, we develop a conceptual model of a filter, which is based on the electrostatic deposition of air droplets potentially containing pathogens. Within the model, we explore the effect of polarization of neutral spherical droplets moving in an air flow past cylindrical electrodes. The strength of electric field they produce is low enough to eliminate corona discharge and minimize air ionization, but high enough to capture the droplets. Based on the proposed model, we calculate the filtration efficiency in a quasi-continuous approximation. Also, we conduct numerical simulations of the deposition of respiratory droplets on a compact periodic array of cylindrical electrodes located at the nodes of a square lattice. The droplets with a radius of 10-20 μm pose the greatest threat in spreading the respiratory infections, and as we demonstrate, the proposed system is very effective in capturing droplets of this size, specifically, for a filter element with a width of about 5 mm, the filtration efficiency exceeds 90%.

Plasmonic devices – equivalent circuit representations

An equivalent circuit model for plasmonic slot waveguide-based devices is presented. Taking advantage of the high mode confinement provided by this waveguide geometry, we express plasmonic waveguide geometries using transmission line parameters and express T-junctions using lumped equivalent circuit elements. By combining these fundamental building blocks, we subsequently introduce equivalent circuit models for stub filters and branch-line couplers. We show that plasmonic circuits, if designed with sharp discontinuities, feature low losses that are comparable to losses from RF circuits and even the corresponding photonic circuits. The framework presented here gives insight into the design of novel microwave-inspired plasmonic devices and circuits and significantly speeds up the design time, as a large part of the geometry optimization can be performed in the equivalent circuit domain. For instance, we use this framework in a follow-up paper to design ultra-compact plasmonic hybrids, such as those needed for coherent detection.

Planning for complex inferior vena cava filter retrievals: the implementation and effectiveness of 3D printed models

BackgroundInferior vena cava filter (IVC) retrieval is most often routine but can be challenging with high morbidity in complex cases, especially those with an extended dwelling time. While risk of morbidity in complex retrievals is decreased with advanced filter retrieval techniques, deciding when and which to use these requires detailed pre-procedural planning. The purpose of our study was to evaluate patient-specific 3D printed anatomic IVC filter models for aiding complex IVC filter retrievals.MethodsAll IVC filter retrieval patients between June 2021 and September 2022 at one academic medical hospital were prospectively screened. Nine met criteria for complex retrieval, and their CT images were used to 3D print patient-specific IVC and filter models. Models were used in pre-procedural planning and clinical utility was assessed using the Anatomic Model Utility Likert Questionnaire and estimations of the procedural and fluoroscopy time saved.ResultsThe usage of 3D printed models in pre-procedural planning had high clinical utility based on the Likert questionnaire (Anatomic Model Utility Points 366.7 ± 103.1). Using a model significantly increased confidence in planning (p = 0.03) and modified the treatment plan in seven cases. It also led to cost-efficient use of resources in the procedure suite with estimated reduction in procedure and fluoroscopy time of 29.0 [20.3] (p = 0.003) and 10.2 [6.7] (p = 0.002) minutes, respectively.Conclusion3D printed anatomic models for patients who require complex IVC filter retrieval demonstrated Likert-based high clinical utility and led to estimated reductions of procedural and fluoroscopy time.

Development of a mathematical model of a collinear filter based on Quartz (SiO2) and its practical implementation

Purpose of the work. Development and testing of a theoretical model of an acousto-optic quasi-collinear tunable filter on crystalline quartz, operating in the spectral range of 0.25-0.4 microns with a spectral resolution of 0.2 nm.Methods. The paper analyzes the basic properties of acousto-optic tunable filters based on crystals of various classes. Based on literature analysis and calculations, an experimental AOTF model was proposed and practically implemented. The study of the physical properties of light filtration on an α-SiO2 crystal was carried out using an experimental method.Practical studies of the spectral tuning of the device at experimental optical frequencies were carried out in the proposed ultrasonic standing wave system in accordance with the tuning characteristic. Based on known experimental data, a theoretical model for describing the passband taking into account piezoelectric effects is proposed.Results. The influence of crystal anisotropy on their acoustoelectric properties is shown. The optimal crystallographic parameters for the operation of filters on a silicon oxide crystal have been determined. A computer model of AOTF has been developed, implemented on the basis of numerical calculations in the Wolfram Mathematica environment. It was experimentally revealed that in the red part of the spectrum under study, the maximum intensity of the frequencies of diffracted light is observed, which corresponds to those closest to the natural frequencies of the piezoelectric transducer, while at the same time, in the blue and violet spectra, the lowest transmission was observed. Practical studies have confirmed that the broadening of the filter passbands occurs due to an increase in the divergence of light.Conclusions. A mathematical model of an acousto-optic quasi-collinear tunable filter on crystalline quartz was substantiated and developed, on the basis of which the calculation of the AOTF operating in the spectral range of 0.25 - 0.4 μm with a spectral resolution of about 0.2 nm was performed.

When latent features meet side information: A preference relation based graph neural network for collaborative filtering

As recommender systems shift from rating-based to interaction-based models, graph neural network-based collaborative filtering models are gaining popularity due to their powerful representation of user-item interactions. However, these models may not produce good item ranking since they focus on explicit preference predictions. Further, these models do not consider side information since they only capture latent feature information of user-item interactions. This study proposes an approach to overcome these two issues by employing preference relation in the graph neural network model for collaborative filtering. Using preference relation ensures the model will generate a good ranking of items. The item side information is integrated into the model through a trainable matrix, which is crucial when the data is highly sparse. The main advantage of this approach is that the model can be generalized to any recommendation scenario where a graph neural network is used for collaborative filtering. Experimental results obtained using the recent RS datasets show that the proposed model outperformed the related baselines.

Addressing data sparsity and cold-start challenges in recommender systems using advanced deep learning and self-supervised learning techniques

ABSTRACT The efficacy of e-commerce conversion rates relies on precise and personalized product recommendations within recommendation systems (RS). While collaborative filtering-based RS has demonstrated success, challenges such as sparsity and cold-start issues in the user-item matrix can impede optimal functionality. To address these challenges, there is a need to integrate additional information sources, encompassing item/user profiles and textual reviews. This study introduces an innovative RS architecture that seamlessly combines self-supervised learning (SSL) and collaborative filtering techniques with BERT-DNN to surmount these obstacles. The distinctiveness of our approach lies in integrating self-supervised learning with collaborative filtering and contextualized data obtained from BERT-DNN, providing a profound understanding of item profiles to enhance comprehension of user preferences and item characteristics. This refined understanding, operational in conjunction with collaborative filtering models like ItemKNN and UserKNN, empowers the system to generate highly personalized recommendations. The proposed method entails several pivotal steps: developing the BERT language model for textual embeddings in item profiles, conducting dimensionality reduction, constructing a Deep Neural Network, implementing self-supervised learning with both UserKNN and ItemKNN CF methods, and employing an ensemble learning technique. Empirical results substantiate the efficacy of our approach, with a specific focus on the innovative fusion of BERT-DNN with self-supervised learning and KNN CF methodologies, showcasing substantial improvements across diverse performance metrics. This underscores the practical importance of leveraging contextualized BERT-DNN data, strengthening the recommendation mechanism, and ultimately enhancing the overall performance of the RS.

Calibrated Eckhardt’s filter versus alternative baseflow separation methods: A silica-based approach in a Brazilian catchment

The Eckhardt’s filter, a recursive digital filter widely used in hydrology to separate baseflow, relies on two parameters: the recession constant (a) and the maximum allowable long-term ratio of baseflow to streamflow (BFImax). While a can be determined through regression analyses, BFImax is often assigned an arbitrary value because of limited catchment information for a more objective determination. This study aimed to assess and calibrate BFImax for a 1.2 km2 rural catchment in southern Brazil using dissolved silica data. Calibration involved minimizing differences between filter-generated baseflow hydrographs and those derived from dissolved silica measurements and mass balance across 13 monitored rainfall-runoff events. The calibrated model was compared to three established models: a modified graphical method based on local minima, a backward filter model, and a one-parameter filter. The results yielded an optimal BFImax of 0.653 with high model performance (PBias ≈ 0, NSE = 0.85, KGE = 0.74, and NRMSD = 13 %) against silica-derived baseflow. Applying the calibrated filter to a six-year streamflow dataset yielded a long-term baseflow index (BFI) value of 54 %, aligned with regional and global averages. Comparisons with other models showed that the backward filter model produced unrealistic baseflow values nearly identical to streamflow values; the local minima method overestimated baseflow by +19 % and failed to accurately represent the smoothness and peaks of the baseflow hydrographs; and the one-parameter filter represented the shape and timing of the baseflow hydrographs well, but underestimated magnitude by −19 %. These findings validate the effectiveness of the Eckhardt’s filter in generating accurate baseflow hydrographs and confirm the importance of calibrating BFImax for optimal performance. The research also endorses the use of dissolved silica measurements and the mass balance method as a reliable approach for calibrating BFImax for long-term assessment of baseflows.

The Economy of Critical Attention: A Computational Analysis of Roland Barthes’s S/Z

Abstract This article argues for the importance of analyzing distributions of literary-critical attention to understand the shape of our discipline and how it has evolved. The article sees critical attention as a scarce resource in literary economies, both within a single text and at much larger scales—even across time. Circulations of critical attention are directed by filtering models, a concept borrowed from cognitive psychology that refers to how competing demands for attention are prioritized. The accrual of critical attention implicitly signals the location of value in a literary economy. When this accrual (or lack thereof) is quantified using computational methodologies, we can track where critical attention goes, how much of it, and why. As a proof of this concept, the article offers a quantitative analysis of critical attention in Roland Barthes’s S/Z. This work reveals the hidden logics of Barthes’s distribution of critical attention and demonstrates how such analyses can identify the filtering models that operate in our critical tools and practices.

Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system’s interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system’s performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren’t needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA.

Improvement of Spatio-Temporal Inconsistency of Time Series Land Cover Products

In recent years, time series land cover products have been developed rapidly. However, the traditional classification strategy rarely considers time continuity and spatial consistency, which leads to the existence of unreasonable changes among the multi-period products. In order to solve the existing problems, this paper proposes a matrix decomposition model and an optimized hidden Markov model (HMM) to improve the consistency of the time series land cover maps. It also compares the results with the spatio-temporal window filtering model. The spatial weight information is introduced into the singular value decomposition (SVD) model, and the regression model is constructed by combining the eigenvalues and eigenvectors of the image to predict the unreasonable variable pixels and complete the construction of the matrix decomposition model. To solve the two problems of reliance on expert experience and lack of spatial relationships, this paper optimizes the model and proposes the HMM Land Cover Transition (HMM_LCT) model. The overall accuracy of the matrix decomposition model and the HMM_LCT model is 90.74% and 89.87%, respectively. It is found that the matrix decomposition model has a better effect on consistency adjustment than the HMM_LCT model. The matrix decomposition model can also adjust the land cover trajectory to better express the changing trend of surface objects. After consistent adjustment by the matrix decomposition model, the cumulative proportion of the first 15 types of land cover trajectories reached 99.47%, of which 83.01% were stable land classes that had not changed for three years.

The Zero-Velocity Correction Method for Pipe Jacking Automatic Guidance System Based on Fiber Optic Gyroscope.

The pipe jacking guidance system based on a fiber optic gyroscope (FOG) has gained extensive attention due to its high degree of safety and autonomy. However, all inertial guidance systems have accumulative errors over time. The zero-velocity update (ZUPT) algorithm is an effective error compensation method, but accurately distinguishing between moving and stationary states in slow pipe jacking operations is a major challenge. To address this challenge, a "MV + ARE + SHOE" three-conditional zero-velocity detection (TCZVD) algorithm for the fiber optic gyroscope inertial navigation system (FOG-INS) is designed. Firstly, a Kalman filter model based on ZUPT is established. Secondly, the TCZVD algorithm, which combines the moving variance of acceleration (MV), angular rate energy (ARE), and stance hypothesis optimal estimation (SHOE), is proposed. Finally, experiments are conducted, and the results indicate that the proposed algorithm achieves a zero-velocity detection accuracy of 99.18% and can reduce positioning error to less than 2% of the total distance. Furthermore, the applicability of the proposed algorithm in the practical working environment is confirmed through on-site experiments. The results demonstrate that this method can effectively suppress the accumulated error of the inertial guidance system and improve the positioning accuracy of pipe jacking. It provides a robust and reliable solution for practical engineering challenges. Therefore, this study will contribute to the development of pipe jacking automatic guidance technology.

An enhanced time synchronization method for a network based on Kalman filtering

This paper introduces an enhanced time synchronization method different from IEEE 1588 (PTP). The proposed algorithm employs a unique synchronous message packet structure with a fixed length of 10 bytes and the highest system priority. This design enables preemptive transmission, effectively reducing transmission and queuing delays. Additionally, it applies a Kalman filtering model to mitigate noise interference, including clock drift, network delay jitter, and network asymmetry. The algorithm also features a clock drift compensation mechanism to continuously adjust the clock, ensuring high precision and stability in time synchronization. The algorithm proposed in this paper has the characteristics of being easy to implement, requiring minimal hardware resources, and being applicable to a variety of networks. Simulation results show that, with an 80 MHz crystal oscillator, the time offset between master and slave clocks does not exceed ± 1 clock cycle in symmetric communication links. In asymmetric links, the maximum time offset is within ± 3 clock cycles. Compared to the original PTP algorithm and the Kalman filter-based time synchronization algorithm, this method reduces the time offset from several microseconds to less than 40 ns.

Multi-stopband filter based on frequency selective surface

This paper presents the design of a terahertz multi-stopband filter based on a metasurface structure which consisting of multiple metallic strips. By adjusting the dimensions and arrangement of these metallic strips placed on a dielectric substrate, the precise control of the filter performance is achieved. This structure exploits the electromagnetic resonances of metallic strips at terahertz frequencies to generate stopbands, thereby enabling selective filtering. Numerical studies demonstrates that these configurations can produce multiple stopbands within the 4.2 to 15.5 THz frequency range, with tunable center frequencies and bandwidths. Through the research of the transverse and longitudinal filter model structure, the band-stop filter with more stopbands can be realized, and the transmission valley value is kept within 5 %, which is crucial for enhancing filter performance. Moreover, the study reveals that tuning the dimensions of the metallic strips can modulate the resonances, resulting in a redshift of the filter's central frequency and an expansion of the passband width. These findings provide a new method for the performance improvement of terahertz filters, advanced filtering technology and electromagnetic wave control technology.

A neural network-based collaborative filtering model for social recommendation systems

A neural network-based collaborative filtering model for social recommendation systems

KF-PEV: a causal Kalman filter-based particle event velocimetry

Event-based pixel sensors asynchronously report changes in log-intensity in microsecond-order resolution. Its exceptional speed, cost effectiveness, and sparse event stream make it an attractive imaging modality for particle tracking velocimetry. In this work, we propose a causal Kalman filter-based particle event velocimetry (KF-PEV). Using the Kalman filter model to track the events generated by the particles seeded in the flow medium, KF-PEV yields the linear least squares estimate of the particle track velocities corresponding to the flow vector field. KF-PEV processes events in a computationally efficient and streaming manner (i.e., causal and iteratively updating). Our simulation-based benchmarking study with synthetic particle event data confirms that the proposed KF-PEV outperforms the conventional frame-based particle image/tracking velocimetry as well as the state-of-the-art event-based particle velocimetry methods. In a real-world water tunnel event-based sensor data experiment performed on what we believe to be the widest field view ever reported, KF-PEV accurately predicted the expected flow field of the SD7003 wing, including details such as the lower velocity in the wake and the flow separation around the underside of an angled wing.

Delineation of Shoreline and Associated Land Use/Land Cover Changes along the Coast of Chattogram, Bangladesh Based on Remote Sensing and GIS Techniques

This study aimed at delineating the shoreline by using the Digital Shoreline Analysis System (DSAS 5.0) tool and detected the changes of land use/land cover (LULC) by Google Earth Engine (GEE) platform. The shoreline is divided into two zones, whereas Zone I covered 87.12[Formula: see text]km and Zone II possessed 168.05[Formula: see text]km. According to End Point Rate (EPR), the mean shoreline change rate of Zone I is 3.55[Formula: see text]m/year and Zone II is [Formula: see text]6.84[Formula: see text]m/year. Likewise, based on Linear Regression Rate (LRR), the mean shoreline change rate of Zone I is 5.46[Formula: see text]m/year and Zone II is [Formula: see text]4.71[Formula: see text]m/year, respectively. Apart from that, the Net Shoreline Movement (NSM) recorded in Zone I is 109.42[Formula: see text]m as well as Zone II is [Formula: see text]213.25[Formula: see text]m, which also revealed how much the shoreline has changed during the last 32 years. This study also used the Kalman filter model to forecast the shoreline positions for 20 years. The most destructive signal is that more than 70% of the coastline is vulnerable due to erosion, whereas 6% is highly vulnerable. By contrast, the results of LULC changes demonstrated the increasing trend of water bodies, built up, and agricultural land while vegetation along with bare land is reduced continuously. The overall accuracy is recorded above 88%, and the kappa co-efficient is found above 0.87 for all three years. The outcome of this study will provide fruitful insight into coastal land use management and adaptation measures against the ongoing along with future threats of shoreline changes to coastal ecosystems and livelihoods.