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In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects unstable RSSI. The adaptive location estimation step utilizes a modified proximity technique when identified as close to an AP, employing trilateration otherwise. Finally, in the post-processing step, an efficient filtering algorithm is applied. For the static state experiment, the accuracy of the proposed algorithm is found to be improved by about 28% compared to the method measured using only the trilateration technique applying the RSSI error correction step and post-processing step. The proposed algorithm improved the positioning accuracy of the entire area by improving accuracy in regions with weak signals without additional devices. [ABSTRACT FROM AUTHOR]

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance. [ABSTRACT FROM AUTHOR]

This document is a response to a critique of a scientific paper published in Nature Communications. The authors of the response address the criticisms made by Dr. Goldsmith and his colleagues regarding the enzyme activities described in their paper. They clarify that their results were measured using a different method than assumed by Dr. Goldsmith. The authors also present their own experiments that challenge the proposed substrate for one of the enzymes involved in the synthesis of β-ODAP, a compound found in grass peas that can cause a neurodegenerative disorder called lathyrism. They conclude that the synthesis of a key compound involved in β-ODAP synthesis is likely limited in the cytosol of grass pea plants. The article highlights the proposed mechanism for β-ODAP synthesis and provides experimental evidence to support it, while also addressing criticisms and emphasizing the importance of understanding this process for improving the crop. [Extracted from the article]

In the approach of metric-based geometric parameterization, the concept of database filtration and proper orthogonal decomposition manipulation quantitatively enhances the generation of new geometric parameters so that the problem-oriented information is reflected in aerodynamic design. Compared with the conventional approach that applies only geometric filtration, this paper incorporates performance filtration so that the problem-oriented information is no longer limited to geometric constraints, thereby enabling the guidance of optimization direction in a more informed manner. This paper focuses on the pressure distribution of samples and their relationship with geometric variations. A variational autoencoder is employed to extract the dominant features of the aerodynamic potentials that an initial geometry can attain in optimization. The authors believe that the aerodynamic potential is reflected in how the pressure distribution differs from that of the initial geometry. Then, samples have to meet the performance filtration criterion based on pressure distribution proposed by designers according to their aerodynamic understanding of the optimization problem. Finally, the remaining collection of qualified samples generates new geometric parameters via proper orthogonal decomposition. The conventional and new approaches were validated in a two-dimensional airfoil case of drag coefficient optimization; the lift-to-drag ratio improved by 17.07% using the traditional method, and by 38.20% using the new approach. The pressure distribution shows that the performance filtration approach produces more supercritical properties than the geometric filtration approach. This implies that the performance filtration approach can capture more intricate details related to the aerodynamic performance of the airfoil. [ABSTRACT FROM AUTHOR]

Some users consider modern hydrostatic drives and controls to be unreliable and difficult to maintain. This view is often due to operational problems caused by issues with obtaining and then maintaining the appropriate cleanliness class of the working fluid. Recommendations on the selection of appropriate filtration system elements can be found in the literature, but there is no numerical model that could be helpful in a detailed analysis of the phenomenon. In the article, the authors tried to fill the research gap regarding the lack of a filtration model based on the filtration efficiency coefficient of filter elements used in hydraulic drives and controls. The developed model allows users to determine the influence of selected filtration system parameters on the separation of contaminants by filter elements. The model is intended to help designers and users of hydraulic drives and controls in optimizing the filtration system in order to obtain and then maintain the required cleanliness class of the hydraulic fluid. This paper also includes the results of the sensitivity analysis of selected filtration-system operating parameters in terms of the highest efficiency. In order to verify the developed model, experimental tests were also carried out, with the results presented in this paper. Based on the numerical analyses and experimental studies, recommendations that may be helpful in the selection or development of filtration systems used in hydrostatic drives and controls were developed. [ABSTRACT FROM AUTHOR]

To solve the high error phenomenon of microelectromechanical systems (MEMS) due to their poor signal-to-noise ratio, this paper proposes an online compensation algorithm wavelet threshold back-propagation neural network (WT-BPNN), based on a neural network and designed to effectively suppress the random error of MEMS arrays. The algorithm denoises MEMS and compensates for the error using a back propagation neural network (BPNN). To verify the feasibility of the proposed algorithm, we deployed it in a ZYNQ-based MEMS array hardware. The experimental results showed that the zero-bias instability, angular random wander, and angular velocity random wander of the gyroscope were improved by about 12 dB, 10 dB, and 7 dB, respectively, compared with the original device in static scenarios, and the dispersion of the output data was reduced by about 8 dB in various dynamic environments, which effectively verified the robustness and feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

The study of the slurry reinforcement mechanism is mainly focused on the interaction between slurry and soil. The seepage effect of the slurry always exists no matter what way the slurry interacts with the soil around the pile. In the process of slurry diffusion, the porosity of the soil, the permeability of the slurry, and the slurry pressure vary due to some cement particles being blocked by the soil particle skeleton. Therefore, the study of the slurry filtration effect is of great significance for predicting the permeation and diffusion law of slurry. In this paper, a macroscopic linear filtration model was introduced and the changes of slurry properties in the permeation diffusion process were considered. Firstly, a spherical (cylindrical) permeation diffusion model, which takes the linear filtration effect and the variation of slurry viscosity into account, was derived based on the conservation of mass. Furthermore, in order to more accurately reflect the influence of the filtration effect on the slurry permeation diffusion model, a polynomial nonlinear filtration model was proposed, and the numerical solution for the permeation diffusion model was derived using finite difference and finite element methods. Finally, the numerically simulated values, the measured values, and the values from the spherical permeation diffusion model that does not consider slurry viscosity variations were compared. The results indicate that the grout pressure is inconsistent with the measured value without considering the effect of the filtration. The initial grouting pressure calculated by the model in this paper is slightly larger, and the required grouting pressure over time is greater than that without considering the filtration effect, regardless of whether the grout diffuses in a spherical or cylindrical manner. The results of this study can contribute to a better understanding of grouting engineering and provide some theoretical guidance for actual grouting. [ABSTRACT FROM AUTHOR]

As emerging pollutants, pharmaceuticals and personal care products (PPCPs) in water have attracted more and more attention because of their harmfulness to the ecosystem and human health. Due to the perpetual input from sewage/wastewater effluents, landfill leachates, urban/agricultural runoff, etc., PPCPs in the aquatic environment are generally "pseudo-persistent". Conventional filtration in the water treatment process cannot effectively remove PPCPs, while biofiltration, a synergistic combination of adsorption and biodegradation, is an effective upgrade method that has received great attention and application in recent years. This paper systematically reviewed the principle of biofiltration and its efficiency in the removal of PPCPs. The important operational parameters influencing biofiltration performance such as filter media, temperature, backwash conditions, empty bed contact time, etc. were summarized. In addition, the limitations and prospects of the current research on biofiltration were also pointed out. [ABSTRACT FROM AUTHOR]

Collecting data from users is a frequent practice for websites to improve various aspects of their products and services, such as performance, usability, and security. Monitoring user activity on websites helps to comprehend visitor behavior and assess the impact of the site. Numerous applications involve the collection of user data by websites, enabling the prediction of user preferences. This, in turn, facilitates personalized content recommendations. Recommender systems serve as a mechanism to propose analogous items and concepts tailored to an individual's unique mindset. Fundamentally, there are two categories of recommender systems: Collaborative Filtering and Content-Based Filtering. This paper provides a comparative study of collaborative filtering and content-based filtering. [ABSTRACT FROM AUTHOR]

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a "chemical synonym"). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem's crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem's filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem. [ABSTRACT FROM AUTHOR]

Recently, discriminative correlation filters (DCF) have been successfully applied for visual tracking. However, traditional DCF trackers tend to separately solve boundary effect and temporal degradation problems in the tracking process. In this paper, a variational online learning correlation filter (VOLCF) is proposed for visual tracking to improve the robustness and accuracy of the tracking process. Unlike previous methods, which use only first-order temporal constraints, this approach leads to overfitting and filter degradation. First, beyond the standard filter training requirement, our proposed VOLCF method introduces a model confidence term, which leverages the temporal information of adjacent frames during filter training. Second, to ensure the consistency of the temporal and spatial characteristics of the video sequence, the model introduces Kullback–Leibler (KL) divergence to obtain the second-order information of the filter. In contrast to traditional target tracking models that rely solely on first-order feature information, this approach facilitates the acquisition of a generalized connection between the previous and current filters. As a result, it incorporates joint-regulated filter updating. Through quantitative and qualitative analyses of the experiment, it proves that the VOLCF model has excellent tracking performance. [ABSTRACT FROM AUTHOR]

This paper presents a compact wideband circularly polarized cross-dipole antenna with a low-pass filter response. It consists of two pairs of folded cross-dipole arms printed separately on both sides of the top substrate, and the two dipole arms on the same surface are connected by an annular phase-shifting delay line to generate circular polarization. A bent metal square ring and four small metal square rings around the cross-dipoles are employed to introduce new resonant frequencies, effectively extending the impedance and axial-ratio bandwidth. Four square patches printed on the middle substrate are connected to the ground plane by the vertical metal plates in order to reduce the antenna height. Thus, a compact wideband circularly polarized antenna is realized. In addition, a transmission zero can be introduced at the upper frequency stopband by the bent metal square rings, without using extra filter circuits. For verification, the proposed model is implemented and tested. The overall size of the model is 90 mm × 90 mm × 33 mm ( 0.37 λ 0 × 0.37 λ 0 × 0.14 λ 0 ; λ 0 denotes the center operating frequency). The measured impedance bandwidth and 3 dB axial-ratio (AR) bandwidth are 53.3% and 41%, respectively. An upper-band radiation suppression level greater than 15 dB is realized, indicating a good low-pass filter response. [ABSTRACT FROM AUTHOR]

In this paper, we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. We assume that, for numerical reasons, one has to time-discretize the diffusion process, which typically leads to filtering that is subject to discretization bias. The approach in [A. Jasra, K. J. H. Law and F. Yu, Unbiased filtering of a class of partially observed diffusions, Adv. Appl. Probab.54 (2022), 3, 661–687] establishes that, when only having access to the time discretized diffusion, it is possible to remove the discretization bias with an estimator of finite variance. We improve on this method by introducing a modified estimator based on the recent work [A. Jasra, M. Maama and H. Ombao, Antithetic multilevel particle filters, preprint (2023), https://arxiv.org/abs/2301.12371]. We show that this new estimator is unbiased and has finite variance. Moreover, we conjecture and verify in numerical simulations that substantial gains are obtained. That is, for a given mean square error (MSE) and a particular class of multi-dimensional diffusion, the cost to achieve the said MSE falls. [ABSTRACT FROM AUTHOR]

In the paper, the maiden application of Tilted Integral Fractional Derivative with Filter plus Fractional Derivative controller (TIDμ1NDμ2$TI{D^{{\mu _1}}}N{D^{{\mu _2}}}$), called TIFDNFD controller, is proposed to achieve load frequency control (LFC) of two area‐multi source power system. In the study, both areas include thermal power, hydropower, and gas turbine power systems. In this study, generation rate constraint and governor dead‐band are integrated into the power system to evaluate the performance of the controller in a non‐linear environment. The wild horse optimizer (WHO) is used for controller parameter tuning in the LFC problem for the first time. The performance of the proposed controller is demonstrated by comparing it with the recently published studies including different controllers. In this study, to obtain more realistic results, solar photovoltaic (PV) power, wind power, and load demand with stochastic form have been added to the power system. In addition, the dynamic effect of the hydrogen storage system, consisting of an electrolyser and a fuel cell, is considered a grid supporter. The superiority of the WHO algorithm in optimization has been verified by comparing it with three optimization algorithms recently proposed. Finally, the resilience and robustness of the power system are evaluated in perturbed system parameters. [ABSTRACT FROM AUTHOR]

Identifying a means of effectively separating and adsorbing the harmful constituents from flue gas emissions is always crucial for the protection of human health and the environment. All-atom molecular dynamics were employed to analyze the dynamic behaviour of flue gas molecules in Au nanopores. The influences of various system temperatures, gas concentrations, and pore sizes on the adsorption conformation, diffusion coefficient, average adsorption energy, and adsorption probability of the flue gases inside an Au nanopore were examined. Results showed that when the temperature rose to 300∼400 K, the gaseous H2S constituent in the flue gas was swiftly adsorbed to the nanopore walls due to the strong H2S-Au interactions, enabling an effective separation of H2S from the flue gas. In addition, increases in pore size (d ≥ 40 Å) reduced the adsorption probability of CO2 and CO on the nanopore surfaces, which also promoted constituent separations of H2S from the flue gas. When the concentration of flue gas exceeded 6.6 mol/L, hydrogen bonds of H2O clusters and their networks entangled, impeding molecular diffusion and thereby reducing the functionality of nanopore-trapping molecules. The results presented in this paper provide a valuable reference for filter material design for flue gas depuration. [ABSTRACT FROM AUTHOR]

In the distribution system, voltage sags and swell have adverse effects on sensitive and critical loads. Dynamic Voltage Restorer (DVR) play a vital role to mitigate voltage sags and swell through its organized control when connected in series with the system. Various conventional control schemes have been reported for the control of DVR. The performance of DVR with these schemes has been successful in the mitigation of voltage sags/swell, but drawbacks, such as undershoot, and overshoot during sag dynamics are observed in the regulated load terminal voltage. These drawbacks may cause malfunctioning of the equipment connected and hence their elimination is indispensable. With this inspiration, a novel Adaline filter-based adaptive control scheme using learning-rate parameter-based Least-Mean-Square algorithm has been proposed. This scheme has the capability to overcome the drawbacks with the conventional control schemes thus improving the performance of DVR. In this paper, the performance of DVR using proposed algorithm is compared with that using synchronous reference frame (SRF)-based conventional control scheme. The performance is studied and validated through simulation results under different voltage sag and swell conditions in MATLAB software using Sim Power System toolboxes. [ABSTRACT FROM AUTHOR]

Computer vision technology is being applied at an unprecedented speed in various fields such as 3D scene reconstruction, object detection and recognition, video content tracking, pose estimation, and motion estimation. To address the issues of low accuracy and high time complexity in traditional image feature point matching, a fast image-matching algorithm based on nonlinear filtering is proposed. By applying nonlinear diffusion filtering to scene images, details and edge information can be effectively extracted. The feature descriptors of the feature points are transformed into binary form, occupying less storage space and thus reducing matching time. The adaptive RANSAC algorithm is utilized to eliminate mismatched feature points, thereby improving matching accuracy. Our experimental results on the Mikolajcyzk image dataset comparing the SIFT algorithm with SURF-, BRISK-, and ORB-improved algorithms based on the SIFT algorithm conclude that the fast image-matching algorithm based on nonlinear filtering reduces matching time by three-quarters, with an overall average accuracy of over 7% higher than other algorithms. These experiments demonstrate that the fast image-matching algorithm based on nonlinear filtering has better robustness and real-time performance. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) is crucial to the safety and reliability of the lithium-ion battery management system (BMS). However, the performance of a lithium-ion battery deteriorates nonlinearly and is heavily affected by capacity-regeneration phenomena during practical usage, which makes battery RUL prediction challenging. In this paper, a rest-time-based regeneration-phenomena-detection module is proposed and incorporated into the Coulombic efficiency-based degradation model. The model is estimated with the particle filter method to deal with the nonlinear uncertainty during the degradation and regeneration process. The discrete regeneration-detection results should be reflected by the model state instead of the model parameters during the particle filter-estimation process. To decouple the model state and model parameters during the estimation process, a dual-particle filtering estimation framework is proposed to update the model parameters and model state, respectively. A kernel smoothing method is adopted to further smooth the evolution of the model parameters, and the regeneration effects are imposed on the model states during the updating. Our proposed model and the dual-estimation framework were verified with the NASA battery datasets. The experimental results demonstrate that our proposed method is capable of modeling capacity-regeneration phenomena and provides a good RUL-prediction performance for lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively. [ABSTRACT FROM AUTHOR]

The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network was constructed, and the observability of the system was analyzed. The sensor nodes are paired to measure relative angle information. Subsequently, the coordinated consistency theory is employed to achieve a unified state estimation of the maneuvering target. The DCIF method enhances the observability of the system, addressing the issues of large accuracy errors and divergence in traditional nonlinear filtering algorithms. Building upon the theoretical proof of consistency convergence in DCIF, four simulation experiments were conducted for comparison. These experiments validate the effectiveness and superiority of the DCIF method in bearing-only sensor networks. [ABSTRACT FROM AUTHOR]

Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper comprehensively reviews the application and research progress of CO2 fracturing fluids in China, highlights the existing issues and puts forward suggestions for future development. Three types of fracturing fluid systems containing CO2, namely, CO2 dry fracturing fluid, CO2 energized fracturing fluid, and CO2 foam fracturing fluid, are categorized based on the mass ratio and process difference between CO2, water, and treatment agents. Field applications in China reveal several problem to be resolved: (1) The application scope of CO2 fracturing fluids is restricted to depleted reservoirs, re-fracturing of old wells, and medium-deep reservoirs with low formation pressure coefficients; (2) different types of CO2 fracturing fluids require different processes and ground supporting equipment; (3) optimization of CO2 compatibility, functionality, temperature and salt tolerance, as well as the cost of treatment agents is necessitated; (4) existing CO2 fracturing fluid system fail to perform well with low friction, low filtration, and high sand-carrying capacity. (5) there lacks a targeted industry standard for evaluation of performance of CO2 fracturing fluid system and treatment agents. Therefore, in order to meet the goals of CCUS-EOR, CCUS-EGR, or integration of fracturing, displacement and burial by CO2, efforts should be made in the aspects that followed, including in-depth investigation of the mechanism of CO2 fracturing fluids, the adaptability and compatibility between existing equipment, different CO2 fracturing fluid systems and processes, and construction of treatment agents, low-density proppants and highperformance systems of recyclability and industrial-grade. In addition, optimization of CO2 fracturing fluid system based fracturing design is also crucial taking such related factors such as overall reservoir geological conditions, petrophysical properties, CO2 transportation, and well site layout into consideration. [ABSTRACT FROM AUTHOR]

This paper proposes a new automatic design method based on continuous and binary optimization for dual-band and wideband filtering power dividers (FPDs). The designs utilize an optimization process to manipulate the multiple modes of a patch radiator. In this way, both dual-band and wideband operations can be achieved. Meanwhile, the filtering property is also taken into consideration during the optimization, which finally produces power dividers with filtering functions. The design process is highly automatic to minimize manual calculations and simulations. Finally, a dual-band filtering power divider whose center frequencies are 2.4 GHz and 6 GHz and a wideband filtering power divider operating from 3.7 GHz to 5.25 GHz are designed and fabricated for validation. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applications, such as estimating scene properties and refining inverse-rendered images. The main application is joint edge-preserving filtering, which can preferably reflect the edge information of a guidance image from an additional sensor. The drawback of edge-preserving filtering lies in its long computational time; thus, many acceleration methods have been proposed. However, most accelerated filtering cannot handle multiple guidance information well, although the multiple guidance information provides us with various benefits. Therefore, we extend the efficient edge-preserving filters so that they can use additional multiple guidance images. Our algorithm, named decomposes multilateral filtering (DMF), can extend the efficient filtering methods to the multilateral filtering method, which decomposes the filter into a set of constant-time filtering. Experimental results show that our algorithm performs efficiently and is sufficient for various applications. [ABSTRACT FROM AUTHOR]

The estimation of the target number and individual tracks are two major tasks in multi-target tracking. The main shortcoming of traditional tracking methods is the cumbersome data association between measurements and targets. The cardinalized probability hypothesis density filter (CPHD) proposed in recent years can achieve the requirement for multitarget tracking. This kind of filter jointly estimates the cardinality distribution and the posterior density, which can achieve a more stable estimate of the target number. However, targets with complex micro-Doppler signatures (drones, birds, etc.) may generate target-dependent false alarms, which is contrary to the traditional uniform distribution assumption. In this case, the estimates of traditional CPHD filter will suffer from the abnormal transfer of PHD mass, causing the degradation of filtering performance. This paper studies the individual tracking of group targets with an improved GM-CPHD filter. First, the target-dependent false alarms are modeled with a general independent and identically distributed (I.I.D.) cluster process. Second, the update equations of cardinality and PHD density in target-dependent false alarms are derived. Finally, a practical solution using the Gaussian mixture method is proposed. The effectiveness of the proposed filter is verified by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

In retinex model, images are considered as a combination of two components: illumination and reflectance. However, decomposing an image into the illumination and reflectance is an ill-posed problem. This paper presents a new approach to estimate the illumination for low-light image enhancement. This work contains three major tasks: estimation of structure-aware initial illumination, refinement of the estimated illumination, and the final correction of lightness in refined illumination. We have proposed a novel approach for structure-aware initial illumination estimation leveraging a new multi-scale guided filtering approach. The algorithm refines proposed initial estimation by formulating a new multi-objective function for optimization. Further, we proposed a new adaptive illumination adjustment for correction of lightness using the estimated illumination. The qualitative and quantitative analysis on low-light images with varying illumination shows that the proposed algorithm performs image enhancement with color constancy and preserves the natural details. The performance comparison with state-of-the-art algorithms shows the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new control strategy, along with an innovative switching pattern, is proposed for the transfer of charge from one cell to another, whether they are adjacent or nonadjacent, or even to multiple cells simultaneously. Therefore, this strategy improves the efficiency of state of charge balancing, which is critical for lithium polymer (LiPo) batteries. Unlike in previous studies, all these functions are realized with a simple structure that employs standard DC–DC buck‐boost converters, without any additional complexities. By implementing the proposed control strategy, the effects of inaccuracies in the gain of current sensors, as well as multiplicative measurement noises, are reduced. The proposed method is applied to a set of five series LiPo cells, and a comprehensive investigation is conducted to assess both its balancing algorithm and operation. The simulation results illustrate the effective balancing performance and the achievement of the desired charge transfer through the proposed approach. An experimental setup, which includes five LiPo cells with a capacity of 1530 milliampere‐hours (mAh), is used to verify the proposed method. The outcomes demonstrate that the balancing of all cells is accurately performed under different initial conditions, within an acceptable time frame. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel personalized music recommendation system that leverages collaborative filtering and semantic analysis techniques. The system aims to generate customized music recommendations tailored to individual users' preferences and listening histories. By employing collaborative filtering, the system identifies users with similar music tastes and suggests music they enjoy. Additionally, semantic analysis is utilized to analyze the current sentiment of a user, enabling the system to recommend songs suitable for the user's mood for initial recommendations. A dataset containing user listening histories and music metadata is utilized to evaluate the system's performance. The results demonstrate that the proposed system surpasses traditional collaborative filtering approaches in identifying the coldstart issue for new users. This capability enhances the music listening experience by providing more relevant and personalized recommendations while facilitating the discovery of new and captivating music. The system's unique approach of combining collaborative filtering and semantic analysis provides a fresh perspective on personalized music recommendations, contributing to the advancement of music recommendation systems. The evaluation outcomes signify the promising nature of the proposed method. Future research can focus on further optimizing the system's performance through additional refinements and evaluations. [ABSTRACT FROM AUTHOR]

The aim of this work is to investigate, in a wide range of gamma-ray energies, the validity of the "Virtual Point Detector" (VPD) approach to evaluate Full-Energy-Peak Efficiency (FEPE) values for a Cerium-doped Lanthanum Bromide scintillator (LaBr 3 :Ce). With VPD approximation, the detector is assumed equivalent to a virtual point inside the crystal where all the interactions can be considered to occur. The distance of VPD from detector cap is evaluated through experimental point source measurements. The knowledge of the trend of VPD depth as a function of gamma radiation energy allows the evaluation of FEPE values starting from an experimental reference value. The validation of the procedure has been performed by applying the VPD approach to measurement geometries already characterized such as a disk source, an Havar foil irradiated inside a target of a medical cyclotron, and a parallelepided sample geometry, an atmospheric particulate paper filter reduced to 6 cm × 6 cm × 0.7 cm dimensions. FEPE values determined by VPD approach lead to radionuclide activities very close to the ones already obtained with measurements carried out with other detectors. • Validity of Virtual Point Detector approach for a LaBr 3 :Ce scintillator. • Efficiency evaluation for a LaBr 3 :Ce scintillator through a VPD approximation. • Experimental validation of VPD approach also for disk and volume sources. • Knowledge of the detector size and composition is not necessary. [ABSTRACT FROM AUTHOR]

The European Green Deal has set a target for Europe to achieve net-zero greenhouse gas emissions by 2050, necessitating a transition to more sustainable energy sources. Hydrogen gas (H 2) has emerged as a promising solution, with methane pyrolysis presenting a viable method for its production. This study explores the optimization of methane plasma pyrolysis for hydrogen and high-quality carbon production. Employing a statistical approach by a design of experiment software, critical process parameters are systematically analyzed to predict their impact within a defined range. Additionally, the paper conducts comprehensive characterization of the solid carbon produced during pyrolysis using imaging, spectroscopic and elemental analysis, and gas sorption analysis methods. The experimental investigation was conducted using a thermal plasma reactor with several settings of influential parameters including methane gas (CH 4) content in the plasma gas, electric current, and arc length. The DC-transferred plasma arc is formed using a variable gas mixture of argon gas (Ar) and CH 4, with a constant flow rate of 5 Nl/min. Thirteen tests were designed, evaluating responses such as power input, process stability, and H 2 yield. The H 2 yield indicates the hydrogen produced from CH 4 , with 100% representing total conversion. While the process exhibited inconstancy, attributed to reactor design constraints, a high H 2 yield of 67%–100% was achieved. The results indicate that a higher CH 4 content in the plasma gas and extended arc lengths disturb the plasma arc, hence reducing the H 2 yield. Increased power input, achieved through higher amperage levels, and a wider reaction zone eased by extending the arc length both led to an improved H 2 yield. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) revealed microstructural differences, with carbon samples from the filter exhibiting finer textures and carbon samples from the reactor larger sizes and dendritic particles. Raman spectroscopy confirmed crystalline graphitic-like structures with low defect concentrations, a finding supported by X-ray diffraction (XRD) analysis. Inductively coupled plasma mass spectroscopy (ICP-MS) analysis confirmed high-purity carbon with slight impurities from initial filter contamination. Brunauer-Emmett-Teller (BET) specific surface area calculations based on gas sorption analysis showed significant variations, with filter-collected samples exhibiting 40–170 m2/g and reactor-collected ones showing 7–30 m2/g. [Display omitted] • This study investigates a unique approach to optimizing the thermal plasma pyrolysis of methane. • A H 2 yield of 67%–100% was achieved utilizing a DC-transferred plasma arc furnace and an Ar and CH 4 mixture plasma gas. • A lower CH 4 content in the plasma gas and an optimized arc length improve the plasma arc and the H 2 yield. • The carbon shows crystalline graphitic-like structures with low defect concentrations. [ABSTRACT FROM AUTHOR]

In order to meet the demand for multi-stopband filters and high filtering efficiency in optical nanodevices, a surface plasmon band-stop filter coupled with a straight waveguide and an arch-type resonator is proposed in this paper. Simulation analysis of visible and near-infrared spectra and field distribution using the finite element method shows that the transmittance spectrum of this structure has five stop bands in the 800 nm band range with a minimum transmittance of 9.10%; when the key parameters such as the bottom size of the resonant cavity are changed, the transmission curves are red-shifted and the performance of some of the stop bands is significantly enhanced; the sensitivity, quality factor and figure of merit can reach 1780 nm/RIU, 108.86 and 102.49 RIU−1, respectively. Finally, two optimized structures are proposed to further enhance the filter performance, allowing a minimum transmittance as low as 0.41%. The filter has a wide application prospect in optical integrated circuits. [ABSTRACT FROM AUTHOR]

The research results presented in the article constitute the next stage of a planned series of experiments on the process of refining steel from a dispersive non-metallic phase by its filtration method. The paper presents the results of experiments carried out using an innovative design of multi-hole ceramic filters, the filter holes of which have an elliptical shape. Analyses of the macro and microstructure and purity of the steel before and after the filtration process reflect the course of the research and confirm that this can be an effective and cheap method of its refining from non-metallic inclusions. The results presented are the first of their kind in the world and confirm that the shape of the filter holes of multi-hole ceramic filters may have a significant impact on the efficiency of the steel filtration process. The non-metallic phase identified at the ceramic filter-steel interface corresponds to the reaction products of the deoxidation of aluminum steel. [ABSTRACT FROM AUTHOR]

Size-based particle filtration has become indispensable in numerous biomedical and environmental applications. In this study, bioinspired by the filter-feeding mechanism (lobe filtration) of manta rays, we designed a U-shaped biomimetic gill rake filter that combined lobe filtration and Dean flow to filter monodisperse suspensions, bi-disperse suspensions and yeast cells. Compared with other equipment using the inertial focusing technology, our equipment can perform high-throughput (up to 8 mL min-1) and high-efficiency filtration of particles (maximum filtration efficiencies of 96.08% and 97.14% for 10 and 15 µm monodisperse suspensions at the optimum flow rate of 6 mL min-1). The complex velocity field of the micro-fluidic flow within the filter is numerically simulated, and in combination with experiments, a threshold for the flow rate is identified. When the inlet flow rate exceeds the threshold value, the efficiency of particle filtration is increased rapidly. Afterwards, by analysing the filtration mechanism, we develop three novel filtration processes. The equilibrium positions of the particles and yeast cells in the main channel are close to the outer wall at high flow rate, which diminishes the likelihood of particles and yeast cells entering the side channel. This configuration establishes a self-cleaning mechanism, ensuring prolonged and efficient operation of the filter with high-throughput processing. Furthermore, the influence of the filter lobe angle and channel width on the filtration efficiency and outlet flow rate ratio are explored, and an optimisation plan is prepared. [ABSTRACT FROM AUTHOR]

The proliferation of available Web services presents a big challenge in selecting suitable services. Various methods have been devised to predict Quality of Service (QoS) values, aiming to address the service selection problem. However, these methods encounter numerous limitations that hinder their prediction accuracy. A key issue stems from the dynamic nature of the service environment, leading to fluctuations in QoS values due to factors like network load and hardware issues. To mitigate these challenges, QoS selection methods have leveraged contextual information from the surrounding environments, such as service invocation time, user, and service locations. Among these methods, Collaborative Filtering (CF) has gained notable importance. In recent years, several CF methods have incorporated service invocation time into their prediction processes, giving rise to what is commonly known as time-aware CF methods. Despite the increasing adoption of time-aware CF methods, there remains a notable absence of a dedicated and comprehensive literature review on this topic. Addressing this gap, this paper conducts an analysis of the literature, reviewing the forty (40) most prominent studies in this domain. It offers a thematic categorization of these studies along with an insightful analysis outlining their objectives, advantages, and limitations. The review also identifies key research gaps and proposes potential directions for future investigations. Overall, this literature review serves as an up-to-date resource for researchers engaged in service-oriented computing research. [ABSTRACT FROM AUTHOR]

This study proposes a blind speech separation algorithm that employs a single-channel technique. The algorithm's input signal is a segment of a mixture of speech for two speakers. At first, filter bank analysis transforms the input from time to time-frequency domain (spectrogram). Number of sub-bands for the filter is 257. Non-Negative Matrix Factorization (NNMF) factorizes each sub-band output into 28 sub-signals. A binary mask separates each sub-signal into two groups; one group belongs to the first speaker and the other to the second speaker. The binary mask separates each sub-signal of the (257×28) 7196 sub-speech signals. That separation cannot identify the speaker. Identification of the sub-signal speaker for each sub-signal is achieved by speaker clustering algorithms. Since speaker clustering cannot process without speaker segmentation, the standard windowed-overlap frames have been used to partition the speech. The speaker clustering process fetches the extracted phase angle from the spectrogram (of the mixture speech) and merges it into the spectrogram (of the recovered speech). Filter bank synthesizes these signals to produce a full-band speech signal for each speaker. Subjective tests denote that the algorithm results are accepted. Objectively, the researchers experimented with 66 mixture chats (6 females and 6 males) to test the algorithm. The average of the SIR test is 11.1 dB, SDR is 1.7 dB, and SAR is 2.8 dB. [ABSTRACT FROM AUTHOR]

In this work we study the design of H2$$ {H}_2 $$ filters for phase‐type (PH) semi‐Markov jump linear systems, considering partial information on the system's operating mode and possible parameter uncertainties. With respect to the mode of operation, it is assumed that the state space of the semi‐Markov chain can be written as the union of disjoint sets, called clusters, and the only information available to the filter is which cluster the state of the semi‐Markov chain belongs to. A new linear matrix inequality (LMI) parameterization employing the slack variable technique is introduced for the design of switching full‐order filters according to the cluster observations so that suitable bounds on the H2$$ {H}_2 $$ norm of the estimation error are guaranteed for the uncertain system. If the system is restricted to be a Markov jump linear system and the Markov chain is assumed to be perfectly measured, the design conditions are shown to be also necessary leading to the optimal full‐order filter. Furthermore, the general filter can be particularized into an observer form for the case in which the system matrices are the same within each cluster and there are no parameter uncertainties except for possible uncertainties affecting the transition rates of the jumping process. The paper concludes with an illustrative example in the context of networked control systems. [ABSTRACT FROM AUTHOR]

Cellulose nanocrystals (CNCs) can be used as environmentally friendly additives in drilling fluids. However, their practical performance and utilization principles still need to be further clarified. This paper investigated the effectiveness of carboxylated cellulose nanocrystals (C-CNCs) and sulfonated cellulose nanocrystals (S-CNCs) on the rheological and filtration properties of water-based drilling fluids (WBDFs). The results indicated that at room temperature, 150 ℃, and 180 ℃, both C-CNCs and S-CNCs could be used as rheology modifiers for bentonite WBDFs, and 1.0% of C-CNCs performed the best outstanding viscosity-increasing effect and shear-thinning behavior. Under room temperature and 150 ℃, C-CNCs and S-CNCs with higher concentrations (1.0%) had a better fluid loss reduction effect. However, at 180 ℃, C-CNCs and S-CNCs with lower concentrations (0.5%) showed superior filtration properties, while 1.0% of C-CNCs and S-CNCs unexpectedly increased the fluid loss. Microstructure analysis demonstrated that the addition of CNCs with high concentrations was beneficial for improving the colloidal stability of WBDFs at 150 ℃, while it would produce adverse influence when the temperature was raised to 180 ℃. It was reported that the stiff network among bentonite, CNCs, and immobilized water molecules was destroyed to varying degrees after aging at high temperatures. Especially when a higher concentration of CNCs existed, CNC particles were greatly easier to attract each other, agglomerate, and form many permeation channels, thereby resulting in a decrease of the viscosity and an increase in fluid loss. Therefore, the use of CNCs should be determined according to their characteristics, formation temperature, and other actual operation conditions. [ABSTRACT FROM AUTHOR]

The weather has a detrimental effect on outdoor vision systems and raises the probability of traffic crashes and road accidents. The scattering of atmospheric particles degrades outdoor images captured in poor weather conditions such as haze and fog. The reduced visibility has a significant impact on driving assistance systems designed for automatic vehicles. As a result, clear visibility is critical for outdoor computer vision systems. Image dehazing is one of the ill-posed problems because evaluating transmission depth is challenging. It is essential to estimate transmission depth with the greatest degree of accuracy. In order to estimate or optimize the transmission depth, this paper employs the adaptive Gaussian notch filter and the concept of gamma correction to recover the final scene radiance. The results of the experiments are assessed and compared both quantitatively and qualitatively with state-of-the-art techniques. The experimental results demonstrate that the proposed indicators ensure high consistency in qualitative and quantitative evaluation using six performance metrics: two blind assessment indicators (e, r), contrast gain (C gain) , visual contrast measure (VCM), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and probability. [ABSTRACT FROM AUTHOR]

Abstract—The paper presents the results of applying the Discrete Perfect Set (DPS) topological filtering algorithm to analyze the spatial clustering of seismic epicenters in the Baikal region. The study in based on the earthquake data recorded by the seismic network of the Baikal Branch of the Geophysical Survey of the Russian Academy of Sciences in the region between (48° N, 58° N) and (99° E, 122° E) from 1964 to 2018. Clustering characteristics are obtained for (i) the recording period from 1989 to 2018 with different parameters of the DPS algorithm and four levels of the minimum energy class KР of seismic events and (ii) six nonoverlapping time intervals from 1964 to 2018 and seismic events of energy class KР ≥ 8.6 with fixed parameters of the DPS algorithm. The dynamics of the clustering parameters from 1964 to 2018 possibly characterizes the variability of seismicity in the region. Namely, the decrease in the linear size of the identified groups of epicenters from about a thousand km to tens km may indicate a significant change in seismic activity in the Baikal region at the end of the 1990s and the beginning of the 2000s compared to the period between 1964 and 1997. [ABSTRACT FROM AUTHOR]

The rapid proliferation of data and the intricate nature of user behavior in the online realm have presented new hurdles for recommendation systems, which aim to suggest pertinent items to users. Among the notable challenges, ensuring the provision of accurate recommendations across diverse domains stands out. This research paper proposes an innovative solution to tackle this challenge by developing a cross-domain recommendation system that leverages the collaborative filtering technique to generate precise and varied recommendations spanning multiple domains. The system gathers user behavior and item attribute data from various domains and employs a collaborative filtering algorithm integrated with a deep neural network. The model makes use of a Neural Network with CReLU activation along with the embeddings of the user and the item followed by concealed layers. The output layer employs tanh activation to guarantee that recommendations fall within [-1, 1]. Adam optimizer, MSE loss, and an accuracy metric are utilised in training. This architecture captures user-item interactions effectively, resulting in precise personalised recommendations resulting an accuracy of 96.5% - 97.5%. To validate the effectiveness of the model, extensive evaluations are conducted on real-world datasets encompassing movies and books. The results demonstrate that the proposed system outperforms state-of-the-art recommendation systems in terms of accuracy, as measured by precision(89%), recall(90%), and AUC(0.74) scores. Furthermore, the system exhibits robust performance even in scenarios characterized by cold-start problems and data sparsity. [ABSTRACT FROM AUTHOR]

A novel multi-combined-step-size normalized subband adaptive filtering algorithm is proposed. Different from the traditional combined-step-size method, by employing a variable mixing parameter on each subband, our designed algorithm is able to combine a large step size with a small one more effectively for each subband. The subband mixing parameters are derived from the variance of the noise-free a priori subband error signals. In this design, a noniterative shrinkage strategy is also utilized to estimate the noise-free a priori subband error signals. Moreover, the mean-square and steady-state performances of the proposed algorithm are studied. Simulation results illustrate that the proposed algorithm outperforms other algorithms mentioned in this paper in terms of tracking capability, convergence speed and steady-state error. [ABSTRACT FROM AUTHOR]

To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the "Tanh cost function framework." By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) is able to penetrate sparse vegetation to obtain highly accurate height information on the ground surface. LiDAR point cloud filtering is an important prerequisite for downstream tasks such as digital terrain model (DTM) extraction and point cloud classification. Aiming at the problem that existing LiDAR point cloud filtering algorithms are prone to errors in complex terrain environments, an ALS point cloud filtering method based on supervoxel ground saliency (SGSF) is proposed in this paper. Firstly, a boundary-preserving TBBP supervoxel algorithm is utilized to perform supervoxel segmentation of ALS point clouds, and multi-directional scanning strip delineation and ground saliency computation are carried out for the clusters of supervoxel point clouds. Subsequently, the energy function is constructed by introducing the ground saliency and the optimal filtering plane of the supervoxel is solved using the semi-global optimization idea to realize the effective distinction between ground and non-ground points. Experimental results on the ALS point cloud filtering dataset openGF indicate that, compared to state-of-the-art surface-based filtering methods, the SGSF algorithm achieves the highest average values across various terrain conditions for multiple evaluation metrics. It also addresses the issue of recessed structures in buildings being prone to misclassification as ground points. [ABSTRACT FROM AUTHOR]

This paper studies the pricing of derivatives in an arbitrage-free but incomplete market. We provide a new theorem and an economic-based approach for studying this issue using information reduction to identify a unique equivalent martingale measure for the pricing of derivatives. This new martingale measure, called the uplifted martingale measure, implies that non-hedged risks are non-priced. Our approach contrasts two distinct markets - the original and a fictitious - each associated with a different filtration, and employs the first and second fundamental theorems of asset pricing in both of these markets. [ABSTRACT FROM AUTHOR]

The utilization of polarization conversion metasurfaces (PCMs) has enormously facilitated the capabilities of numerous wireless systems and optical devices. However, the cross‐polarization conversion performed by the PCM inevitably results in a parasitic filtering effect that severely hinders its capacity to receive multiple polarization waves. In this paper, a high‐efficiency transmissive PCM based on the non‐reciprocal metasurface integrated with the NPN transistors is proposed, which can convert both input x‐ and y‐polarized waves into the y‐polarized transmitted wave, meanwhile, the parasitic filtering effect of the polarization converter is effectively eliminated. The total transmission efficiency of orthogonal polarizations is higher than 50% within 3.26–3.39 GHz, excluding the gain from the transistor‐based circuits, and the peak efficiency reaches 85.4% at 3.35 GHz. In addition, the proposed PCM opens up the possibilities for expanding the range of received polarization states. For the conceptual demonstration, the conversion from arbitrary linear polarizations to y‐polarization has been achieved with the transmission coefficients higher than ‐0.66 dB at 3.35 GHz. The performances of designs have been verified by experiments and tests. These designs show potential for breaking the constraints on multiple polarization conversion and promoting the development of versatile PCMs. [ABSTRACT FROM AUTHOR]

This paper studies the dissipative filtering of uncertain singular time-varying delay Markovian jump systems (MJSs) with nonlinear perturbations and generally uncertain transition rates (GUTRs). We focus on the design of a mixed filter, which can include mode-dependent and mode-independent filters in a unified and simple structure. First, a new Lyapunov–Krasovskii functional is proposed through the delay-decomposition approach, and improved Wirtinger-based integral inequalities are used to obtain a sufficient condition to ensure the stochastic admissibility of the considered uncertain nonlinear singular time-varying delay MJSs with dissipative performance. Based on these ingredients, the explicit expression of the desired mixed filter is obtained. Finally, some numerical examples and the filter design of a two-loop circuit network reflect the less conservatism and effectiveness of the method in this article. [ABSTRACT FROM AUTHOR]

The digital media shared over the Internet is the backbone of the current generation of communication. Multimedia is becoming increasingly popular with no governing body for intellectual property rights, hence the need for protecting it efficiently while preserving the feature set of given data. Several watermarking methods have been proposed for the copyright protection of audio and speech signals yet the Recording Industry Association of America reported illegal downloading costs around $12.5 billion in the year 2021. Furthermore, the challenge to find the right balance between the three contrasting characteristics of watermarking; robustness, imperceptibility, and payload persists. This paper puts forward a new, hybrid, and purely data-driven way of watermarking the audio and speech signals using iterative filtering and singular value decomposition. The input audio is processed using iterative filtering to generate a band of intrinsic mode functions (IMFs) and a residue component. Depending upon the application, singular value decomposition is applied on residue or an IMF for insertion of bits of the watermark to minimize the effect of watermarking on the audio signal. The method focuses on the robustness of the audio signals against various signal processing attacks while providing a significant boost in imperceptibility and payload as well. Thus, providing a strikingly excellent balance between the characteristics of watermarking. With an average SNR of 38.06 db and normalized correlation close to unity, the proposed method validates its superiority compared to the state of the art. [ABSTRACT FROM AUTHOR]