Multilevel Filter Research Articles

Surface-engineered graphene-based multi-level filter materials for one-step separation of complex oil-in-water emulsions

Global daily oil consumption reaches nearly 100 million barrels, a figure which consequently leads to the generation of substantial volumes of oily wastewater during the processes of oil production and transportation. Existing advanced materials exhibit remarkable properties for emulsions stabilized by specific surfactants. However, these materials face limitations in practical application due to the intricate composition of surfactants present in real-world oily wastewater. In this paper, we aim to tackle this issue by presenting a solution that involves the utilization of surface-engineered graphene-based multi-level filter materials (GMFMs). These specially prepared GMFMs incorporate positively charged, negatively charged, and uncharged graphene sheets, which synergistically combine to form a porous structure with unique wettability. This innovative design allows the filter material to exhibit exceptional separation capabilities for complex emulsions. The surface-engineered GMFMs exhibited exceptional efficiency in the separation of mixed surfactant-stabilized emulsions and industrial wastewater. Operating at a high throughput of approximately 5000 L m−2 h−1, these GMFMs achieved an impressive oil removal efficiency of 99.8 % or higher after just one-step separation. The combined efficiency, reusability, and broad applicability of GMFMs facilitate efficient separation of oil-in-water emulsions on a large scale, promising a sustainable solution to the pressing issue of oily wastewater treatment.

Multi-level filter network for low-overlap point cloud registration

Multi-level filter network for low-overlap point cloud registration

Restaurant Liquid Waste Treatment into Clean Water Using Graded Filter-Ultrafiltration Membrane Polyethersulfone (PES) Method

Restaurant liquid waste contains elevated pollutant levels and is commonly discharged directly into the environment, leading to adverse impacts on ecosystems and aquatic life. The traditional methods for processing such wastewater often involve extensive land usage and complicated operations, posing challenges for effective treatment. Therefore, there is a need for a more practical technology to manage restaurant liquid waste, and one promising approach is the utilization of membrane technology for wastewater treatment. In this study, the researchers employed a combination process of multilevel filters with downflow flow and PES membrane ultrafiltration to treat the restaurant liquid waste. The multilevel filter comprised several layers of filter media, including silica sand, activated carbon, zeolite, and gravel, with specific thicknesses on a PVC pipe. For the PES membrane, two different concentrations were tested: 15% and 20% PES. To evaluate the efficiency of the treatment process, pollutant parameters such as pH, TSS (Total Suspended Solids), COD (Chemical Oxygen Demand), and turbidity were measured. The results indicated significant pollutant reduction: pH levels could reach 6.9, TSS degradation achieved up to 98.37%, COD degradation up to 88.14%, and turbidity degradation up to 97.03%. Based on the outcomes, the most effective treatment for converting restaurant wastewater into clean water involved the combined use of multilevel filters and a 20% PES membrane ultrafiltration system.

Blackwater Communal Septic Tank Domestic Waste Treatment into Clean Water with Multilevel Filter Method, Wetland, Lotus Fish Pond, Continued to Aquaponics IPAL in Terms of TSS and TDSA

Blackwater or domestic waste is a type of liquid waste that requires adequate treatment so as not to pollute the environment. The effectiveness of wastewater treatment can be increased by using multilevel filters and aquatic plants as remediation agents. This study aims to improve the quality of communal domestic wastewater (WWTP) by reducing the value of domestic waste quality parameters. This study used an aquatic plant used as a Blackwater remediation agent, namely Roots Wangi (Vetiveria zizanoides). The independent variables in this study were plant distance, residence time, and plant root depth. The parameters observed were TSS (total suspended solid), TDS (total dissolved solid). The stratified filter consists of an arrangement of gravel, charcoal and sand, and the residence time is divided into a maximum of 60 days while waiting for the plants to thrive. The methods used are: Cleaning all WWTP ponds, preparing vetiver and hydroponic plants, preparing 3” PVC and holes, preparing multilevel filter gabions with weir mesh and plates, preparing multilevel filter materials, namely sand, gravel, and coconut shell charcoal. The implementation is: 3 multilevel filter packages, installation of fragrant root wetland plants in the 2nd pond, making fish and lotus ponds, aquaponics in WWTP. The data analysis method used is factor analysis. The results of the analysis show statistical similarities between vetiver plants and wastewater quality parameters, the formula is TSS = 142,395–1,865 * Wetland + e dan TDS = 1007,441–9,839 * Wetland + e. Therefore, it can be concluded that the use of multilevel filters, vetiver ponds, fish lotus ponds, aquaponics will significantly reduce the TSS and TDS parameters.
 Keywords: clean water, waste water, wetland, lotus fish pond

Active Compensation-Based Harmonic Reduction Technique to Mitigate Power Quality Impacts of EV Charging Systems

This article presents a novel active compensation-based harmonic reduction (ACHR) technique in order to mitigate the existing third harmonic component in the input current of the power factor corrector (PFC) circuit in electric vehicle (EV) charging systems. Traditional PFC control scheme includes an outer voltage loop to control the output dc voltage and an inner current loop to ensure that the grid current is sinusoidal. The work shows how third and other high-order harmonic components are created, while the signal is transmitting through the voltage control loop, and then, an analytical model is developed to calculate the dominant third harmonic magnitude according to the circuit specifications and control parameters. The ACHR approach takes advantage of multilevel filter design to prevent the presence of high-order odd harmonics in addition to mitigating the existing third harmonic component created by the voltage control loop before it is passed through the current control loop. ACHR is designed optimally, and its performance is analyzed in detail to have a robust and stable closed-loop system while keeping the characteristics of fundamental harmonic. A totem-pole boost PFC proof-of-concept is built to verify the proposed ACHR technique. The results show that the third harmonic magnitude is reduced from 244 mA in conventional control scheme to 130 mA in the proposed ACHR technique. Moreover, total harmonic distortion (THD) in input current is decreased from 4.88% to 4.03%.

Representation theory and multilevel filters

We present a general setting where wavelet filters and multiresolution decompositions can be defined, beyond the classical \({\mathbf {L}}^2({\mathbb {R}},dx)\) setting. This is done in a framework of iterated function system (IFS) measures; these include all cases studied so far, and in particular the Julia set/measure cases. Every IFS has a fixed order, say N, and we show that the wavelet filters are indexed by the infinite dimensional group G of functions from X into the unitary group \(U_N\). We call G the loop group because of the special case of the unit circle.

A Wasserstein coupled particle filter for multilevel estimation

In this article, we consider the filtering problem for partially observed diffusions, which are regularly observed at discrete times. We are concerned with the case when one must resort to time-discretization of the diffusion process if the transition density is not available in an appropriate form. In such cases, one must resort to advanced numerical algorithms such as particle filters to consistently estimate the filter. It is also well known that the particle filter can be enhanced by considering hierarchies of discretizations and the multilevel Monte Carlo (MLMC) method, in the sense of reducing the computational effort to achieve a given mean square error (MSE). A variety of multilevel particle filters (MLPF) have been suggested in the literature, e.g., in Jasra et al., SIAM J, Numer. Anal., 55, 3068–3096. Here we introduce a new alternative that involves a resampling step based on the optimal Wasserstein coupling. We prove a central limit theorem (CLT) for the new method. On considering the asymptotic variance, we establish that in some scenarios, there is a reduction, relative to the approach in the aforementioned paper by Jasra et al., in computational effort to achieve a given MSE. These findings are confirmed in numerical examples. We also consider filtering diffusions with unstable dynamics; we empirically show that in such cases a change of measure technique seems to be required to maintain our findings.

Regression and Classification of Alzheimer's Disease Diagnosis Using NMF-TDNet Features From 3D Brain MR Image.

With the development of deep learning and medical imaging technology, many researchers use convolutional neural network(CNN) to obtain deep-level features of medical image in order to better classify Alzheimer's disease (AD) and predict clinical scores. The principal component analysis network (PCANet) is a lightweight deep-learning network that mainly uses principal component analysis (PCA) to generate multilevel filter banks for the centralized learning of samples and then performs binarization and generates blockwise histograms to obtain image features. However, the dimensions of the extracted PCANet features reaching tens of thousands or even hundreds of thousands, and the formation of the multilevel filter banks is sample data dependent, reducing the flexibility of PCANet. In order to solve these problems, in this paper, we propose a data-independent network based on the idea of PCANet, called the nonnegative matrix factorization tensor decomposition network (NMF-TDNet). Specifically, we use nonnegative matrix factorization (NMF) instead of PCA to create multilevel filter banks for sample learning, then uses the learning results to build a higher-order tensor and perform tensor decomposition (TD) to achieve data dimensionality reduction, producing the final image features. Finally, our method use these features as the input of the support vector machine (SVM) for AD classification diagnosis and clinical score prediction. The performance of our method is extensively evaluated on the ADNI-1, ADNI-2 and OASIS datasets. The experimental results show that NMF-TDNet can achieve data dimensionality reduction and the NMF-TDNet features as input achieved superior performance than using PCANet features as input.

Pelatihan Pembuatan Filter Air Sederhana Skala Rumah Tangga di Kelurahan Gambut

Clean water is one of the basic needs for human life. Making simple raw water treatment equipment using a filtration system is expected to help the community to process dirty water into raw water on the scale of household needs. Poor water quality will affect the health of existing residents. There is a need for innovation in the manufacture of water filters in order to improve the quality of water for daily needs so that it becomes a more useful product or material. The method used in achieving the goal is to provide training in making simple water filters. The right method used for processing turbid water from household dug wells into clean water in an area is to use the multilevel filter method. This filtering process is to remove suspended solids or the separation process between solids/colloids and liquid solids. The conclusion in this activity is that the training on making simple water filters can be accepted by local residents to get clean and not cloudy water. It is hoped that with this activity there will be more efforts and willingness from the community to improve the quality of healthy life by paying attention to aspects of the availability and use of clean water

Dual-Branch Feature Fusion Network for Salient Object Detection

Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view.

Defining of the Minimum Wavelet Filter Coefficients Bit-Width for 3D Medical Imaging

Medical imaging uses a variety of modalities to provide visual information about a patient. Various methods are used to process this data. Many of them are based on discrete wavelet transform (DWT). Its use will allow effective denoising and compression of 2D and 3D images. This paper proposes a new approach to linear time-invariant wavelet filtering using quantized filter coefficients when using which the computational errors have different signs and allow to partially compensate each other as a result of which the processed image is of high quality. The analysis of the quantization noise of the direct multilevel DWT filter coefficients is carried out. The derived formulas demonstrate the relationship between the quantization accuracy of these coefficients and the processing quality of digital 3D images. The derived formulas for calculating the minimum accuracy of the wavelet filter coefficients representation in the computing devices memory allow minimizing the effect of quantization noise on the result of 3D images processing. Modelling of 3D medical tomographic images DWT processing showed that a decrease in the ratio of the average voxel brightness to the maximum allowable value with increasing color depth of images leads to faster achievement of high quality compared to the results of theoretical analysis with an increase in the value of the scaling degree of the wavelet filter coefficients. The obtained theoretical and practical results open up the possibility for reducing the computational complexity of software and hardware implementation of wavelet processing of 3D medical visual data on modern microelectronic devices (field-programmable gate arrays, application-specific integrated circuits, etc.).

Compressing CNNs Using Multilevel Filter Pruning for the Edge Nodes of Multimedia Internet of Things

Multimedia Internet-of-Things (IoT) systems have been widely utilized in various computer vision tasks and significantly integrated computer vision and networking capabilities. In these systems, convolutional neural networks (CNNs) perform a preliminary analysis of the collected video or image information in the edge devices. However, the high computational cost and huge storage consumption of the complex CNNs prevent their deployment on mobile-edge devices that have limited computational resource and memory. In this article, we aim to simultaneously accelerate and compress CNNs via a multilevel filter pruning (MFP) algorithm, to alleviate the dependence on the hardware of IoT edge nodes. First, a global pruning sensitivity order is defined, which could guide us to perform preliminary pruning from the perspective of convolutional layers' sensitivity. Then, the functional index of each filter is judged by the image entropy of its output feature map, which contributes to further pruning from the perspective of filter function importance. Finally, the moderate fine tuning is adopted to recover the network capability. The experimental results show that the proposed MFP algorithm could reduce 54.5% floating-point operations and 31.9% graphics memory for VGG-16 on CIFAR-10, and achieve 5.45 × floating-point acceleration and 19.70 × storage reduction for VGG-16 on ImageNet. In the reconstruction phase, the algorithm could recover the network capability much faster than the existing pruning algorithms.

National happiness index monitoring using Twitter for bilanguages

Nowadays, social media have become one of the most important methods of communication that provide a real-time and rich source of information, including sentiments. Understanding the population sentiment is a key goal for organisations and governments. In recent years, quite a lot of research has been done on sentiment analysis from social media. However, all the work in the state of the art is focused on a specific pre-defined subset of tweets, e.g. sentiment analysis via keywords search from tweets for relevant brands, products, services, events and so forth. Monitoring the general sentiment at national level through the whole social media stream is not done due to the challenges of filtering sentiment-irrelevant information, diversity of vocabulary usage in general tweets across topics causing low accuracy and the need for bilingual or multilingual models. This paper proposes a system for general population sentiment monitoring from a social media stream (Twitter), through comprehensive multi-level filters, and our proposed improved latent Dirichlet allocation (LDA) (Wang et al. in ACM Trans Internet Technol 18(1):1–23, 2017; Wang and Al-Rubaie in Appl Soft Comput 33:250–262, 2015; https://patents.google.com/patent/US20170293597A1/en) method for sentiment classification. Experiments show that our proposed improved LDA for sentiment analysis yields the best results, and also validate our proposed system for national sentiment monitoring in Abu Dhabi using twitter.

АЛГОРИТМ ПОВЫШЕНИЯ ТКАНЕВОГО КОНТРАСТА ПРИ ВИЗУАЛИЗАЦИИ СТРУКТУРЫ СИЛЬНО РАССЕИВАЮЩИХ СРЕД МЕТОДОМ ОПТИЧЕСКОЙ КОГЕРЕНТНОЙ ТОМОГРАФИИ

A phantom of a human blood vessel is used to study the quality of structural images in endoscopic OCT. Small-angle raster scanning, averaging and multilevel filtering are used. Compression of the structural image with raster averaging increases the contrast of the structural image by reducing the level of speckle noise. File compression is part of the imaging process and is performed for each pixel. The graphical dependence of the upper lines of the A-scans of the structural image of the blood vessel phantom wall is presented. The proposed algorithm allows obtaining high-quality structural images for gastroenterology, urology, gynecology, and in vivo cardiovascular diagnostics using conventional and endoscopic OCT; characterized by high processing speed of initial data and optimal file size; reduces the requirements for the detector bandwidth of the endoscopic OCT system. For calculations, an HP Z640 Workstation with two six-core E5-2620v3c processors and a 4GB NVIDIA Quadro K4200 video card was used.

Fuzzy logic-based multi-level shunt active power filter for harmonic reduction

In this paper, using a three level diode clamped multilevel inverter and DC capacitor, a shunt active power filter (SAPF) is implemented to mitigate the supply current harmonics and compensate reactive power drawn from nonlinear load. The advantage of using three-level inverter paves way to reduced harmonic distortion and switching losses. Fuzzy logic control and unit sine vector control are proposed in this paper for generating reference current for the SAPF. The advantage of fuzzy control is that it is based on a linguistic description and does not require a mathematical model of the system. The implementation of fuzzy logic control (FLC) algorithm is executed using MATLAB fuzzy logic tool box. The proposed pulse width modulation (PWM) method produces the switching signals to the inverter from the sampled reference phase voltage magnitudes as in the case of conventional space vector PWM (SVPWM). The simulation results illustrate that the proposed three-level SAPF with low harmonic content in supply current and in phase with the line voltage. The simulation results are validated with prototype model for demonstrating the effectiveness of the system.

An Industrial Dynamic Skyline Based Similarity Joins For Multidimensional Big Data Applications

In the era of data deluge, data analysis has become a key task for many industrial applications, e.g., master data management, and data integration. In particular, similarity join is an important primitive operator to support data analysis, which is to find similar pairs based on similarity functions and thresholds. In this article, we first propose a new similarity join operation called the dynamic skyline join without having to specify any similarity function or similarity threshold, which measures the similarity through multicriteria optimization. The dynamic skyline join operator makes the similarity join more flexible to support different criteria in multidimensional space. However, it is nontrivial to achieve dynamic skyline joins as both join operations and dynamic skyline queries are computationally complex in the increasing volume of real-world data. Therefore, we further propose Grid-SkyJoin, a framework to enable efficient parallel dynamic skyline joins on a shared-nothing cluster. Specifically, we use a grid partitioning to facilitate the data filtering and grouping strategies to provide load balancing and reduce the number of replicas. We also propose a multilevel filtering scheme to prune away a large fraction of unpromising points that do not fit into join results without actual join operations. Extensive experiments using benchmark datasets demonstrate that our filtering scheme can greatly reduce the number of data points to be joined, and our approach is about two times faster compared with the straightforward method in average.

Multilevel particle filters for L\xe9vy-driven stochastic differential equations

We develop algorithms for computing expectations with respect to the laws of models associated to stochastic differential equations driven by pure Lévy processes. We consider filtering such processes as well as pricing of path dependent options. We propose a multilevel particle filter to address the computational issues involved in solving these continuum problems. We show via numerical simulations and theoretical results that under suitable assumptions regarding the discretization of the underlying driving Lévy proccess, the cost to obtain MSE mathcal {O}(epsilon ^2) scales like mathcal {O}(epsilon ^{-2}) for our method, as compared with the standard particle filter mathcal {O}(epsilon ^{-3}).

Power Quality Improvement of High Voltage DC Link using Modified Shunt Active Power Filter

A new control strategy for active power filters is proposed, modeled and implemented in order to improve the power quality of a line commutated converter High voltage DC link. The ability of reactive power and harmonics reductions are generally met by using passive and active power filters. In this paper, modified active power filter with a modified harmonics pulse width modulation algorithm is used to minimize the source harmonics and force the AC supply current to be in the same phase with AC voltage source at both sending and receiving sides of a line commutated converter high voltage DC link. Therefore, it is considered as power factor corrector and harmonics eliminator with random variations in the load current. The modified harmonics pulse width modulation algorithm is applicable for active power filter based on a three-phase five-level and seven-level cascaded H-bridge voltage source inverter. Simulation results show that the suggested modified multilevel active power filters improve total harmonics distortion of both voltage and current with almost unity effective power factor at both AC sides of high voltage DC link. Therefore, modified active power filter is an effective tool for power quality improvement and preferable for line commutated converter high voltage DC link at different load conditions.

Novel Segmentation of Iris Images for Biometric Authentication Using Multi Feature Volumetric Measure

The aim of the research is to improve the efficiency of biometric authentication using different features of iris image. The biometric authentication and verification has become more popular where the authentication is more essential in many organizations. There are many approaches has been discussed to segment the iris image and to perform verification but suffers with the problem of accuracy in feature extraction and segmentation. To resolve such problems and to improve the efficiency of iris segmentation and recognition, we propose a novel segmentation algorithm which uses multi level filter which removes the eyelids and eyelash features and performs the edge detection to identify the inner and outer eye regions. Once the regions has been identified then, we compute various measures like the size of inner and outer eyes and extract the features of both and convert them in to feature vectors. The generated feature vectors are used to perform classification in biometric authentication approach. The multi feature volumetric measure is computed on the feature vector of each eye image where the feature vector has various features like the size of both inner and outer eyes, width and height, the original binary features, the number of binary ones and the number of pixels damaged by any form of disease and so on. Based on these features the MFVM is computed to classify the iris image towards a big data set of biometric features to perform authentication. The proposed method has improved the efficiency of iris segmentation and improved the efficiency of iris recognition based biometric authentication. Also the approach has reduced the time complexity and improved the efficiency also.

Research and Realization on Image Preprocessing Technology Based on Improved Median Filter

Aiming at the problem that traditional median filter algorithm cannot process collected images quickly and efficiently, this paper adopts improved median filter and makes use of advantages, such as fast running speed, parallel running of inner program, to design an image preprocessing system with high real time ability and high flexibility. At last, compared with MATLAB median filter simulation figure and multilevel median filter, it has shown that using FPGA to process and improve median filter can not only conduct median filter to images successfully, but also has the ability of fast operation speed and low energy consumption.