Usage Of Filters Research Articles

Effectiveness of Aerosol Filtration by Mechanical Filter, Electrostatic Filter, and Electrostatic Filter with Heat Moisture Exchanger in Healthy Volunteers Nebulized with Normal Saline: A Randomized Crossover Trial

Background: The COVID-19 outbreak in Thailand has raised concerns that aerosol generated from the nebulizer might be contaminated with SARS-CoV-2. To prevent this, various types of viral filters might be useful in decreasing the spread of COVID-19. Objective: To compare the effectiveness of mechanical filter, electrostatic filter, and electrostatic filter with heat moisture exchanger (HME) to no filter in filtration nebulized normal saline solution (NSS). Materials and Methods: The present study was a randomized, single-blind crossover trial. Forty-eight healthy volunteers were given nebulized NSS via the mouthpiece. At the end of the system, a filter or no filter was attached according to the randomization series. The primary outcome was the number of particles per cubic foot measured at two locations, distal to the filter, which is referred to as ‘near distant’ and 180 centimeters away from the system, which is referred to as ‘far distant’ during nebulization. The measurement was recorded every minute from the beginning of nebulization until the fifth minute. Results: Forty-eight healthy volunteers were studied with four types of filtration methods. The number of particles per cubic foot was compared by intervention using a multi-level mixed effect linear regression, a statistical method that accounted for the repeated measurements within each participant. The highest number of particles was measured at the near distant in no filter group, with a mean of 188,034 particles per cubic feet and a maximum of 370,816 particles per cubic feet and lowest number was measured at the far distant in mechanical filter group with a mean of 199 per cubic feet and a maximum of 316 particles per cubic feet. The ranking of the number of particles from the highest to the lowest were no filter, electrostatic filter, electrostatic filter with HME, and mechanical filter. Each type of filter significantly decreased the number of particles compared to no filter usage (p<0.001). However, no significant difference was found among each type of filter. Conclusion: Mechanical filter, electrostatic filter, and electrostatic with HME lessened the number of particle dispersion into the environment compared with no filter attached to the nebulization equipment.

Investigating how AI Technologies Influence Changing Social Norms and Behaviors on Social Media, Particularly in Areas like Communication and Self-Presentation

Every community This study investigates the influence of Artificial Intelligence (AI) technologies on communication styles and self-presentation on social media platforms. The primary objectives include examining how AI tools impact user behavior and measuring their effects on engagement metrics. A quantitative analysis of 350 participants revealed a significant positive correlation between AI filter usage and engagement, with correlation coefficients of 0.65 for likes and 0.58 for comments. Additionally, the reliance on AI-driven communication tools was associated with a decrease in spontaneity, reflected by a regression coefficient of -0.65. Demographic analysis showed significant variations in AI tool usage across age and gender groups, suggesting that younger users and females are more inclined to utilize these technologies. The findings highlight the dual role of AI in enhancing user engagement while raising concerns about authenticity and social norms. The study concludes that while AI tools can significantly boost interactions, they may also diminish the richness of social connections. Recommendations for promoting authentic communication and diverse self-presentation practices are discussed, alongside the necessity for further research into the long-term effects of AI on social behaviors.

Omics exploration of Tetraselmis chuii adaptations to diverse light regimes.

Microalgae are significantly influenced by light quality and quantity, whether in their natural habitats or under laboratory and industrial culture conditions. The present study examines the adaptive responses of the marine microalga Tetraselmis chuii to different light regimes, using a cost-effective filtering method and a multi-omics approach. Microalgal growth rates were negatively affected by all filtered light regimes. After six days of cultivation, growth rate for cultures exposed to blue and green filtered light was 67%, while for red filter was 83%, compared to control cultures. Transcriptomic analysis revealed that the usage of green filters resulted in upregulation of transcripts involved in ribosome biogenesis or coding for elongation factors, exemplified by a 2.3-fold increase of TEF3. On the other hand, a 2.7-fold downregulation was observed in photosynthesis-related petJ. Exposure to blue filtered light led to the upregulation of transcripts associated with pyruvate metabolism, while photosynthesis was negatively impacted. In contrast, application of red filter induced minor transcriptomic alterations. Regarding metabolomic analysis, sugars, amino acids, and organic acids exhibited significant changes under different light regimes. For instance, under blue filtered light sucrose accumulated over 6-fold, while aspartic acid content decreased by 4.3-fold. Lipidomics analysis showed significant accumulation of heptadecanoic and linoleic acids under green and red light filters. Together, our findings indicate that filter light can be used for targeted metabolic manipulation.

2D Materials for Potable Water Application: Basic Nanoarchitectonics and Recent Progresses.

Water polluted by toxic chemicals due to waste from chemical/pharmaceuticals and harmful microbes such as E. Coli bacteria causes several fatal diseases; and therefore, water filtration is crucial for accessing clean and safe water necessary for good health. Conventional water filtration technologies include activated carbon filters, reverse osmosis, and ultrafiltration. However, they face several challenges, including high energy consumption, fouling, limited selectivity, inefficiencies in removing certain contaminants, dimensional control of pores, and structural/chemical changes at higher thermal conditions and upon prolonged usage of water filter. Recently, the advent of 2D materials such as graphene, BN, MoS2, MXenes, and so on opens new avenues for advanced water filtration systems. This review delves into the nanoarchitectonics of 2D materials for water filtration applications. The current state of water filtration technologies is explored, the inherent challenges they face are outlines, and the unique properties and advantages of 2D materials are highlighted. Furthermore, the scope of this review is discussed, which encompasses the synthesis, characterization, and application of various 2D materials in water filtration, providing insights into future research directions and potential industrial applications.

The Yearning to Be Everything to Everyone: Examining the Homogenization of Social Media Platforms in the Context of Convergence

Social media platforms are continually adding new features and enhancing existing ones to attract users and extend their screen time. These platforms constantly innovate to capture users' attention and gain an edge in the competitive landscape of social media. This study focuses on identifying the similarities between features of popular social media platforms and examines whether these similarities lead to homogenization. Specifically, presenting the chronological development of these features over time is crucial for understanding the historical development and evolution of social media. The framework of this research is based on the concept of convergence. The convergence concept is used to assess whether there are commonalities in the tools, interfaces, and interaction modes offered by the platforms to their users. In this context, the study investigates how the features provided by social media platforms become similar over time and how user experiences become homogenized. The 'walkthrough method' was employed to analyze the interfaces of the selected platforms. This method involves a step-by-step examination of user experiences on the platforms to understand how features are presented and how they are utilized by users. Data were collected through notes and recordings made by researchers while using the platforms. The results indicate a significant similarity and convergence in the features offered by popular social media platforms. For instance, features such as story sharing, live streaming, filter usage, and the explore tab have become widespread across different platforms. Platforms that consider users' habits and preferences offer familiar and easily accessible experiences through these similar features. Consequently, it can be said that social media platforms create a form of homogenization in user experiences by providing similar features over time. This phenomenon offers important insights into the evolution and future development of social media. Additionally, the study suggests that the concept of convergence not only encompasses the merging of features from different technologies but also indicates a tendency for similar platforms to converge internally.

An Implementation of the Particle Flow Filter in an Atmospheric Model

Abstract The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high-dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an observing system simulation experiment (OSSE) in a simplified atmospheric general circulation model and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a yearlong cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future. Significance Statement Data assimilation is an important tool in weather forecasting. However, fundamental issues arising from linear and Gaussian approximations still exist, which can limit our utilization of observations. The particle flow filter is a promising method that avoids these approximations, but efficient algorithms for this method have yet to be developed. In this study, we develop an algorithm for the particle flow filter that can be implemented in high-dimensional geophysical models. We have demonstrated, for the first time, that the particle flow filter runs efficiently for a yearlong experiment in an atmospheric model. The new algorithm also improves the results over a commonly used data assimilation method. The results in this work demonstrate the potential usage of the particle flow filter in the weather forecasting and other high-dimensional forecasting problems.

Filtered reality: exploring the motives and socio-demographic factors of smartphone beauty filter usage among university students in Ghana

Filtered reality: exploring the motives and socio-demographic factors of smartphone beauty filter usage among university students in Ghana

The effect of using the smartphone blue light filter feature on tear production

Smartphones today have been used to support human activities. It makes the human eye always exposed to smartphone screens that use light-emitting diodes (LEDs) that emit light with a blue electromagnetic spectrum known as blue light. The effect of blue light on human eyes is still being studied. This study aims to determine the effect of using smartphone blue light filter features on tear production. This was a quasi-experimental, pre-post study, involving 40 samples who met the inclusion and exclusion criteria. Subjects were asked to use a smartphone within 1 week without activating a blue light filter. After this, tear production was measured using the Schirmer test under topical anesthesia. Then, subjects were asked to use a smartphone again within 1 week by activating the blue light filter and being measured again using the Schirmer test under topical anesthesia. Data obtained were analyzed using SPSS 25 software. There was a statistically significant (sig=0.000) difference in Schirmer test values before and after the usage of smartphone blue light filters. Each increase in the duration of smartphone usage for one minute will decrease Schirmer test results by 0.000625 mm, and so will the multiplication. The duration of smartphone usage has a 21.6% contribution of influence on the results of the Schirmer test. In conclusion, there is an effect of using the blue light filter feature on tear production between the use of a smartphone without activating the blue light filter with the use of a smartphone that activates the blue light filter feature.

The effect of using jug-type water filters on selected metals concentration in tap water – a case study

ABSTRACT In this study, the effect of the filtration process on Na, K, Mg, Ca, Fe and Zn concentration in water, using filters from one of the leading European manufacturers, was investigated. The increase in Na (up to 300%) and K concentration (up to 320%) at the beginning of jug filter usage was reported. A decrease in Ca, Fe and Zn concentration was observed. Standard filters remove 80–90% of Mg from tap water at the beginning of the usage, while magnesium-enriching ones slightly increase its concentration – from around 8 to 12–25 mg/L. Significant changes in the filter's operating characteristics were also observed for other studied elements as they wore out. Using Mg-enriching filters can increase magnesium intake from 4–5 to 6–15% of the recommended daily dose from water consumption. The results were also discussed regarding the amounts of macroelements found in commercially available bottled water. The magnesium concentration in tap water after filtration through magnesium-enriching filters was lower compared to bottled mineral waters. The authors note a scope for the development of water filter production technology, in particular, the need to develop filters that more efficiently enrich water with magnesium and do not increase sodium concentration.

Association of IVCF use with mortality and intracranial hemorrhage in patients with selected cancers and brain metastasis

AbstractWe investigated the association of inferior vena cava filter (IVCF) usage with early mortality and intracranial hemorrhage (ICH) in patients with cancer and venous thromboembolism (VTE) with and without brain metastasis. We used the California Cancer Registry data linked to hospitalization and emergency department databases to identify patients (all ages) with melanoma, kidney, breast, or lung cancers who had acute VTE between 2005 and 2017 at hospital admission. The primary outcomes were 30-day mortality and 180-day ICH post-index VTE hospitalization. Of the 16 847 patients with cancer and VTE, 19.1% had brain metastasis. Patients with brain metastasis were more likely to receive an IVCF (odds ratio, 2.24; 95% confidence interval [CI], 2.01-2.50). Among patients with active bleeding, IVCF placement was associated with ∼50% reduction in 30-day mortality (hazard ratio [HR], 0.53; 95% CI, 0.42-0.68), regardless of the presence or absence of brain metastasis. In patients without active bleeding, 30-day mortality decreased by nearly 30% among those with brain metastasis who received IVCF (HR, 0.72; 95% CI, 0.60-0.85), with no difference among those without brain metastasis who had an IVCF inserted. Patients with brain metastasis had an elevated hazard of 180-day mortality (HR, 5.14; 95% CI, 2.99-8.83), but no association was found between IVCF insertion and 180-day ICH. Our study suggests a potential mortality benefit of IVCF use among patients with selected cancers and VTE, particularly among patients with active bleeding and those with brain metastasis with no bleeding. IVCF use was not associated with 180-day ICH. Randomized clinical trials are warranted to confirm our results.

Unraveling the impact of self-esteem on the utilization of Instagram filters: the mediating role of fear of negative evaluation.

The popularity of social media platforms, such as Instagram, has given rise to a surge in photo editing and filtering practices among users. Understanding the underlying psychological factors that drive individuals to employ filters is crucial in comprehending the impact of such behavior on self-perception and online interactions. This study aims to investigate the influence of self-esteem on the importance attributed to the use of Instagram filters, with a particular focus on the mediating role played by the fear of negative evaluation. A sample of participants (N = 2,657) from diverse demographic backgrounds has been recruited to complete a series of questionnaires measuring self-esteem, fear of negative evaluation, and Instagram filter usage patterns. Mediation analysis has been employed to assess the extent to which the fear of negative evaluation mediates the relationship between self-esteem and filter usage importance. In line with the hypotheses, the results revealed that participants self-esteem negatively predicted fear of negative evaluation, which in turn had a positive effect on use of filters. Furthermore, fear of negative evaluation fully mediated the relationship between self-esteem and use of filters. Overall, the implications of this research extend to both theoretical and practical domains. The findings can contribute to the existing literature on self-esteem, social media behavior, and body image by shedding light on the factors influencing digital self-presentation.

Digital Identities and Aesthetic Expression: Uncovering Young Indonesians Argumentation in Filter Usage on Social Media

This study delves into the diverse motivations and impacts of Instagram filter usage among young Indonesians, focusing on how these digital tools influence selfrepresentation and social interaction within the digital sphere. While Instagram filters are traditionally viewed as aesthetic enhancements, this research highlights their broader roles, including entertainment, education, and personal branding. Utilizing a qualitative approach, this paper collects data through interviews and netnography, employing snowball sampling to gather in-depth insights from eight young Instagram users. Findings indicate that young people use filters not only for visual enhancement but also for fun, educational engagement, and the expression of personal traits like humor and intelligence. This usage underscores young people's agency as active consumers, manipulating digital tools to craft their public personas and navigate social media spaces. This study underscores the need for further quantitative research to explore the widespread patterns of filter consumption among youth, aiming to enrich the understanding of digital identity formation in a globalized context.

Prediction of Diabetic Retinopathy using Deep Learning with Preprocessing

INTRODUCTION: When Diabetic Retinopathy (DR) is not identified promptly; it frequently results in sight impairment. To properly diagnose and treat DR, preprocessing of picture methods and precise prediction models are essential. With the help of numerous well-liked filters and a Deep CNN (Convolutional Neural Network) model, the comprehensive method for DR image preparation and prognosis presented in this research is described. Using the filters that focus boundaries and contours in the ocular pictures is the first step in the initial processing stage. This procedure tries to find anomalies linked to DR. By the usage of filters, the excellence of pictures can be developed and minimize disturbances, preserving critical information. The Deep CNN algorithm has been trained to generate forecasts on the cleaned retinal pictures following the phase of preprocessing. The filters efficiently eliminate interference without sacrificing vital data. Convolutional type layers, pooling type layers, and fully associated layers are used in the CNN framework, which was created especially for image categorization tasks, to acquire data and understand the relationships associated with DR.
 OBJECTIVES: Using image preprocessing techniques such as the Sobel, Wiener, Gaussian, and non-local mean filters is a promising approach for DR analysis. Then, predicting using a CNN completes the approach. These preprocessing filters enhance the images and prepare them for further examination. The pre-processed images are fed into a CNN model. The model extracts significant information from the images by identifying complex patterns. DR or classification may be predicted by the CNN model through training on a labeled dataset.
 METHODS: The Method Preprocessing is employed for enhancing the clarity and difference of retina fundus picture by removing noise and fluctuation. The preprocessing stage is utilized for the normalization of the pictures and non-uniform brightness adjustment in addition to contrast augmentation and noise mitigation to remove noises and improve the rate of precision of the subsequent processing stages.
 RESULTS: To improve image quality and reduce noise, preprocessing techniques including Sobel, Wiener, Gaussian, and non-local mean filters are frequently employed in image processing jobs. For a particular task, the non-local mean filter produces superior results; for enhanced performance, it may be advantageous to combine it with a CNN. Before supplying the processed images to the CNN for prediction, the non-local mean filter can assist reduce noise and improve image details.
 CONCLUSION: A promising method for DR analysis entails the use of image preprocessing methods such as the Sobel, Wiener, Gaussian, and non-local mean filters, followed by prediction using a CNN. These preprocessing filters improve the photos and get them ready for analysis. After being pre-processed, the photos are sent into a CNN model, which uses its capacity to discover intricate patterns to draw out important elements from the images. The CNN model may predict DR or classification by training it on a labeled dataset. The development of computer-aided diagnosis systems for DR is facilitated by the integration of CNN prediction with image preprocessing filters. This strategy may increase the effectiveness of healthcare workers, boost patient outcomes, and lessen the burden of DR.

Echo cancellation using Artificial Intelligence technique

To improve the audio quality in telecommunication, echo cancellation was proposed to prevent signals from re-appearing. Generally speaking, the echo canceler first recognizes the original transmitted signal that appears again due to delay in the transmitted or recieved signal. Then, the echo canceler subtracts the echo in the signals. Commonly, echo cancelers are calledacoustic echo cancellation (AEC) in general, whether they aim for acoustic echo, line echo, or both. However, in modern times, new problems have been emerging which require more innovative, more advanced methods to tackle the echo issue. Thus, the paper reviews methods regarding Artificial Intelligence (AI) to improve the current means for echo cancellation. In this paper, we mainly discuss the approaches to echo cancellation, the identification of echo using adaptive AI and the usage of adaptive filters and neural network for echo cancellation. For instance, deep learning approaches can facilitate the issues of AEC (active echo control) and ANC (active noise control),the optimization of LMS algorithm can be achieved by combining Swarm Intelligence (SI), combining adaptive digital filter and recurrent neural network for acoustic echo cancellation etc. Using AI in echo cancellation can help us achieve high-quality, full-duplex telecommunication in modern telephony system.

A Robust Approach for Licence Plate Detection Using Deep Learning

Intelligent transport systems must be developed due to the rising use of vehicles, particularly cars. In the field of computer vision, the identification of a vehicle's licence plate (LP) has been crucial. Various methods and algorithms have been used for the detection process. It becomes challenging to find similar photos, nevertheless, because the features of these plates change depending on colour, font, and language of characters. The research proposes a powerful deep learning framework based on feature extraction using convolutional neural networks and localization using canny-edge detection. Three steps make up the model's operation. An improved approach integrating the usage of bilateral filters and Canny edge detection is used for the processes of segmentation and localization. Further, a CNN architecture is used to extract features from images and classify the presence of licence plates in unseen vehicles. If present, the stage is followed by recognition of numbers written on the plates. An extensive experimental investigation takes place using three datasets namely Stanford Cars, Car Licence Plate Detection dataset and Indian Licence Plates Database. The attained simulation outcome ensures a superior performance over existing techniques in a significant way.

Harmonic Resonance Study for Wind Power Plant

Harmonic resonance is one of an important aspect being considered in a large network such as a wind power plant (WPP). This paper analyses several factors that affect resonance characteristic in a WPP and possible mitigation measures. The first part of the paper provides an analysis of resonances in WPP. Two methods of identifying resonances are performed which are a common impedance scan method and a harmonic resonance modal method for different operating conditions. Several factors that affect resonances including the usage of passive filters as harmonic mitigation in the network are analyzed. A case study of a 400 MW offshore wind power plant is analyzed for the harmonic resonance including with the integration of a passive filter.

Antibacterial and Antiviral Activities of Silver Nanocluster/Silica Composite Coatings Deposited onto Air Filters.

The indoor air quality should be better controlled and improved to avoid numerous health issues. Even if different devices are developed for air filtration, the proliferation of microorganisms under certain conditions must be controlled. For this purpose, a silver nanocluster/silica composite coating was deposited via a cosputtering technique onto fiber glass and polymeric based substrates. The aim of this work is focused on the evaluation of the antibacterial and antiviral effects of the developed coating. The preliminary results of the compositional and morphological tests showed an evenly distributed coating on filters surfaces. Several antibacterial tests were performed, confirming strong effect both in qualitative and quantitative methods, against S. epidermidis and E.coli. To understand if the coating can stop the proliferation of bacteria colonies spread on it, simulation of everyday usage of filters was performed, nebulizing bacteria solution with high colonies concentration and evaluating the inhibition of bacteria growth. Additionally, a deep understanding of the virucidal action and mechanism of Ag nanoclusters of the coating was performed. The effect of the coating both in aqueous medium and in dry methods was evaluated, in comparison with analysis on ions release. The virucidal performances are assessed against the human coronavirus OC43 strain (HCoV-OC43).

A Convolutional Framework for Color Constancy.

We introduce a convolutional framework (CF) for computational color constancy, building upon the established low-level image feature-based framework, which utilized simple image statistics for illuminant estimation. Our framework expands upon this through an end-to-end learnable neural architecture. This adaptation enables the learning and usage of advanced filters that are not restricted to Gaussian kernels operating on individual color channels, thus generalizing the capabilities of the original framework. Additionally, our general framework supports deeper convolutional architectures, thus increasing its computational power. It can also be efficiently applied to estimate multiple spatially varying illuminants within a single scene. Our experimental results on standard datasets demonstrate that the CF outperforms the best methods in the low-level framework, improving the illuminant estimation accuracy by up to 34% for single illuminant estimation and 30% for multiple illuminants estimation. Additionally, our framework exhibits superior performance even when the number of training images is reduced. Finally, we document the inference speedup of our implementation reaching up to 30 × , making the CF especially suitable for applications where efficiency is critical. Source code and trained models available at: https://github.com/MarcoBauzz/convolutional-color-constancy.

Tackling the problem of noisy IoT sensor data in smart agriculture: Regression noise filters for enhanced evapotranspiration prediction

In smart agriculture, the accurate prediction of evapotranspiration plays a crucial role in optimizing water usage and maximizing crop yield. However, the increasing adoption of IoT sensor technologies has resulted in the accumulation of large amounts of data, which are frequently contaminated by noise and pose a significant challenge to extract reliable knowledge through data modeling. This research addresses the problem of noisy IoT sensor data and its impact on evapotranspiration prediction, an essential aspect of agricultural practices. The effect of noise on sensor variables and evapotranspiration is extensively analyzed by simulating different noise levels in evapotranspiration datasets collected from various agricultural areas in Spain, enabling a comprehensive evaluation of its impact on the performance of data science models. Despite the potential consequences of this type of errors, a noise preprocessing stage is often overlooked in existing literature in this field, which is necessary to improve data quality prior to modeling. In order to address this challenge, this paper proposes the usage of regression noise filters as approach to mitigate the detrimental effects of noisy IoT sensor data on evapotranspiration prediction. Additionally, we introduce the rgnoisefilt R package, which offers a practical and efficient implementation of noise filtering techniques for regression datasets, providing a valuable solution for handling noisy data in smart agriculture applications. The experimental results obtained emphasize the negative impacts of noise on evapotranspiration prediction performance and highlight the importance of an appropriate data treatment to mitigate system deterioration. Furthermore, the findings of this research emphasize the efficacy of the regression noise filters implemented in the rgnoisefilt software, enhancing the performance of the models built and providing a valuable tool for improving data quality in smart agriculture.

Equalizer of Amplitude-Frequency Response of Acoustic Channels and its Adaptive Filter Based on Recursive Least Squares Algorithm with Diagonalized Correlation Matrix

The paper presents an equalizer of the amplitude-frequency response of the acoustic wave propagation channels in the rooms. The considered equalizer uses the so-called modified architecture based on the adaptive filter with the input signal filtering, which weights are calculated using the Recursive Least Squares (RLS) algorithm. The equalizer of the acoustic channels amplitude-frequency response usually requires the usage of the adaptive filters with the large number of weighs that means the large computational complexity of the adaptive filter. The adaptive filters based on the fast RLS algorithms are efficient in the terms of their complexity but often unstable if the number of adaptive filter weights is large. Therefore, the algorithms are poorly suited for usage in the amplitude-frequency response equalizers of the acoustic channels. Adaptive filters based on the RLS algorithm, which use the Matrix Inversion Lemma (MIL), are stable, but computationally complex. The equalizer presented in this paper uses a simplified adaptive filter based on the MIL RLS algorithm with a diagonalized correlation matrix of the filter input signal. In addition, in the filter, each of the square submatrices on the simplified correlation matrix diagonal is inverted using a computationally efficient version of the MIL that takes into account the symmetrical structure of the mentioned submatrices relatively their main diagonals. Computer simulation confirms the performance of the proposed equalizer and demonstrates its efficiency comparing to the unsimplified equalizer. The demo includes the graphs of the equalizer steady-state amplitude-frequency response, the acoustic channel amplitude-frequency responses before and after the equalization, and the power spectral density plots of the equalizer input signal and the signals passed through the equalizer and the acoustic medium. The equalizer can be used in the equipment for the high-quality voice and music reproduction in the conditions of the limited computing resources for the implementation of the equalizer.