Basic Filter Research Articles

Minimal Realization Plus Current Output CC-based Biquad Circuit

This paper presents a minimal design of a biquad circuit using only one plus current output type-II current conveyor (CCII), one differential voltage current conveyor (DVCC) and grounded passive components. The circuit enables the implementation of all 5 basic filter types: low-pass (LP), band-pass (BP), high-pass (HP), band-stop (BS) and all-pass (AP) by the selecting and adding of the input and output currents with no component matching constraints. Moreover, the circuit parameters ω0 and Q can be set simply by adjusting the circuit components. The proposed biquad circuit performance has very low sensitivity to circuit components due to its simple structure and a small number of devices (2 active and 4 grounded passive components). This allows the circuit to work at high frequencies. The performance of the proposed topology was evaluated through PSPICE simulator using the 0.18 mm CMOS technology from the Taiwan Semiconductor Manufacturing Company (TSMC).

Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model

In this paper, we study the dynamics of the CoVid-19 outbreak in Semarang, Indonesia, using a fractional CoVid-19 model. We first determine the effects of the isolation rate ∊ and infection rate β on the reproduction number R0 and infected number V. We find that R0 is directly proportional to β and inversely proportional to ∊. For V, the effect of physical distancing is not as significant as changing ∊. As ∊ increases, V decreases, the number of susceptible individuals increases, the number of quarantined individuals decreases sharply, and the number of recovered individuals decreases. Moreover, the effect of vaccination is also considered. The combination of physical distancing, isolation, and vaccination has a significant impact on reducing the number of infected individuals. Analysis of dynamical systems allows us to understand the characteristics of our model, such as its boundedness and non-negativity, the existence of equilibrium points, the existence and uniqueness of solutions, and the local and global stability. To validate our fractional CoVid-19 model, we introduce the fractional extended Kalman filter (FracEKF) as a prediction method and compare the results against reported CoVid-19 data. FracEKF is a modified version of the basic extended Kalman filter with a time-fractional memory effect. The prediction results illustrate the accuracy of this model in terms of the root mean square error (RMSE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) for each fractional-order. Varying ∊ reproduces the trends observed in the reported data for the number of infected individuals, i.e., when ∊ increases, the infected number decreases. Moreover, a higher fractional-order results in higher model accuracy. Furthermore, higher values of the process noise Qf give smaller errors, whereas higher values of the observation noise Rf produce higher errors. Qf and the fractional-order α are inversely proportional to RMSE,NRMSE, and MAPE, whereas Rf is directly proportional to RMSE,NRMSE, and MAPE.

Ecological role of reed belts in lakeside zone: Impacts on nutrient retention and bacterial community assembly during Hydrilla verticillata decomposition

Reed belts acting as basic nutrient filters are important parts of lake buffer riparian zones. However, little is known about their impacts on nutrient release and bacterial community during plant litter decomposition. In this study, a field experiment was conducted in west-lake Taihu to monitor the changes in nutrients, bacterial enzymatic activities, and bacterial community in plant debris during Hydrilla verticillata (H. verticillata) decomposition in open water (HvC) and reed belts (HvL) area for 126 days. We found that there was lower temperature but higher nutrient concentrations in overlying water in HvL than HvC. Partial least squares path modeling revealed that environmental parameters in overlying water had important impacts on bacterial activities and nutrient release (such as alkaline phosphatase, cellulase, and soluble sugar) and therefore affected dissolved organic matter components in plant debris. According to Illumina sequencing, 46,003 OTUs from 10 dominant phyla were obtained and Shannon index was higher in HvL than HvC at the same sampling time. Neutral community model explained 49% of bacterial community variance and immigration rate by the estimate of dispersal in HvC (Nm: 27,154) and HvL (Nm: 25,765), respectively. Null model showed stochastic factors governed the bacterial community assembly in HvC (66.67%) and HvL (87.28%). TP and pH were key factors affecting the bacterial community structure at the phylum level. More hubs and complex interactions among bacteria were observed in HvL than HvC. Function analysis showed bacterial community had important role in carbon, organic phosphorus, and nitrogen removal but phosphorus-starvation was detected in debris of H. verticillata. This study provides useful information for understanding the changes in nutrients and bacterial community in litter during H. verticillata decomposition and highlights the role of reed belts on retained plant litter to protect lake from pollution.

Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments

With the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside and outside the artillery itself. In this study, we creatively propose a fault diagnosis model based on an attention mechanism, the AdaBoost method and a wavelet noise reduction network to address the difficulty in obtaining high-quality motor signals in complex noisy interference environments. First, multiple fusion wavelet basis, soft thresholding, and index soft filter optimization were used to train multiple wavelet noise reduction networks that could recover sample signals under different noise conditions. Second, a convolutional neural network (CNN) classification module was added to construct end-to-end classification models that could correctly identify faults. The above basis classification models were then integrated into the AdaBoost method with an improved attention mechanism to develop a fault diagnosis model suitable for complex noisy environments. Finally, two experiments were conducted to validate the proposed method. Under motor signals with varying signal-to-noise ratios (SNRs) noises, the proposed method achieved an average accuracy of 92%, surpassing the conventional method by over 8.5%.

Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing

Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer vision model inaccuracies. This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects. Due to the nature of the reported problem caused by many misses and mismatches, the power of quantum computing with the alternating direction method of multipliers (ADMM) optimizer was leveraged. A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking (MOT) algorithms. This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value. Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy (MOTA) by 16% more than the regular YOLOv5-DeepSORT model when using a quantum optimizer. Also, a 6% multiple object tracking precision (MOTP) increases and a 6% identification metrics (F1) score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4. This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration.

Development of a Distributed Adaptive Dual MPC Scheme with Application to Control of an Octuple Tank System

The distributed model predictive control (dMPC) provides a computationally efficient framework for designing controllers for a large dimensional plant. The performance of such dMPC scheme critically depends upon the quality of the control relevant models used for predictions. In continuously operated processes, model plant mismatch (MPM) arises due to the time-varying nature of process parameters and shift in the operating conditions due to economic considerations and this results in performance degradation. Recently an adaptive version of dMPC (dAMPC) has been developed that achieves better control performance using online model parameter update. A conventional adaptive MPC scheme, however, requires external dither signal to be injected to generate sufficient excitation needed for parameter estimation. In this work, dAMPC is cast in the dual control framework (dADMPC) which ensured sufficient input excitation as and when needed for better online parameter estimation. The proposed controller is based on black-box models parameterized using generalized orthogonal basis filters (GOBF). The Fourier coefficients of the GOBF models are dynamically updated online using recursive parameter estimators to account for MPM. The efficacy of the proposed scheme is demonstrated by simulating servo and regulatory problems associated with the benchmark octuple tank process. The simulation study reveals that performance of the proposed dADMPC scheme is comparable to a centralized ADMPC while achieving a considerable reduction in the online computation time.

Influence of models approximating the fractional-order differential equations on the calculation accuracy

In recent years, the usage of fractional-order (FO) differential equations to describe objects and physical phenomena has gained immense popularity. Due to the high computational complexity in the exact calculation of these equations, approximation models make the calculations executable. Regardless of their complexity, these models always introduce some inaccuracy which depends on the model type and its order. This article shows how the selected model affects the obtained solution. This study revisits seven fractional approximation models, known as the Continued Fraction Expansion method, the Matsuda method, the Carlson method, a modified version of the Stability Boundary Locus fitting method, and Basic, Refined, and Xue Oustaloup filters. First, this work calculates the steady-state values for each of the models symbolically. In the next step, it calculates the unit step responses. Then, it shows how the model selection affects Nyquist plots and compares the results with the plot determined directly. In the last stage, it estimates the trajectories for an example of an FO control system by each model. We conclude that when employing approximation models for fractional integro-differentiation, choosing the appropriate type of model, its parameters, and order is very important. Selecting the wrong model type or wrong order may lead to incorrect conclusions when describing a real phenomenon or object.

Semi-Supervised Alert Filtering for Network Security

Network-based intrusion detection systems play a pivotal role in cybersecurity, but they generate a significant number of alerts. This leads to alert fatigue, a phenomenon where security analysts may miss true alerts hidden among false ones. To address alert fatigue, practical detection systems enable administrators to divide alerts into multiple groups by the alert name and the related Internet Protocol (IP) address. Then, some groups are deliberately ignored to conserve human resources for further analysis. However, the drawback of this approach is that the filtering basis is so coarse-grained that some true alerts are also ignored, which may cause critical security issues. In this paper, we present a new semi-supervised and fine-grained filtering method that uses not only alert names and IP addresses but also semi-supervised clustering results from the alerts. We evaluate our scheme with both a private dataset from a security operations center and a public dataset from the Internet. The experimental results demonstrate that the new filtering scheme achieves higher accuracy and saves more human resources compared to the current state-of-the-art method.

Identifying a Suitable Signal Processing Technique for MI EEG Data

Motor imagery (MI) electroencephalography (EEG) technology is acquiring great attention from researchers due to its remarkable real-world applications. EEG signals inherit a high degree of non-stationarity, making their analysis not modest. Hence, choosing an appropriate signal processing approach becomes crucial. This comparative paper aims to identify a suitable signal processing method among famous approaches, namely short-time Fourier transform (STFT), continuous wavelet transform (CWT), and two variations of discrete wavelet transform maximal overlap DWT (MODWT) and MODWT multiresolution analysis (MODWTMRA). Different mother wavelet basis filters experimented with wavelet methods: Morse, Amor, Bump, Symlets, Daubechies, Coiflets, and Fejér-Korovkin. The different methods were tested on the classification of the right-hand and left-hand motor imagery tasks using the brain-computer interface (BCI) competition IV 2b dataset. A shallow convolutional neural network containing a single convolution layer was first trained and then used for classification. The experimental outcomes verified that MI EEG signals can be better analyzed and recognized using the maximal overlap-based signal processing methods. The classification accuracy proved that MODWT and MODWTMRA with the Symlets wavelet outperformed the other methods.

Research on Multi-Sensor Data Fusion Positioning Method of Unmanned Ships Based on Threshold- and Hierarchical-Capacity Particle Filter

To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter (TCPF) is designed. First, the ZigBee-GPS/BDS multi-sensor measurement data is preprocessed to achieve a consistent space–time reference and transformed into the same coordinate system by projection. Then, the fault data is weighted and corrected through the consistency inspection of ZigBee-GPS/BDS multi-sensor positioning data, and the corresponding confidence factor is given according to the confidence distance of the positioning data; furthermore, the confidence factor is associated with stratified sampling. After that, the multi-sensor positioning data is filtered and denoised using a basic particle filter. Finally, a TCPF data fusion algorithm is designed, and the navigation positioning data of the unmanned ship is fused and filtered to obtain its positioning information. Numerical tests show that compared with other filtering algorithms, the mean square root error and standard deviation of the proposed TCPF algorithm decrease by an average of 25.0% and 28.0%, respectively, which verifies its high filtering accuracy and its advantages in suppressing particle degradation and avoiding sample scarcity. The experimental tests show that compared with other fusion algorithms, the proposed TCPF algorithm can not only realize the precise positioning during unmanned ship navigation, but also in the positioning and fault tolerance test, the average positioning error, root-mean-square error, and standard deviation of the former decrease by 36.0%, 38.0%, and 37.0%, respectively, and the corresponding performance indicators of the latter decrease by an average of 20.0%, 19.5%, and 17.5%, which verifies that it has the advantages of high data reliability and good filtering fault tolerance, and helps to improve the positioning accuracy of unmanned ships.

Swedish Biodiversity Data Infrastructure (SBDI): Insights from the Swedish ALA installation

The Swedish Biodiversity Data Infrastructure (SBDI) is a biodiversity informatics infrastructure and is the key national resource for data-driven biodiversity and ecosystems research. SBDI rests on three pillars: mobilisation and access to biodiversity data; development and operation of tools for analysing these data; and user support. SBDI is funded by the Swedish Research Council (VR) and eleven of Sweden’s major universities and research government authorities (Fig. 1). mobilisation and access to biodiversity data; development and operation of tools for analysing these data; and user support. SBDI is funded by the Swedish Research Council (VR) and eleven of Sweden’s major universities and research government authorities (Fig. 1). SBDI was formed in early 2021 and represents the final step in an amalgamation of national infrastructures for biodiversity and ecosystems research. SBDI includes the Swedish node of the Global Biodiversity Information Facility (GBIF), the key international infrastructure for sharing biodiversity data. SBDI's predecessor Biodiversity Atlas Sweden (BAS) was an early adopter of the Atlas of Living Australia (ALA) platform. SBDI pioneered the container-based deployment of the platform using Docker and Docker Swarm. This container-based approach helps simplify deployment of the platform, which is characterised by a microservice architecture with loosely coupled services. This enables scalability, modularity, integration of services, and new technology insertions. SBDI has customised the BioCollect module to remove region-specific constraints so that it can be more readily improved for environmental monitoring in Sweden. To further support this, there are plans to develop services for the distribution of terrestrial map layers, which will provide important habitat information for artificial intelligence and machine learning research projects. The Amplicon Sequence Variants (ASVs) portal, an interface to sequence-based observations, is an example of integration and new technology insertion. The portal developed in SBDI and seamlessly integrated with the ALA platform provides basic functionalities for searching ASVs and occurrence records using the Basic Local Alignment Search Tool (BLAST) or filters on sequencing details and taxonomy and for submitting metabarcoding dataset Fig. 2. Future developments for SBDI include a continued focus on eDNA and monitoring data as well as the implementation of procedures for handling sensitive data.

Evaluating denoising performances of basic filters in the detection of microcalcifications on mammogram images

This research evaluates the denoising abilities of some image-processing filters used in facilitating the early detection of microcalcifications in breast tissues. The mean, median and Gaussian filters were employed to denoise mammogram images of microcalcification breast phantoms of various densities. The performances of the filters were assessed by evaluating the mean squared error (MSE), peak signal-to-noise ratio (PSNR), and signal-to-noise ratio (SNR). All experiments were carried out on MATLAB R2020a platform. The results revealed that the Gaussian filter recorded optimal performance in denoising images with all 3 types of added noises compared to the mean and median filters. The PSNR value of the heterogeneous phantom (PVAL/H) was superior to those of the less dense (PVAL/E), dense (PVAL), and extremely dense (PVAL/G) phantoms for all the tested filters. The results of this work agree with the high contrast recorded by the original image of PVAL/H.

High performance of photonics crystals Demux with an integrated channel drop filter based on annular periodic ring cavity

Abstract In this work, we are interested in the design of a basic filter structure based on an annular ring cavity where we study its characteristics and its performance in terms of transmission and selectivity of the signal. The basic structure is a 2D photonic crystal (PC) with holes in the substrate which are more suitable for different PC manufacturing processes. Based on the optimization of the filter structure results, we created a high performance structure and improved a 4 channels demultiplexer (DEMUX) with an optical parameters which are the transmission efficiency, the quality factor and the crosstalk with average values of 97.25, 3171 and −26.58 dB, respectively, it had a significant role in optical communication networks. The compact size of the proposed DEMUX is 211.7 μm2 which is suitable for the integrated optics. To determine the photonic band gap (PBG) of our proposed structure, we used the plane wave expansion (PWE) method and the finite difference time domain (FDTD) method used to study normalized transmission.

Frequency Domain Analysis of Load Frequency Control Using PIDTune Model Standard

The load frequency control system aims to regulate the frequency in the electric power system at a normal value with a predetermined tolerance limit. In practice, the load frequency control system does not always operate optimally, therefore a controller is needed to be added to the load frequency control system. The controller designed is a variation of the combination of Proportional-Integral-Differential (PID) controllers with the standard model PIDTune method consisting of proportional (P) controller, proportional integral (PI) controller, proportional differential (PD) controller, Proportional Integral Differential (PID) controller, Proportional Differential controller with first-order filter on differential section (PDF), and Proportional Integral Differential controller with first-order filter on differential section (PIDF). This study is aiming to carry out simulation and analysis in the frequency domain and then analyze the robustness of the reheat type power frequency control system and then design a PID controller for the reheat type power frequency control system in basic configuration, filter configuration, feedback configuration, feedforward configuration, and cascade configuration using PIDtune Model Standard on Matlab software. From the results of simulation and analysis, the controller that complies the design criteria and can make the reheat type of load frequency control system work optimally based on frequency domain analysis and robustness analysis is a proportional-integral (PI) controller in a feedback configuration with gain margin (Kg) equals to 38.11 dB, phase margin equals to 59.6°, infinity bandwidth, peak resonance value (Mr) equals to 1.19, maximum sensitivity peak value (MS) equals to 1.24, and complementary maximum sensitivity peak value (MT) equals to 1,17.

Gestión de cartera de inversión renta variable aplicando la Teoría de Portafolios de Markowitz

Investment Portfolio Management equities is based on the investor reasoning behavior minimizing risks and maximizing profits, benefits offered by Markowitz Portfolios Theory (TPM onwards). The goal is to manage investment Portfolios equities applying TPM to determine from this one if a financial assets Portfolios negotiated in Standard y Poor's 500 (SyP 500) deals with the maximizing investor profits considering a minimal variance. The population was made by 505 enterprises composed by 11 economic sectors SyP 500 rate. Some basic analysis filters were used in order to obtain the same and 34 enterprises our of the main economical sections of SyP 500 rate were identified to which TPM was applied to investment Portfolio Management equities and financial tools such as FINVIZ, Yahoo finance, Select Sector supported by Microsoft Excel were used. The research design was pre-experimental with a quantitative-qualitative approach. One of the conclusions was that the Investment Portfolio Management equities had a 52,379% performance producing a 3,086% and 5,892% monthly expected profits risk.

РОБОТОТЕХНІЧНА СИСТЕМА З ОДНОТИПНИМИ ЧАСТОТНО-ЗАЛЕЖНИМИ КОМПОНЕНТАМИ У ТРАКТІ ОБРОБКИ СИГНАЛІВ ДАТЧИКІВ

Abstract. The issue of building a sensor signal processing path on the same type of components in computer robotic systems, which can comprehensively rebuild their characteristics by software and hardware means depending on the operating conditions to increase the system efficiency, is considered. Different types of connections of analog and digital second-order frequency-dependent components are considered, since a high-order processing path is easier to form from the same type of low-order components. This is due to the low cost of calculating the coefficients of the transfer function and the number of coefficients, ease of adjustment or reconstruction, moderate power consumption and processing time. It is shown that a serial connection is best suited for the sensor signal processing path, since signal distortion occurs in other connections. To increase the order of the processing path, it is suggested to use a serial connection of the same type of frequency-dependent components of the second order. Obtained ratios that allow to calculate the frequency of the main typical component based on the required frequency of several components connected in series. These ratios are very difficult to implement in robotic systems, as they do not have large computing resources. Since the range of frequencies and the number of components are limited, it is recommended to calculate the necessary frequencies in advance and summarize them in the table. When working in real time, it is advisable to use these tables for rebuilding. When connecting analog and digital basic filters of the same type in series, it is advisable to use the main filters of the second order of Butterworth and Chebyshev of the second kind with flat frequency response in the passband, which will ensure a good increase in the steepness of the frequency response decline and make it possible to predict the value of the cutoff frequency for the low-pass filter. And for bandpass filters, only Butterworth filters. Keywords: Industry 4.0-5.0, robotic systems, frequency-dependent components, component connection types, series connection, main typical component, amplitude-frequency characteristic.

Multi Frequency Controllable In-Band Suppressions in a Broad Bandwidth Microstrip Filter Design for 5G Wi-Fi and Satellite Communication Systems Utilizing a Quad-Mode Stub-Loaded Resonator

The key elements used for receiving and processing signals in communication systems are the bandpass filters. Initially, a common operating mechanism was applied for the design of broadband filters, i.e., by cascading low-pass filters or high-pass filters using multiple line resonators with length quarter-half- or full-wavelength with central frequency, but using these approaches, the design topology becomes expensive and complex. The above mechanisms can be possibly overcome using a planar microstrip transmission line structure due to its simple design fabrication procedure and low cost. So, pointing out the above problems in bandpass filters such as low-cost, low insertion loss, and good out-of-band performance, this article presents a broadband filter with multifrequency suppression capability at 4.9 GHz, 8.3 GHz, and 11.5 GHz using a T-shaped shorted stub-loaded resonator with a central square ring coupled to the basic broadband filter. Initially, the C-shaped resonator is utilized for the formation of a stopband at 8.3 GHz for a satellite communication system, and then a shorted square ring resonator is added to the existing C-shaped structure for the realization of two more stopbands at 4.9 GHz and 11.5 GHz for 5G (WLAN 802.11j) communication, respectively. The overall circuit area covered with the proposed filter is 0.52 λg × 0.32 λg (λg is the wavelength of the feed lines at frequency 4.9 GHz). All the loaded stubs are folded in order to save the circuit area, which is an important requirement of next-generation wireless communication systems. The proposed filter has been analyzed using a well-known transmission line theory, even–odd-mode, and simulated with the 3D software HFSS. After the parametric analysis, some attractive features were obtained, i.e., compact structure, simple planar topology, low insertion losses of 0.4 dB over the entire band, good return loss greater than 10 dB, and independently controlled mutli stopbands, which make the proposed design unique and can be used in various wireless communication system applications. Finally, a Rogers RO-4350 substrate is selected for the fabrication of the prototype using an LPKF S63 ProtoLaser machine and then measured using a ZNB20 vector network analyzer for matching the simulated and measured results. After testing the prototype, a good agreement was found between the results.

Pattern synthesis of nonuniform linear antenna array based on FFDM

To optimize the number, spacing and excitation of nonuniform linear array at the same time, a Fourier basis filter diagonalization method (FFDM) is proposed in this paper. By constructing a set of Hamiltonian operator functions that can be decomposed by orthogonal eigenvectors, the synthesis of nonuniform linear arrays is transformed into a generalized eigenvalue decomposition under Fourier basis. The introduction of Fourier basis can improve the computational rate. The least square method (LSM) is used to solve the array element excitation, which improves the matching accuracy. A number of numerical simulations shown that the proposed algorithm can improve the accuracy of the array factor (AF) pattern of many types of practical arrays reconstruction.

DIRECTIONS FOR IMPROVING THE EFFICIENCY OF THE RECRUITMENT PROCESS WITH THE USE OF DIGITAL TECHNOLOGIES FOR UKRAINIAN IT-BUSINESS IN THE CONDITIONS OF MARITAL STATE

The article states that the Russian full-scale invasion of Ukraine in February 2022 had a critical impact on the macroeconomic system of Ukraine, and because of this, preservation and ensuring the functioning of the labor market of Ukraine took on special importance. Current trends in the labor market of the IT business have been analyzed. It is noted that the IT business was the least affected by the war, but the personnel market for most companies has changed significantly, which poses new challenges to the personnel recruitment process. It has been proven that employees of IT companies are a particularly valuable resource. Using their intellectual potential allows the Ukrainian IT business to develop and successfully compete on the international market. It is indicated that personnel recruiting directly affects the company's provision of qualified personnel with the necessary competencies, and the digitization of its processes is one of the factors of ensuring an effective personnel selection process. 7 main trends that are currently operating in the IT business during martial law are highlighted. It is indicated that the main task is to ensure further stabilization and growth of the IT industry, to preserve the intellectual potential of its employees and stop the outflow of personnel. It is proven that the research of technology and issues of recruiting services in Ukraine is quite new, since the theory and practice of recruiting in high-tech spheres of activity is currently at the stage of development, therefore there is no single approach to solving existing problems and tasks. There are 3 main online resources for online recruitment. The advantages and disadvantages of each of them are indicated. Current tools that help in the selection of highly qualified personnel are considered. The query creation methodology, basic search filters and logical operators are considered. As a result of the research, it is proven that the improvement of personnel selection methods is carried out thanks to digital technologies, which allow you to directly optimize the selection process and improve its quality indicators.

Low-complexity equalizers and applications

Equalizers for audio signal processing play an important role in microphones, mixing systems, mastering processors, monitor loudspeakers, and consumer devices such as smart tablets, smart phones, wireless headphones, and hearing devices. We will introduce low-complexity filter designs for equalizers based on first- and second order recursive filters. Low-complexity filter design means having the lowest number of parameters and design equations for first- and second-order difference equation realizing prescribed filter transfer functions such as low-pass, high-pass, band-pass, and all-pass filters. Based on these basic filters, we derive all-pass realizations of these standard filters and then apply them to design parametric low- and high-frequency shelving filters and peak filters. These last two versions of weighting filters are based on three parameters, namely, the cut-off or center frequency, the bandwidth or Q factor, and the gain in dB for a low-, mid-, or high frequency band and are, therefore, named parametric equalizers. These parametric equalizers' PEQs occur in a channel-strip of a mixing console and are an integral part of the mixing process. We will describe several further applications of PEQs for amp and loudspeaker modeling, headphone equalization and head-related transfer function modeling, and audio coding. All applications allow a low-complexity filter design, parameter update, and efficient recursive filter implementations with much lower complexity compared to non-recursive filters.