Logic Filter Research Articles

Research on capacitance-coupled electrical impedance imaging fusion algorithm based on wavelet transform.

Capacitively Coupled Electrical Impedance Tomography (CCEIT), as a new electrical tomography method, effectively overcomes the drawbacks of traditional electrical resistance tomography that requires direct contact with liquids, so as to achieve non-contact measurement. In order to make full use of the complete electrical impedance information (real and imaginary) obtained by CCEIT and further improve the quality of image reconstruction, an image fusion algorithm based on wavelet transform was proposed. First, the Landweber algorithm is used to image the real part information and the imaginary part information, and the wavelet transform algorithm is used to decompose the two images at multiple scales, in which the low-frequency coefficient is fused by the improved logic filter algorithm based on the adaptive threshold, the high-frequency coefficient is selected based on the absolute value maximum method for fusion, and finally, the fused image is obtained by wavelet reconstruction. Numerical simulations and experiments verify the feasibility of the proposed method, and the results show that the relative error is reduced by up to 17% and the correlation coefficient is increased by up to 10%.

Infrared and Visible Image Fusion Algorithm Based on Double-Domain Transform Filter and Contrast Transform Feature Extraction

Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the problem of incomplete detail extraction that exists in the traditional transform domain image decomposition, an adaptive high-pass filter is proposed to decompose images into high-frequency and low-frequency portions. Second, in order to address the issue of fuzzy fusion target caused by contrast loss during the fusion process, a novel feature extraction algorithm is devised based on a novel nonlinear transform function. Finally, the fusion results are optimized and color-corrected by our proposed spatial-domain logical filter, in order to solve the color loss and edge blur generated in the fusion process. To validate the benefits of the proposed algorithm, nine classical algorithms are compared on the LLVIP, MSRS, INO, and Roadscene datasets. The results of these experiments indicate that the proposed fusion algorithm exhibits distinct targets, provides comprehensive scene information, and offers significant image contrast.

A Hypersensitive Intelligent Filter for Detecting Explicit Content in Learning Environments

In today’s digital age, educational institutions aim to ensure safe learning environments in the light of pervasive explicit and inappropriate content. This study proposes an innovative approach to enhance safety by integrating convolutional neural networks (CNNs) for visual analysis with an intuitionistic fuzzy logic (IFL) filter for explicit content identification. Additionally, it utilizes GPT-3 to generate contextual warnings for users. A large-scale dataset comprising explicit and educational materials is used to evaluate the system. The results show that this hypersensitive filter has high accuracy performance, particularly in handling ambiguous or borderline content. The proposed approach provides an advanced solution to tackle the challenges of detecting explicit content and promotes safer learning environments by showcasing the potential of combining generative AI techniques across various domains.

An Image Construction Based on the VTLF Fusion Technique for Oil–Water Two-Phase Flow of Noncontact Electrical Impedance Tomography

Electrical tomography (ET) has attracted extensive attention in the petroleum multiphase flow field due to its advantages of nonintrusion, nonradiation, and simple equipment structure. Some contactless impedance imaging methods, such as capacitively coupled electrical resistance tomography (CCERT) and phase-based dielectric spectroscopy tomography, or displacement current phase tomography (DCPT), have been studied for the parameter measurement of two-phase flow. The mentioned research mainly focuses on the separate imaging by real part, imaginary part, or phase information of the voltage/current. However, the distribution change of media in the region of interest (RoI), especially the presence of high permittivity and conductivity materials, involves the distribution change of conductivity, permittivity, and loss factor. Developing a proper image fusion technique based on the three components can provide more comprehensive image information for tomographic imaging. This article proposed an improved logic filter image fusion algorithm based on a variable threshold (VTLF), and a noncontact electrical impedance tomography (NEIT) system for imaging water–oil two-phase flow is carried out. Image reconstruction is implemented by the VTLF image fusion algorithm under different media distributions, conductivities, and excitation frequencies, and the image fusion algorithms of a weighted average (WA) and logic filter (LF) are also used for comparison. The simulation results show that the reconstructed images by the proposed fusion method of VTLF had the lowest artifacts and the highest correlation coefficient (above 0.7) compared with other methods. Also, in the experiment, the VTLF also got the best performance, so it is effective to improve the reconstruction accuracy.

A Matrix Model for Creating Logical Filters of an Electronic Catalog of Prosthetic Modules for Customized Prostheses

Introduction. When synthesizing a prosthesis from ready-made prosthesis units, the prosthetist is faced with the problem of selecting from a large range of components that differ in properties and characteristics. This challenge can be overcome by the creation of a system for processing the patient's biomedical information and its further use as criteria for selecting prosthetic nodes from a global database. For this purpose, an appropriate knowledge base must be incorporated into the system software.Aim. Substantiation of the expediency of presenting the knowledge base about the requirements for the lower limb prosthesis nodes in the form of a matrix model for creating a system of logical filters in the process of selecting nodes from an electronic catalog.Materials and methods. Theoretical research methods were used, including analysis, synthesis and analogy. An expert survey among leading specialists was carried out. To unify the description of the structural and functional state of a disabled person, the terms of the International Classification of Functioning (ICF), Disability and Health were used.Results. At the main stage of filtering, prosthetic modules optimally meeting the patient’s needs are selected using a specialized software application, depending on the patient’s health status and various healthrelated factors. A model of the knowledge base is presented, which describes the logic of selecting prosthetic nodes and their filtering in an electronic catalog.Conclusion. The matrix representation of the knowledge base that contains rules for selecting components of lower limb prostheses, taking into account the patient's condition, is a basis for creating a system of logical filters when searching for prosthetic modules in an electronic catalog for creating customized prostheses. The use of the ICF conceptual language for describing the factors influencing the choice of prosthetic modules is a step towards the formation of a patient’s digital profile, which corresponds to the strategy of transition to digital medicine technologies.

Algebraic Synthesis of Safety Logical Filter on Manufacturing Systems

This work focuses on the management of safety constraints for the control of cyber-physical manufacturing systems. A methodology for constructing a set of constraints to ensure the safety of an existing control law is proposed. The resolution of this constraint set is performed using algebraic synthesis. This tool facilitates the implementation of a logic filter in a way that complies with IEC 61131-3.

Simplified Control Structure of Fuzzy Logic and Kalman Filter for Induction Motor Drive

The paper deals with the utilization of Kalman filter and fuzzy logic control in induction motor drive with direct torque control (DTC). In order to lower ripple of stator current vector in DTC drive, pulse width modulation technique with high switching frequency is applied. However, the performance of the DTC also depends on the accuracy of both stator resistance and stator current vector. In the paper, the stator resistance and stator current components are assumed to be distorted by Gaussian noises. In order to reduce the effect of noises especially at low speed and very low speed regions, a simple Kalman filter is applied for filtering current components, and fuzzy logic theory is used to increase the flexibility of proportional-integral (PI) compensator in the speed controller of the drive structure. Simulations are implemented in conditions of high-level noises of stator current and stator resistance, and a wide range of load torque. An ITAE-based criterion is utilized to evaluate the performance of drive structures. Results confirmed the expected dynamic properties of the proposed drive structure. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

‘Cold European civilisation hasn’t arrived here yet’ – negotiations within the Cypriot regime of (im)mobility

ABSTRACT This article explores the mobility/labour nexus in the Republic of Cyprus, a borderland region between Europe and the Middle East and a ‘state of exception’ since its creation. Contrary to perceptions of Cypriot migration policies as rogue aberrations from an idealised European norm, the article contends that zones of legal ambiguity display their own filtering logic towards migrant labour, offering different kinds of constraints and opportunities to migrants.

Impact of Polyphase Induction Motor on Photovoltaic Water Pumping System

An improved dynamic behavior of water pumping system is presented in this paper. The system consists of a seven-phase induction motor powered by a photovoltaic generator (PVG) and a battery bank, via two static converters controlled independently. A boost converter based on fuzzy logic controller is utilized to adapt the voltage of the photovoltaic generator in order to extract maximum power. A three levels neutral point clamped inverter (NPC) is introduced to attain a low voltage harmonic distortion in the system. In addition, an improved direct field oriented control with minimizing non-sequential currents for the seven-phase induction motor using fuzzy logic filter is implemented. To verify and validate the proposed system, an optimal operating point is chosen to locate the dynamic and static operating limits of the system. Moreover, the proposed system is simulated and analyzed. The minimization of the non-sequential currents component induces a quality energy improvement with minimizing of the Joule losses, which increase the system efficiency. The use of the seven-phase machine brings a degree of additional freedom to the system, its tolerance to faults is a capital issue for the isolated places.

Mass smart detection for breast cancer diagnosis in mammographic images

Background: Breast cancer is one of the most common types of cancer among women. Mammography is the most standard method for diagnosing breast cancer, which can be minimize the human error using computer systems of human error. Methods: In this paper, using the image processing techniques, the mass was detected and identified in the photograph and then intelligent system outlined its margin After removing the image noise, using the fuzzy inference system, the fuzzy edge improvement has been applied and then using the coordinate logic filter, the mass areas have been detected and shown in the image. Results: The proposed smart system have p <0.001 for the correct diagnosis compared to the human diagnostic methods. Conclusion: The smart system results have been tested on 322 MIAS database images. In this database, 120 cases have benign and malignant tumors and 202 are healthy. The smart system was able to detect 115 cancer cases (true positive) and 190 healthy people (true negative) correctly. The number of false positive and false negative are 12 and 5, respectively. Therefore, the accuracy of the smart system for the database is 95%, and the sensitivity and specificity are 96% and 94%, respectively.

Framework and implementation of a fuzzy logic filter-an optimization strategy for the BWMS based on stakeholders' perspectives.

The International Convention for the Control and Management of Ships' Ballast Water and Sediments (IMO 2018 Edition) was adopted in 2004 and came into force on 8 September 2017, aiming to introduce global regulations to control the transfer of potentially invasive species. Large efforts have been made by the maritime industry in creating reliable strategies for the installation of systems on board (Register 2014). Environmental considerations (INTERTANKO 2018) and optimization for the management systems (IMO 2017 Report) are factors broadly considered to tackle this matter. A consistent implementation strategy must be stated before the ballast water management system (BWMS) installation project starts-the management of stakeholders (e.g. ship owners, classification societies, administration, shipyards) is an important aspect of this process. This relies on their expertise, which in turn results in a high level of engagement and supports the implementation plan into the organizations. The creation of a framework for the optimization process, considering the implementation project of a BWMS on board of oil tankers, is the first part of this research. The use of fuzzy logic principles in the second part-as an evaluation instrument from a ranking obtained by multicriteria principles-sums up the aim of this paper, where the peculiarities about oil tankers' modelling will be discussed throughout the analysis of 2 optimization cases (Suezmax and Aframax).

Prediction of Harvested Energy for Wireless Sensor Node

Energy harvesting wireless sensor nodes are interesting for the Internet of Things, since they can provide continuous operation by adapting workload not only to the current energy reserves, but to the amount of energy that can be harvested in the future also. We present a multistage day ahead hourly solar energy prediction algorithm. The predictor uses cloud cover and precipitation probability predictions from weather forecast obtained once per day for 24 hours in advance. To compensate for short-term weather changes until the next weather forecast data is obtained, forecast errors of humidity and atmospheric pressure are fed to the fuzzy logic filter. The filter adjusts predictions of cloud cover and precipitation probability, which are applied to the clear-sky radiation model in order to obtain prediction of solar energy. The prediction of solar energy is additionally corrected based on the energy prediction error in the preceding time slot. The results show that the proposed predictor outperforms the state-of-the-art predictors in terms of prediction error. Proposed predictor and state-of-the-art predictors were also evaluated using a simulated wireless sensor node with the simple energy management algorithm, where the proposed predictor was the most efficient at maintaining energy neutrality.

Завадостійка система імпульсної лазерної дальнометрії

Pulsed laser rangefinders prove to be cost-effective and practical devices when used at distances of several tens of kilometers due to their compactness, portability and energy efficiency. However, the measurement accuracy is significantly reduced by the presence of pulsed interference affecting the input of the optical receiver both during the sensing period and when the reflected signal is being received. Using the algorithms with the accumulation and subsequent processing of the results of several successive measurements reduces the speed of decision-making and does not guarantee the convergence of the results to the real value of the distance. The paper proposes a structural diagram of a laser rangefinder with the ability to detect pulsed interference in the range interval and correct errors that occur in the structure of the signal reflected from the target. The basis of the rangefinder circuit is a logical consistent filter, the structure of which contains multipliers (multiplication operations). The following requirements were formulated for the structure of the probe signal: — the first element should always be set to +1 to synchronize the receiver decider; — the weight of the coding sequence is equal to half its length; — the length of the coding sequence is even. Based on the requirements for coding sequences, the optimal structures of binary probing signals of length 8 were found, providing the best corrective ability. Comparison of the correlation properties of the found sequences and the sequences that are constructed using the Walsh functions showed the advantage of the optimal sequences by the criterion of the minimum level of the ACF side lobes. The simulation of the rangefinder under pulsed noise conditions has shown that the logical filter is advisable to use for those cases when the duration of the obstacle does not exceed 1/3 of the duration of the probing signal.

30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

Logical filter approach for early stage cyber-attack detection

The planned in advance cyber-attacks cause the most damage for the users of the information systems. Such attacks can take a very long time, require considerable financial and human resources, and therefore, they can only be organized by large interest groups. Furthermore, current intrusion detection systems, intrusion prevention systems and intrusion response systems used to protect against cyber-attacks have several shortcomings. Such systems respond only to the attack itself when it is too late to take a preventive action and they are not suitable for detecting an attack in early stages when it is possible to block the attack and minimize the losses. Early detection requires detailed monitoring of network and system parameters to be able to accurately identify the early stages of the attack when it is still possible to kill the attack chain. In this paper, we propose to consider an attack chain consisting of nine stages. The method to detect early stage cyberattack based on the attack chain analysis using hardware implementation of logical filters is suggested. The performed experiment acknowledges the possibility to detect the attack in the early stages.

Method of Early Staged Cyber Attacks Detection in IT and Telecommunication Networks

Increasing digitization together with the benefits has also brought a lot of problems related to the challenges in cyberspace. Due to the ongoing cyberattacks yearly increase, losses in sectors that are using Telecommunication and IT services are growing. The events of the past 10 years have shown that there are particularly dangerous incidents in the cyberspace, which are pre-planned, well-prepared and carried out by terrorist groups or even by some governments. Pre-planned cyber-attacks have some stages so it is possible to distinguish the early stages where attacks do not bring significant damage to data and information. This article examines the features of the attacks and their characteristics and is the first part of the study's generalization. There is proposed a method for early staged detection of such attacks using a number of the logical filters. Proposed methodology provides a network analysis structure, logical filter configuration and attack detection algorithms that enable the detection of network flow parameters that characterize potential attack vectors. The results of theoretical simulation have shown that proposed method is capable of determining early-staged cyberattacks.In the next paper, the logical mathematical model, an estimation of the sensitivity of such method and assessment of the probability of each initial stage will be presented.DOI: http://dx.doi.org/10.5755/j01.eie.24.3.20981

A combined monitoring scheme with fuzzy logic filter for plant-wide Tennessee Eastman Process fault detection

Principal Component Analysis (PCA) is the most common Multivariate Statistical Process Control (MSPC) method that is widely used for Fault Detection and Diagnosis (FDD). Since early abnormality detection with high accuracy is required for safe and reliable process operation, False Alarms Rate (FAR), Missed Detection Rate (MDR) and the detection time delay are the major factors that must be taken into consideration when developing any process monitoring scheme. Unfortunately, the PCA performance, with fixed limits, is weak in terms of the stated factors. In contrast, conventional Moving Window PCA (MWPCA) is an adaptive technique which updates both the PCA model and the thresholds once a new normal observation is available. Yet, MWPCA methodology still does not reduce the MDR and the detection delay. In this paper, a Modified MWPCA (MMWPCA) with Fuzzy Logic Filter (FLF) is proposed to enhance the monitoring performance of PCA. It is an adaptive approach with a fixed model that combines both aforementioned techniques. The aim of using FLF is to ensure robustness to false alarms without affecting the Fault Detection (FD) performance. The application of the proposed method has been carried out on the Tennessee Eastman Process (TEP). Hold-one and hold-five MMWPCA with FLF are applied and compared to recent FDD work in the literature. The obtained results demonstrate the superiority of the proposed technique in detecting different types of faults with high accuracy and with shorter time delay.

Exploring Directional Path-Consistency for Solving Constraint Networks

Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention. However, enforcing PC is computationally expensive and sometimes even unnecessary. Directional path-consistency (DPC) is a weaker notion of PC that considers a given variable ordering and can thus be enforced more efficiently than PC. This paper shows that DPC (the DPC enforcing algorithm of Dechter and Pearl) decides the constraint satisfaction problem (CSP) of a constraint language if it is complete and has the variable elimination property (VEP). However, we also show that no complete VEP constraint language can have a domain with more than 2 values. We then present a simple variant of the DPC algorithm, called DPC*, and show that the CSP of a constraint language can be decided by DPC* if it is closed under a majority operation. In fact, DPC* is sufficient for guaranteeing backtrack-free search for such constraint networks. Examples of majority-closed constraint classes include the classes of connected row-convex (CRC) constraints and tree-preserving constraints, which have found applications in various domains, such as scene labeling, temporal reasoning, geometric reasoning, and logical filtering. Our experimental evaluations show that DPC* significantly outperforms the state-of-the-art algorithms for solving majority-closed constraints.

Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning.

In addition to routine clinical examination, unobtrusive and physical monitoring of Rheumatoid Arthritis (RA) patients provides an important source of information to enable understanding the impact of the disease on quality of life. Besides an increase in sedentary behaviour, pain in RA can negatively impact simple physical activities such as getting out of bed and standing up from a chair. The objective of this work is to develop a method that can generate fine-grained actigraphies to capture the impact of the disease on the daily activities of patients. A processing methodology is presented to automatically tag activity accelerometer data from a cohort of moderate-to-severe RA patients. A study of procesing methods based on machine learning and deep learning is provided. Thirty subjects, 10 RA patients and 20 healthy control subjects, were recruited in the study. A single tri-axial accelerometer was attached to the position of the fifth lumbar vertebra (L5) of each subject with a tag prediction granularity of 3 s. The proposed method is capable of handling unbalanced datasets from tagged data while accounting for long-duration activities such as sitting and lying, as well as short transitions such as sit-to-stand or lying-to-sit. The methodology also includes a novel mechanism for automatically applying a threshold to predictions by their confidence levels, in addition to a logical filter to correct for infeasible sequences of activities. Performance tests showed that the method was able to achieve around 95% accuracy and 81% F-score. The produced actigraphies can be helpful to generate objective RA disease-specific markers of patient mobility in-between clinical site visits.

Self-adjusting threshold mechanism for pixel detectors

Readout chips of hybrid pixel detectors use a low power amplifier and threshold discrimination to process charge deposited in semiconductor sensors. Due to transistor mismatch each pixel circuit needs to be calibrated individually to achieve response uniformity. Traditionally this is addressed by programmable threshold trimming in each pixel, but requires robustness against radiation effects, temperature, and time. In this paper a self-adjusting threshold mechanism is presented, which corrects the threshold for both spatial inequality and time variation and maintains a constant response. It exploits the electrical noise as relative measure for the threshold and automatically adjust the threshold of each pixel to always achieve a uniform frequency of noise hits. A digital implementation of the method in the form of an up/down counter and combinatorial logic filter is presented. The behavior of this circuit has been simulated to evaluate its performance and compare it to traditional calibration results. The simulation results show that this mechanism can perform equally well, but eliminates instability over time and is immune to single event upsets.