Electrical impedance tomography (EIT), as a non-ionizing tomography method, has been widely used in various fields of application, such as engineering and medical fields. This study applies an iterative process to reconstruct EIT images using the simultaneous algebraic reconstruction technique (SART) algorithm combined with K-means clustering. The reconstruction started with defining the finite element method (FEM) model and filtering the measurement data with a Butterworth low-pass filter. The next step is solving the inverse problem in the EIT case with the SART algorithm. The results of the SART algorithm approach were classified using the K-means clustering and thresholding. The reconstruction results were evaluated with the peak signal noise ratio (PSNR), structural similarity indices (SSIM), and normalized root mean square error (NRMSE). They were compared with the one-step gauss-newton (GN) and total variation regularization based on iteratively reweighted least-squares (TV-IRLS) methods. The evaluation shows that the average PSNR and SSIM of the proposed reconstruction method are the highest of the other methods, each being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest, which is 0.04. The performance evaluation also shows that the proposed method is faster than the other methods.
Reconstruction of electrical impedance tomography images based on the expecta...ISA Interchange
Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing.
Chracterization of LabVIEW based 16-electrode 2D EIT systeminventionjournals
Characterization of EIT system is crucial for validation and calibration. Parameters used for characterization are divided into two groups- first dealing with data quality (SNR, accuracy) and second dealing with image quality (CNR, RNG, reconstructed area).These parameters are useful while selecting a particular EIT system for any new application. Injected current directly affects the data and hence the quality of reconstructed image. Therefore we have studied effect of amplitude of injected current on the EIT data and reconstructed image experimentally. Guidelines are evolved for setting the EIT system to collect useful data for further characterization. We report a set of experiments carried out to characterize a Lab VIEW based 16- electrode 2D EIT system developed in our laboratory. SNR and accuracy for all 208 channels involved in the Sheffield measurement pattern has been calculated. Resolution of EIT system is an important parameter and it depends on both data and image quality and is a combined index for the performance. Uniformity of resolution over the object is also important to preserve shape characteristics of target. We have obtained the resolution of our system experimentally at the centre as well as near the electrodes. It is shown to follow the limits proposed by Seagar relating to the model used for reconstruction and by Isaacson relating to the noise in the measurement.
Performance of low-cost solar radiation loggerIJECEIAES
In solar power systems, irradiance value data are among the most important parameters. Such data can be used in installing photovoltaic (PV) modules, such as determining the exact location, tilt angle, and required area, for optimal power efficiency. In this study, the comprehensive simulation and implementation of a solar radiation meter with a PV cell and temperature sensor are presented. The irradiance measurement value is based on the power reading generated by the small capacity of the PV cell at a specific load converted into a digital value in the microcontroller using the implicit Newton polynomial interpolation (NPI) equation as a low-cost alternative method. The effect of temperature is included in the conversion to obtain precise measurement results. Firstly, the structure and characteristics of the PV cell are discussed. Secondly, the parameters, measuring method, and conversion of the measurement reading data using the NPI equation are presented to assess the results. Finally, the simulation of the solar radiation meter using the PSIM and implementation of the hardware are conducted to validate the concepts and compare their results. The proposed hardware has an average error of 2.72% in the implementation of the measurement test.
Image reconstruction through compressive sampling matching pursuit and curvel...IJECEIAES
An interesting area of research is image reconstruction, which uses algorithms and techniques to transform a degraded image into a good one. The quality of the reconstructed image plays a vital role in the field of image processing. Compressive Sampling is an innovative and rapidly growing method for reconstructing signals. It is extensively used in image reconstruction. The literature uses a variety of matching pursuits for image reconstruction. In this paper, we propose a modified method named compressive sampling matching pursuit (CoSaMP) for image reconstruction that promises to sample sparse signals from far fewer observations than the signal’s dimension. The main advantage of CoSaMP is that it has an excellent theoretical guarantee for convergence. The proposed technique combines CoSaMP with curvelet transform for better reconstruction of image. Experiments are carried out to evaluate the proposed technique on different test images. The results indicate that qualitative and quantitative performance is better compared to existing methods.
Artificial Neural Network in the Design of Rectangular Microstrip Antennaaciijournal
A simple design to compute accurate resonant frequencies and the electric fields of rectangular microstrip
antennas using artificial neural networks (ANN) is proposed. The ANN is developed to calculate the
frequency and antenna's field. ANN is designed using multilayer perceptron networks (MLP). The results
that were obtained accord the trained and tested data of ANN models. As a result, the ANN model is
presented as a substitutional method to the detailed electromagnetic design of rectangular microstrip
antenna.
A Novel Approach for Precise Motion Artefact Detection in Photoplethysmograph...AM Publications
PPG signal is a useful tool for quick and critical diagnosis related to cardiovascular output via wearable or portable devices. Its drawback is unreliable during non-stationary states due to occurrences of frequency overlap of the desired and motion artifact signals. The accelerometer is usually used to reflect the motion artifact when the adaptive noise cancellation technique is implemented to address this obstacle, but it failed to predict the value of real induced noise accurately. In this work, we investigate a new concept that is capable of providing the entire motion artifact separately by recruiting twin photodetectors to formulate the influential signals. The main function of photo-detector (MPD) is to generate the corrupted PPG signal. While the second photo-detector (CPD) that covered up from the light effect, will be used to reflect the corruption effect that exists in both sources simultaneously by counting the generated dark photocurrent (GDPC). To validate the GDPC approach, experiments were executed to analyze the response of two methods during steady and motion state. Results showed resemblance responses for both methods regarding the’ amplitude fluctuations and high positive correlations in the time domain. Furthermore, the FFT peak plots in frequency domain indicated the potential of CPD to reflect all fundamental frequencies caused by motion, unlike the acceleration approach. Therefore, the proposed concept is a sure-fire method to obtain precise measurements at a lower cost.
This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic module inputs (temperature and solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours. In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power. The entire system is implemented in the Matlab / Simulink environment where simulation results obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&O.
Chebyshev filter applied to an inversion technique for breast tumour detectioneSAT Journals
Abstract Microwave imaging has been extensively studied in the past several years as a new technique for early stage breast cancer detection. The rationale of microwave imaging for breast tumour detection is significant contrast in the dielectric properties of normal tissue and malignant tumours. However, in practice noise present from the environments during screening/examination degrades the quality of the image. Inaccurate reconstructed image caused false/misleading interpretation of the image which leads to inappropriate diagnose or treatment to the patient. In the simulation works, noise is added to imitate the actual environment scenario. The two-dimensional (2D) object that identical to breast model is developed using numerical simulation to imitate the breast model. A filter is integrated with an iterative inversion technique for breast tumour detection to eliminate the noise. To assess the effectiveness of this approach, we consider the reconstruction of the electrical parameter profiles of 2D objects from measurements of the transient total electromagnetic field data contaminated with noise. Additive white Gaussian noise is utilized to mimic the effect of random processes that occur in the nature. This paper presents the filter settings and characteristics that affect the reconstruction of the image in order to obtain the most reliable and closer to the actual image. Selection of filter settings or design is important in order to achieve desired signal, most accurate image and provide reliable information of the object. Chebyshev low pass filter is applied in the Forward-Backward Time-Stepping (FBTS) algorithm to filter the noisy data and to improve the quality of reconstructed image. Keywords: Chebyshev low pass filter, microwave imaging and breast tumour detection
Reconstruction of electrical impedance tomography images based on the expecta...ISA Interchange
Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing.
Chracterization of LabVIEW based 16-electrode 2D EIT systeminventionjournals
Characterization of EIT system is crucial for validation and calibration. Parameters used for characterization are divided into two groups- first dealing with data quality (SNR, accuracy) and second dealing with image quality (CNR, RNG, reconstructed area).These parameters are useful while selecting a particular EIT system for any new application. Injected current directly affects the data and hence the quality of reconstructed image. Therefore we have studied effect of amplitude of injected current on the EIT data and reconstructed image experimentally. Guidelines are evolved for setting the EIT system to collect useful data for further characterization. We report a set of experiments carried out to characterize a Lab VIEW based 16- electrode 2D EIT system developed in our laboratory. SNR and accuracy for all 208 channels involved in the Sheffield measurement pattern has been calculated. Resolution of EIT system is an important parameter and it depends on both data and image quality and is a combined index for the performance. Uniformity of resolution over the object is also important to preserve shape characteristics of target. We have obtained the resolution of our system experimentally at the centre as well as near the electrodes. It is shown to follow the limits proposed by Seagar relating to the model used for reconstruction and by Isaacson relating to the noise in the measurement.
Performance of low-cost solar radiation loggerIJECEIAES
In solar power systems, irradiance value data are among the most important parameters. Such data can be used in installing photovoltaic (PV) modules, such as determining the exact location, tilt angle, and required area, for optimal power efficiency. In this study, the comprehensive simulation and implementation of a solar radiation meter with a PV cell and temperature sensor are presented. The irradiance measurement value is based on the power reading generated by the small capacity of the PV cell at a specific load converted into a digital value in the microcontroller using the implicit Newton polynomial interpolation (NPI) equation as a low-cost alternative method. The effect of temperature is included in the conversion to obtain precise measurement results. Firstly, the structure and characteristics of the PV cell are discussed. Secondly, the parameters, measuring method, and conversion of the measurement reading data using the NPI equation are presented to assess the results. Finally, the simulation of the solar radiation meter using the PSIM and implementation of the hardware are conducted to validate the concepts and compare their results. The proposed hardware has an average error of 2.72% in the implementation of the measurement test.
Image reconstruction through compressive sampling matching pursuit and curvel...IJECEIAES
An interesting area of research is image reconstruction, which uses algorithms and techniques to transform a degraded image into a good one. The quality of the reconstructed image plays a vital role in the field of image processing. Compressive Sampling is an innovative and rapidly growing method for reconstructing signals. It is extensively used in image reconstruction. The literature uses a variety of matching pursuits for image reconstruction. In this paper, we propose a modified method named compressive sampling matching pursuit (CoSaMP) for image reconstruction that promises to sample sparse signals from far fewer observations than the signal’s dimension. The main advantage of CoSaMP is that it has an excellent theoretical guarantee for convergence. The proposed technique combines CoSaMP with curvelet transform for better reconstruction of image. Experiments are carried out to evaluate the proposed technique on different test images. The results indicate that qualitative and quantitative performance is better compared to existing methods.
Artificial Neural Network in the Design of Rectangular Microstrip Antennaaciijournal
A simple design to compute accurate resonant frequencies and the electric fields of rectangular microstrip
antennas using artificial neural networks (ANN) is proposed. The ANN is developed to calculate the
frequency and antenna's field. ANN is designed using multilayer perceptron networks (MLP). The results
that were obtained accord the trained and tested data of ANN models. As a result, the ANN model is
presented as a substitutional method to the detailed electromagnetic design of rectangular microstrip
antenna.
A Novel Approach for Precise Motion Artefact Detection in Photoplethysmograph...AM Publications
PPG signal is a useful tool for quick and critical diagnosis related to cardiovascular output via wearable or portable devices. Its drawback is unreliable during non-stationary states due to occurrences of frequency overlap of the desired and motion artifact signals. The accelerometer is usually used to reflect the motion artifact when the adaptive noise cancellation technique is implemented to address this obstacle, but it failed to predict the value of real induced noise accurately. In this work, we investigate a new concept that is capable of providing the entire motion artifact separately by recruiting twin photodetectors to formulate the influential signals. The main function of photo-detector (MPD) is to generate the corrupted PPG signal. While the second photo-detector (CPD) that covered up from the light effect, will be used to reflect the corruption effect that exists in both sources simultaneously by counting the generated dark photocurrent (GDPC). To validate the GDPC approach, experiments were executed to analyze the response of two methods during steady and motion state. Results showed resemblance responses for both methods regarding the’ amplitude fluctuations and high positive correlations in the time domain. Furthermore, the FFT peak plots in frequency domain indicated the potential of CPD to reflect all fundamental frequencies caused by motion, unlike the acceleration approach. Therefore, the proposed concept is a sure-fire method to obtain precise measurements at a lower cost.
This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic module inputs (temperature and solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours. In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power. The entire system is implemented in the Matlab / Simulink environment where simulation results obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&O.
Chebyshev filter applied to an inversion technique for breast tumour detectioneSAT Journals
Abstract Microwave imaging has been extensively studied in the past several years as a new technique for early stage breast cancer detection. The rationale of microwave imaging for breast tumour detection is significant contrast in the dielectric properties of normal tissue and malignant tumours. However, in practice noise present from the environments during screening/examination degrades the quality of the image. Inaccurate reconstructed image caused false/misleading interpretation of the image which leads to inappropriate diagnose or treatment to the patient. In the simulation works, noise is added to imitate the actual environment scenario. The two-dimensional (2D) object that identical to breast model is developed using numerical simulation to imitate the breast model. A filter is integrated with an iterative inversion technique for breast tumour detection to eliminate the noise. To assess the effectiveness of this approach, we consider the reconstruction of the electrical parameter profiles of 2D objects from measurements of the transient total electromagnetic field data contaminated with noise. Additive white Gaussian noise is utilized to mimic the effect of random processes that occur in the nature. This paper presents the filter settings and characteristics that affect the reconstruction of the image in order to obtain the most reliable and closer to the actual image. Selection of filter settings or design is important in order to achieve desired signal, most accurate image and provide reliable information of the object. Chebyshev low pass filter is applied in the Forward-Backward Time-Stepping (FBTS) algorithm to filter the noisy data and to improve the quality of reconstructed image. Keywords: Chebyshev low pass filter, microwave imaging and breast tumour detection
Chebyshev filter applied to an inversion technique for breast tumour detectioneSAT Journals
Abstract Respiration Rate is one of the vital signs which require regular monitoring among diseased people. There are a number of medical devices developed to monitor human health condition among them RR monitor is one. The Respiration rate monitor is a device that measures the subject’s respiration rate non-invasively. The objective of the proposed work is to design and develop a low cost Respiration rate monitor for clinical applications. The main parameter to be used is the temperature of respired air i.e. both inspired and expired air. Hence this device uses Thermistor as the source sensor which will provide the temperature feedback of the inspired and expired air. The proposed work uses the ATMEL AT89S52 microprocessor with external ADC0809. The magnitude voltage during the inhalation and exhalation is converted into digital signal using ADC. The further process involves a peak detection technique. The number of peaks obtained in duration of one minute gives the Respiration Rate. The so obtained Respiration Rate is sent to the concerned physician’s cell phone through GSM modem. The device gives an alarm and sends request via SMS if there is tachyopnea and bradyapnea. Keywords: Respiratory Rate, Peak Detection, ADC, GSM, SM, Threshold.
Ensembling techniques in solar panel quality classification IJECEIAES
Solar panel quality inspection is a time consuming and costly task. This study tries to develop as reliable method for evaluating the panels quality by using ensemble technique based on three machine learning models namely logistic regression, support vector machine and artificial neural network. The data in this study came from infrared camera which were captured in dark room. The panels are supplied with direct current (DC) power while the infrared camera is located perpendicular with panel surface. Dataset is divided into four classes where each class represent for a level of damage percentage. The approach is suitable for systems which has limited resources as well as number of training images which is very popular in reality. Result shows that the proposed method performs with the accuracy is higher than 90%.
Extraction of photovoltaic generator parameters through combination of an an...IJECEIAES
In the present work, we propose an improved method based on a combination of an analytical and iterative approach to extract the photovoltaic (PV) module parameters using the measured current-voltage characteristics and the simple diode model. First, we calculate the series resistance using a set of analytical formulas for the base values of the three current-voltage curves. Then, the three other parameters are analytically expressed as functions of serial resistance and ideality factor based on the linear least-squares method. Finally, the ideality factor is calculated applying an iterative algorithm to minimize the normalized root mean square error (NRMSE) value. The proposed method was validated with a real experimental set of two PV generators, which showed the best fit to the I-V curve. Moreover, the proposed method needs only the initial value of the ideality factor.
This paper presents an electromagnetically-actuated micropump for microfluidic application. The system comprises two modules; an electromagnetic actuator module and a diffuser module. Fabrication of the diffuser module can be achieved using photolithography process with a master template and a PDMS prepolymer as the structural material. The actuator module consists of two power inductors and two NdFeB permanent magnets placed between the diffuser elements. The choice of this actuation principle merits from low operating voltage (1.5 Vdc) and the flow direction can be controlled by changing the orientation of the magnet vibration. Maximum volumetric flow rate of the fabricated device at zero backpressure is 0.9756 μLs-1 and 0.4659 μLs-1 at the hydrostatic backpressure of 10 mmH2O at 9 Hz of switching speed.
Comparative analysis of evolutionary-based maximum power point tracking for ...IJECEIAES
The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.
Design of a Selective Filter based on 2D Photonic Crystals Materials IJECEIAES
Two dimensional finite differences temporal domain (2D-FDTD) numerical simulations are performed in cartesian coordinate system to determine the dispersion diagrams of transverse electric (TE) of a two-dimension photonic crystal (PC) with triangular lattice. The aim of this work is to design a filter with maximum spectral response close to the frequency 1.55 μm. To achieve this frequency, selective filters PC are formed by combination of three waveguides W 1 K A wherein the air holes have of different normalized radii respectively r 1 /a=0.44, r 2 /a=0.288 and r /a= 0.3292 (a: is the periodicity of the lattice with value 0.48 μm). Best response is obtained when we insert three small cylindrical cavities (with normalized radius of 0.17) between the two half-planes of photonic crystal strong lateral confinement.
Ecological impact due to the implementation of a modeled and optimized hybri...IJECEIAES
This paper presents a very alarming forecasts about our future and particularly for a medium and long-term future. That is to say, several actions are being carried out by different civil and state parties to deal with these very concrete threats. And it is within this framework that this paper fits, and its objective is to highlight the hybrid systems and more precisely the photovoltaic-wind hybrid systems coupled with storage batteries, as an efficient alternative to the classical means of electricity production. This work will adopt a method of obtaining results called performance evaluation, so this manuscript will present firstly the mathematical model of this hybrid system to best conceive what it is about, then as second part will determine the exact figures of the largest amount of carbon dioxide (CO2) that can be avoided using this technology and following a precise methodology, this can be applied to our situation, i.e., a simple house or to any other type of installation.
NEW SYSTEM OF CHAOTIC SIGNAL GENERATION BASED ON COUPLING COEFFICIENTS APPLI...University of Malaya (UM)
The nonlinear behavior (chaotic) of light traveling in an optical fiber ring resonator such as an add/drop system
is presented. The chaotic behavior is considered to be a beneficial effect that can be used in the communication
system. Such a system can be used to secure the information signals, therefore, the ability of chaotic carriers to synchronize in a communication system is performed. The used optical material is InGaAsP/InP regarding to suitable parameters of the system. The nonlinear refractive index is fixed to n2 = 3.8 × 10−20 m2
/W, and the 20,000 iterations of round-trip within the system is simulated. The input powers are selected at 1 W, where the coupling coefficient of the system varies according to two critical cases, where 0 0.1
and 0.1 1. As a results, larger coupler coefficient corresponds to lower input power for the case of
0 0.1 and smaller coupling coefficient of the system is corresponds to lower input power when
0.1 1. To optimize the microring systems, Lower input power is recommended in many applications in optical optical communication systems.
The fourier transform for satellite image compressioncsandit
The need to transmit or store satellite images is growing rapidly with the development of
modern communications and new imaging systems. The goal of compression is to facilitate the
storage and transmission of large images on the ground with high compression ratios and
minimum distortion. In this work, we present a new coding scheme for satellite images. At first,
the image will be downloaded followed by a fast Fourier transform FFT. The result obtained
after FFT processing undergoes a scalar quantization (SQ). The results obtained after the
quantization phase are encoded using entropy encoding. This approach has been tested on
satellite image and Lena picture. After decompression, the images were reconstructed faithfully
and memory space required for storage has been reduced by more than 80%
OVERVIEW AND APPLICATION OF ENABLING TECHNOLOGIES ORIENTED ON ENERGY ROUTING ...ijaia
Energy routers are recent topics of interest for scientific community working on alternative energy.
Enabling technologies supporting installation and monitoring energy efficiency in building are discussed in
this paper, by focusing the attention on innovative aspects and on approaches to predict risks and failures
conditions of energy router devices. Infrared (IR) Thermography and Augmented Reality (AR) are
indicated in this work as potential technologies for the installation testing and tools for predictive
maintenance of energy networks, while thermal simulation, image post-processing and data mining
improve the analysis of the prediction process. Image post- processing has been applied on thermal images
and for WiFi AR. Concerning data mining we applied k-Means and Artificial Neural Network –ANNobtaining
outputs based on measured data. The paper proposes some tools procedure and methods
supporting the Building Information Modeling- BIM- in smart grid applications. Finally we provide some
ISO standards matching with the enabling technologies by completing the overview of scenario
A novel methodology for time-domain characterization of a full anechoic chamb...IJECEIAES
In this paper we present a novel methodology for time-domain characterization of a full anechoic chamber using the finite integral method. This approach is considered fast, accurate and not intensive for computer resources. The validation of this approach is carried out on CST-microwave studio for a full anechoic chamber intended for antennas measurement applications and electromagnetic exposure evaluation for cellular network. Low, medium and high gain sources are used in this study. The simulations are realized on a personal computer of medium performances (i7 CPU and 16 GB of RAM). The stability and the convergence of our approach are obtained thanks to local mesh and auto-regressive linear filtering techniques. The minimization of the simulation time is based on use of the Huygens sources in the place of the antennas. The maximum error of the chamber as well as the wave depolarization into the chamber are at one with the previous work and the catalogs of the principles chambers manufacturers for the proposed tests in this paper. The Full simulations time is about 15 hours in average.
EFFECTIVE PEEC MODELING OF TRANSMISSION LINES STRUCTURES USING A SELECTIVE ME...EEIJ journal
The transmission lines structures are quite common in the system of electromagnetic compatibility (EMC)
analysis. The increasing complexities of physical structures make electromagnetic modeling an
increasingly tough task, and computational efficiency is desirable. In this paper, a novel selective mesh
approach is presented for partial element equivalent circuit (PEEC) modeling where intense coupling parts
are meshed while the remaining parts are eliminated. With the proposed approach, the meshed ground
plane is dependent on the length and height of the above transmission lines. Relevant compact formulae for
determining mesh boundaries are deduced, and a procedure of general mesh generation is also given. A
numerical example is presented, and a validation check is accomplished, showing that the approach leads
to a significant reduction in unknowns and thus computation time and consumed memories, while
preserving the sufficient precision. This approach is especially useful for modeling the electromagnetic
coupling of transmission lines and reference ground, and it may also be beneficial for other equivalent
circuit modeling techniques.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
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Chebyshev filter applied to an inversion technique for breast tumour detectioneSAT Journals
Abstract Respiration Rate is one of the vital signs which require regular monitoring among diseased people. There are a number of medical devices developed to monitor human health condition among them RR monitor is one. The Respiration rate monitor is a device that measures the subject’s respiration rate non-invasively. The objective of the proposed work is to design and develop a low cost Respiration rate monitor for clinical applications. The main parameter to be used is the temperature of respired air i.e. both inspired and expired air. Hence this device uses Thermistor as the source sensor which will provide the temperature feedback of the inspired and expired air. The proposed work uses the ATMEL AT89S52 microprocessor with external ADC0809. The magnitude voltage during the inhalation and exhalation is converted into digital signal using ADC. The further process involves a peak detection technique. The number of peaks obtained in duration of one minute gives the Respiration Rate. The so obtained Respiration Rate is sent to the concerned physician’s cell phone through GSM modem. The device gives an alarm and sends request via SMS if there is tachyopnea and bradyapnea. Keywords: Respiratory Rate, Peak Detection, ADC, GSM, SM, Threshold.
Ensembling techniques in solar panel quality classification IJECEIAES
Solar panel quality inspection is a time consuming and costly task. This study tries to develop as reliable method for evaluating the panels quality by using ensemble technique based on three machine learning models namely logistic regression, support vector machine and artificial neural network. The data in this study came from infrared camera which were captured in dark room. The panels are supplied with direct current (DC) power while the infrared camera is located perpendicular with panel surface. Dataset is divided into four classes where each class represent for a level of damage percentage. The approach is suitable for systems which has limited resources as well as number of training images which is very popular in reality. Result shows that the proposed method performs with the accuracy is higher than 90%.
Extraction of photovoltaic generator parameters through combination of an an...IJECEIAES
In the present work, we propose an improved method based on a combination of an analytical and iterative approach to extract the photovoltaic (PV) module parameters using the measured current-voltage characteristics and the simple diode model. First, we calculate the series resistance using a set of analytical formulas for the base values of the three current-voltage curves. Then, the three other parameters are analytically expressed as functions of serial resistance and ideality factor based on the linear least-squares method. Finally, the ideality factor is calculated applying an iterative algorithm to minimize the normalized root mean square error (NRMSE) value. The proposed method was validated with a real experimental set of two PV generators, which showed the best fit to the I-V curve. Moreover, the proposed method needs only the initial value of the ideality factor.
This paper presents an electromagnetically-actuated micropump for microfluidic application. The system comprises two modules; an electromagnetic actuator module and a diffuser module. Fabrication of the diffuser module can be achieved using photolithography process with a master template and a PDMS prepolymer as the structural material. The actuator module consists of two power inductors and two NdFeB permanent magnets placed between the diffuser elements. The choice of this actuation principle merits from low operating voltage (1.5 Vdc) and the flow direction can be controlled by changing the orientation of the magnet vibration. Maximum volumetric flow rate of the fabricated device at zero backpressure is 0.9756 μLs-1 and 0.4659 μLs-1 at the hydrostatic backpressure of 10 mmH2O at 9 Hz of switching speed.
Comparative analysis of evolutionary-based maximum power point tracking for ...IJECEIAES
The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.
Design of a Selective Filter based on 2D Photonic Crystals Materials IJECEIAES
Two dimensional finite differences temporal domain (2D-FDTD) numerical simulations are performed in cartesian coordinate system to determine the dispersion diagrams of transverse electric (TE) of a two-dimension photonic crystal (PC) with triangular lattice. The aim of this work is to design a filter with maximum spectral response close to the frequency 1.55 μm. To achieve this frequency, selective filters PC are formed by combination of three waveguides W 1 K A wherein the air holes have of different normalized radii respectively r 1 /a=0.44, r 2 /a=0.288 and r /a= 0.3292 (a: is the periodicity of the lattice with value 0.48 μm). Best response is obtained when we insert three small cylindrical cavities (with normalized radius of 0.17) between the two half-planes of photonic crystal strong lateral confinement.
Ecological impact due to the implementation of a modeled and optimized hybri...IJECEIAES
This paper presents a very alarming forecasts about our future and particularly for a medium and long-term future. That is to say, several actions are being carried out by different civil and state parties to deal with these very concrete threats. And it is within this framework that this paper fits, and its objective is to highlight the hybrid systems and more precisely the photovoltaic-wind hybrid systems coupled with storage batteries, as an efficient alternative to the classical means of electricity production. This work will adopt a method of obtaining results called performance evaluation, so this manuscript will present firstly the mathematical model of this hybrid system to best conceive what it is about, then as second part will determine the exact figures of the largest amount of carbon dioxide (CO2) that can be avoided using this technology and following a precise methodology, this can be applied to our situation, i.e., a simple house or to any other type of installation.
NEW SYSTEM OF CHAOTIC SIGNAL GENERATION BASED ON COUPLING COEFFICIENTS APPLI...University of Malaya (UM)
The nonlinear behavior (chaotic) of light traveling in an optical fiber ring resonator such as an add/drop system
is presented. The chaotic behavior is considered to be a beneficial effect that can be used in the communication
system. Such a system can be used to secure the information signals, therefore, the ability of chaotic carriers to synchronize in a communication system is performed. The used optical material is InGaAsP/InP regarding to suitable parameters of the system. The nonlinear refractive index is fixed to n2 = 3.8 × 10−20 m2
/W, and the 20,000 iterations of round-trip within the system is simulated. The input powers are selected at 1 W, where the coupling coefficient of the system varies according to two critical cases, where 0 0.1
and 0.1 1. As a results, larger coupler coefficient corresponds to lower input power for the case of
0 0.1 and smaller coupling coefficient of the system is corresponds to lower input power when
0.1 1. To optimize the microring systems, Lower input power is recommended in many applications in optical optical communication systems.
The fourier transform for satellite image compressioncsandit
The need to transmit or store satellite images is growing rapidly with the development of
modern communications and new imaging systems. The goal of compression is to facilitate the
storage and transmission of large images on the ground with high compression ratios and
minimum distortion. In this work, we present a new coding scheme for satellite images. At first,
the image will be downloaded followed by a fast Fourier transform FFT. The result obtained
after FFT processing undergoes a scalar quantization (SQ). The results obtained after the
quantization phase are encoded using entropy encoding. This approach has been tested on
satellite image and Lena picture. After decompression, the images were reconstructed faithfully
and memory space required for storage has been reduced by more than 80%
OVERVIEW AND APPLICATION OF ENABLING TECHNOLOGIES ORIENTED ON ENERGY ROUTING ...ijaia
Energy routers are recent topics of interest for scientific community working on alternative energy.
Enabling technologies supporting installation and monitoring energy efficiency in building are discussed in
this paper, by focusing the attention on innovative aspects and on approaches to predict risks and failures
conditions of energy router devices. Infrared (IR) Thermography and Augmented Reality (AR) are
indicated in this work as potential technologies for the installation testing and tools for predictive
maintenance of energy networks, while thermal simulation, image post-processing and data mining
improve the analysis of the prediction process. Image post- processing has been applied on thermal images
and for WiFi AR. Concerning data mining we applied k-Means and Artificial Neural Network –ANNobtaining
outputs based on measured data. The paper proposes some tools procedure and methods
supporting the Building Information Modeling- BIM- in smart grid applications. Finally we provide some
ISO standards matching with the enabling technologies by completing the overview of scenario
A novel methodology for time-domain characterization of a full anechoic chamb...IJECEIAES
In this paper we present a novel methodology for time-domain characterization of a full anechoic chamber using the finite integral method. This approach is considered fast, accurate and not intensive for computer resources. The validation of this approach is carried out on CST-microwave studio for a full anechoic chamber intended for antennas measurement applications and electromagnetic exposure evaluation for cellular network. Low, medium and high gain sources are used in this study. The simulations are realized on a personal computer of medium performances (i7 CPU and 16 GB of RAM). The stability and the convergence of our approach are obtained thanks to local mesh and auto-regressive linear filtering techniques. The minimization of the simulation time is based on use of the Huygens sources in the place of the antennas. The maximum error of the chamber as well as the wave depolarization into the chamber are at one with the previous work and the catalogs of the principles chambers manufacturers for the proposed tests in this paper. The Full simulations time is about 15 hours in average.
EFFECTIVE PEEC MODELING OF TRANSMISSION LINES STRUCTURES USING A SELECTIVE ME...EEIJ journal
The transmission lines structures are quite common in the system of electromagnetic compatibility (EMC)
analysis. The increasing complexities of physical structures make electromagnetic modeling an
increasingly tough task, and computational efficiency is desirable. In this paper, a novel selective mesh
approach is presented for partial element equivalent circuit (PEEC) modeling where intense coupling parts
are meshed while the remaining parts are eliminated. With the proposed approach, the meshed ground
plane is dependent on the length and height of the above transmission lines. Relevant compact formulae for
determining mesh boundaries are deduced, and a procedure of general mesh generation is also given. A
numerical example is presented, and a validation check is accomplished, showing that the approach leads
to a significant reduction in unknowns and thus computation time and consumed memories, while
preserving the sufficient precision. This approach is especially useful for modeling the electromagnetic
coupling of transmission lines and reference ground, and it may also be beneficial for other equivalent
circuit modeling techniques.
Similar to Enhanced image reconstruction of electrical impedance tomography using simultaneous algebraic reconstruction technique and K-means clustering (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
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Application
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Enhanced image reconstruction of electrical impedance tomography using simultaneous algebraic reconstruction technique and K-means clustering
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 3987~3997
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp3987-3997 3987
Journal homepage: http://ijece.iaescore.com
Enhanced image reconstruction of electrical impedance
tomography using simultaneous algebraic reconstruction
technique and K-means clustering
Arfan Eko Fahrudin1,2
, Endarko1
, Khusnul Ain3
, Agus Rubiyanto1
1
Laboratory of Medical Physics and Biophysics, Institut Teknologi Sepuluh Nopember, East Java, Indonesia
2
Physics Study Program, Universitas Lambung Mangkurat, Banjarbaru, Indonesia
3
Department of Physics, Universitas Airlangga, Surabaya, Indonesia
Article Info ABSTRACT
Article history:
Received Nov 21, 2022
Revised Jan 5, 2023
Accepted Feb 4, 2023
Electrical impedance tomography (EIT), as a non-ionizing tomography
method, has been widely used in various fields of application, such as
engineering and medical fields. This study applies an iterative process to
reconstruct EIT images using the simultaneous algebraic reconstruction
technique (SART) algorithm combined with K-means clustering. The
reconstruction started with defining the finite element method (FEM) model
and filtering the measurement data with a Butterworth low-pass filter. The
next step is solving the inverse problem in the EIT case with the SART
algorithm. The results of the SART algorithm approach were classified using
the K-means clustering and thresholding. The reconstruction results were
evaluated with the peak signal noise ratio (PSNR), structural similarity
indices (SSIM), and normalized root mean square error (NRMSE). They
were compared with the one-step gauss-newton (GN) and total variation
regularization based on iteratively reweighted least-squares (TV-IRLS)
methods. The evaluation shows that the average PSNR and SSIM of the
proposed reconstruction method are the highest of the other methods, each
being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest,
which is 0.04. The performance evaluation also shows that the proposed
method is faster than the other methods.
Keywords:
Butterworth low-pass filter
Electrical impedance
tomography
Image reconstruction
K-means clustering
Simultaneous algebraic
reconstruction technique
algorithm
This is an open access article under the CC BY-SA license.
Corresponding Author:
Endarko
Laboratory of Medical Physics and Biophysics, Institut Teknologi Sepuluh Nopember
Kampus ITS-Sukolilo Surabaya, Surabaya-60111, East Java, Indonesia
Email: endarko@physics.its.ac.id
1. INTRODUCTION
In recent years, research on electrical impedance tomography (EIT) has overgrown with applications
in various fields, such as engineering (materials, civil, and chemical) and medical imaging. Usefulness in
engineering, such as [1] for visualizing the distribution of conductivity on carbon nanotube (CNT) composite
layers, for analysis of building moisture conditions [2], and for imaging of chemical engineering process [3].
Meanwhile, some examples of the application of EIT in the medical field include the imaging of stroke
patients [4], imaging of breast cancer detection [5], imaging of lung ventilation [6]. and cardiopulmonary
monitoring [7]. The advantages of EIT being chosen for some of these applications are that EIT is available
in a low-cost system, non-ionizing, and non-invasive to the object being measured or observed [8].
Research on EIT development generally has two topics: hardware and software development.
Research on hardware development, among others, focuses on the design and fabrication of constant current
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sources [9], [10], and there is also a focus on the design and fabrication of the overall EIT system, both based
on microcontrollers [11], microprocessors [12], [13] and also field programmable gate arrays (FPGA) [14].
Research in software development for EIT includes the development of regularization-based reconstruction
algorithms [15]–[18], with the iterative method [19], [20] simulated annealing [21], [22], genetic algorithms
[23] and deep learning [24], [25]. In addition to developing reconstruction algorithms, there are studies on
using digital filters [26] and clustering to support the EIT image reconstruction algorithm [27].
The iterative method is one of the reliable approaches for image reconstruction, such as X-ray and
impedance tomography. Research that uses iterative methods on EIT reconstruction, among others, using
optimal current patterns to distinguish the actual conductivity from the estimated conductivity between each
iteration of the block Kaczmarz algorithm to solve the inverse problem [28] and a modified Landweber
iterative algorithm that is based on an updated sensitivity matrix [29]. The structured sparse representation is
added to the iterative process of the Symkaczmarz algorithm for EIT image reconstruction to enhance the
quality of the reconstruction presented in [30]. In another research, a linear back-projection (ILBP) is
suggested to reduce the presence of artifacts around objects and increase the accuracy of object position and
shape [20]. In [31], EIT reconstruction was conducted based on homotopy perturbation iteration (HPI), which
accelerated with Nesterov’s strategy. While those that use iterative methods on X-ray imaging are
modifications of the simultaneous algebraic reconstruction technique (SART) algorithm with total variation
regularization [32], modification of the SART algorithm with one step late (OSL) technique [33], modified
SART based on the Perona-Malik (PM) model [34], and modified SART algorithm with fast total variation
for positron-emission tomography (PET) imaging [35]. Meanwhile, in another study SART algorithm was
applied to ionospheric tomography [36] and microwave imaging [37].
Based on the advantages of these iterative methods, especially the SART algorithm, this study uses
the iterative SART method as the EIT reconstruction algorithm. The SART algorithm is then combined with
the K-means clustering algorithm and followed by thresholding to improve the quality of the reconstructed
image output. The study was conducted by simulation and experiment using a two-dimensional (2D)
cylindrical phantom with an anomaly object as the output of the reconstructed image.
2. METHOD
In this study, the EIT image reconstruction process was carried out by simulation and experiment
implemented with electrical impedance and diffuse optical reconstruction software (EIDORS) [38] and Air
Tool [39] in MATLAB. The simulation process started by creating the phantom model. The phantom or
object measured for the simulation was a 2D cylinder with 16 electrodes. The finite element method (FEM)
model of the phantom was created with Netgen within EIDORS. Figure 1 shows an example of a phantom
with an anomaly, Figure 1(a) shows it with the show_fem function, and Figure 1(b) displays it in the
show_slices function of EIDORS. The anomaly given to the phantom is in a circle and rectangle form in the
simulation, as shown in Figure 2. The background conductivity of the phantom is 1 S/m, and the anomalous
object has a conductivity of 2.2 and 0.1 S/m. So that the EIT simulation is similar to the actual conditions, in
the simulation process, noise is given to the measurement data with a gaussian noise of 40 dB signal to noise
ratio (SNR). The measurement data of simulation obtained from the electrodes when current is driving into
the phantom. In The EIODRS, type of current driving and voltage measurement method must be set first.
Current driving and voltage measurements are performed using the commonly used adjacent method [40].
The principle of the adjacent method is illustrated in Figure 3, an example of the initial step of the adjacent
method is shown in Figure 3(a), and the following step is in Figure 3(b).
(a) (b)
Figure 1. Phantom with an anomaly (a) displayed with show_fem and (b) displayed with show_slices
3. Int J Elec & Comp Eng ISSN: 2088-8708
Enhanced image reconstruction of electrical impedance tomography using … (Arfan Eko Fahrudin)
3989
Figure 2. Anomalous models (conductivity of models A, B, and E is 2.2 S/m, models C, D, and F is 0.1 S/m)
(a) (b)
Figure 3. Illustration adjacent method in the current (I) driving and voltage (V) measurement: (a) initial step
and (b) second step
The next stage of simulation is the reconstruction process and evaluation with steps as illustrated in
Figure 4. The reconstruction process started with defining of FEM model for reconstruction. This study used
a standard model of FEM on EIDORS with mesh elements of 6,400, as shown in Figure 5. Before the data of
EIT measurement is processed with a reconstruction algorithm, a filtering process is carried out with a
first-order Butterworth low pass filter to reduce noise from the measurement data. The filtered measurement
data is the difference in the voltage read on the phantom when there is an anomaly (inhomogeneous) and no
anomaly (homogeneous). The low pass filter has a normalized cut-off frequency of 0.45.
Figure 4. Steps of the reconstruction process and evaluation
Figure 5. FEM model of the image reconstruction
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In the inverse problem for EIT, by ignoring the high-order terms, the linear approximation of the
EIT model can be expressed in (1) [41],
𝛿𝑈 = 𝐽𝛿𝜎 (1)
where 𝛿𝑈 ∈ 𝑅𝑚𝑥1
is the number m measurement data, 𝛿𝜎 ∈ 𝑅𝑛𝑥1
is the reconstructed image with the number
of n pixels, and 𝐽 ∈ 𝑅𝑚𝑥𝑛
is the reconstruction matrix or sensitivity matrix with dimensions mn,
representing the partial derivative of the voltage associated with conductivity. To solve the linear system
problem of EIT, the SART algorithm, as one of the iterative methods, is used to solve the inversion problem
for image reconstruction. The (1) can be restated as 𝑏 = 𝑎𝑥, where 𝑏 = 𝛿𝑈, 𝑎 = 𝐽, and 𝑥 = 𝛿𝜎, so that the
solution to the linear equation with the SART algorithm becomes (2), with λ (lambda) is a relaxation
parameter satisfying 0<λ<2, and as default in Air Tool λ is set to 1. The application of the SART algorithm
begins by providing an initial value of x0 (usually x0=0) as input and then iterates until the convergent
condition is met. The iteration process of the SART algorithm is expressed in (2) [35].
𝑥𝑗
𝑘+1
= 𝑥𝑗
𝑘
+
𝜆
∑ 𝑎𝑖,𝑗
𝑀
𝑖=1
∑ 𝑎𝑖,𝑗
𝑏𝑖−∑ 𝑎𝑖,𝑙𝑥𝑙
𝑘
𝑀
𝑙=1
∑ 𝑎𝑖,𝑙
𝑀
𝑙=1
𝑀
𝑗=1 (2)
The output of the SART algorithm is then classified using the K-means algorithm, specifically the
K-means++ algorithm, an improved version of K-means. An explanation of the K-means++ clustering
algorithm can be seen in [42]. The SART algorithm output in a 6,4001 matrix that is classified into six
classes, and the centroids of these classes are sorted ascending. The class with the lowest centroid shows the
smallest conductivity; otherwise, the class with the largest centroid has the highest conductivity. After the
classification, the thresholding process is carried out to display only anomalous objects in the reconstructed
image. For anomalies whose conductivity is higher than the background, the thresholding process is carried
out with the maximum value of the matrix, which belongs to the largest centroid class, so that the maximum
value is the object and the background is the minimum value; for smaller conductivities, the opposite applies.
The next step is to reconstruct the image by applying the results of classification and thresholding into the
mesh of the FEM model used.
Furthermore, the reconstruction results from the proposed method are compared with one-step
gauss-newton (GN) [43] and total variation regularization based on iteratively reweighted least-squares
(TV-IRLS) [17]. The implementation of both methods (one-step (GN) and TV-IRLS) is performed with the
inbuilt algorithm of EIDORS. The optimal reconstruction result of these compared methods is achieved by
determining the hyperparameter or regularization parameter with the L-curve method. The parameters used to
evaluate the reconstruction results are peak signal noise ratio (PSNR), structural similarity indices (SSIM),
and normalized root mean square error (NRMSE). PSNR is used as an objective measure of image quality
which is expressed by (3) [44],
𝑃𝑆𝑁𝑅 = 10𝑙𝑜𝑔10 (
𝑝𝑒𝑎𝑘𝑣𝑎𝑙2
𝑀𝑆𝐸
) (3)
where peakval is the maximum pixel value of the reference image (ground truth), and MSE is the mean
square error between the reconstructed image and the reference image. SSIM is a measure of image quality
that represents the similarity of two images, where SSIM is obtained by calculating the components of
luminance (I), contrast (c), and correlation coefficient (s) so that SSIM is defined by [45]:
𝑆𝑆𝐼𝑀(𝑥, 𝑦) = [𝐼(𝑥, 𝑦)]𝛼
[𝑐(𝑥, 𝑦)]𝛽
[𝑠(𝑥, 𝑦)]𝛾
with
𝐼(𝑥, 𝑦) =
2𝜇𝑥𝜇𝑦+𝐶1
𝜇𝑥
2+𝜇𝑦
2+𝐶1
,
𝑐(𝑥, 𝑦) =
2𝜎𝑥𝜎𝑦+𝐶2
𝜎𝑥
2+𝜎𝑦
2+𝐶2
,
𝑠(𝑥, 𝑦) =
𝜎𝑥𝑦+𝐶3
𝜎𝑥𝜎𝑦+𝐶3
5. Int J Elec & Comp Eng ISSN: 2088-8708
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where 𝜇𝑥, 𝜇𝑦, 𝜎𝑥, 𝜎𝑦 and 𝜎𝑥𝑦 are the local mean, standard deviation, and cross-covariance of the image.
Meanwhile, C1, C2 and C3 are constant, with 𝐶1 = (𝐾1𝐿)2
, 𝐶2 = (𝐾2𝐿)2
where 𝐾1 ≪ 1, 𝐾2 ≪ 1, each is a
small constant, and L is the dynamic range of the pixel values. To simplify the SSIM formula, the value of
α=β=γ=1 and C3=C2/2 so that the SSIM formula becomes:
𝑆𝑆𝐼𝑀(𝑥, 𝑦) =
(2𝜇𝑥𝜇𝑦+𝐶1)(2𝜎𝑥𝑦+𝐶2)
(𝜇𝑥
2+𝜇𝑦
2+𝐶1)(𝜎𝑥
2+𝜎𝑦
2+𝐶2)
(4)
Another metric used to evaluate reconstruction results is NRMSE which is defined as (5) [46],
𝑁𝑅𝑀𝑆𝐸 = √∑(𝐼𝑟𝑒𝑓(𝑟) − 𝐼(𝑟))2 ∑(𝐼𝑟𝑒𝑓(𝑟))2
⁄ (5)
where 𝐼𝑟𝑒𝑓(𝑟) denotes reference image and 𝐼(𝑟) is a reconstructed image.
Experimental data on EIT was obtained from a microcontroller-based EIT hardware system with a
design as shown in Figure 6. The measuring object or phantom was a cylinder with a diameter of 14 cm and
16 electrodes with dimensions of 1×2 cm2
. The cylinder is then filled with saline water with a conductivity of
912 μS/cm, as high as 1.5 cm. The anomalous objects are a brass cylinder rod with a diameter of 2.2 cm, a
copper block with a side of 2.5 cm, a painted wooden cylinder with a diameter of 2.7 cm, and a wooden cube
with a side of 2.7 cm. Reconstructing the experimental data has the same steps as reconstruction through
simulation. The reconstruction results are then evaluated qualitatively by comparing the reconstructed image
to the actual condition of the object.
Figure 6. EIT hardware design diagram
3. RESULTS AND DISCUSSION
In this research, an iterative method with the SART algorithm for EIT image reconstruction is used
to detect anomalous objects in cylindrical phantom, which have higher and lower conductivity than
background conductivity. The results of applying the low pass filter with the SART algorithm are shown in
Figure 7. From the figure, it can be seen that there is a reduction in noise from the reconstructed image. This
noise reduction can be seen from the increase in the PSNR value. In addition, visually, it can be seen that the
anomalies in the reconstructed image are more similar in shape to the original image.
A comparison of the reconstruction results of the proposed method with one-step GN and
TV-IRLS from the simulation is shown in Figure 7. Evaluation results with PSNR, SSIM, and NRMSE
reconstruction parameters from all methods indicate that the reconstructed image with the proposed method
results in the more optimal result of the image. This optimal result is shown from the PSNR and SSIM
values, which increase after clustering and thresholding; otherwise, the values of NRMSE decrease. The
average of PSNR and SSIM values of the proposed method are 24.24 and 0.94, respectively, whereas the
highest value of PSNR and SSIM from other methods, meanwhile the average of NRMSE is 0.04, which is
the lowest value from other methods. Comparison of PSNR, SSIM, and NRMSE values from each model for
different methods are also shown in Figures 8 to 10, it is demonstrated that the proposed method has the
highest PSNR and SSIM values and the lowest value of NRMSE. The optimal value of this parameter is due
to the image’s background noise, which can be optimally reduced so that the reconstruction results are close
to the ground truth.
In addition to indicating the reconstructed image quality, the NRMSE value is also related to the
reconstructed image accuracy. The smaller the NRMSE value, the more accurate the resulting reconstructed
image will be. This accuracy can be seen from the shape and position of the reconstructed image. Based on
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the reconstructed image, as shown in Figure 7, the sixth column, it can be seen that the smaller the RMSE
value, the shape and position of the reconstructed image are more accurate and closer to the reference image,
and vice versa.
Figure 7. Reconstruction results through simulation
Ground Truth One-step GN TV-IRLS SART SART+ Filter SART+Filter +
K-means
PSNR:
SSIM:
NRMSE:
22.69
0.44
0.15
23.82
0.45
0.13
24.00
0.47
0.11
24.44
0.48
0.10
27.16
0.94
0.05
PSNR:
SSIM:
NRMSE:
22.92
0.47
0.14
22.83
0.48
0.14
21.87
0.49
0.15
22.30
0.49
0.15
26.09
0.92
0.06
PSNR:
SSIM:
NRMSE:
21.69
0.47
0.09
21.53
0.47
0.09
20.94
0.48
0.09
20.98
0.48
0.09
23.05
0.96
0.03
PSNR:
SSIM:
NRMSE:
18.81
0.45
0.19
18.92
0.48
0.14
18.09
0.49
0.15
18.10
0.49
0.15
19.49
0.91
0.06
PSNR:
SSIM:
NRMSE:
21.96
0.43
0.17
23.32
0.45
0.13
23.47
0.46
0.12
23.99
0.47
0.11
27.16
0.95
0.04
PSNR:
SSIM:
NRMSE:
21.47
0.46
0.09
21.19
0.47
0.09
20.59
0.48
0.09
20.66
0.48
0.09
22.48
0.95
0.03
Average of PSNR:
SSIM:
NRMSE:
21.69
0.46
0.13
21.93
0.47
0.12
21.49
0.48
0.12
21.74
0.48
0.11
24.24
0.94
0.04
7. Int J Elec & Comp Eng ISSN: 2088-8708
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The results of the reconstruction by experiment are shown in Figure 11. Based on the experimental
results, it can be seen that the results of the reconstruction with the SART+filter method obtained results that
were similar to the results of the reconstruction with a simulation, where the resulting image compared with
the actual conditions obtained similar results, both from the shape and position. The reconstructed image shows
better results than the one-step GN and TV-IRLS methods. Likewise, the reconstruction results obtained by
combining the SART algorithm with K-means clustering show significantly better results than the reconstruction
results using other methods, where the reconstruction results clearly show the anomalous objects.
Apart from being observed from the reconstruction results, the proposed method’s performance is
also tested based on the computation time. Table 1 shows the computation time of the three methods used during
the experiment, which were run on a laptop equipped with an Intel(R) Core(TM) i7-6600U CPU and 8 GB of
RAM. The Table 1 shows that the proposed method has a low-cost computation time compared to other
methods, proving that the algorithm is efficient for solving EIT image reconstruction problems.
Figure 8. Comparison of PSNR values for different methods
Figure 9. Comparison of SSIM values for different methods
Figure 10. Comparison of NRMSE values for different methods
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Ground Truth Model One-step GN TV-IRLS SART+Filter SART+Filter+
K-means
A
B
C
D
E
F
Figure 11. Reconstruction results through experiment
Table 1. Time of computation of each method based on the experiment
Method Reconstruction time (s)
Model A Model B Model C Model D Model E Model F
One step GN 4.32 4.38 4.36 4.36 4.18 4.39
TV-IRLS 21.86 23.59 22.02 21.98 24.44 21.87
SART+Filter+K-means 1.80 1.76 1.79 1.86 1.80 1.78
4. CONCLUSION
The reconstructed image from the EIT with the SART algorithm combined with K-means clustering
demonstrated provides a reasonably optimal image of the reconstruction result. This optimal result is shown
from the evaluation results of the reconstructed image through simulation, in which quantitatively obtained
average PSNR and SSIM are the highest compared to other methods; the values are 24.24 and 0.94,
respectively, and the lowest NRMSE with the value 0.04. Meanwhile, referring to the experiment,
qualitatively, the reconstructed image obtained is similar to the image reconstruction using simulation, and
the results are close to the actual conditions of the reconstructed object. In the performance evaluation of the
experiment compared with one-step GN and TV-IRLS, the proposed method is faster than the other methods.
ACKNOWLEDGEMENTS
The author would like to thank the Ministry of Education, Culture, Research, and Technology,
which has provided Domestic Postgraduate Education Scholarships (BPPDN) and gratefully acknowledge
the financial support from the Institut Teknologi Sepuluh Nopember with scheme collaboration research
Number: 1233/PKS/ITS/2021.
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BIOGRAPHIES OF AUTHORS
Arfan Eko Fahrudin received the Bachelor’s degree in Physics (2004) from
Institut Teknologi Sepuluh Nopember (ITS) and a master’s degree in Electrical Engineering
(2010) specializing in neural network and image processing from Universitas Gadjah Mada
(UGM). He is currently reading for a doctorate at Institut Teknologi Sepuluh Nopember (ITS),
Indonesia, funded by BPPDN Scholarships. His interdisciplinary research interests focus on
biophysics, biomedical instrumentation, and image processing. He can be contacted at
arfan_eko@ulm.ac.id.
Endarko received the Bachelor’s degree in Electronics and Instrumentation
(1998) from Universitas Gadjah Mada (UGM), a master’s degree in Physics (2003)
specializing in instrumentation physics from Institut Teknologi Bandung (ITB), and a Ph.D.
degree in Electronic and Electrical Engineering from University of Strathclyde, Glasgow,
UK (2012). Since 1998, he has worked at the Department of Physics, Institut Teknologi
Sepuluh Nopember (ITS) in Surabaya. His interdisciplinary research interests focus on
biophysics, medical physics, and medical instrumentation. He can be contacted at email:
endarko@physics.its.ac.id.
11. Int J Elec & Comp Eng ISSN: 2088-8708
Enhanced image reconstruction of electrical impedance tomography using … (Arfan Eko Fahrudin)
3997
Khusnul Ain received the Bachelor’s degree in Physics (1995) from Universitas
Gadjah Mada (UGM), a master’s degree in Physics specializing in computational physics
(2002) from Universitas Gadjah Mada (UGM), and a Ph.D. degree in Medical Physics from
Institut Teknologi Bandung (ITB) (2014). Since 1997, he has been working at the Department
of Physics, Universitas Airlangga, in Surabaya. His interdisciplinary research interests focus
on medical physics and medical instrumentation. He can be contacted at k_ain@fst.unair.ac.id.
Agus Rubiyanto received the Bachelor’s degree in Physics (1988) from Institut
Teknologi Sepuluh Nopember (ITS), a master’s degree in Optoelectronics and Applied Laser
(1993) from Universitas Indonesia, and a Ph.D. degree in Applied Physics from University of
Paderborn, Germany (2000). Since 1989, he has worked at the Department of Physics,
Institut Teknologi Sepuluh Nopember (ITS) in Surabaya. His interdisciplinary research
interests focus on optoelectronics, medical physics, and biophysics. He can be contacted at
email: arubi@physics.its.ac.id.