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This study examines the effect of low molecular weight acid (LMA), moisture and oxygen on the thermal ageing characteristics of refined, bleached, and deodorised palm oil (RBDPO). The paper moisture was varied between 0.5 and 3.5. The oil was initially subjected to 0.2 g of LMA and 20 mbar of oxygen pressure. The thermal ageing experiment was performed at 120 °C and 140 °C for 28 days. Several dielectric and physiochemical parameters were measured which included dielectric dissipation factor, relative permittivity, resistivity, moisture in oil, acidity, and thermogravimetric analysis (TGA). It is found that LMA and moisture in paper do not affect the relative permittivity of RBDPO and mineral oil (MO). The dielectric dissipation factor of RBDPO and MO reveals slight increment trends within the ageing time. The decrements of resistivities occur after 7 days of ageing for both RBDPO and MO while only RBDPO shows decrement trend of moisture in oil. The ageing patterns of relative permittivities, dielectric dissipation factors and resistivities for RBDPO are similar to MO. The increment of acidity for RBDPO is more apparent that MO throughout the ageing time. All RBDPOs are more resistant to ageing than MO based on the TGA.

This study examines the effect of low molecular weight acid (LMA), moisture and oxygen on the thermal ageing characteristics of refined, bleached, and deodorised palm oil (RBDPO). The paper moisture was varied between 0.5 and 3.5. The oil was initially subjected to 0.2 g of LMA and 20 mbar of oxygen pressure. The thermal ageing experiment was performed at 120 °C and 140 °C for 28 days. Several dielectric and physiochemical parameters were measured which included dielectric dissipation factor, relative permittivity, resistivity, moisture in oil, acidity, and thermogravimetric analysis (TGA). It is found that LMA and moisture in paper do not affect the relative permittivity of RBDPO and mineral oil (MO). The dielectric dissipation factor of RBDPO and MO reveals slight increment trends within the ageing time. The decrements of resistivities occur after 7 days of ageing for both RBDPO and MO while only RBDPO shows decrement trend of moisture in oil. The ageing patterns of relative permittivities, dielectric dissipation factors and resistivities for RBDPO are similar to MO. The increment of acidity for RBDPO is more apparent that MO throughout the ageing time. All RBDPOs are more resistant to ageing than MO based on the TGA.

Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns those traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.

Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system's performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.

Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system's performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.

Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system's performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.

Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system's performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.

Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system's performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.

Observing the vehicles movement becomes an urgent necessity due to exponentially increasing numbers of vehicles in the world. However, to this regard, a good deal of research had been presented to estimate the exact physical position of the vehicle. The major challenges faced vehicle localization systems are large coverage areas required, positioning at diverse environments and positioning during a high-speed movement. However, in this paper, the challenges of employing the vehicle localization techniques, which rely on the propagation signal properties, are discussed. Moreover, a comparison between these techniques, in terms of accuracy, responsiveness, scalability, cost, and complexity, is conducted. The presented positioning technologies are classified into five categories: satellite based, radio frequency based, radio waves based, optical based, and sound based. The discussion shows that, both of satellite-based technology and cellular-based technology are emerge solutions to overcome the challenges of vehicle positioning. Satellite-based can provide a high accurate positioning in open outdoor environment, whereas the cellular-based can provide accurate and reliable vehicle localization in urban environment, it can support non-line of sight (NLOS) positioning and provide large coverage and high data transmission. The paper also shows that, the standalone localization technology still has limitations. Therefore, we discussed how the presented techniques are integrated to improve the positioning performance.

This paper presents a modulation strategy applied to a 13-level three-phase transistor clamped H-bridge (TCHB) inverter, aimed at a renewable and electric vehicle drives application. A comparison is performed between the TCHB inverter and a traditional cascaded H-bridge (CHB) inverter, considering circuit complexity, switching losses, and total harmonic distortion (THD) attained from each multilevel inverter topologies. The TCHB inverter achieves a 13-level output with only 15 switches, whereas the conventional CHB inverter requires 24 switches. The modulation technique, employing a nearest level control, yields improved output quality for both the TCHB and CHB multilevel inverters. The results demonstrate that this strategy effectively minimizes the overall THD. Notably, previous modulation techniques mainly focused on carrier-based PWM or SVPWM, making this approach distinctive. The FFT analysis reveals a voltage THD of 5.49 for TCHB and 5.15 for CHB, indicating a marginal difference in THD content for each multilevel inverter. Despite the CHB inverter experiencing double the switching stress compared to TCHB, since less switches are required in the TCHB inverter, consequently, the system's total cost and complexity are reduced. The achieved results are verified through the use of simulations carried out in the MATLAB Simulink. © 2024, Institute of Advanced Engineering and Science. All rights reserved.

This paper presents a modulation strategy applied to a 13-level three-phase transistor clamped H-bridge (TCHB) inverter, aimed at a renewable and electric vehicle drives application. A comparison is performed between the TCHB inverter and a traditional cascaded H-bridge (CHB) inverter, considering circuit complexity, switching losses, and total harmonic distortion (THD) attained from each multilevel inverter topologies. The TCHB inverter achieves a 13-level output with only 15 switches, whereas the conventional CHB inverter requires 24 switches. The modulation technique, employing a nearest level control, yields improved output quality for both the TCHB and CHB multilevel inverters. The results demonstrate that this strategy effectively minimizes the overall THD. Notably, previous modulation techniques mainly focused on carrier-based PWM or SVPWM, making this approach distinctive. The FFT analysis reveals a voltage THD of 5.49 for TCHB and 5.15 for CHB, indicating a marginal difference in THD content for each multilevel inverter. Despite the CHB inverter experiencing double the switching stress compared to TCHB, since less switches are required in the TCHB inverter, consequently, the system's total cost and complexity are reduced. The achieved results are verified through the use of simulations carried out in the MATLAB Simulink. © 2024, Institute of Advanced Engineering and Science. All rights reserved.

This paper proposes a novel approach to predicting child alimony under Islamic shariah law using a hybrid fuzzy inference system, integrating Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy systems. Machine learning algorithms have become valuable tools for legal decision-making, but judicial process delays can lead to adverse effects. Our model aims to expedite decision-making and minimize legal fees by accurately determining the proper amount of alimony for children after divorce. We collected data from 94 alimony cases and evaluated the model’s performance using accuracy, precision, recall, and F1-score metrics. The hybrid fuzzy system achieved promising results with 69% accuracy, 70% precision, 75% recall and 69% F1 score. Notably, the model reduced bias and standardization in decision-making, promoting fairness. However, the study suggests potential areas for improvement and emphasizes trans-parent judgment processes and coordination among judges in assessing alimony costs based on sufficiency and ma’ruf criteria. This research significantly contributes to machine learning applications in the judicial domain. It provides a valuable decisionmaking tool for judges and lawyers to enhance the judicial process’s efficiency and ensure children’s welfare in divorce cases under Islamic shariah law. Further research can enhance the model’s effectiveness and reliability, opening avenues for continued exploration in this field.

Big number operation has always been a bottleneck to computer system as it imposes high demand on computing power. With a limited power available, operations such as exponentiation and multiplication involving large integer belonging to encryption process requires grave scrutiny. One way to address this issue is by replacing an original complex computation into a sequence of small computations that in the end produces the same results. This paper takes an evolutionary approach to survey numerous articles that have contributed to the advancement of integer representation. Numerous representations were proposed, those that come into play concentrated on reducing non-zero digits and limiting non-zero spacing other than allowing subtraction operation. A comparison was made to distinguish the properties of each method from the others. This detailed outlook can be a guide for identifying the correct representation to be chosen for implementation within specific application.

This paper proposes a novel approach to predicting child alimony under Islamic shariah law using a hybrid fuzzy inference system, integrating Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy systems. Machine learning algorithms have become valuable tools for legal decision-making, but judicial process delays can lead to adverse effects. Our model aims to expedite decision-making and minimize legal fees by accurately determining the proper amount of alimony for children after divorce. We collected data from 94 alimony cases and evaluated the model’s performance using accuracy, precision, recall, and F1-score metrics. The hybrid fuzzy system achieved promising results with 69% accuracy, 70% precision, 75% recall and 69% F1 score. Notably, the model reduced bias and standardization in decision-making, promoting fairness. However, the study suggests potential areas for improvement and emphasizes trans-parent judgment processes and coordination among judges in assessing alimony costs based on sufficiency and ma’ruf criteria. This research significantly contributes to machine learning applications in the judicial domain. It provides a valuable decisionmaking tool for judges and lawyers to enhance the judicial process’s efficiency and ensure children’s welfare in divorce cases under Islamic shariah law. Further research can enhance the model’s effectiveness and reliability, opening avenues for continued exploration in this field.

Big number operation has always been a bottleneck to computer system as it imposes high demand on computing power. With a limited power available, operations such as exponentiation and multiplication involving large integer belonging to encryption process requires grave scrutiny. One way to address this issue is by replacing an original complex computation into a sequence of small computations that in the end produces the same results. This paper takes an evolutionary approach to survey numerous articles that have contributed to the advancement of integer representation. Numerous representations were proposed, those that come into play concentrated on reducing non-zero digits and limiting non-zero spacing other than allowing subtraction operation. A comparison was made to distinguish the properties of each method from the others. This detailed outlook can be a guide for identifying the correct representation to be chosen for implementation within specific application.

Low-back pain (LBP) is a complex health problem requiring accurate diagnosis and effective treatment. However, the current decision support system (DSS) for LBP only considers the patient’s pain intensity and treatment suitability, which may not lead to optimal outcomes. This paper proposes a novel DSS that combines machine learning (ML) and expert input to classify LBP types and provide more reliable and personalized recommendations. We used an open-source dataset to train and test various ML models, including an ensemble model that combines multiple classifiers. We also performed data analysis and feature extraction to enhance the model’s predictive power. We developed a prototype tool to demonstrate the model’s performance and usability. Our results show that the ensemble model achieved the highest accuracy of 92.02%, followed by random forest (RF) (91.01%), multilayer perceptron (MP) (91.01%), and support vector machine (SVM) (87.88%). Our findings suggest that ML can help LBP specialists diagnose and treat LBP more effectively by learning from historical data and predicting LBP categories. Our DSS can potentially improve the quality of life for LBP patients and reduce the burden on the healthcare system.

This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.

The paper presents the description for diagnostic methods of induction motor's stator windings fault. The presented methods use Frequency Response Analysis (FRA) technique for detection of Winding Faults in Induction Motor . This method is previously reliable method for faults diagnosis and detection in many parts of transformers including transformer windings. In this paper, this method was used for motor windings faults detection. This paper presents the FRA response interpretation on internal short circuit (SC) fault at stator winding on three cases studies of different three-phase induction motors (TPIM), were analysed according to two status: healthy induction motor at normal winding status and same motor with windings shorted of main windings. A conclusion of this paper provides the interpretation of and validation the FRA response due to internal SC fault case by using NCEPRI algorithm, which is considered as one of certified statistical indicators. The proposed method in this paper had a useful result for detect and diagnosis of stator windings faults of TPIM. The applications of developed method can be used to detece the other machines types faults.

This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.

The paper presents the description for diagnostic methods of induction motor's stator windings fault. The presented methods use Frequency Response Analysis (FRA) technique for detection of Winding Faults in Induction Motor . This method is previously reliable method for faults diagnosis and detection in many parts of transformers including transformer windings. In this paper, this method was used for motor windings faults detection. This paper presents the FRA response interpretation on internal short circuit (SC) fault at stator winding on three cases studies of different three-phase induction motors (TPIM), were analysed according to two status: healthy induction motor at normal winding status and same motor with windings shorted of main windings. A conclusion of this paper provides the interpretation of and validation the FRA response due to internal SC fault case by using NCEPRI algorithm, which is considered as one of certified statistical indicators. The proposed method in this paper had a useful result for detect and diagnosis of stator windings faults of TPIM. The applications of developed method can be used to detece the other machines types faults.

The diagnosis of wind energy conversion systems (WECS) turns out to be necessary because of their relatively high cost of operation and maintenance. Wind turbines are hard-to-access structures, and they are often located in remote areas. Therefore, a remote diagnosis (e-diagnosis) is required. This paper proposes an alternative approach for the e-diagnosis of a WECS based on the discrete wavelet transform (DWT) and frequency analysis of the aero generator stator currents. To validate this approach, real-time hardware in the loop (HIL) is used to simulate in real-time the mathematical model of the induction generator on the OPAL-RT OP5600 platform to generate the stator currents and the rotor speed. The DWT is applied to the current signal, to generate the DWT signal, which has a huge number of points that are not supported for direct transmission by the Arduino Mega RobotDyn because of its limited sample time. The absolute values of the DWT peak points (MDWT) are sent as point’s packages form to the diagnosis station via the ESP8266 integrated Wi-Fi board of the Arduino Mega RobotDyn to monitor the SCIG states and determine the number of broken bars.

Federated learning (FL) has emerged as one of the most effective solutions to deal with the rapid utilization of internet of things (IoT) in big data markets. Through FL, local data at each IoT device can be trained locally without sharing the local data to the cloud server. However, this conventional FL may still suffer from privacy leakage when the local data are trained, and the trained model is shared to the cloud server to update the global prediction model. This paper proposes a FL framework with privacy awareness to protect data including the trained model for IoT devices. First, a data/model encryption method using fully homomorphic encryption is introduced, aiming at protecting the data/model privacy. Then, the FL framework for the IoT with the encryption method leveraging logistic regression approach is discussed. Experimental results using random datasets show that the proposed framework can obtain higher global model accuracy (up to 4.84%) and lower global model loss (up to 66.4%) compared with other baseline methods.

In this paper, two types of ring-cavity fiber laser structures that operate as a single wavelength laser were investigated on the threshold performance. The two structures are namely Brillouin fiber laser and Brillouin erbium fiber laser. In the first structure, the Brillouin pump signal was amplified before being injected into the laser cavity which namely as Brillouin fiber laser. Meanwhile, for second structure respectively, the Brillouin pump signal was pre-amplified in the laser cavity which namely as Brillouin Erbium fiber laser. We found that the stimulated Brillouin scattering threshold power was lowered significantly by circulating the spontaneous Brillouin scattering in the gain medium utilizing the pre-amplification technique. The optimum stimulated Brillouin scattering threshold power was about 1.4 mW, and this was achieved at optimum output coupling ratio of 95%. By comparing to the first structure in which the Brillouin pump signal was amplified before entering the laser cavity, stimulated Brillouin scattering threshold power was only achieved at 2.62 mW at a similar wavelength. The pre-amplification technique proposed in this paper has been shown to improve the performance of single-wavelength ring-cavity fiber lasers via significant reduction of the stimulated Brillouin scattering threshold power which was around 1.22 mW.

In this paper, two types of ring-cavity fiber laser structures that operate as a single wavelength laser were investigated on the threshold performance. The two structures are namely Brillouin fiber laser and Brillouin erbium fiber laser. In the first structure, the Brillouin pump signal was amplified before being injected into the laser cavity which namely as Brillouin fiber laser. Meanwhile, for second structure respectively, the Brillouin pump signal was pre-amplified in the laser cavity which namely as Brillouin Erbium fiber laser. We found that the stimulated Brillouin scattering threshold power was lowered significantly by circulating the spontaneous Brillouin scattering in the gain medium utilizing the pre-amplification technique. The optimum stimulated Brillouin scattering threshold power was about 1.4 mW, and this was achieved at optimum output coupling ratio of 95%. By comparing to the first structure in which the Brillouin pump signal was amplified before entering the laser cavity, stimulated Brillouin scattering threshold power was only achieved at 2.62 mW at a similar wavelength. The pre-amplification technique proposed in this paper has been shown to improve the performance of single-wavelength ring-cavity fiber lasers via significant reduction of the stimulated Brillouin scattering threshold power which was around 1.22 mW.

Photovoltaic (PV) parameters monitoring is very important for the implementation and optimum utilization of solar energy as the electricity source. This work proposes a design of simple, cost-efficient, and low consumption wireless PV monitoring system enhanced by a driving software for recording the PV system parameters. The circuit is designed with a minimum number of components to manage four types of sensors to measure four parameters that are essential for real-time analysis and prediction of PV performance. The only pair of XBee RF modules as an active component, while others are passive, includes resistors and capacitors for PV current, and voltage those for signal conditioning, temperature and irradiance sensors were also involved. The paper presents a prototype system for high voltage series-connected PV array in the range (100-310) V and 3 ampere as a maximum current, while the sampling frequency can be configured up to 14 sample/Sec in applications of the short period for data logging for the four channels. The proposed system succeeds in providing real-time monitoring with lower cost and can be extended for more functions such as controlling tracking system and failure diagnosis. Photovoltaic (PV) parameters monitoring is very important for the implementation and optimum utilisation of solar energy as electricity source. This work proposes a design of simple, cost-efficient, and low consumption wireless PV monitoring system enhanced by a driving software for recording the PV system parameters. The circuit is designed with a minimum number of components to manage four types of sensors to measure four parameters that are essential for real-time analysis and prediction of PV performance. Only pair of XBee RF modules as an active components, while othera are passive, includes resistors and capacitors for PV current, and voltage those for signal conditioning, temperature and irradiance sensors were also involved. The paper presents a prototype system for high

Photovoltaic (PV) parameters monitoring is very important for the implementation and optimum utilization of solar energy as the electricity source. This work proposes a design of simple, cost-efficient, and low consumption wireless PV monitoring system enhanced by a driving software for recording the PV system parameters. The circuit is designed with a minimum number of components to manage four types of sensors to measure four parameters that are essential for real-time analysis and prediction of PV performance. The only pair of XBee RF modules as an active component, while others are passive, includes resistors and capacitors for PV current, and voltage those for signal conditioning, temperature and irradiance sensors were also involved. The paper presents a prototype system for high voltage series-connected PV array in the range (100-310) V and 3 ampere as a maximum current, while the sampling frequency can be configured up to 14 sample/Sec in applications of the short period for data logging for the four channels. The proposed system succeeds in providing real-time monitoring with lower cost and can be extended for more functions such as controlling tracking system and failure diagnosis. Photovoltaic (PV) parameters monitoring is very important for the implementation and optimum utilisation of solar energy as electricity source. This work proposes a design of simple, cost-efficient, and low consumption wireless PV monitoring system enhanced by a driving software for recording the PV system parameters. The circuit is designed with a minimum number of components to manage four types of sensors to measure four parameters that are essential for real-time analysis and prediction of PV performance. Only pair of XBee RF modules as an active components, while othera are passive, includes resistors and capacitors for PV current, and voltage those for signal conditioning, temperature and irradiance sensors were also involved. The paper presents a prototype system for high

Fast Fourier transform (FFT) processor is a prevailing tool in converting signal in time domain to frequency domain. This paper provides signal-to-noise ratio (SNR) study on 16-point pipelined FFT processor implemented on field-programable gate array (FPGA). This processor can be used in vast digital signal applications such as wireless sensor network, digital video broadcasting and many more. These applications require accuracy in their data communication part, that is why SNR is an important analysis. SNR is a measure of signal strength relative to noise. The measurement is usually in decibles (dB). Previously, SNR studies have been carried out in software simulation, for example in Matlab. However, in this paper, pipelined FFT and SNR modules are developed in hardware form. SNR module is designed in Modelsim using Verilog code before implemented on FPGA board. The SNR module is connected directly to the output of the pipelined FFT module. Three different pipelined FFT with different architectures were studied. The result shows that SNR for radix-8 and R4SDC FFT architecture design are above 40dB, which represent a very excellent signal. SNR module on the FPGA and the SNR results of different pipelined FFT architecture can be consider as the novelty of this paper.

This paper presents development of a simulation to demonstrate a relatively new hybrid approach in improving energy resources that is applicable in automotive industry. The existing hybrid approach in automotive industry is considerably efficient in terms of energy saving by switching between fuel and electricity for energy resources. However, both energy resources confront various challenges. While the electricity resources require recharging, the fuel resources are scarce and expensive. Therefore, in this paper we aim to propose a relatively new hybrid approach, referred to as energy recycling concept equipped with coordination algorithm. To simulate the proposed energy recycling concept, a prototype of Electrical Control Unit (ECU) car is built. Then, an algorithm that coordinates battery charging is developed and integrated with the ECU. Finally, the simulation of the proposed energy recycling concept equipped with the coordination algorithm is evaluated on the prototype of the ECU car. The results show that the proposed energy recycling concept that allows switching between two sources of energy is applicable to operate the ECU car prototype.

This paper implement the antonym procedure in substitution-based approach for linguistic steganography that is expected to be an alternative to the existing substitution approach that using synonym. This method is hidden the message as existing approach, however that is changing the semantic of the stego text from cover text as the medium. This paper proposed the development method is named Antonym Substitution-based that applied based on implementation linguistic steganography in covering the secret message. This approach refines the word choices makes antonym substitution-based approach linguistically is comparable with the previous effort of synonym substitution. Furthermore, this paper also used the verification and validation approaches as the procedure to evaluate performance of this method. The evaluation through verification approach used in order to determine the effectiveness and quality performance. This proposed method is expected has become the new technique implementation of linguistic steganography in process securing secret message that hide in the text medium

This paper reviews the method of classification of the types of images used in data concealment based on the perspective of the researcher’s efforts in the past decade.Therefore, all papers were analyzed and classified according to time periods.The main objective of the study is to infer the best types of images that researchers have discussed and used, several reasons will be shown in this study, which started from 2006 to 2017, through this paper the pros and the cons in the use of favourite types in the concealment of data through previous studies.

Data hiding is a technique used to protect confidential information.The aim of a particular data hiding scheme is to make a more secure and robust method of information exchange so that confidential and private data can be protected against attacks and illegal access. The aim of this paper is to review on different data hiding schemes, covering the decoding, decrypting and extracting schemes.This paper also highlighted three major schemes that are widely used in research and real practice.The discussion include findings on the most recent work on decryption schemes.

Steganography is one of the categories in information hiding that is implemented to conceal the hidden message to ensure it cannot be recognized by human vision. This paper focuses on steganography implementation in text domain namely text steganography.Text steganography consists of two groups, which are word-rule based and feature-based techniques.This paper analysed these two categories of text steganography based on effectiveness and security evaluation because the effectiveness is critically important in order to determine that technique has the appropriate quality.Meanwhile, the security is important due to the intensity performance in securing the hidden message. The main goal of this paper is to review the evaluation of text steganography in terms of effectiveness and security that have been developed by previous research efforts. It is anticipated that this paper will identify the performance of text steganography based on effectiveness and security measurement.

Internet banking is a method for conducting financial transactions online that makes use of the internet as a platform. Customers could transact at any time and from any location. Numerous aspects relating to the adoption of internet banking have been analyzed and studied in recent in-depth studies from the literature. This paper will merge these numerous predefined factors into a model by drawing on the various ideas related to the acceptance model. It was decided to adapt the analytical hierarchy process (AHP) method for finding the important numerous components for the model. The finest mathematical calculation method is AHP, which enables decision-makers to prioritize their ranking in order to satisfy various criteria. The goal of this research is to rank the elements that influence the use of online banking. Three main factors such as technical information, website and service availability were chosen as the main factors of the model based on the literature review. Sub-factors such as ease of use, responsiveness, privacy, reliability, security, communication and efficiency were also suggested and combined into a single integrated framework. Utilizing the systematic literature review (SLR) methodology, several factors were found. As a result, the article will enhance understanding of the unique elements supporting the adoption of internet banking.

Due to the limitations of battery life and capacity, power supply has been the bottleneck for scaling of a wireless sensor network in thousands or millions of nodes. RF energy harvester (RFEH) is a promising solution to power up sensors and wireless devices due to increasing accessibility of RF energy sources, better silicon integration of the harvester circuit and compatibility with wireless networks. One of the significant limitations of RF energy harvester is low power efficiency rectifier where the main function of the micropower rectifier is to convert RF energy into DC energy. To achieve higher power conversion energy (PCE), this paper presents a five-stage charge pump rectifier, with implementation of diode-connected MOS transistors and an auxiliary circuit to produce compensation voltage to the charge pump to achieve higher efficiency over a wide input range. This work is designed and implemented using 130nm CMOS technology and achieved a wide input power range of 15 dBm with efficiency higher than 20%; and at -21 dBm sensitivity for 1V output is achieved while driving 1 MΩ load at 920MHz.

Internet banking is a method for conducting financial transactions online that makes use of the internet as a platform. Customers could transact at any time and from any location. Numerous aspects relating to the adoption of internet banking have been analyzed and studied in recent in-depth studies from the literature. This paper will merge these numerous predefined factors into a model by drawing on the various ideas related to the acceptance model. It was decided to adapt the analytical hierarchy process (AHP) method for finding the important numerous components for the model. The finest mathematical calculation method is AHP, which enables decision-makers to prioritize their ranking in order to satisfy various criteria. The goal of this research is to rank the elements that influence the use of online banking. Three main factors such as technical information, website and service availability were chosen as the main factors of the model based on the literature review. Sub-factors such as ease of use, responsiveness, privacy, reliability, security, communication and efficiency were also suggested and combined into a single integrated framework. Utilizing the systematic literature review (SLR) methodology, several factors were found. As a result, the article will enhance understanding of the unique elements supporting the adoption of internet banking.

Due to the limitations of battery life and capacity, power supply has been the bottleneck for scaling of a wireless sensor network in thousands or millions of nodes. RF energy harvester (RFEH) is a promising solution to power up sensors and wireless devices due to increasing accessibility of RF energy sources, better silicon integration of the harvester circuit and compatibility with wireless networks. One of the significant limitations of RF energy harvester is low power efficiency rectifier where the main function of the micropower rectifier is to convert RF energy into DC energy. To achieve higher power conversion energy (PCE), this paper presents a five-stage charge pump rectifier, with implementation of diode-connected MOS transistors and an auxiliary circuit to produce compensation voltage to the charge pump to achieve higher efficiency over a wide input range. This work is designed and implemented using 130nm CMOS technology and achieved a wide input power range of 15 dBm with efficiency higher than 20%; and at -21 dBm sensitivity for 1V output is achieved while driving 1 MΩ load at 920MHz.

Educational learning settings exploit cognitive factors as ultimate feedback to enhance personalization in teaching and learning. But besides cognition, the emotions of the learner which reflect the affective learning dimension also play an important role in the learning process. The emotions can be recognized by tracking explicit behaviors of the learner like facial or vocal expressions. Despite reasonable efforts to recognize emotions, the research community is currently constraints by two issues, namely : i) the lack of efficient feature descriptors to accurately represent and prospectively recogniz e (detecting) the emotions of the learner ; ii) lack of contextual datasets to benchmark performances of emotion recognizers in the learning - speci fic scenarios, resulting in poor generalizations. This paper presents a facial emotion recognition technique (FERT). The FERT is realized through results of preliminary analysis across various facial feature descriptors. Emotions are classified using the m ultiple kernel learning (MKL) method which reportedly possesses good merits. A contextually relevant simulated learning emotion ( SLE ) dataset is introduced to validate the FERT scheme. Recognition performance of the FERT scheme generalizes to 90.3% on the SLE dataset. On more popular but noncontextually datasets, the scheme achi e ved 90.0% and 82.8% respectively extended Cohn Kanade (CK+) and acted facial expressions in the wild ( AFEW ) datasets. A test for the null hypothesis that there is no significant difference in the performances accuracies of the descriptors rather proved otherwise ( x 2 = 14 . 619 , df = 5 , p = 0 . 01212 ) for a model considered at a 95% confidence interval.

Educational learning settings exploit cognitive factors as ultimate feedback to enhance personalization in teaching and learning. But besides cognition, the emotions of the learner which reflect the affective learning dimension also play an important role in the learning process. The emotions can be recognized by tracking explicit behaviors of the learner like facial or vocal expressions. Despite reasonable efforts to recognize emotions, the research community is currently constraints by two issues, namely : i) the lack of efficient feature descriptors to accurately represent and prospectively recogniz e (detecting) the emotions of the learner ; ii) lack of contextual datasets to benchmark performances of emotion recognizers in the learning - speci fic scenarios, resulting in poor generalizations. This paper presents a facial emotion recognition technique (FERT). The FERT is realized through results of preliminary analysis across various facial feature descriptors. Emotions are classified using the m ultiple kernel learning (MKL) method which reportedly possesses good merits. A contextually relevant simulated learning emotion ( SLE ) dataset is introduced to validate the FERT scheme. Recognition performance of the FERT scheme generalizes to 90.3% on the SLE dataset. On more popular but noncontextually datasets, the scheme achi e ved 90.0% and 82.8% respectively extended Cohn Kanade (CK+) and acted facial expressions in the wild ( AFEW ) datasets. A test for the null hypothesis that there is no significant difference in the performances accuracies of the descriptors rather proved otherwise ( x 2 = 14 . 619 , df = 5 , p = 0 . 01212 ) for a model considered at a 95% confidence interval.

Educational learning settings exploit cognitive factors as ultimate feedback to enhance personalization in teaching and learning. But besides cognition, the emotions of the learner which reflect the affective learning dimension also play an important role in the learning process. The emotions can be recognized by tracking explicit behaviors of the learner like facial or vocal expressions. Despite reasonable efforts to recognize emotions, the research community is currently constraints by two issues, namely : i) the lack of efficient feature descriptors to accurately represent and prospectively recogniz e (detecting) the emotions of the learner ; ii) lack of contextual datasets to benchmark performances of emotion recognizers in the learning - speci fic scenarios, resulting in poor generalizations. This paper presents a facial emotion recognition technique (FERT). The FERT is realized through results of preliminary analysis across various facial feature descriptors. Emotions are classified using the m ultiple kernel learning (MKL) method which reportedly possesses good merits. A contextually relevant simulated learning emotion ( SLE ) dataset is introduced to validate the FERT scheme. Recognition performance of the FERT scheme generalizes to 90.3% on the SLE dataset. On more popular but noncontextually datasets, the scheme achi e ved 90.0% and 82.8% respectively extended Cohn Kanade (CK+) and acted facial expressions in the wild ( AFEW ) datasets. A test for the null hypothesis that there is no significant difference in the performances accuracies of the descriptors rather proved otherwise ( x 2 = 14 . 619 , df = 5 , p = 0 . 01212 ) for a model considered at a 95% confidence interval.

This paper presents a multi-input, multi-output power converter system using a single-phase matrix converter (SPMC) circuit topology. In particular, this technology is of vital importance in floating production such as offshore oil and gas platforms where space is crucial, therefore requiring a reduction in equipment size and weight. The proposed circuit topology only employed a single circuit to perform energy conversion of direct current (DC) to alternating current (AC), DC to DC, AC to DC, and AC to AC operations, thus can reduce the power losses resulting in high power density. As a result, it can promise technological advancement and convergence, hence, support the manufacturing sector transition to industry 4.0, and in line with the United Nation’s sustainable development goals. The proposed converter model will be validated in terms of electrical circuit operations through the computer simulation (MATLAB/Simulink) software.

As well-known, linear motors are widely applied to various industrial applications due to their abilities in providing directly straight movement without auxiliary mechanical transmissions. This paper addresses the sensorless control problem of tubular linear synchronous motors, which belong to a family of permanent magnet linear motor. To be specific, a novel velocity observer is proposed to deal with an unmeasurable velocity problem, and asymptotic convergence of the observer error is ensured. Unlike other studies on sensorless control methods for linear motors, our proposed observer is designed by regrading unknown disturbance load in the tracking control problem whereas considering theoretical demonstrations. By adjusting controller parameters properly, the position and velocity tracking error converge in arbitrary small values. Finally, the effectiveness of the proposed method is verified in two illustrative examples.

The inclusion of a high energy density permanent magnet into magnetic gear improves the machine's torque density. However, it also contributes to eddy current loss, especially in a high-speed application such in electric vehicle. In this paper, the losses from eddy current and iron loss are investigated on concentric magnetic gear (CMG). Torque multiplier CMG is designed with 8/3 gear ratio for this study. Iron loss and eddy current loss are compared and discussed. Based on this study, eddy current loss contributes to almost 96% of the total loss. This finding is hoped to direct the researcher to focus more on reducing loss associated with eddy current loss.

For more than 50 years, transcutaneous electrical stimulation method has been used to cure the spinal cord injury, stroke or cerebral palsy. This method works by activating the excitable nerves, muscle fibers by electrical current stimulation through electrode to skin interface. Electrode to skin interface requires equivalent circuit to overcome the inability of measuring the skin resistivity directly. We have learned several previous models, which are from Lawler, Moineau and Keller and Kuhn. Unfortunately, Moineau model neglects the capacitance effect, while Lawler and Keller and Kuhn include capacitive and resistive nature of skin in their equivalent circuits. Both models consisted of only one parallel RC block. Therefore, this paper presents the simulation results of the proposed equivalent circuit model using two parallel RC circuits. Simulation of the proposed model is conducted in MATLAB 2015a and compared with two previous models using certain parameters. Results show that the proposed model obtained the impedance of 10.830 kΩ when it is simulated using 100Hz frequency, for Lawler model the impedance is 5.340 kΩ and Keller and Kuhn model the impedance obtained is 6.490 kΩ. The proposed model has the refined impedance compared with other models and is expected to deliver better electrical stimulation.

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework. © 2021, Institute of Advanced Engineering and Science. All rights reserved.

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

In diagnosing coronary artery disease, measurement of the cross-sectional area of the lumen, maximum and minimum diameter is very important. Mainly, it will be used to confirm the diagnosing, to predict the stenosis if any and to ensure the size of the stent to be used. However, the measurement only offers by the existing software and some of the software needs human interaction to complete the process. The purpose of this paper is to present the algorithm to measure the region of interest (ROI) on intravascular ultrasound (IVUS) using an image processing technique. The methodology starts with image acquisition process followed by image segmentation. After that, border detection for each ROI was detected and the algorithm was applied to calculate the corresponding region. The result shows that the measurement is accurate and could be used not only for IVUS but applicable to solid circle and unsymmetrical circle shape.

In diagnosing coronary artery disease, measurement of the cross-sectional area of the lumen, maximum and minimum diameter is very important. Mainly, it will be used to confirm the diagnosing, to predict the stenosis if any and to ensure the size of the stent to be used. However, the measurement only offers by the existing software and some of the software needs human interaction to complete the process. The purpose of this paper is to present the algorithm to measure the region of interest (ROI) on intravascular ultrasound (IVUS) using an image processing technique. The methodology starts with image acquisition process followed by image segmentation. After that, border detection for each ROI was detected and the algorithm was applied to calculate the corresponding region. The result shows that the measurement is accurate and could be used not only for IVUS but applicable to solid circle and unsymmetrical circle shape.

Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.