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Mythology Study and Comparison for Quadratic DC-DC Step-up Converters 2 2 Electronic systems reliant on solar sources need DC voltage over 50 volts; hence, the use of converters is essential to satisfy client requirements. Converters modify the output voltage based on the input voltage. Quadratic DC-DC step-up converters are often used to enhance voltage transfer gain and efficiency. This sort of converter circumvents the issues associated with regular cascaded converters. Alongside the primary aims of its use, the researcher must address the practical aspects of the suggested approach, including duty cycle operational range, output voltage fluctuations, reduction of component consumption, cost, and complexity. This article examines and compares quadratic step-up converter topologies from recent years, highlighting researchers' endeavours to attain high voltage transfer gain, regulated output, and efficiency. The comparison results of the high-gain converter are shown (Table 1) to assist in selecting an appropriate high-gain topology for a particular application. Cross-references should be used there. 1 10 2024/03/01 1402/12/11 2025/01/22 1403/11/3 ZAHRAA TALIB ZAHRAA TALIB Department of Electrical and Electronic Engineering, University of Karbala, Holy Karbala, Iraq. Iraq zahraa.t@uokerbala.edu.iq Quadratic boost Quadratic step-up Quadratic boost topologies Boost converter QBC comparison. [1] R. Kadri, J. Gaubert, G. Champenois, and M. Mostefaï. "Performance analysis of transformless single switch quadratic boost converter for grid connected photovoltaic systems", In: Proc. of International Conf. On Electrical Machines-ICEM, Rome, Italy, pp. 1-7, 2010, doi: 10.1109/ICELMACH.2010.5608092.##[2] Z. Talib, A. Meri, M. Nasr, S. Alwash, and M. Dauwed. 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Enhancing Privacy by Large Mask Inpainting and Fusion-Based Segmentation in Street View Imagery 2 2 Protecting privacy in street view imagery is a critical challenge in urban analytics, requiring comprehensive and scalable solutions beyond localized obfuscation techniques such as face or license plate blurring. To address this, we propose a novel framework that automatically detects and removes sensitive objects, such as pedestrians and vehicles, ensuring robust privacy preservation while maintaining the visual integrity of the images. Our approach integrates semantic segmentation with 2D priors and multimodal data from cameras and LiDAR to achieve precise object detection in complex urban scenes. Detected regions are seamlessly filled using a large-mask inpainting technique based on fast Fourier convolutions (FFC), enabling efficient generalization to high-resolution imagery. Evaluated on the SemanticKITTI dataset, our method achieves a mean Intersection over Union (mIoU) of 64.9%, surpassing state-of-the-art benchmarks. Despite its reliance on accurate sensor calibration and multimodal data availability, the proposed framework offers a scalable solution for privacy-sensitive applications such as urban mapping, and virtual tourism, delivering high-quality anonymized imagery with minimal artifacts. 11 28 2024/03/012024/05/27 1403/3/7 2025/01/222025/02/15 1403/11/27 Mahdi Khourishandiz Mahdi Khourishandiz School of Automotive Engineering, Iran University of Science and Technology (IUST), Tehran 16846-13114, Iran. Iran mahdikhoorishandiz@gmail.com Abdollah Amirkhani Abdollah Amirkhani School of Automotive Engineering, Iran University of Science and Technology (IUST), Tehran 16846-13114, Iran. Iran amirkhani@iust.ac.ir Privacy Protection Street View Imagery Large Mask Inpainting Semantic Segmentation Multi-modality Lidar. [1] F. Xu, A. Jin, X. Chen, and G. 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EKV Model Based Analog/RF CMOS Design Pre-SPICE Tool 2 2 A novel simplified EKV model base analog/RF CMOS design pre-SPICE tool is presented in this paper. Addition to facilitating the sizing process, this CAD tool can also optimize circuit characteristics. By having a web address, users can access it without installing any software. Using a graphical and a numerical view, the designer can select degrees of freedom and observe the MOS circuit performance. Through the use of charts versus IC, the graphical view can show tradeoffs in circuit performance in real-time. Charts can be displayed simultaneously in both linear and logarithmic scales. IC CRIT , is also available and can be displayed on the charts. This tool is not limited to one process and it is possible to select different processes. It is efficient for pre-SPICE designs, enhancing intuitive understanding and the designer's experience for future projects while eliminating the need for trial-and-error simulations. Furthermore, the predicted results align well with simulation outcomes, demonstrating the effectiveness of the design and optimization method presented. Two methodologies for selecting optimum ICs are presented by this tool. These are illustrated by the study of linearity indices, AIP3 and IIP3, in one-stage and two-stage differential amplifiers and the design of a single-ended OTA. 29 47 2024/03/012024/05/272024/07/03 1403/4/13 2025/01/222025/02/152025/02/14 1403/11/26 Gholamreza Khademevatan Gholamreza Khademevatan Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. Iran g_khademevatan@sbu.ac.ir ali jalali ali jalali Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. Iran a_jalali@sbu.ac.ir Enz Krummenacher Vittoz Radio Frequency Simulation Program with Integrated Circuit Emphasis Computer-aided design Inversion Coefficient Critical inversion coefficient. Operational Transconductance Amplifier. [1] B. Razavi, “Design of Analog CMOS Integrated Circuit”, 2nd-Edition. McGraw Hill, January 20, 2016.##[2] A.I. Ouali, A. Oualkadi, M. Moussaoui, et al. Design Optimization Methodology Based on IC Parameter for CMOS RF Circuits. AJSE 39, 8935–8946 (2014).##[3] A. G. Girardi, L. C. Severo and P. C. de Aguirre, “Design Techniques for Ultra-Low Voltage Analog Circuits Using CMOS Characteristic Curves: a practical tutorial,” Journal of Integrated Circuits and Systems, vol. 17, NO.1, 2022.##[4] Cao W, Bu H, Vinet M, Cao M, Takagi S, Hwang S, Ghani T, Banerjee K, “The future transistors,”. Nature. 2023 Aug;620(7974):501-515. doi: 10.1038/s41586-023-06145-x. Epub 2023 Aug 16. Erratum in: Nature. 2023 Sep;621(7979): E43. doi: 10.1038/s41586-023-06576-6. PMID: 37587295.##[5] Radamson, Henry H., Yuanhao Miao, Ziwei Zhou, Zhenhua Wu, Zhenzhen Kong, Jianfeng Gao, Hong Yang, Yuhui Ren, Yongkui Zhang, Jiangliu Shi, and et al. 2024. "CMOS Scaling for the 5 nm Node and Beyond: Device, Process and Technology, “Nanomaterials 14, no. 10: 837. https://doi.org/10.3390/nano14100837.##[6] G. Guitton, M. de Souza, A. Mariano and T. Taris,” Design Methodology Based on the Inversion Coefficient and its Application to Inductorless LNA Implementations,” in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 10, pp. 3653-3663, Oct. 2019.##[7] D. Binkley, “Tradeoffs and Optimization in Analog CMOS Design,” 1st ed. New York: Wiley, 2008.##[8] A. Mangla, “Modeling nanoscale quasi-ballistic MOS transistors”, PhD Thesis at EPFL University 2014.##[9] H. Lu, J. W. Kim, D. Esseni and A. Seabaugh,” Continuous semiempirical model for the current-voltage characteristics of tunnel fets,” 2014 15th International Conference on Ultimate Integration on Silicon (ULIS), 2014, pp. 25-28, doi: 10.1109/ULIS.2014.6813897.##[10] M. A. Chalkiadaki, “Characterization and modeling of nanoscale MOSFET for ultralow power RF IC design,” Ph.D. dissertation, EPFL, Switzerland, Dissertation No.7030, 2016.##[11] Enz, C.C., Krummenacher, F. and Vittoz, E.A. An analytical MOS transistor model valid in all regions of operation and dedicated to low-voltage and low-current applications. Analog Integr Circ Sig Process 8, 83–114 (1995). ##[12] C. Enz and E. A. Vittoz, “Charge-Based MOS Transistor Modeling: The EKV Model for Low-Power and RF IC Design,” Hoboken, NJ, USA: Wiley 2006.##[13] C. Enz, F. Chicco and A. Pezzotta,” Nanoscale MOSFET Modeling: Part 1: The Simplified EKV Model for the Design of Low-Power Analog Circuits,” in IEEE Solid-State Circuits Magazine, vol. 9, no. 3, pp. 26-35, Summer 2017.##[14] C. Enz, F. Chicco and A. Pezzotta,” Nanoscale MOSFET Modeling: Part2: Using the Inversion Coefficient as the Primary Design Parameter,” in IEEE Solid-State Circuits Magazine, vol. 9, no. 4, pp. 73-81, Fall 2017.##[15] G. Guitton, “Design Methodologies for multi-mode and multi-standard Low-Noise Amplifiers”, Ph.D. Thesis, Dept. of the Engineering and Computer Science of University of Bordeaux,12 December 2018.##[16] G. Khademevatan and A. Jalali, "Inversion Coefficient as a Key Design Parameter in MOS Device Performance," 2024 32nd International Conference on Electrical Engineering (ICEE), Tehran, Iran, Islamic Republic of, 2024, pp. 1-7, doi: 10.1109/ICEE63041.2024.10668114.##[17] W. Sansen,” Biasing for Zero Distortion: Using the EKVBSIM6 Expressions,” in IEEE Solid-State Circuits Magazine, vol. 10, no. 3, pp. 48-53, Summer 2018, doi: 10.1109/MSSC.2018.2844607.##[18] C. C. Enz and E. A. Vittoz,” CMOS low-power analog circuit design,” Emerging Technologies: Designing Low Power Digital Systems, 1996, pp. 79-133, doi: 10.1109/ETLPDS.1996.508872.##[19] D. M. Binkley, C. E. Hopper, S. D. Tucker, B. C. Moss, J. M. Rochelle and D. P. Foty,” A CAD methodology for optimizing transistor current and sizing in analog CMOS design,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 22, no. 2, pp. 225-237, Feb. 2003, doi: 10.1109/TCAD.2002.806606##[20] W. R. Torres, “An Empirical Methodology for Foundry Specific Submicron CMOS Analog Circuit Design”, Ph.D. Thesis, Dept. of the Engineering and Computer Science University of Florida Atlantic, Boca Raton, Florida, December 2013.##[21] E. Afacan, “Inversion Coefficient Optimization Based Analog/RF Circuit Design Automation”, Microelectronics Journal, vol. 83, pp. 86-93, 2019.##[22] “IC LAB of EPFL University,” epfl.ch/labs/. https://archiveweb.epfl.ch/iclab.epfl.ch/index.html%3Fp=521.html (accessed April. 30, 2024).##[23] K. Singh and P. Jain, “BSIM3v3 to EKV2.6 Model Parameter Extraction and Optimization using LM Algorithm on 0.18um Technology node”, Intl Journal of Electronics and Telecommunications, 2018, vol. 64, no. 1, pp. 5–11.##[24] E. Afacan, “Inversion Coefficient Optimization Based Analog/RF Circuit Design Automation”, Microelectronics Journal, vol. 83, pp. 86-93, 2019. 1916-1932, Sept. 2007.##[25] E. Afacan and G. Dundar, “Inversion Coefficient Optimization Assisted Analog Circuit Sizing Tool”, 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), Giardini Naxos, Italy, 12-15 JUNE 2017.##[26] W. Sansen,” Minimum Power in Analog Amplifying Blocks: Presenting a Design Procedure,” in IEEE Solid-State Circuits Magazine, vol. 7, no. 4, pp. 83-89, Fall 2015, doi: 10.1109/MSSC.2015.2474237.##[27] B. Razavi, “RF Microelectronics”, Prentice Hall Communications Engineering and Emerging Technologies, 2nd-Edition. 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Calculation and Analysis of the Electric Field of the OIP Bushings under Internal Humidity and Surface Polluted Conditions using FEM 2 2 Bushings are one of the most important components of electrical equipment such as power transformers, reactors, capacitors. Most of the installed bushings have Oil-Immersed Paper (OIP) insulation structure. Bushing failure is caused by various reasons such as poor manufacturing process, overloading and also poor installation process, but moisture ingress is one of the main reasons of OIP bushing defect during its operation. In this paper, the electric field distribution of OIP bushings in multiple situations are simulated and effects of moisture distribution are analyzed. The simulations are stablished in polluted and clean surfaces of the studied bushing and done by COMSOL Multiphysics Software. The results show that non-uniform moisture distribution has a significant effect on electric fields of OIP insulation. This effect strongly increases with increasing the pollution on the external insulator of the bushing. 48 59 2024/03/012024/05/272024/07/032024/07/09 1403/4/19 2025/01/222025/02/152025/02/142025/03/17 1403/12/27 Mohammad Abouhosseini Darzi Mohammad Abouhosseini Darzi Department of Electrical & Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran. Iran md.abouhosseini@stu.nit.ac.ir Mohammad Mirzaie Mohammad Mirzaie Department of Electrical & Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran. Iran mirzaie@nit.ac.ir Amir Abbas Shayegani Akmal Amir Abbas Shayegani Akmal Department of Electrical Engineering, University of Tehran, Tehran, Iran. Iran shayegani@ut.ac.ir Ebrahim Rahimpour Ebrahim Rahimpour Institute of Electrical Power Engineering and High Voltage Technology, THWS, Schweinfurt, Germany. Germany ebrahim.rahimpour@thws.de Finite Element Method (FEM) Moisture OIP Bushing Pollution Transformer [1] A. Petersen, “The Risk of transformer fires and strategies which can be applied to reduce the risk,” in CIGRE Session, Paris, 2010. ##[2] W. Youyuan, “Study of the impact of initial moisture content in oil impregnated insulation paper on thermal aging rate of condenser bushing,” Energies, vol. 8, p. 14298–1431, 2015. ##[3] E. Kuffel, High Voltage Engineering Fundamentals, Second edition, 2000. ##[4] L. Zhou, W. Liao and S. Wang, “A high-precision diagnosis method for damp status of OIP bushing,” IEEE Trans. on Instrumentation and Measurement, vol. 70, pp. 1-10, 2021. ##[5] A. Mikulecky and Z. Štih, “Influence of temperature, moisture content and ageing on oil impregnated paper bushings insulation,” IEEE Trans. on Dielectrics and Electrical Insulation, vol. 20, no. 4, pp. 1421-1427, Aug. 2013.##[6] L. Zhou, G. Wu and J. Liu, “Modeling of transient moisture equilibrium in oil-paper insulation,” IEEE Trans. on Dielectrics & Electrical Insulation, vol. 15, no. 3, pp. 872-878, Jun. 2008.##[7] M. Poljak, “Electric field at sharp edge as a criterion for dimensioning condenser-type insulation systems,” Electric Power Systems Research, vol. 152, p. 485–492, 2017. ##[8] H. Yao and e. al., “Evaluation method for moisture content of oil-paper insulation based on segmented frequency domain spectroscopy: From curve fitting to machine learning,” IET Sci. Meas. Technical, vol. 15, pp. 517-526, 2021. ##[9] Q. Dai, Y. Lio and G. Cheng, “Frequency domain spectroscopy for non-uniformly distributed moisture detection in transformer bushings,” IEEE Access, vol. 8, pp. 210429-210434, 2020. ##[10] D. Garcia, “A review of moisture diffusion coefficients in transformer solid,” Electrical Insulation Magazine, vol. 29, pp. 46-54, 2013. ##[11] D. Wang and L.Zhou, “Simulation for transient moisture distribution and effects on the electric field in stable condition: 110kV oil-immersed insulation paper bushing,” IEEE Access, vol. 7, pp. 162991-163002, 2019.##[12] D. Wang and L.Zhou, “Moisture estimation for oil-immersed bushing based on FDS method- field application,” IET Gener. Transm. Distrib., vol. 12, no. 11, pp. 2762-2769, 2018. ##[13] H. Yao, H. Mu, N. Ding, D. Zhang and Z. Liang, “Evaluation method for moisture content of oil-paper insulation based on segmented frequency domain spectroscopy: From curve fitting to machine learning,” IET Science, Measurement & Technology, vol. 15, no. 6, pp. 517-526, 2021.##[14] M. A. Salam, H. Ahmad and T. Tamsir, “Calculation of time to flashover of contaminated insulator by Dimensional Analysis technique,” Computer and Electrical Engineering, vol. 27, no. 6, pp. 419-427, 2001. ##[15] A. Arshad, S. Nekahi, G. McMeekin and M. Farzaneh, “Numerical computation of electric field and potential along silicone rubber insulators under contaminated and dry band conditions,” 3D Research, vol. 7, pp. 1-10, 2016.##[16] J. Zhao, “Effect of external insulation environment on electric field distribution of 500kV porcelain bushing,” in 3rd IEEE conference on Energy Internet and Energy System Integration, Changsha, China, 2019.##[17] E. Akbari, M. Mirzaie, M. Asadpoor and A. Rahimnejad, “Effects of disc insulator type and corona ring on electric field and voltage distribution over 230-kV insulator string by numerical method,” IJEEE, vol. 9, pp. 58-66, 2013. ##[18] B. Qi, Q. Dai and C. Li, “The mechanism and diagnosis of insulation deterioration caused by moisture ingress into oil-impregnated paper bushing,” Energies, vol. 11, 2018. ##[19] C. Ekanayake and S. Gubanski, “Frequency response of oil impregnated pressboard and paper samples for estimating moisture in transformer insulation,” IEEE Trans. on Power Delivery, vol. 21, no. 3, pp. 1309-1317, 2006. ##[20] D. Wang and L.Zhou, “Moisture estimation for oil‐immersed bushing based on FDS method at a reference,” IET Gener. Transm. Distrib., vol. 12, no. 10, pp. 2480-2486, 2018. ##[21] Z. Fazarinc, “Computation of electric fields from electric charges: Coulomb's law or poisson's equation,” Comput. Appl. Eng. Educ., no. 1, pp. 445-454, 1993. ##[22] Selection and dimensioning of high-voltage insulators intended for use in polluted conditions- Part1: Definitions, information and general principles, IEC Tech. Spec. 60815-1, 2008. ##[23] J. Rasolonjanahary, L. Kräenbühl and A. Nicolas, “Computation of electric fields and potential on polluted insulators using a boundary element method,” IEEE Trans. on Magnetics, vol. 28, no. 2, pp. 1473-1476, 1992. ##[24] E. Asenjo, N. Morales and A. Valdenegro, “Solution of low frequency complex fields in polluted insulators by means of the finite element method,” IEEE Trans. On Dielectric and Electrical Insulation, vol. 4, pp. 10-16, 1997. ##[25] L. Yang, Z. Kuang and e. al, “Study on surface rainwater and arc characteristics of high-voltage bushing with booster sheds under heavy rainfall,” IEEE Access, vol. 8, pp. 146865-146875, 2020. ##[26] M. Akbari, M. Allahbakhshi and R. Mahmoodia, “Heat analysis of the power transformer bushings in the transient and steady states considering the load variations,” Applied Thermal Eng., vol. 121, pp. 999-1010, 2017. ##[27] J. Araya, “Electric field distribution and leakage currents in glass insulator under different altitudes and pollutions conditions using FEM simulations,” IEEE Latin America Transactions, vol. 19, no. 8, pp. 278-1285, 2021.## ##

BIMLP Model Based on Deep Learning for Predicting Electrical Load Demand 2 2 The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. However, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. To address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. Furthermore, the proposed system, BIMLP (Bagging-Improved MLP), is designed to iteratively improve its parameters using a binary search algorithm and reduce the learning error using bagging, a technique for ensemble learning. The proposed system was applied to the Electric Power Consumption data set and achieved a value of 0.9734 in the r2 criterion. The results of implementing and evaluating the proposed system demonstrate its satisfactory performance compared to existing techniques. 60 73 2024/03/012024/05/272024/07/032024/07/092024/07/21 1403/4/31 2025/01/222025/02/152025/02/142025/03/172025/01/01 1403/10/12 Somayeh Talebzadeh Somayeh Talebzadeh Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran. Iran talebzadeh7@gmail.com Reza Radfar Reza Radfar Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran r.radfar@srbiau.ac.ir Abbas Toloei Ashlaghi Abbas Toloei Ashlaghi Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran toloie@gmail.com MLP Bagging Regression Electrical load demand [1] A. A. Ibrahim, and K. M. A. Elzaridi, "Xgboost algorithm for orecasting electricity consumption of germany," AURUM Journal of Engineering Systems and Architecture, vol. 7, no. 1, pp. 99-108, 2023.##[2] L. Guo, L. Wang, and H. Chen, "Electrical load forecasting based on LSTM neural networks," in 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019), 2019: Atlantis Press, pp. 107-111. ##[3] R. F. Engle, C. Mustafa, and J. Rice, "Modelling peak electricity demand," Journal of forecasting, vol. 11, no. 3, pp. 241-251, 1992.##[4] I. Ghalehkhondabi, E. Ardjmand, G. R. Weckman, and W. A. Young, "An overview of energy demand forecasting methods published in 2005–2015," Energy Systems, vol. 8, pp. 411-447, 2017.##[5] A. Mosavi, S. Faizollahzadeh Ardabili, and S. Shamshirband, "Demand prediction with machine learning models: State of the art and a systematic review of advances," 2019.##[6] T.-Y. Kim and S.-B. Cho, "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, vol. 182, pp. 72-81, 2019.##[7] T. Le, M. T. Vo, B. Vo, E. Hwang, S. Rho, and S. W. Baik, "Improving electric energy consumption prediction using CNN and Bi-LSTM," Applied Sciences, vol. 9, no. 20, p. 4237, 2019.##[8] W. Yucong and W. Bo, "Research on EA-xgboost hybrid model for building energy prediction," in Journal of Physics: Conference Series, 2020, vol. 1518, no. 1: IOP Publishing, p. 012082. ##[9] F. U. M. Ullah, A. Ullah, I. U. Haq, S. Rho, and S. W. Baik, "Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks," IEEE Access, vol. 8, pp. 123369-123380, 2019.##[10] J. Huang, M. Algahtani, and S. Kaewunruen, "Energy forecasting in a public building: a benchmarking analysis on long short-term memory (LSTM), support vector regression (SVR), and extreme gradient boosting (XGBoost) networks," Applied Sciences, vol. 12, no. 19, p. 9788, 2022.##[11] M. Abumohsen, A. Y. Owda, and M. Owda, "Electrical Load Forecasting Based on Random Forest, XGBoost, and Linear Regression Algorithms," in 2023 International Conference on Information Technology (ICIT), 2023: IEEE, pp. 25-31. ##[12] X. Luo, L. O. Oyedele, A. O. Ajayi, O. O. Akinade, H. A. Owolabi, and A. Ahmed, "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, vol. 131, p. 109980, 2020.##[13] S. Ghimire, T. Nguyen-Huy, M. S. AL-Musaylh, R. C. Deo, D. Casillas-Pérez, and S. Salcedo-Sanz, "A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction," Energy, vol. 275, p. 127430, 2023.##[14] L. Cao, Y. Li, J. Zhang, Y. Jiang, Y. Han, and J. Wei, "Electrical load prediction of healthcare buildings through single and ensemble learning," Energy Reports, vol. 6, pp. 2751-2767, 2020.##[15] M. Massaoudi, S. S. Refaat, I. Chihi, M. Trabelsi, F. S. Oueslati, and H. Abu-Rub, "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, vol. 214, p. 118874, 2021.##[16] A. Ghasempour and M. Martínez-Ramón, "Electric load forecasting using multiple output gaussian processes and multiple kernel learning," in 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA), 2023: IEEE, pp. 1-6. ##[17] S. Tsegaye, P. Sanjeevikumar, L. B. Tjernberg, and K. A. Fante, "Short term load forecasting of electrical power distribution system using enhanced deep neural networks (DNNs)," IEEE Access, 2024.##[18] Electric Power Consumption. [Online]. Available: https://www.kaggle.com/datasets/fedesoriano/electric-power-consumption##[19] N. Kshetrimayum, K. R. Singh, and N. Hoque, "PConvLSTM: an effective parallel ConvLSTM-based model for short-term electricity load forecasting," International Journal of Data Science and Analytics, pp. 1-18, 2024.## ##

An Experimental Investigation of the Bluetooth Smart for Use in Wearable Home-Care Monitoring Systems 2 2 As the demand for continuous online remote monitoring of patients grows, the energy consumption of wearable home-care monitoring systems (WHMSs) requires careful evaluation. Selecting the right communication protocol therefore is crucial to minimize energy usage and extend device lifecycles. Recent versions of Bluetooth Smart (IEEE 802.15.1 are promising for WHMSs, offering low energy consumption and extended coverage range. However, their energy consumption in WHMSs remains underexplored. This paper investigates the energy consumption and maximum coverage range of Bluetooth V4.2, V5/1MB and V5/2MB in various home-care environments. We propose a software and hardware-based energy monitoring framework to practically measure the energy consumption of the protocols, conducting extensive experiments in typical home scenarios with obstacles like kitchen cabinets, brick walls, and the human body. Our results show similar power consumption for BLE v4.2 and BLE v5 modules, but the BLE v5/2MB has lower energy usage than BLE v5/1MB due to faster transmission. Additionally, obstacles significantly impact energy consumption and range, with BLE v5/1MB achieving a maximum range of 108m in line-of-sight conditions, which drops to 45m and 29m with brick walls and human bodies, respectively. Finally, the BLE v5/2MB effective range in all experimental scenarios is about 80% of BLE v5/1MB. 74 89 2024/03/012024/05/272024/07/032024/07/092024/07/212024/07/29 1403/5/8 2025/01/222025/02/152025/02/142025/03/172025/01/012025/02/24 1403/12/6 Nasibeh Heshmati Moulaei Nasibeh Heshmati Moulaei School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. Iran n_heshmati@comp.iust.ac.ir Seyed Ali Seyedalian Seyed Ali Seyedalian School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. Iran seyed_ali@comp.iust.ac.ir Alireza Sinaee Oskouie Alireza Sinaee Oskouie School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. Iran sinayi.alireza@gmail.com Eisa Zarepour Eisa Zarepour School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. Iran zarepour@iust.ac.ir Bluetooth Low Energy Energy Consumption Analysis Wearable Sensors Internet of Things Remote Health Monitoring. [1] H. M. Kaidi, M. A. M. Izhar, R. A. Dziyauddin, N. E. Shaiful and R. Ahmad, A Comprehensive Review on Wireless Healthcare Monitoring: System Components, IEEE Access, vol. 12, pp. 35008-35032, 2024.##[2] P. C. Ng, J. She and P. Spachos, Energy-Efficient Overlay Protocol for BLE Beacon-Based Mesh Network, IEEE Transactions on Mobile Computing, vol. 22, no. 5, pp. 2709-2724, 1 May 2023.##[3] P. C. Ng and J. She, Remote Proximity Sensing With a Novel Q-Learning in Bluetooth Low Energy Network, IEEE Transactions on Wireless Communications, vol. 21, no. 8, pp. 6156-6166, 2022.##[4] D. Marco, P. Park, M. Pratesi and F. Santucci, A Bluetooth-Based Architecture for Contact Tracing in Healthcare Facilities, Journal of Sensor and Actuator Networks, vol. 10, no. 1, 2021.##[5] T. Rault, A. Bouabdallah, Y. Challal, and F. Marin. A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications. Pervasive and Mobile Computing, 37:23 – 44, 2017.##[6] N. H. Molaei, S. Ali Seyedalian, A. S. Oskouie and E. Zarepour, Characterizing The Energy Consumption and Maximum Coverage of 802.15.1 V4.2 for Wearable Home-care Monitoring Systems, 2020 25th International Computer Conference, Computer Society of Iran (CSICC), Tehran, Iran, 2020, pp. 1-9,##[7] B. Karthik, L. D. Parameswari, R. Harshini, and A. Akshaya. Survey on iot & arduino based patient health monitoring system. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3(1):1414–1417, 2018.##[8] S. Basu, S. Saha, S. Pandit, and S. Barman (Mandal). Smart health monitoring system for temperature, blood oxygen saturation, and heart rate sensing with embedded processing and transmission using iot platform. In A. K. Das, J. Nayak, B. Naik, S. K. Pati, and D. Pelusi, editors, Computational Intelligence in Pattern Recognition, pages 81–91, Singapore, 2020. Springer Singapore.##[9] H. Mshali, T. Lemlouma, M. Moloney, and D. Magoni. A survey on health monitoring systems for health smart homes. International Journal of Industrial Ergonomics, 66:26 – 56, 2018.##[10] I. G. R. Sri, S. Konduru, P. Madiraju, J. B. Mahitha, and V. K. Rao. IOT based health monitoring system. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pages 501–504, Mar. 2019.##[11] S. Raza, P. Misra, Z. He, and T. Voigt. Bluetooth smart: An enabling technology for the internet of things. In 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pages 155–162, Oct 2015.##[12] M. Collotta, G. Pau, T. Talty, and O. K. Tonguz. Bluetooth 5: A concrete step forward toward the iot. IEEE Communications Magazine, 56(7):125–131, July 2018.##[13] P. P. Ray and S. Agarwal. Bluetooth 5 and internet of things: Potential and architecture. In 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pages 1461– 1465, Oct 2016.##[15] T. T. Habte, H. Saleh, B. Mohammad, and M. Ismail. Ultra Low Power ECG Processing System for IoT Devices (Analog Circuits and Signal Processing). Springer, 2018.##[16] H. Mshali, T. Lemlouma, M. Moloney, and D. Magoni. A survey on health monitoring systems for health smart homes. International Journal of Industrial Ergonomics, 66:26 – 56, 2018.##[17] T. Rault, A. Bouabdallah, Y. Challal, and F. Marin. A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications. Pervasive and Mobile Computing, 37:23 – 44, 2017.##[18] T. Salman and R. Jain. A survey of protocols and standards for internet of things. arXiv preprint arXiv:1903.11549, 2019.##[19] F.J. Dian, A. Yousefi, S. Lim. A practical study on Bluetooth Low Energy (BLE) throughput. 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Efficient Tactile Perception in Robotics: Reducing Data Redundancy through Compression and Normalization in Spiking Graph Convolutional Networks 2 2 Touch, one of the fundamental human senses, is essential for understanding the environment by enabling object identification and stable movements. This ability has inspired significant advancements in artificial neural networks for object recognition, texture identification, and slip detection applications. However, despite their remarkable capacity to simulate tactile perception, artificial neural networks consume considerable energy, limiting their broader adoption. Recent developments in electronic skin technology have brought robots closer to achieving human-like tactile perception by enabling asynchronous responses to temperature and pressure changes, thereby enhancing robotic precision in tasks like object manipulation and grasping. This research presents a Spiking Graph Convolutional Network (SGCN) designed for processing tactile data in object recognition tasks. The model addresses the redundancy in spiking-format input data by employing two key techniques: (1) data compression to reduce the input size and (2) batch normalization to standardize the data. Experimental results demonstrated a 93.75% accuracy on the EvTouch-Objects dataset, reflecting a 4.31% improvement, and a 78.33% accuracy on the EvTouch-Containers dataset, representing an 18% improvement. These results underscore the SGCN's effectiveness in reducing data redundancy, decreasing required time steps, and optimizing tactile data processing to enhance robotic performance in object recognition. 90 101 2024/03/012024/05/272024/07/032024/07/092024/07/212024/07/292024/09/16 1403/6/26 2025/01/222025/02/152025/02/142025/03/172025/01/012025/02/242025/01/13 1403/10/24 Elahe Rezaee Ahvanooii Elahe Rezaee Ahvanooii Electrical and Computer Engineering Department, Semnan University, Semnan, Iran Iran e_rezaee@semann.ac.ir Sheis Abolmaali Sheis Abolmaali Electrical and Computer Engineering Department, Semnan University, Semnan, Iran Iran shabolmaali@semnan.ac.ir Tactile Perception Graph Convolutional Network Spiking Neural Network Redundancy Reduction Batch Normalization. [1] Z. Yi, Y. Zhang, and J. Peters. "Bioinspired tactile sensor for surface roughness discrimination," Sensors and Actuators A: Physical, vol. 255, pp. 46-53, 2017. ##[2] M. Alameh et al., "Smart Tactile Sensing Systems Based on Embedded CNN Implementations," Micromachines, vol. 11, p. 103, 2020. ##[3] N. Funk et al., "Evetac: An event-based optical tactile sensor for robotic manipulation," IEEE Transactions on Robotics, vol. 40, pp. 3812-3832, 2024.##[4] L. Huaping, and F. Sun, Robotic tactile perception and understanding. Berlin, Germany: Springer, 2018.##[5] J. W. Fastier-Wooller et al., "Pressure and temperature sensitive e-skin for in situ robotic applications," Materials & Design, vol. 208, p. 109886, 2021. ##[6] F. Gu et al., "TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition," in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), IEEE, 2020, pp. 9876-9882. ##[7] W. A. Friedl, and M. A. Roa., "Experimental evaluation of tactile sensors for compliant robotic hands," Frontiers in Robotics and AI, vol. 8, p. 704416, 2021. ##[8] T. Taunyazoz et al., “Event-driven visual-tactile sensing and learning for robots,” in Proceedings of Robotics: Science and Systems, July 2020. ##[9] K. Ganguly et al., "Gradtac: Spatio-temporal gradient based tactile sensing," Frontiers in Robotics and AI, vol. 9, p. 898075, 2022. ##[10] S. B. Shrestha and G. Orchard, “Slayer: Spike layer error reassignment in time,” in Advances in Neural Information Processing Systems, 2018, pp. 1412–1421. ##[11] S. Snyder et al., "Transductive spiking graph neural networks for loihi," in Proceedings of the Great Lakes Symposium on VLSI 2024, ACM, 2024, pp. 608-613. ##[12] T. J. Pannen, S. Puhlmann, and O. Brock, "A low-cost, easy-to-manufacture, flexible, multi-taxel tactile sensor and its application to in-hand object recognition," In 2022 International Conference on Robotics and Automation (ICRA), IEEE, 2022, pp. 10939-10944. ##[13] J. Tegin, and J. Wikander, "Tactile sensing in intelligent robotic manipulation–a review," Industrial Robot: An International Journal, vol. 32, no. 1, pp. 64-70, 2005.##[14] M. M. Kamel, A. H. M. El-Sayed, M. I. Awad, and G. A. Abou-Elmagd, "TACTILE OBJECT RECOGNITION ROBOTIC SYSTEM USING SUPERVISED MACHINE LEARNING," Journal of Advanced Engineering Trends, vol. 42, no. 2, pp. 87-99, 2023. ##[15] U. A. Bhatti et al., "Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence." International Journal of Intelligent Systems, vol. 2023, no. 1, p. 8342104, 2023. ##[16] T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks," arXiv preprint arXiv:1609.02907, 2016. ##[17] M. Defferrard et al., "Convolutional neural networks on graphs with fast localized spectral filtering," in Advances in Neural Information Processing Systems, vol. 29, 2016, pp. 3844–3852. ##[18] A. H. Naghshbandy, K. Naderi, and U. D. Annakkage. "A Laplacian Approach to Locate Source of Forced Oscillations Under Resonance Conditions Based on Energy-Driven Multilateral Interactive Pattern," Iranian Journal of Electrical & Electronic Engineering, vol. 18, no. 3, 2022. ##[19] S. Zhang, H. Tong, J. Xu, and R. Maciejewski. "Graph convolutional networks: a comprehensive review." Computational Social Networks, vol. 6, no. 1, pp. 1-23, 2019. ##[20] Y. Wang, Y. Liu, and H. Zhao. "FL-SGCN: federated learning on spiking graph convolutional networks." in Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), vol. 12594, SPIE, 2023, pp. 673-678. ##[21] Z. Zhu et al., "Spiking graph convolutional networks," arXiv preprint arXiv:2205.02767, 2022.##[22] H. Li, M. Xu, J. Pei, and Rong Zhao. "Efficient GCN Deployment with Spiking Property on Spatial-Temporal Neuromorphic Chips," in Proceedings of the 2023 International Conference on Neuromorphic Systems, ACM, 2023, pp. 1-8.##[23] Sanaullah, S. Koravuna, U. Rückert, and T. Jungeblut, "Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications," Frontiers in Computational Neuroscience, vol. 17, p. 1215824, 2023. ##[24] J. K. Eshraghian et al., "Training spiking neural networks using lessons from deep learning," Proceedings of the IEEE, vol. 111, no. 9, pp. 1016-1054, 2023. ##[25] H. Fang et al., "Brain-inspired graph spiking neural networks for commonsense knowledge representation and reasoning," arXiv preprint arXiv:2207.05561, 2022.##[26] J. Yang et al., "GGT-SNN: Graph learning and Gaussian prior integrated spiking graph neural network for event-driven tactile object recognition," Information Sciences, vol. 677, p. 120998, 2024. ##[27] N. Yin et al., "Dynamic spiking graph neural networks," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 15, 2024, pp. 16495-16503.##[28] Y. Li, R. Yin, Y. Kim, and P. Panda. "Efficient human activity recognition with spatio-temporal spiking neural networks," Frontiers in Neuroscience, vol. 17, p. 1233037, 2023.##[29] H. Zhao, X. Yang, C. Deng, and J. Yan, "Dynamic reactive spiking graph neural network," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 15, 2024, pp. 16970-16978. ##[30] H. Zhang et al., "Direct training high-performance spiking neural networks for object recognition and detection," Frontiers in Neuroscience, vol. 17, p. 1229951, 2023. ##[31] T. Dalgaty et al., "Hugnet: Hemi-spherical update graph neural network applied to low-latency event-based optical flow," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 3953-3962.##[32] C. 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2D DOA Estimation of Wideband and FH Signals Using Improved K-means Clustering and Implementation Considerations 2 2 This paper presents a two-dimensional (2D) direction of arrival (DOA) estimation method based on the popular correlative interferometer (CI) approach, incorporating practical considerations. Leveraging the flexibility of software-defined radio (SDR) platforms, the proposed array antenna model is designed according to the specifications of a dual-channel synchronous USRP B210 receiver and an appropriate RF switch. To enhance the speed and accuracy of 2D DOA estimation for narrowband, wideband (WB), and frequency hopping (FH) signals, this study introduces a method that integrates power spectrum density (PSD) and spectrogram analysis of the receiver’s instantaneous bandwidth with an optimized filter bank, to precisely detect active frequencies and their intervals. Additionally, a fast, modified K-means clustering algorithm is developed to refine DOA estimation for FH and WB signals across multiple active subchannels. Simulation results demonstrate improved DOA estimation accuracy in multipath conditions, particularly at longer distances, with further enhancements achieved through the proposed clustering method. 102 116 2024/03/012024/05/272024/07/032024/07/092024/07/212024/07/292024/09/162024/11/01 1403/8/11 2025/01/222025/02/152025/02/142025/03/172025/01/012025/02/242025/01/132025/04/03 1404/1/14 Zahra Memarian Zahra Memarian Department of Electrical and Computer Engineering, University of Kashan, Kashan, Iran. Iran zahra.memarian@grad.kashanu.ac.ir Mahdi Majidi Mahdi Majidi Department of Electrical and Computer Engineering, University of Kashan, Kashan, Iran. Iran m.majidi@kashanu.ac.ir 2D DOA Estimation Wideband Frequency Hopping Filter Bank Modified K-means Clustering. [1] A. Rembovsky, A. Ashikhmin, V. Kozmin, and S. Smolskiy, "Radio monitoring," Problems, methods and equipment. Lecture notes in electrical engineering. Springer, 2009.##[2] H. Zamani, H. Zayyani, and F. Marvasti, "An iterative dictionary learning-based algorithm for DOA estimation," IEEE Communications Letters, vol. 20, no. 9, pp. 1784-1787, 2016.##[3] S. Kulkarni, A. Thakur, S. Soni, A. Hiwale, M. H. Belsare, and A. B. Raj, "A comprehensive review of direction of arrival (DoA) estimation techniques and algorithms," Journal of Electronics and Electrical Engineering, pp. 138–186-138–186, 2025.##[4] M. Qu, W. Si, and R. Liu, "Array design and phase interferometer-based DOA estimation for diversely polarized antenna arrays," Measurement, vol. 242, p. 116222, 2025.##[5] P. Ramezanpour, M. Aghababaie, M. Mosavi, and D. de Andrés, "Multi-stage beamforming using DNNs," Iranian Journal of Electrical & Electronic Engineering, vol. 18, no. 2, 2022.##[6] T. Sallam, Q. Wang, and A. M. 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An Improved Strategy for Torque Ripple Reduction of Dual Three-Phase Brushless DC Motor Fed by Two Diode-Clamped, Three-Level Inverters Using Model Predictive Control 2 2 Multiphase electric motors are useful for industrial and military applications that need high power, fault tolerance control, smooth torque, and the ability to share power and torque compared to conventional three-phase electric motors. One type of Multiphase electric machine is Brushless DC Motors (BLDCM) which uses conventional strategies such as hysteresis current controllers. It has important challenges such as high torque ripple, low efficiency, vibrations, and noise that are undesirable for high power applications such as submarines. This paper proposes a new finite control set model predictive control (FCS-MPC) approach with reduction of computational for diode-clamped three-level (DC3L) inverter fed to dual three-phase BLDCM (DTP-BLDCM) by selecting optimal vectors to solve the above problems. Also, an approach of balancing the voltage of the capacitors in two of the DC3L inverters to reduce torque ripple has been proposed. The results of the suggested MPC method are contrasted and verified with the multiband hysteresis current (MHC) method through simulation. The simulation results specify that the suggested MPC controller works superior than the MHC controller. Also, due to the simplicity and low complexity of the suggested MPC strategy used, the real implementation possibility and performance of the controller are checked by simulations for a 4125-V/2.7-MW/350-RPM DTP-BLDCM. 117 136 2024/03/012024/05/272024/07/032024/07/092024/07/212024/07/292024/09/162024/11/012024/11/13 1403/8/23 2025/01/222025/02/152025/02/142025/03/172025/01/012025/02/242025/01/132025/04/032025/04/03 1404/1/14 Zahra Emami Zahra Emami Department of Electrical & Computer Engineering, University of Kashan, Kashan, Iran. Iran zahra_emami1765@yahoo.com Abolfazl Halvaei Niasar Abolfazl Halvaei Niasar Department of Electrical & Computer Engineering, University of Kashan, Kashan, Iran. Iran halvaei@kashanu.ac.ir DTP-BLDCM model predictive control (MPC) the diode-clamped three-level (DC3L) inverter multiband hysteresis current (MHC) controller. [1] F. Barrero and M. J. Duran, "Recent advances in the design, modeling, and control of multiphase machines—Part I," IEEE Transactions on Industrial Electronics, vol. 63, no. 1, pp. 449-458, Januray 2015.##[2] E. Levi, "Multiphase electric machines for variable-speed applications," IEEE Transactions on industrial electronics, vol. 55, no. 5, pp. 1893-1909, May 2008.##[3] M. Mengoni, L. Zarri, A. Tani, L. Parsa, G. Serra, and D. Casadei, "High-torque-density control of multiphase induction motor drives operating over a wide speed range," IEEE Transactions on Industrial Electronics, vol. 62, no. 2, pp. 814-825, July 2014.##[4] P. Bogusz, M. Korkosz, and J. Prokop, "A study of dual-channel brushless DC motor with permanent magnets," in 2016 13th Selected Issues of Electrical Engineering and Electronics (WZEE), May 2016: IEEE, pp. 1-6. ##[5] Z. Fu, J. Liu, and Z. Xing, "Performance analysis of dual-redundancy brushless DC motor," Energy Reports, vol. 6, pp. 829-833, December 2020.##[6] I. Shchur and V. Turkovskyi, "Open-end winding dual three-phase BLDC motor drive system with integrated hybrid battery-supercapacitor energy storage for electric vehicle," in 2021 IEEE International Conference on Modern Electrical and Energy Systems (MEES), September 2021: IEEE, pp. 1-6. ##[7] Z. Zhu, S. Wang, B. Shao, L. Yan, P. Xu, and Y. Ren, "Advances in dual-three-phase permanent magnet synchronous machines and control techniques," Energies, vol. 14, no. 22, p. 7508, Novamber 2021.##[8] Y. Ren, Z. Zhu, J. E. Green, Y. Li, S. Zhu, and Z. Li, "Improved duty-ratio-based direct torque control for dual three-phase permanent magnet synchronous machine drives," IEEE Transactions on Industry Applications, vol. 55, no. 6, pp. 5843-5853, December 2019.##[9] Y. Hu, Z.-Q. Zhu, and K. Liu, "Current control for dual three-phase permanent magnet synchronous motors accounting for current unbalance and harmonics," IEEE Journal of Emerging Selected Topics in Power Electronics, vol. 2, no. 2, pp. 272-284, June 2014.##[10] A. Dey, P. Rajeevan, R. Ramchand, K. Mathew, and K. Gopakumar, "A space-vector-based hysteresis current controller for a general n-level inverter-fed drive with nearly constant switching frequency control," IEEE Transactions on Industrial Electronics, vol. 60, no. 5, pp. 1989-1998, May 2012.##[11] K.-H. Kim and M.-J. Youn, "DSP-based high-speed sensorless control for a brushless DC motor using a DC link voltage control," Electric Power Components Systems, vol. 30, no. 9, pp. 889-906, November 2002.##[12] B.-K. Lee and M. Ehsani, "Advanced simulation model for brushless dc motor drives," Electric power components systems, vol. 31, no. 9, pp. 841-868, June 2003.##[13] F. Barrero, M. R. Arahal, R. Gregor, S. Toral, and M. J. Durán, "A proof of concept study of predictive current control for VSI-driven asymmetrical dual three-phase AC machines," IEEE Transactions on Industrial Electronics, vol. 56, no. 6, pp. 1937-1954, June 2009.##[14] B. Cao, B. M. Grainger, X. Wang, Y. Zou, G. F. Reed, and Z.-H. Mao, "Direct torque model predictive control of a five-phase permanent magnet synchronous motor," IEEE Transactions on Power Electronics, vol. 36, no. 2, pp. 2346-2360, February 2020.##[15] W. Xie et al., "Finite-control-set model predictive torque control with a deadbeat solution for PMSM drives," IEEE Transactions on Industrial Electronics, vol. 62, no. 9, pp. 5402-5410, September 2015.##[16] Y. Zhang, B. Zhang, H. Yang, M. Norambuena, and J. Rodriguez, "Generalized sequential model predictive control of IM drives with field-weakening ability," IEEE Transactions on Power Electronics, vol. 34, no. 9, pp. 8944-8955, September 2018.##[17] J. J. Aciego, I. G. Prieto, and M. J. Duran, "Model predictive control of six-phase induction motor drives using two virtual voltage vectors," IEEE Journal of Emerging Selected Topics in Power Electronics, vol. 7, no. 1, pp. 321-330, March 2018.##[18] I. Gonzalez-Prieto, M. J. Duran, J. J. Aciego, C. Martin, and F. Barrero, "Model predictive control of six-phase induction motor drives using virtual voltage vectors," IEEE Transactions on Industrial Electronics, vol. 65, no. 1, pp. 27-37, January 2017.##[19] C. Xiong, H. Xu, T. Guan, and P. Zhou, "A constant switching frequency multiple-vector-based model predictive current control of five-phase PMSM with nonsinusoidal back EMF," IEEE Transactions on industrial Electronics, vol. 67, no. 3, pp. 1695-1707, March 2019.##[20] M. A. Frikha, J. Croonen, K. Deepak, Y. Benômar, M. El Baghdadi, and O. Hegazy, "Multiphase motors and drive systems for electric vehicle powertrains: State of the art analysis and future trends," Energies, vol. 16, no. 2, p. 768, Jan 2023.##[21] D. Ronanki and S. S. Williamson, "A simplified space vector pulse width modulation implementation in modular multilevel converters for electric ship propulsion systems," IEEE Transactions on Transportation Electrification, vol. 5, no. 1, pp. 335-342, December 2018.##[22] K. Thantirige, A. K. Rathore, S. K. Panda, G. Jayasignhe, M. A. Zagrodnik, and A. K. Gupta, "Medium voltage multilevel converters for ship electric propulsion drives," in 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS), March 2015: IEEE, pp. 1-7. ##[23] M. Schweizer, T. Friedli, and J. W. Kolar, "Comparative evaluation of advanced three-phase three-level inverter/converter topologies against two-level systems," IEEE Transactions on industrial electronics, vol. 60, no. 12, pp. 5515-5527, December 2012.##[24] C. Xia, G. Zhang, Y. Yan, X. Gu, T. Shi, and X. He, "Discontinuous space vector PWM strategy of neutral-point-clamped three-level inverters for output current ripple reduction," IEEE Transactions on Power Electronics, vol. 32, no. 7, pp. 5109-5121, July 2016.##[25] G. Zhang, Y. Su, Z. Zhou, and Q. Geng, "A carrier-based discontinuous PWM strategy of NPC Three-level inverter for common-mode voltage and switching loss reduction," Electronics, vol. 10, no. 23, p. 3041, December 2021.##[26] F. Barrero, M. R. Arahal, R. Gregor, S. Toral, and M. J. Durán, "One-step modulation predictive current control method for the asymmetrical dual three-phase induction machine," IEEE Transactions on Industrial Electronics, vol. 56, no. 6, pp. 1974-1983, June 2009.##[27] M. J. Duran, J. Prieto, F. Barrero, and S. Toral, "Predictive current control of dual three-phase drives using restrained search techniques," IEEE Transactions on Industrial Electronics, vol. 58, no. 8, pp. 3253-3263, August 2010.##[28] M. Habibullah, D. D.-C. Lu, D. Xiao, and M. F. Rahman, "Finite-state predictive torque control of induction motor supplied from a three-level NPC voltage source inverter," IEEE Transactions on Power Electronics, vol. 32, no. 1, pp. 479-489, Jaunary 2016.##[29] Y. Hu, Z.-Q. Zhu, and M. Odavic, "Comparison of two-individual current control and vector space decomposition control for dual three-phase PMSM," IEEE Transactions on Industry Applications, vol. 53, no. 5, pp. 4483-4492, May 2017.##[30] I. Zoric, M. Jones, and E. Levi, "Vector space decomposition algorithm for asymmetrical multiphase machines," in 2017 International Symposium on Power Electronics (Ee), Oct. 2017: IEEE, pp. 1-6. ##[31] B. Wu and M. Narimani, High-power converters and AC drives. John Wiley & Sons, January 2017.##[32] P. Drozdowski, "Modelling of BLDCM with a double 3-phase stator winding and back EMF harmonics," Archives of Electrical Engineering, vol. 64, no. 1, March 2015.##[33] M. Gu, Z. Wang, K. Yu, X. Wang, and M. Cheng, "Interleaved model predictive control for three-level neutral-point-clamped dual three-phase PMSM drives with low switching frequencies," IEEE Transactions on Power Electronics, vol. 36, no. 10, pp. 11618-11630, October 2021.##[34] [R. A. Raj, M. Shreelakshmi, and S. George, "Multiband hysteresis current controller for three level BLDC motor drive," in 2020 International Conference on Power, Instrumentation, Control and Computing (PICC), December 2020: IEEE, pp. 1-6. ##[35] C. Bian, X. Li, and G. Zhao, "The peak current control of permanent magnet brushless DC machine with asymmetric dual-three phases," CES Transactions on Electrical Machines Systems, vol. 2, no. 1, pp. 129-135, March 2018.##[36] N. Celanovic and D. 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A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method 2 2 In determining position using GPS, due to local effects, pseudo-range errors cannot be mitigated by methods such as the use of reference stations or mathematical models; however, by using precise carrier phase observations and deploying a statistically optimal filter such as Phase-Adjusted Pseudo-range (PAPR) algorithm, the error can be significantly reduced. Additionally, the correlation between observations is a factor affecting positioning accuracy. In this paper, by using both pseudo-range and carrier phase observations and taking into account the effect of spatial correlation between observations to determine the variance-covariance matrix, the accuracy of position determination using the recursive Least Squares method is increased. For this purpose, the PAPR algorithm was implemented to reduce error. Next, a non-diagonal variance-covariance matrix was introduced to estimate the variance of the observations based on their spatial correlations. Experimental results on real data show that the proposed method improves positioning accuracy by at least 10% compared to previous methods. To evaluate the complexity of the proposed models, we employed an ARM STM32H743 processor. The findings indicate a modest increase in the proposed model complexity compared to earlier models, along with a substantial improvement in positioning accuracy. 137 146 2024/03/012024/05/272024/07/032024/07/092024/07/212024/07/292024/09/162024/11/012024/11/132024/11/19 1403/8/29 2025/01/222025/02/152025/02/142025/03/172025/01/012025/02/242025/01/132025/04/032025/04/032025/03/04 1403/12/14 Nerjes Rahemi Nerjes Rahemi The authors are with the School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. Iran Rahemi@iust.ac.ir Kurosh Zarrinnegar Kurosh Zarrinnegar The authors are with the School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. Iran k_zarrinnegar@elec.iust.ac.ir Mohammad Reza Mosavi Mohammad Reza Mosavi The authors are with the School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. Iran M_Mosavi@iust.ac.ir GPS Phase-Adjusted Pseudo-range Algorithm Recursive Least Squares Spatial Correlations Variance-Covariance Matrix. 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R., “Positioning accuracy improvement in high‐speed GPS receivers using sequential extended Kalman filter,” IET Signal Processing, Vol. 15, No. 4, pp. 251-264, 2021.##[15] Mirmohammadian F., Asgari J., Verhagen S. and Amiri-Simkooei A., “Improvement of multi-GNSS precision and success rate using realistic stochastic model of observations,” Remote Sensing, Vol. 14, No. 1, pp. 60, 2021. ##[16] Luo X., Lin Y., Dai X., Bian S. and Chen D., “An improved stochastic model for the geodetic GNSS receivers under ionospheric scintillation at low latitudes,” Space Weather, Vol. 22, No. 2, p.e2023SW003632, 2024. ##[17] Deng J., Zhao X., Zhang A. and Ke F., “A robust method for GPS/BDS pseudorange differential positioning based on the Helmert variance component estimation,” Journal of Sensors, Vol. 2017, No. 1, pp. 8172342, 2017.##[18] Zhang Q., Zhao L. and Zhou J., “A novel weighting approach for variance component estimation in GPS/BDS PPP”, IEEE Sensors Journal, Vol. 19, No. 10, pp. 3763-3771, 2019.##[19] Martin A., Parry M., Soundy A.W., Panckhurst B.J., Brown P., Molteno T.C. and Schumayer D., “Improving real-time position estimation using correlated noise models”, Sensors, Vol. 20, No. 20, pp. 5913, 2020.##[20] Li F., Gao J., Psimoulis P., Meng X. and Ke F., “A novel dynamical filter based on multi-epochs Least-Squares to integrate the carrier phase and pseudorange observation for GNSS measurement,” Remote Sensing, Vol. 12, No. 11, pp. 1762, 2020.##[21] Kermarrec G. and Schön S., “A priori fully populated covariance matrices in Least-Squares adjustment-case study: GPS relative positioning,” Journal of Geodesy, Vol. 91, pp. 465-484, 2017.##[22] Zhu M., Yu F., Xiao S., Fan S. and Wang Z., “An improved posteriori variance-covariance components estimation applied to unconventional GPS and multiple low-cost imus integration strategy”, IEEE Access, Vol. 7, pp. 136892-136906, 2019.##[23] Prochniewicz D., Wezka K. and Kozuchowska J., “Empirical stochastic model of multi-GNSS measurements,” Sensors, Vol. 21, No. 13, pp. 4566, 2021.## ##

Optimal Multi-Objective Design for Hybrid Renewable Energy System in Distribution System Considering Multi-Scenarios 2 2 This paper presents an effective approach for determining optimal integration of renewable energy distributed generator (RE-DGs) of solar farms (SFs) and wind farms (WFs) in IEEE 69-node power distribution network (PDN) with target of minimizing (1) the single objective function of total active power loss and (2) multi-objective function including a) total active power loss, b) total reactive power loss, c) the voltage deviation and d) imported energy from the main power gird. Intelligent and adaptive meta-heuristic optimization algorithm called bonobo optimizer (BO) is introduced to address optimization problem considering the changing four seasons of winter, spring, summer and autumn from both generation and consumption. The obtained results from BO show its outstanding performance in determining the suitable installation of SFs and WFs compared with many published methods and implemented methods for two cases of single and multi-objective functions. 147 163 2024/03/012024/05/272024/07/032024/07/092024/07/212024/07/292024/09/162024/11/012024/11/132024/11/192024/12/21 1403/10/1 2025/01/222025/02/152025/02/142025/03/172025/01/012025/02/242025/01/132025/04/032025/04/032025/03/042025/04/03 1404/1/14 Nguyen Nhat Tung Nguyen Nhat Tung Faculty of Electrical and Electronics Engineering, Thuyloi University, Hanoi, Vietnam. Vietnam tungnn@tlu.edu.vn Solar farms Wind farms Bonobo optimizer Total power loss The voltage deviation. [1] T. Ahmad, S. Manzoor, D. Zhang, “Forecasting high penetration of solar and wind power in the smart grid environment using robust ensemble learning approach for large-dimensional data,” Sustainable Cities and Society, vol. 75, p. 103269, 2021. DOI: 10.1016/j.scs.2021.103269##[2] D. 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A Review of Ultrasound Imaging Methods and Techniques to Enhance Their Frame Rate 2 2 Increasing the frame rate of ultrasound imaging while keeping image quality is important for following fast movements, especially the heart. There are different modalities for B-mode image recording, including line-by-line scanning with linear, phased, convex array, synthetic aperture imaging (STA), plane waves (PWI), then the combination of plane waves (CPWI), and so on. Researchers have tried to increase the frame rate in each case using different methods. Three approaches for this aim are data acquisition, post-processing, and beamforming. This article reviews these approaches and their solutions for compensating image quality reduction. Ultrafast ultrasound imaging, which provides exceptional temporal resolution (high frame rate), is promising in diagnosing heart diseases due to its ability to capture rapid heart movements. It can record images faster than conventional imaging, usually exceeding 1000 frames per second. This can be achieved through plane wave imaging (PWI). However, high frame rate data acquisition can lead to a decrease in image quality. Transmitting at different angles and then combining plane wave imaging is a popular method to enhance PWI quality but reduces the frame rate by the number of angles. As a result, researchers have aimed to increase the temporal resolution while compensating for the loss of quality. 164 184 2024/03/012024/05/272024/07/032024/07/092024/07/212024/07/292024/09/162024/11/012024/11/132024/11/192024/12/212024/12/23 1403/10/3 2025/01/222025/02/152025/02/142025/03/172025/01/012025/02/242025/01/132025/04/032025/04/032025/03/042025/04/032025/03/17 1403/12/27 Seyyedeh Ensiyeh Hashemi Seyyedeh Ensiyeh Hashemi The Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran. 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