Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Turk, Kadir(7)
- Akinoglu, Bulent G.(6)
- Bingol, Ozkan(5)
- Ozden, Talat(5)
- Simsek, Cemaleddin(5)
- Albayrak, Cenk(4)
- Basoglu, Mustafa Engin(4)
- Dogan, Ramazan Ozgur(4)
- Ekinci, Murat(4)
- Herencsar, N.(4)
- Kayikcioglu, Temel(4)
- Dogan, Hulya(3)
- Gedikli, Eyup(3)
- Yanarates, Cagfer(3)
- Özden, Talat(3)
- Altan, Aytac(2)
- Altas, Ismail H.(2)
- Benli, Emrah(2)
- Cakmak, Recep(2)
- Demirbag, Kubra Simsek(2)
- Essaaidi, M.(2)
- Genemo, Musa Dima(2)
- Gul, Fatih(2)
- Kucuk, Mehmet Ali(2)
- Maleki, Masoud(2)
- Manshouri, Negin(2)
- Motai, Yuichi(2)
- Sreeram, G.(2)
- Tonyali, Samet(2)
- Tugcu, Emin(2)
- Turan, Raşit(2)
- Ture, Hayati(2)
- Yazgan, Ayhan(2)
- Yildirim, Nihan(2)
- Zaz, Y.(2)
- Aiordachioaie, D.(1)
- Akkaya, Kemal(1)
- Aklnoǧlu, Bülent Gültekin(1)
- Akmoǧlu, Bülent Gültekin(1)
- Akyurek, Feyzullah(1)
Yayınlar
- MakaleAçık Erişim2026Enhanced Sensorless Current Control in Boost Converters for EV Fast Charging: Equilibrium Optimization-Tuned Cascaded ANN-Fuzzy Logic Control
Altan, Aytac; Yanarates, Cagfer; Alzaidi, Mohammed S.
Efficient current control in boost converters is critical for electric vehicle (EV) battery charging applications, where dynamic operating conditions challenge conventional control strategies. This paper presents a novel cascaded artificial neural network (ANN) and fuzzy logic (ANFIS) controller designed to enhance current regulation in boost converters. The performance of the proposed approach is validated through comparison with single PI, cascaded PI, and single ANFIS control schemes. To improve system performance, a sensorless current estimation technique is implemented, reducing hardware dependency and enhancing robustness, while lowering system cost. Experimental evaluations demonstrate that the proposed cascaded ANFIS controller maintains consistent current levels under rapid load changes, providing improved transient response and steady-state accuracy. Specifically, the proposed method achieves a settling time of 23 ms, compared to 185 ms for single PI and 66 ms for single ANFIS control, corresponding to an 87.6% improvement over PI. In addition, overshoot is reduced from 37.25% (PI) and approximately 20% (ANFIS) to negligible levels, while a fast rise time of 3.62 ms is obtained. These improvements reduce current deviation and transient stress, contributing to more stable and reliable charging operation. Although charging time, efficiency, and battery lifetime are not directly measured, the results indicate strong potential for improved practical charging performance. The proposed approach provides valuable insights into the integration of intelligent control methods in power electronics and supports the development of high-performance EV charging systems.
- BildiriYalnız künye2026Analysis of Temporal Consistency in Video-Based Palmprint ROI Tracking
Sunnetci, Betul Kosmaz; Bingol, Ozkan; Gedikli, Eyup
In real-time palmprint recognition systems, operating under unconstrained conditions, maintaining the defined palmprint region of interest (ROI) throughout hand motion presents a significant challenge. Variations in hand position or orientation may cause the ROI to shift unexpectedly or partially or completely disappear, which can adversely affect subsequent processing steps and their computational cost. In this study, the temporal stability and consistency of palmprint ROIs extracted from video data in the MPW-180 dataset were evaluated using two different approaches. Specifically, the behavior of two methods (optical flow based tracking and keypoint-based MediaPipe Hands detection) was quantitatively analyzed over time using corner position shifts and overlap ratios. Experimental results indicate that although the keypointbased method produces spatially accurate ROI estimates, it may exhibit sudden positional jumps in certain frames. In contrast, the optical flow method provides smoother motion transitions but may introduce positional drift over time due to the accumulation of tracking errors.
- MakaleYalnız künye2026Blue-Shift Effect-Based Positioning for Multi-Color VLC
Kucuk, Mehmet Ali; Turk, Kadir
In this letter, an angle-of-arrival (AoA)-based integrated positioning system is proposed that utilizes the standard optical front-end of multi-color visible light communication (MC-VLC). Unlike traditional AoA methods, which rely on specialized receiver architectures, the proposed approach uses the inherent angular dependence of optical filter transmission responses, known as the blue-shift effect, for directly estimating the angle of incidence from the received signal. In this context, an AoA estimation technique is developed by combining eigenvalue decomposition of the MC-VLC channel with regression analysis. Simulation results show that the proposed system achieves millimeter-level positioning accuracy, enabling robust and high-precision indoor localization without additional hardware complexity or cost.
- BildiriYalnız künye2026Advancing Digital Wallet Security Through Multi-Factor Authentication and Homomorphic Encryption-Based Privacy Preservation
Genemo, Musa Dima; Kumar, Kotha Anil; Sadat, Quazi Taif; Santosh, Kathari; Sreeram, G.; Raj, I. Infant; Dash, R.; Rautray, R.; Mishra, S.K.
Digital wallets have been provided by FinTech businesses thus reengineering the financial services but nonetheless there is a possibility of keyloggers, bogus personas and assaults of the server with conventional systems using PIN authentication. The current multi-factor authentication configuration usually leaves sensitive biometric information exposed as the logins are processed. To address these security issues, the paper proposes the MHESecAuth that employs Multi-Factor Homomorphic Encryption and Smart Contracts to authenticate. The permission to make transactions is secure since the system operates with PINs, OTPs and no one can view the biometrics. As per experimental research, the system can be accurate at 96.4 percent, false acceptance rate is 0.08 percent, false rejection rate is 0.6 percent and encryption consumes 160ms. The framework optimizes security, privacy and performance against both MFA, encryption-only and combined solutions and is therefore suitable in the development of quantum-secure digital wallets and still blockchain-based audit and transparency.
- BildiriYalnız künye2025Multi Objective Reinforcement Tuning for Responsible Generative AI in Automated Financial Advisory Systems
Bhandarkar, Vishakha D; Genemo, Musa Dima; Muda, Iskandar; Murugan, Rekha; Sreeram, G.; Dharani, N.
Generative AI in a financial context poses risks to the areas of regulatory compliance, accuracy in the content being delivered, and appropriateness in terms of emotion. This research proposes ReFiGen, a framework that can be used to create compliant financial content that is compliant with the regulatory framework and the sentiment of the users. Following domain-specific fine-tuning, ReFiGen blends sentiment alignment with reinforcement learning to optimize on compliance. It demonstrates better performance than baseline models with a high data accuracy rate of 98.35 percent and can generate a fluent output that is emotionally congruent, and regulation-minded. The results of experiments indicate that ReFiGen is both clear and coherent and exactly follows financial guidelines. This practice affirms that the combination of unstructured compliance modeling and affective control is a successful aspect of LLMs. ReFiGen is one of the milestones on the journey of trustworthy and ethically aligned AI in the financial advisory market.
- BildiriYalnız künye2025Depth Estimation Based Automatic Pose Correction in Hand Images
Sünnetci Koşmaz, Betül; Bingöl, Özkan; Gedikli, Eyüp; Ekinci, Murat
In palm biometric recognition systems, the hand being at different angles and positions relative to the camera presents a critical challenge in terms of accuracy and reliability. Especially in images captured in free environments, hand misalignment can negatively affect recognition performance by making it difficult to extract biometric attributes. Perspective distortions due to camera position change the geometric structure of the hand, compromising biometric data integrity. While 3D imaging systems can offer successful solutions for pose normalization, performing a similar transformation for 2D images poses a major technical challenge. In this study, depth estimation supported plane fitting is used to remove perspective distortions in 2D images and pose normalization of the hand is obtained. The proposed method uses the information obtained from depth estimation to realign the hand image as if it were standing directly in front of the camera, improving geometric consistency. This will make the extraction of biometric features more reliable and contribute to improving the accuracy of palm recognition systems.
- MakaleAçık Erişim2025Solving the Dynamic Stability Challenge of Automatic Gain-Controlled Phase-Locked Loops: A Robust Alternative via Enhanced SRF-PLL Design and Implementation
Yanarateş, Cağfer; Altan, Aytaç
This study presents a robust alternative to conventional phase-locked loop (PLL) architectures through the design and real-time implementation of an enhanced Synchronous Reference Frame Phase-Locked Loop (SRF-PLL) for single-phase inverter systems. Targeting the inherent dynamic stability limitations of Automatic Gain-Controlled PLLs (AGC-PLLs), the proposed SRF-PLL demonstrates superior performance in transient response and tracking accuracy. Comparative evaluations under diverse dynamic operating conditions—including phase angle jumps, voltage sags/swells, frequency steps, noise, and harmonic distortion—highlight its resilience and adaptability. Experimental results reveal a substantial improvement in dynamic response, achieving 2.5 to 6 times faster settling times depending on the operating condition. Moreover, peak-to-peak oscillations were significantly suppressed, with deviations reduced from 2° to nearly 0° under a 5 Hz frequency step scenario, even in the presence of 3rd order harmonic injection. For a 40° phase angle step, the proposed system achieves a fivefold improvement in attenuation time (0.03 s vs. 0.11 s) compared to the AGC-PLL. Under voltage sag conditions with noise and 3rd order harmonic injection, the enhanced SRF-PLL reduces the attenuation time from 0.2 s to 0.08 s. During voltage swell scenarios, it further improves the response time from 0.025 s to 0.01 s. These findings confirm the practical benefits and efficiency of the enhanced SRF-PLL for applications requiring precise and reliable phase tracking and synchronization.
- BildiriYalnız künye2025Improving Skin Lesion Classification with Pretrained Deep Learning Models
Almasani, Heba; Ture, Hayati
Skin cancer is the most common type of cancer. Cancer can be defined as a disease characterized by the uncontrolled division and spread of cells. As with other types of cancer, early diagnosis and treatment play a crucial role in combating skin cancer. Since diagnosis is made through visual examination, factors such as the level of expertise and image artifacts negatively affect diagnostic accuracy and slow down the process.Computer-aided applications used to analyze such images provide faster and more accurate results. Additionally, these methods eliminate factors that reduce diagnostic accuracy during the classification and segmentation of skin lesions.This study classifies skin cancer to benign or malignant using deep learning and machine learning methods. In particular, Convolutional Neural Networks (CNNs) have shown promising results in medical image classification. The study evaluates six pre-trained CNN models - VGG16, EfficientNetB7, EfficientNetV2L, DenseNet, ConvNeXtBase and MobileNet - for binary skin lesion classification using the ISIC 2019 dataset. The study first preprocesses and balances the data to enhance performance. The results indicate that the ConvNeXtBase model achieved the highest success with accuracy rate of 84.63% after balancing the data that we have.
- BildiriYalnız künye2025Hybrid CNN-LSTM Framework for Automated Detection of Pediatric Heart Murmurs
Al-Taib, Asmaa Abduh; Ture, Hayati; Aslan, Muhammed Emin
This study presents a comprehensive deep learning framework for automated heart murmur detection from phonocardiogram (PCG) signals to address the subjective limitations of cardiac auscultation. Using the CirCor DigiScope dataset of 5,272 pediatric heart sound recordings from 1,568 subjects, we systematically evaluated multiple approaches including traditional machine learning models (Logistic Regression, SVM, KNN, Decision Tree, AdaBoost), basic deep learning architectures (standalone CNN and LSTM), and advanced transfer learning models (DenseNet121, EfficientNet B0/B1/B2, ResNet 50/101, MobileNet V2/V3, GhostNet, AttentionCNN). Our hybrid CNN-LSTM architecture combines mel-spectrogram spatial feature extraction with temporal sequence modeling using 128 traditional audio features and comprehensive preprocessing with data augmentation. The CNN-LSTM model achieved superior performance with 84.6% accuracy, 0.84 F1-score, 0.748 precision, and 0.800 recall, significantly outperforming traditional methods (best SVM: 0.716 F1-score) and advanced architectures (best DenseNet121: 0.72 F1-score), demonstrating robust classification with 0.879 AUC. Results indicate that hybrid deep learning architectures effectively automate cardiac auscultation, providing objective diagnostic tools with potential for improved healthcare accessibility and clinical decision-making.
- BildiriYalnız künye2025Tumor Classification via Subregion Combinations of Cortical L5PC6 Pyramidal Neurons
Ozcan, Zubeyir; Bozkir, Ilknur Kayikcioglu; Kayikcioglu, Temel
In this study, it was aimed to classify benign and malignant tumor samples by utilizing different subregions of a cortical L5PC6 pyramidal neuron. A spike-based learning method was applied to a real dataset, and a decision mechanism based on action potential generation was established by training a separate neuron for each class. During the training process, various types of synaptic connections and spike encoding schemes were evaluated and intensity coding yielded the highest performance for the pyramidal neuron. Regional analyses revealed that the basilar region particularly stood out in terms of classification success. The results demonstrate that the morphological and biophysical diversity of pyramidal neurons provides a significant advantage in classification tasks.
- BildiriAçık Erişim2024In Different Scenarios MobileNet-V1 for Cross-view Gait Recognition
Yaprak, Busranur; Gedikli, Eyup; Badica, C.; Ivanovic, M.; Koprinkova-Hristova, P.; Leon, F.; Manolopoulos, Y.; Yildirim, T.; Ucar, A.
Gait recognition aims to identify people from a distance by analyzing their walking style. Nevertheless, the efficacy of recognition drops significantly under cross-view and, appearance-based variations such as carrying and clothing. In this study, the performance of the MobileNet-V1 deep network is evaluated in various scenarios to address the cross-view gait recognition problem. In the first scenario, the fine-tuned MobileNet-V1 is evaluated on Gait Energy Images (GEI) as input data, while in the second scenario, the fine-tuned MobileNet-V1 is assessed with Optical Flows and masked RGB frames input data. In the last scenario, the first two scenarios are combined over a single fused deep network based on finetuned MobileNet-V1, and a single recognition process is performed using two different fused features data; GEI features with Optical Flow features, and GEI features with masked RGB frame features. In the evaluation process, a comprehensive data set for the cross-view gait recognition problem, CASIA-B is used for the experiments. The obtained results demonstrate that in the last scenario, the contribution of masked RGB frame features to the recognition rate of GEI is more significant.
- BildiriAçık Erişim2024High-Accuracy Hand Segmentation for Palmprint Biometric Systems Improving Auto-Encoder Techniques
Yalcin, Kadir; Dogan, Ramazan Ozgur; Dogan, Hulya; Bingol, Ozkan
Biometric technologies have been widely adopted in various commercial products, ranging from security systems to personal devices, due to their exceptional reliability and user-friendliness. Among these, palmprint biometrics has gained increasing attention for its advantages, such as ease of use, a larger surface area, and the ability to capture more features compared to other biometric methods. A typical palmprint biometric system involves five key stages: (a) Image Acquisition, (b) Hand Segmentation, (c) Pattern Generation, (d) Feature Extraction, and (e) Verification/Identification. The overall success of the system depends on the effectiveness of each stage, with hand segmentation being crucial for identifying the most relevant features for accurate verification. This study aims to achieve high-accuracy biometric recognition by segmenting hands from images captured in uncontrolled environments with complex backgrounds. Accuracy (ACC), Intersection over Union (IoU), F1-Score, and Dice Coefficient are the four critical metrics used to assess the performance of segmentation techniques. The findings demonstrate that the U-Net + ViT and MA-Net + ResNet34 techniques outperform traditional auto-encoder methods in both quantitative performance metrics and visual analysis, leading to significantly improved hand segmentation for biometric systems.
- BildiriAçık Erişim2024Super-Resolution Based ROI Enhancement Approaches for Palmprint Recognition; [Avu Izi Tanima I in S per z n rl k Tabanli ROI Iyilestirme Yaklasimlari]
MacAn, Huseyin Furkan; Bingol, Ozkan; Turk, Salih; Ekinci, Murat
The sizes of regions of interest (ROI) obtained from images captured by cameras in palmprint recognition may vary depending on the camera specifications and the position of the hand. The regions of interest that recognition systems can use are typically rescaled through interpolation techniques and then defined as inputs to the system. In such cases, there may be some loss of biometric data. This study aims to enhance the quality of the palmprint ROI images, which are critical for palmprint recognition systems. To achieve this, various super-resolution techniques were employed to reconstruct low-resolution ROI images to the target sizes while preserving the distinctiveness of their biometric features. This approach is designed to optimize recognition accuracy and improve data reliability in palmprint recognition processes.
- MakaleAçık Erişim2024Enhanced Hysteresis Droop Control for Equivalent Load Sharing in Parallel PWM DC-DC Converters: Optimized Slew Rate by Resonant Filtering
Yanarates, Cagfer; Altan, Aytac
In this study, enhanced hysteresis droop control for equivalent and fast load sharing in parallel PWM DC-DC converters is presented. The basis behind the work encompasses power system stability, reliability, efficiency, reduction of losses and decrease in the operational cost. In this context, phase management, which constitutes the biggest problem, and the resulting factors such as ripple cancellation, thermal stress, transient response behavior and complexity, is aimed to manage with the proposed control scheme. Performance evaluation of the proposed system is based on sensitivity analysis against possible disturbance and noise, and comparative analysis with PI controller against uncertainty and parameter variations using mathematical modeling and simulation techniques. The results show that the proposed controller has improved performance in both noise attenuation and disturbance rejection at typical switching frequencies of DC-DC converters ranging from 100 kHz to a few MHz. Additionally, applying the proposed control technique considering 10% parameter variations led to the largest difference being recorded at 4% while this difference was 18.68% in conventional PI control. According to the findings of the simulation, the proposed control scheme reduces the settling time by a factor of 4.89, whereas it takes 775.862 μs seconds with the PI controller. Furthermore, with the suggested controller, the overshoot - which was 36.09% with the PI controller - is reduced to just 1%.
- MakaleAçık Erişim2024Optimizing Memristor-Based Synaptic Devices for Enhanced Energy Efficiency and Accuracy in Neuromorphic Machine Learning
Gökgöz, Baki; Aydin, Tolga; Gül, Fatih
The traditional Von Neumann computing architecture, which necessitates data transfer between external memory and the processor, incurs significant energy and time costs when running deep learning (DL) and machine learning (ML) architectures. The primary issue with the energy and time efficiency of this architecture stems from the frequent and intensive data transfers between memory and the processor. Therefore, memristive synaptic devices are utilized to overcome this energy and time inefficiency while performing cognitive tasks. The fundamental working principle of memristive devices is to reduce the need for data transfer by combining memory and processing in the same location, thereby significantly decreasing both energy consumption and the time required for operations. However, to achieve the desired level of efficiency in terms of energy and time consumption from neuromorphic systems, the performance of these systems needs to be further improved with respect to accuracy and test error rates for classification applications. Achieving high accuracy performance in such deep learning or machine learning models necessitates optimization processes not only at the hardware level but also at the algorithmic level. In this context, this paper presents a comprehensive examination and comparison of the frequently used SGD and its momentum variants for deep learning and machine learning applications in memristor-based neuromorphic computing systems. The study thoroughly investigates the performance of critical metrics such as the learning properties, energy efficiency, and accuracy rates of the nano-scale titanium dioxide (TiO2) based synaptic device. The experimental results for the MNIST dataset showed AdaDelta 89.48%, AdaGrad 79.00%, Adam 79.13%, AdaMax 79.68%, Momentum 88.55%, Nadam 81.20%, RMSprop 84.91% and SGD 89.47% accuracy. The experimental results for the CIFAR dataset showed AdaDelta 90.51%, AdaGrad 82.08%, Adam 83.10%, AdaMax 81.76%, Momentum 91.25%, Nadam 82.45%, RMSprop 88.11% and SGD 90.21% accuracy. 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
- BildiriYayıncıda açık erişim2024High Performance Automatic ROI Extraction from Hand Images with Convolutional Neural Networks
Kosmaz Sunnetci, Betul; Bingol, Ozkan; Gedikli, Eyup; Ekinci, Murat
Automatic ROI extraction from hand images is a critical step in palmprint recognition. Traditional methods involve steps such as identifying the region of the hand, finding the valleys between the fingers and marking the palm area. Some of these processes are performed manually and some are performed with image processing techniques. However, these methods are time-consuming, vulnerable to human error, and computationally redundant. In this study, a deep learning-based convolutional neural network is developed for high-performance automatic ROI extraction from hand images. The proposed method eliminates the hand segmentation process and provides a solution where the palm region can be automatically selected directly. This reduces processing time, minimizes human error and improves overall accuracy.
- MakaleAçık Erişim2024Optical Filter Design for Multi-Color VLC in Reflective Environments with Multiple Lighting Sources
Kucuk, Mehmet A.; Albayrak, Cenk; Turk, Kadir; Yanikomeroglu, Halim
Multi-color visible light communication (MC-VLC) systems, which aim to transmit data in parallel with multi-color LEDs, use optical filters to separate color channels at the receiver side. However, the relatively wide spectrum of LEDs may cause inter-color interference (ICI). In addition, the passband of the optical filters shifts towards the blue wavelength depending on the angle of incidence (AoI) of the light, which may also increase ICI. In this paper, the problem of designing optical filters for MC-VLC systems in the presence of the blue-shift is investigated. The communication environment is assumed to be a typical room illuminated by multiple fixtures in accordance with lighting standards. Moreover, the walls in the room are reflective, resulting in non-line-of-sight (NLoS) components. The passband edges of the optical filter are optimized to maximize the average signal-to-interference-plus-noise ratio (SINR). The effect of the field-of-view (FOV) of the receiver on the system performance is also analyzed. Finally, a generic optical filter has been designed that can be used in standards-compliant environments for selected LEDs and a given FOV range. The results show that the designed optical filter provides a higher and uniform capacity than those in the literature.
- MakaleYalnız künye2024The Elephant in the Room: New Skills and Work Dimensions of Turkish White Goods Industry Engineers in Industry 4.0 Era
Demirbag, Kubra Simsek; Yildirim, Nihan
While the emergence of the Industry 4.0 (I4.0) concept has heightened concerns about how digital transformation will affect employees, engineers who create, develop, and spread the I4.0 tools and technologies are ignored. Also, the industries with the highest digital maturity should be prioritized since they are the readiest ones for the I4.0 transformation. In this context, our focus is the engineers in the Turkish white goods industry, one of the three industries with the highest level of digital maturity in Turkey. We aim to determine the new skills expected from engineers and to uncover the impact of the perceived usage level of I4.0 technologies by engineers on the dimensions of engineering work. In this article, we conducted two separate surveys for engineers and managers and sought answers to four research questions using factor analysis and regression analysis. The results show that the new skills are divided into intrinsic motivation, technology, and data and information skills. At the same time, the perceived usage level of I4.0 technologies influences the social characteristics dimension of engineering work. We revealed that the sociability level of the engineers who use data analytics, artificial intelligence, simulation, and RFID/RTLS technologies while performing their jobs tends to decrease, while the sociability level of the engineers who use adaptive robots and additive manufacturing technologies gets higher.
- BildiriYalnız künye2023An Extensive Study: Creation of A New Inverted Microscope Image Data Set and Improving Auto-Encoder Models for Higher Accuracy Segmentation of HaCaT Cell Culture Line
Sevim, Zeynep; Dogan, Hulya; Demir, Zeynep; Sezen, Feride Sena; Dogan, Ramazan Ozgur
In cell culture investigations, it is assumed that the number of cells is counted automatically without the need for forward planning, additional tools, or consumables. The automatic cell counting process is greatly facilitated and made functional by the segmentation of cell culture lines. Many studies with high-accuracy results have been developed for cell or tissue segmentation in images acquired from microscopic systems, but this success cannot be achieved for segmentation of cell culture line (especially in images acquired from inverted microscope). There are still restrictions in segmentation of cell culture line studies in inverted microscope systems. In this context, a new inverted microscope image data set is created and higher accuracy auto-encoder models are developed for segmentation of HaCat cell culture line. The analyzes of the developed auto-encoder models are evaluated using four different performance evaluation procedures which are Accuracy, Sensitivity, Specificity and Jaccard. In terms of the performance evaluation procedures used in this study, the achievements of the auto-encoder models are clearly satisfactory for the automated segmentation of the HaCat cell culture line.
- BildiriYalnız künye2023Current Sources and Actual PV Modules based Photovoltaic (PV) Emulator for Testing MPPT Algorithms and PV Inverters
Zhou, Zhongfu; Wang, Yidong; Yanarates, Cagfer; MacAulay, John
PV array emulator has been widely used for testing PV inverters and their control algorithms such as maximum power point tracking (MPPT) algorithms. Various power electronics converter-based PV emulators have been presented in many research papers. This paper presents a PV emulator that is based on indoor solar panels (without solar irradiance) and external current source (dc power supply). The external current source is used to emulate the photo current that is usually generated by solar panel under sunlight. The advantages of the proposed PV emulator are that it can generate similar electrical characteristics of actual PV panel without sunlight, it is suitable for testing PV inverters and MPPT algorithms in indoor conditions where the sunlight is usually very weak. In addition, a PV array emulator that can emulate electrical performance under partial shading conditions was constructed by using two solar panels and two current sources. Test results show that the proposed PV emulator provides a similar performance of real PV array under real sun light. It provides a low-cost solution for testing PV inverters and control algorithms inside a laboratory environment.