The Impact Of A Structured Training Program On The Depth Perception Of Ab Initio Drone Pilots,
2026
Edith Cowan University
The Impact Of A Structured Training Program On The Depth Perception Of Ab Initio Drone Pilots, John Murray, Steven Richardson, Keith Joiner, Graham Wild
Research outputs 2022 to 2026
Highlights: What are the main findings? First empirical assessment of depth-perception improvement from an official RPAS training program. Demonstrates measurable perceptual-skill gains despite no explicit syllabus objective for depth perception. What are the implications of the main findings? Conducted in authentic operational settings under regulated conditions, ensuring strong external validity. Establishes a validated baseline for future comparative and longitudinal studies on perceptual training effectiveness. Flying remotely requires accurate perception of the environment to ensure safe operation. While remotely piloted aircraft (RPA) bring unique opportunities, they also present new challenges for the pilot, including exercising accurate depth perception. The impact of …
The Risc-V Fpga (Rvfpga) Teaching Package,
2026
Complutense University of Madrid
The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Roy Kravitz, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Multiple Additional Authors
Electrical and Computer Engineering Faculty Publications and Presentations
RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …
The Risc-V Fpga (Rvfpga) Teaching Package,
2026
Complutense University of Madrid
The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Julio Villalba-Moreno, Alexander Grinshpun, Freddy Gabbay, Luke Seed, Rui Duarte, Manuel Lopez, Oscar Alonso, Robert Owen
Electrical & Computer Engineering Faculty Research
RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …
Iron-Involved Orr Electrocatalysts Under The Lens Of In-Situ/Operando Mössbauer Spectroscopy,
2026
CAS Key Laboratory of Science and Technology on Applied Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning, China 116023; Mössbauer Effect Data Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning, China 116023
Iron-Involved Orr Electrocatalysts Under The Lens Of In-Situ/Operando Mössbauer Spectroscopy, Sumbal Farid, Jun-Hu Wang
Journal of Electrochemistry
Exploring cost-effective and efficient catalysts for oxygen reduction reaction (ORR) poses a significant challenge, especially in the pursuit of alternatives to precious metals like platinum. Significant advancements have driven electrochemists to develop efficient ORR catalysts using abundant materials, particularly iron (Fe)-based, known for their exceptional performance in ORR. While the crucial function of Fe in boosting ORR catalytic activity is recognized, the connection between material attributes and catalytic performance remains enigmatic. Understanding the dynamic processes involved in oxygen electrocatalysis is paramount for designing precious-metals-free ORR electrocatalysts. Mössbauer spectroscopy stands out as a powerful technique for deciphering the structural characteristics of …
The Ntp Anode For Aqueous Sodium Ion Batteries: Recent Advances And Future Perspectives,
2026
School of Materials Science and Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian, China; School of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P R China
The Ntp Anode For Aqueous Sodium Ion Batteries: Recent Advances And Future Perspectives, Ming-Li Wang, Xue-Ying Su, Zheng-Xiang Shan, Shu-Zhe Yang, Heng-Rui Guo, Hao Luo, Dong-Liang Chao
Journal of Electrochemistry
Aqueous sodium-ion batteries (ASIBs) have attracted great attention in aqueous batteries due to their merit of high safety. However, the constrained work potential and insufficient chemical stability of anode materials in aqueous electrolytes hinder the large-scale application of ASIBs. Sodium titanium phosphate, NaTi2(PO4)3 (NTP), is considered one of the most promising anode materials for ASIBs due to its excellent electrochemical performance and tunable structure. Recently, great achievements have been made in the development of NTP, however, a comprehensive review of existing studies is still lacking. This article firstly introduces the basic properties of NTP and …
Development Status And Existing Problems Of Ion-Solvation Membranes For Electrolysis Of Water,
2026
State Key Laboratory of Advanced Fiber Materials, College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China
Development Status And Existing Problems Of Ion-Solvation Membranes For Electrolysis Of Water, Zheng-Yuan Zhou, Yu-Tao Sun, Zheng-Bang Liu, Chuan-Zheng Wang, Yong-Nan Zhou, Xi Luo, Tian-Chi Zhou, Jin-Li Qiao
Journal of Electrochemistry
Ion-solvaing membranes (ISMs) have received extensive attention in recent years as a key component in electrochemical energy conversion and storage devices. This article provides an overview of structural composition, performance advantages, research progress, ion conduction mechanism and existing issues of ISMs, primarily classifying them according to the matrix structure. A detailed analysis of performance enhancement methods, key performance indicators of ISMs and performance influencing factors is also presented. The article contributes to further optimizing the design and application of ion-solvation membranes, providing theoretical support for the development of fields such as hydrogen production through electrolysis of water and electrochemical energy …
Carbon Supported Octahedral Ptni Nanoparticles (Oct-Ptni/C) As A Cathode Catalyst For Proton Exchange Membrane Fuel Cells (Pemfcs) With Improved Activity And Durability,
2026
Yunnan Key Laboratory of Modern Separation Analysis and Substance Transformation, College of Chemistry and Chemical Engineering, Yunnan Normal University, Kunming, Yunnan, 650500, P.R. China
Carbon Supported Octahedral Ptni Nanoparticles (Oct-Ptni/C) As A Cathode Catalyst For Proton Exchange Membrane Fuel Cells (Pemfcs) With Improved Activity And Durability, Zi-Wei Feng, Hai-Zhong Chen, Xiao Duan, Ling Tang, Yun-Kun Zhao, Long Huang
Journal of Electrochemistry
Proton exchange membrane fuel cells (PEMFCs) are considered as a promising renewable power source. However, the massive commercial application of PEMFCs has been greatly hindered by their high expense and less-satisfied performance mainly due to the sluggish oxygen reduction reaction (ORR) kinetics even on state-of-the-art Pt catalyst. Octahedral PtNi nanoparticles (oct-PtNi NPs) with excellent ORR activity in a half-cell have been widely studied, while their performance in membrane electrode assembly (MEA) has much less reported. Herein, we investigated the MEA performance using the carbon supported oct-PtNi NPs (oct-PtNi/C) as the cathode catalyst. Under the mild acid washing condition, the surface …
In-Situ Eval: A Modular Framework For Custom And Real-Time Rag Benchmarking,
2026
University of South Carolina
In-Situ Eval: A Modular Framework For Custom And Real-Time Rag Benchmarking, Ritvik Garimella, Kaushik Roy, Chathurangi Shyalika, Amit Sheth
Publications
Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementations, and evaluation is fragmented. We present In-Situ Eval, a unified and reproducible framework that operationalizes the full RAG pipeline with configurable subsampling strategies and both RAG-specific and generic evaluation metrics. The platform supports two execution modes: an offline Dataset mode for evaluating precomputed outputs, and a live Retrieval mode for benchmarking RAG variants with state-of-the-art LLMs. Users can flexibly select datasets, …
Hybrid Quantum-Classical Optimization Of The Resource Scheduling Problem,
2026
University of Denver
Hybrid Quantum-Classical Optimization Of The Resource Scheduling Problem, Tyler Christeson, Md Habib Ullah, Ali Arabnya, Amin Khodaei, Rui Fan
Electrical and Computer Engineering: Faculty Scholarship
Resource scheduling is critical in many industries, especially in power systems where the Unit Commitment (UC) problem determines the on/off status and output levels of generators under physical and economic constraints. Traditional exact methods, such as Branch-and-Bound, Branch-and-Cut, dynamic programming and mixed-integer linear programming (MILP), remain the backbone of UC solution techniques, but they often rely on linear approximations or exhaustive search, leading to high computational burdens as system size grows. Metaheuristic approaches, such as genetic algorithms, particle swarm optimization, and other evolutionary methods, have been explored to mitigate this complexity; however, they typically lack optimality guarantees, exhibit sensitivity to …
Fuzzy Pi Controller For Frequency Control Of A Diesel-Pv-Battery-Based Islanded Ac Microgrid,
2026
Process Control Technology Dept., Faculty of Technology and Education, Beni-Suef University, Beni-Suef, Egypt
Fuzzy Pi Controller For Frequency Control Of A Diesel-Pv-Battery-Based Islanded Ac Microgrid, M. S. Elborlsy, Ramadan M. Mostafa, Hossam E. Keshta, Mohamed A. Ghalib
Mansoura Engineering Journal
Effective management of modern electrical grids requires intelligent and adaptable control mechanisms to effectively balance power supply and demand, particularly in times of significant disturbances. Microgrids predominantly harness renewable energy sources (RES), which are inherently variable, for electricity generation. However, due to these fluctuations, conventional control systems often struggle to optimize performance under diverse operational conditions. This study addresses the need for improved frequency regulation in isolated AC microgrids (MGs) by proposing a fuzzy PI (FPI) controller capable of dynamically adjusting control strategies to accommodate disturbances such as three-phase faults, sudden changes in load, and variations in solar irradiance. A …
Developing A Collaborative Tool To Foster Communication In Sustainability Research,
2026
Department of Electrical and Electronic Engineering, Munster Technological University, Cork, Ireland
Developing A Collaborative Tool To Foster Communication In Sustainability Research, Nina Hunter, Noëlle-Laetitia Perret, Martin Klepal
Electrical & Electronic Engineering
Climate change necessitates urgent responses based on knowledge produced by teams that transcend disciplinary boundaries, and with members whose work focuses on the generation of knowledge and on the application of knowledge, with some integrating both. Almost a third of Europe’s building stock consists of heritage buildings requiring renovation that is ideally sustainable as part of an energy transition response. The European transdisciplinary CALECHE study team with use-cases in France, Italy, Sweden and Switzerland aims to support decision-making on the sustainable renovation of heritage buildings, via research that employs co-design. However, team members are from various disciplines, with different skillsets …
Cover And Contents,
2026
TÜBİTAK
Cover And Contents
Turkish Journal of Electrical Engineering and Computer Sciences
No abstract provided.
Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model,
2026
College of Computer Science & Technology Harbin Institute of Technology at Weihai, Weihai, China
Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model, Zibo Wang, Yaofang Zhang, Sicai Lv, Yingzhou Wang, Hongri Liu, Bailing Wang
Turkish Journal of Electrical Engineering and Computer Sciences
Exploitation is one of the most significant ways to launch attacks using vulnerabilities. The increasing number of vulnerabilities and limited allocation of security resources make it impossible to eliminate all exploitations. Because not every vulnerability can be fixed, it is necessary to rank exploitations and subsequently assess the residual risk, which is defined as the remaining threat potential after each elimination. In this paper, a structured and flexible decision support framework based on a hybrid multicriteria decision-making model is proposed for prioritizing exploitations and assessing residual risk. Metrics are treated as criteria in the model. The hybrid model is developed …
A Deep Learning-Based Real-Time No-Reference Image Decolorization Network With Perceptual Preservation,
2026
TÜBİTAK
A Deep Learning-Based Real-Time No-Reference Image Decolorization Network With Perceptual Preservation, Mengjuan Zhao, Yitao Liang, Weiya Shi, Juan Xia
Turkish Journal of Electrical Engineering and Computer Sciences
Currently, grayscale images are preferred as input data for some specific vision tasks. Decolorization is the transformation of a color image into a grayscale image. Efficient decolorization algorithms can improve the overall task efficiency, while perceptual preservation in decolorization can provide more information for further processing. In recent research, traditional methods focus on preserving contrast or detail information with little attention to perceptual features. Deep-learning methods are beginning to consider perceptual preservation, but they run inefficiently. In addition, the decolorization methods lack the optimal target grayscale images for reference. Therefore, we propose a new deep learning-based real-time no-reference decolorization network …
Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators,
2026
TÜBİTAK
Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators, Atena Tazikeh Lemeski, Di̇dem Tekgün, Ozan Keysan, Kemal Leblebi̇ci̇oğlu, Murat Göl
Turkish Journal of Electrical Engineering and Computer Sciences
Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in Türkiye. The effects of DE faults on the SPSG’s magnetic equivalent circuit parameters are then examined under various fault severities. A …
A Deep Learning Model For Accurate Tomato Leaf Disease Identification,
2026
Bahria Univerisy, Islamabad E-8, Pakistan
A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood
Turkish Journal of Electrical Engineering and Computer Sciences
Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …
Influence Of Sinr And Noise Variance On Outage Probability For Mimo-Noma System In 5g And Beyond,
2026
TÜBİTAK
Influence Of Sinr And Noise Variance On Outage Probability For Mimo-Noma System In 5g And Beyond, Sadiq Ur Rehman, Jawwad Ahmed, Muhammad Zubair, Syed Sajjad Hussain Rizvi
Turkish Journal of Electrical Engineering and Computer Sciences
Nonorthogonal multiple access (NOMA) communication presents a promising solution to the limitations of traditional orthogonal multiple access techniques, offering potential improvements in achievable rates. Multiple-input multiple-output (MIMO), when combined with NOMA (MIMO-NOMA), further enhances these benefits by leveraging the diversity advantages of multiple antennas. Looking ahead, the future of wireless communication hinges on deploying heterogeneous networks (HetNets), facilitating the coexistence of various wireless access networks in a hierarchical fashion. However, the advent of 5G and 6G communications brings shorter channel coherence times, rendering channel reciprocity unreliable. Consequently, conventional channel estimation methods relying on uplink (UL) pilots for downlink (DL) transmission …
A Novel Approach To Maximum Weighted Traffic Flow Method For Effective Signal Control,
2026
TÜBİTAK
A Novel Approach To Maximum Weighted Traffic Flow Method For Effective Signal Control, Zülal Hi̇lal Yildiz Budak, Seyi̇t Alperen Çeltek, Aki̇f Durdu
Turkish Journal of Electrical Engineering and Computer Sciences
Traffic signal management is a critical challenge due to its environmental, economic, and public health impacts. The maximum weighted flow method (MaxWeightedFlow) was developed to optimize traffic flow at isolated and coordinated urban intersections. This study proposes a new method, the novel MaxWeightedFlow, which includes two key strategies to enhance the classical approach. The first strategy reduces computational burden by estimating vehicle approach times based on instantaneous speeds, improving real-time performance. The second employs regression analysis to optimize the alpha parameter, representing the vehicle waiting coefficient. The proposed approach, the novel MaxWeightedFlow, was evaluated using real-world traffic data from Kilis, …
Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis,
2026
TÜBİTAK
Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …
Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts,
2026
Center for Technical Intelligence Studies and Research, Air Force Institute of Technology
Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz
Faculty Publications
Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …
