arXiv subjects

Mehmet Parlak

Publications and source records attributed to Mehmet Parlak.

5 recordsLinked to original sources

arXiv2026

From Equations to Algorithms and Data: Transforming Microwave Engineering and Education with Machine Learning

Conventional microwave engineering education relies heavily on analytical methods, canonical circuit topologies, and intuition-driven design, which have proven effective at microwave frequencies. However, as systems increasingly operate in the millimeter-wave and terahertz regimes, parasitic effects, process-dependent electromagnetic interactions, and ultra-wideband performance requirements challenge both topology/layout-constrained traditional design methodologies and existing teaching paradigms. This paper proposes a pedagogical shift in microwave and RFIC (Radio Frequency Integrated Circuit) engineering and education by introducing machine-learning (ML) and data-driven electromagnetic synthesis as a complementary design framework for microwave circuits such as power dividers and combiners, couplers, and baluns. Rather than emphasizing predefined topologies, the proposed approach enables topology-agnostic, performance-oriented exploration of the design space, allowing students to directly engage with electromagnetic behavior through specification-driven synthesis. By integrating machine-learning-based inverse design and multi-objective optimization into the curriculum, the framework enhances physical intuition, encourages design creativity, and better aligns microwave education with emerging industrial practices in high-frequency and ultra-wideband system design.

eess.SP↗

arXiv2026

JCAS-MARL: Joint Communication and Sensing UAV Networks via Resource-Constrained Multi-Agent Reinforcement Learning

Multi-UAV networks are increasingly deployed for large-scale inspection and monitoring missions, where operational performance depends on the coordination of sensing reliability, communication quality, and energy constraints. In particular, the rapid increase in overflowing waste bins and illegal dumping sites has created a need for efficient detection of waste hotspots. In this work, we introduce JCAS-MARL, a resource-aware multi-agent reinforcement learning (MARL) framework for joint communication and sensing (JCAS)-enabled UAV networks. Within this framework, multiple UAVs operate in a shared environment where each agent jointly controls its trajectory and the resource allocation of an OFDM waveform used simultaneously for sensing and communication. Battery consumption, charging behavior, and associated CO$_2$ emissions are incorporated into the system state to model realistic operational constraints. Information sharing occurs over a dynamic communication graph determined by UAV positions and wireless channel conditions. Waste hotspot detection requires consensus among multiple UAVs to improve reliability. Using this environment, we investigate how MARL policies exploit the sensing-communication-energy trade-off in JCAS-enabled UAV networks. Simulation results demonstrate that adaptive pilot-density control learned by the agents can outperform static configurations, particularly in scenarios where sensing accuracy and communication connectivity vary across the environment.

cs.IT↗

arXiv2026

UAV-MARL: Multi-Agent Reinforcement Learning for Time-Critical and Dynamic Medical Supply Delivery

Unmanned aerial vehicles (UAVs) are increasingly used to support time-critical medical supply delivery, providing rapid and flexible logistics during emergencies and resource shortages. However, effective deployment of UAV fleets requires coordination mechanisms capable of prioritizing medical requests, allocating limited aerial resources, and adapting delivery schedules under uncertain operational conditions. This paper presents a multi-agent reinforcement learning (MARL) framework for coordinating UAV fleets in stochastic medical delivery scenarios where requests vary in urgency, location, and delivery deadlines. The problem is formulated as a partially observable Markov decision process (POMDP) in which UAV agents maintain awareness of medical delivery demands while having limited visibility of other agents due to communication and localization constraints. The proposed framework employs Proximal Policy Optimization (PPO) as the primary learning algorithm and evaluates several variants, including asynchronous extensions, classical actor--critic methods, and architectural modifications to analyze scalability and performance trade-offs. The model is evaluated using real-world geographic data from selected clinics and hospitals extracted from the OpenStreetMap dataset. The framework provides a decision-support layer that prioritizes medical tasks, reallocates UAV resources in real time, and assists healthcare personnel in managing urgent logistics. Experimental results show that classical PPO achieves superior coordination performance compared to asynchronous and sequential learning strategies, highlighting the potential of reinforcement learning for adaptive and scalable UAV-assisted healthcare logistics.

cs.LG↗

arXiv2023

Blockchain-based Immutable Evidence and Decentralized Loss Adjustment for Autonomous Vehicle Accidents in Insurance

In case of an accident between two autonomous vehicles equipped with emerging technologies, how do we apportion liability among the various players? A special liability regime has not even yet been established for damages that may arise due to the accidents of autonomous vehicles. Would the immutable, time-stamped sensor records of vehicles on distributed ledger help define the intertwined relations of liability subjects right through the accident? What if the synthetic media created through deepfake gets involved in the insurance claims? While integrating AI-powered anomaly or deepfake detection into automated insurance claims processing helps to prevent insurance fraud, it is only a matter of time before deepfake becomes nearly undetectable even to elaborate forensic tools. This paper proposes a blockchain-based insurtech decentralized application to check the authenticity and provenance of the accident footage and also to decentralize the loss-adjusting process through a hybrid of decentralized and centralized databases using smart contracts.

cs.CR↗

arXiv2023

Use Cases for Time-Frequency Image Representations and Deep Learning Techniques for Improved Signal Classification

Time-frequency images (TFIs) provide a joint time-frequency representation of a signal and have become an effective tool for analyzing, characterizing, and processing non-stationary signals. Deep learning (DL) techniques have become versatile for signal classification, enabling the automatic extraction of relevant features from raw data. In this paper, we present two use cases on the time-frequency transformation and deep learning techniques for signal classification, where signals are first pre-processed and transformed into TFIs, and their features are then extracted through deep learning neural networks and classification algorithms. The specific methods and algorithms used may vary depending on the particular application, therefore different methods for creating TFIs; the Short-Time Fourier Transform (STFT), Fourier-based Synchrosqueezing Transform (FSST), Wigner Ville distribution (WVD), Smoothed Pseudo-Wigner distribution (SPWD), Choi-Williams distribution (CWD), and Continuous Wavelet Transform (CWT) are investigated. The performance of various deep learning, and convolutional neural network (CNN) models such as ResNet-50, ShuffleNet, and Squeezenet are evaluated for their accuracy of classification in different applications and the results are compared with the results of the conventional machine learning and ensemble methods such as Multilayer Perceptrons (MLP), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and XGboost. The results of this research demonstrate that significant improvements in signal classification accuracy can be achieved by leveraging the combined power of TFIs, and deep learning models. These advances have found practical applications in a wide range of fields, including radar signal classification, stability analysis of power systems, speech and music recognition, and biomedical signal characterization.

eess.SP↗