arXiv subjects

Mehmet Kemal Ozdemir

Publications and source records attributed to Mehmet Kemal Ozdemir.

10 recordsLinked to original sources

arXiv2026

Radar-Assisted Beam Management Framework for mmWave NTNs: Overhead Reduction and Physical Layer Security Application

Fast and low-overhead beam management is a critical requirement for the practical deployment of non-terrestrial networks (NTNs) operating at millimeter-wave and higher frequencies. In this paper, we propose a radar-assisted beam selection framework for NTNs that limits the set of candidate beams by utilizing spatial sensing information such as the angle-of-departure (AoD) and distance estimations. To provide theoretical insight into the expected worst-case overhead, we conduct a probabilistic analysis under idealized conditions, where an approximation of the worst-case beam selection overhead is proposed and its statistics are derived under Gaussian error. Additionally, the proposed framework is applied to a physical-layer security (PLS) scenario by leveraging the radar's capability to detect passive targets that represent unintended users. The simulation results show that the unintended user's power is suppressed below -135 dBm, while an additional beamforming gain of roughly 2 dB is attained for the legitimate users.

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arXiv2026

Receiver-Centric TDOA Localization Framework for DAB Signals under Synchronization Impairments

In Global Navigation Satellite System (GNSS)-denied environments, terrestrial signals of opportunity (SoOP) offer an alternative for positioning, but synchronization impairments such as clock offsets, drift, and multipath limit performance. This paper proposes a receiver-centric multi-channel time-difference-of-arrival (TDOA) localization framework based on Digital Audio Broadcasting (DAB) signals. The method exploits the DAB null symbol for coarse timing and the phase reference symbol (PRS) for fine synchronization, followed by sub-sample time-of-arrival (TOA) estimation. A double-difference formulation removes inter-receiver clock offsets, while a peak-to-sidelobe ratio (PSR)-based weighting improves robustness. A bias correction step mitigates errors due to multipath. Finally, a coordinated-turn extended Kalman filter (CT-EKF) further refines position estimates. Results show improved accuracy over conventional TDOA with Gauss-Newton estimation, especially in challenging conditions.

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arXiv2026

Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography

Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its corresponding radiology report. Leveraging CT-RATE, we develop CT-CLIP, a CT-focused contrastive language-image pretraining framework designed for broad applications without the need for task-specific training. We demonstrate how CT-CLIP can be used in multi-abnormality detection and case retrieval, and outperforms state-of-the-art fully supervised models across all key metrics. By combining CT-CLIP's vision encoder with a pretrained large language model, we create CT-CHAT, a vision-language foundational chat model for 3D chest CT volumes. Finetuned on over 2.7 million question-answer pairs derived from the CT-RATE dataset, CT-CHAT underscores the necessity for specialized methods in 3D medical imaging. Collectively, the open-source release of CT-RATE, CT-CLIP, and CT-CHAT not only addresses critical challenges in 3D medical imaging but also lays the groundwork for future innovations in medical AI and improved patient care.

cs.CV↗

arXiv2025

DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray

Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential to aid in the analysis of these X-rays, thereby improving the accuracy of dental diagnoses and treatment plans. Nevertheless, designing automated algorithms for this purpose poses significant challenges, mainly due to the scarcity of annotated data and variations in anatomical structure. To address these issues, we organized the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. This challenge aims to promote the development of algorithms for multi-label detection of abnormal teeth, using three types of hierarchically annotated data: partially annotated quadrant data, partially annotated quadrant-enumeration data, and fully annotated quadrant-enumeration-diagnosis data, inclusive of four different diagnoses. In this paper, we present a comprehensive analysis of the methods and results from the challenge. Our findings reveal that top performers succeeded through diverse, specialized strategies, from segmentation-guided pipelines to highly-engineered single-stage detectors, using advanced Transformer and diffusion models. These strategies significantly outperformed traditional approaches, particularly for the challenging tasks of tooth enumeration and subtle disease classification. By dissecting the architectural choices that drove success, this paper provides key insights for future development of AI-powered tools that can offer more precise and efficient diagnosis and treatment planning in dentistry. The evaluation code and datasets can be accessed at https://github.com/ibrahimethemhamamci/DENTEX

cs.CV↗

arXiv2025

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography

Accurate delineation of anatomical structures in volumetric CT scans is crucial for diagnosis and treatment planning. While AI has advanced automated segmentation, current approaches typically target individual structures, creating a fragmented landscape of incompatible models with varying performance and disparate evaluation protocols. Foundational segmentation models address these limitations by providing a holistic anatomical view through a single model. Yet, robust clinical deployment demands comprehensive training data, which is lacking in existing whole-body approaches, both in terms of data heterogeneity and, more importantly, anatomical coverage. In this work, rather than pursuing incremental optimizations in model architecture, we present CADS, an open-source framework that prioritizes the systematic integration, standardization, and labeling of heterogeneous data sources for whole-body CT segmentation. At its core is a large-scale dataset of 22,022 CT volumes with complete annotations for 167 anatomical structures, representing a significant advancement in both scale and coverage, with 18 times more scans than existing collections and 60% more distinct anatomical targets. Building on this diverse dataset, we develop the CADS-model using established architectures for accessible and automated full-body CT segmentation. Through comprehensive evaluation across 18 public datasets and an independent real-world hospital cohort, we demonstrate advantages over SoTA approaches. Notably, thorough testing of the model's performance in segmentation tasks from radiation oncology validates its direct utility for clinical interventions. By making our large-scale dataset, our segmentation models, and our clinical software tool publicly available, we aim to advance robust AI solutions in radiology and make comprehensive anatomical analysis accessible to clinicians and researchers alike.

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arXiv2024

GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model. Without directly comparable methods in 3D medical imaging, we benchmarked GenerateCT against cutting-edge methods, demonstrating its superiority across all key metrics. Importantly, we evaluated GenerateCT's clinical applications in a multi-abnormality classification task. First, we established a baseline by training a multi-abnormality classifier on our real dataset. To further assess the model's generalization to external data and performance with unseen prompts in a zero-shot scenario, we employed an external set to train the classifier, setting an additional benchmark. We conducted two experiments in which we doubled the training datasets by synthesizing an equal number of volumes for each set using GenerateCT. The first experiment demonstrated an 11% improvement in the AP score when training the classifier jointly on real and generated volumes. The second experiment showed a 7% improvement when training on both real and generated volumes based on unseen prompts. Moreover, GenerateCT enables the scaling of synthetic training datasets to arbitrary sizes. As an example, we generated 100,000 3D CTs, fivefold the number in our real set, and trained the classifier exclusively on these synthetic CTs. Impressively, this classifier surpassed the performance of the one trained on all available real data by a margin of 8%. Last, domain experts evaluated the generated volumes, confirming a high degree of alignment with the text prompt. Access our code, model weights, training data, and generated data at https://github.com/ibrahimethemhamamci/GenerateCT

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arXiv2022

AutoCOR: Autonomous Condylar Offset Ratio Calculator on TKA-Postoperative Lateral Knee X-ray

The postoperative range of motion is one of the crucial factors indicating the outcome of Total Knee Arthroplasty (TKA). Although the correlation between range of knee flexion and posterior condylar offset (PCO) is controversial in the literature, PCO maintains its importance on evaluation of TKA. Due to limitations on PCO measurement, two novel parameters, posterior condylar offset ratio (PCOR) and anterior condylar offset ratio (ACOR), were introduced. Nowadays, the calculation of PCOR and ACOR on plain lateral radiographs is done manually by orthopedic surgeons. In this regard, we developed a software, AutoCOR, to calculate PCOR and ACOR autonomously, utilizing unsupervised machine learning algorithm (k-means clustering) and digital image processing techniques. The software AutoCOR is capable of detecting the anterior/posterior edge points and anterior/posterior cortex of the femoral shaft on true postoperative lateral conventional radiographs. To test the algorithm, 50 postoperative true lateral radiographs from Istanbul Kosuyolu Medipol Hospital Database were used (32 patients). The mean PCOR was 0.984 (SD 0.235) in software results and 0.972 (SD 0.164) in ground truth values. It shows strong and significant correlation between software and ground truth values (Pearson r=0.845 p<0.0001). The mean ACOR was 0.107 (SD 0.092) in software results and 0.107 (SD 0.070) in ground truth values. It shows moderate and significant correlation between software and ground truth values (Spearman's rs=0.519 p=0.0001412). We suggest that AutoCOR is a useful tool that can be used in clinical practice.

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arXiv2022

FM Band Channel Measurements and Modeling

As FM coverage is so ubiquitous around the world, several applications can be considered to better exploit this useful band. Thus, it is of significant interest to investigate and characterize channel properties of the FM Band for the potential two-way digital wireless systems. In this paper, we present the results of field measurements at 86 MHz conducted at Gebze, Kocaeli, Turkey. Through the measurements, some of the FM channel characteristics are identified. Measurements are performed for urban, hilly terrain, and rural areas. Our results show that the FM channel expectedly has a large coverage area but at the same time, it possesses large channel excess delays. While most of COST-207 channel power delay profile models are also applicable for the FM Band, for urban environments and hilly terrain environments, channel clusters and excess delays are higher than those of COST-207 models. As 5G systems aim to utilize lower frequency bands for supplementary links, FM Band can be considered as one of the potential bands and the channel models proposed in this study can then be exploited for performance analysis.

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arXiv2020

Identification of The Number of Wireless Channel Taps Using Deep Neural Networks

In wireless communication systems, identifying the number of channel taps offers an enhanced estimation of the channel impulse response (CIR). In this work, efficient identification of the number of wireless channel taps has been achieved via deep neural networks (DNNs), where we modified an existing DNN and analyzed its convergence performance using only the transmitted and received signals of a wireless system. The displayed results demonstrate that the adopted DNN accomplishes superior performance in identifying the number of channel taps, as compared to an existing algorithm called Spectrum Weighted Identification of Signal Sources (SWISS).

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arXiv2018

Optimization of LTE Radio Resource Block Allocation for Maritime Channels

In this study, we describe the behavior of LTE over the sea and investigate the problem of radio resource block allocation in such SINR limited maritime channels. For simulations of such sea environment, we considered a network scenario of Bosphorus Strait in Istanbul, Turkey with different number of ships ferrying between two ports at a given time. After exploiting the network characteristics, we formulated and solved the radio resource allocation problem by max-min integer linear programming method. The radio resource allocation fairness in terms of Jain's fairness index was computed and it was compared with round robin and opportunistic methods. Results show that the max-min optimization method performs better than the opportunistic and round robin methods. This result in turn reflects that the max-min optimization method gives us the high minimum best throughput as compared to other two methods considering different ship density scenarios in the sea. Also, it was observed that as the number of ships begin to increase in the sea, the max-min method performs significantly better with good fairness as compared to the other two methods.

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