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★★★★★
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USD 100 /hr
Hire Dr. Oktay K.
United Kingdom
USD 100 /hr

Machine Learning & Remote Sensing Researcher & Data Scientist | Python, Geospatial AI, Satellite Image Analysis

Profile Summary
Subject Matter Expertise
Services
Writing Technical Writing, Newswriting, General Proofreading & Editing
Research Scientific and Technical Research, Systematic Literature Review
Consulting Digital Strategy Consulting, Scientific and Technical Consulting
Data & AI Predictive Modeling, Statistical Analysis, Image Processing, Image Analysis, Algorithm Design-ML, Data Visualization, Big Data Analytics, Text Mining & Analytics, Data Cleaning, Data Processing, Data Insights
Product Development Formulation
Work Experience

Group Lead - Remote Sensing Image and Data Analysis

Cardiff University

June 2025 - Present

Deputy Director - Data Science Academy

Cardiff University

November 2022 - Present

Lecturer

Cardiff University

August 2021 - Present

Research Associate

University of Bristol

March 2018 - July 2021

Research Assistant

Izmir Institute of Technology

March 2012 - January 2018

Visiting Scholar

Istituto di Scienza e Tecnologie dell'Informazione Alessandro Faedo Consiglio Nazionale delle Ricerche

February 2017 - April 2017

Research Assistant

Yaşar University

October 2009 - February 2012

Education

PhD (Electrical-Electronics Engineering)

Izmir Institute of Technology

February 2012 - January 2018

MSc (Electronics and Communication Engineering)

Izmir Institute of Technology

September 2009 - January 2012

BSc (Electronics Engineering)

Istanbul Kültür University

2007 - 2009

Certifications
  • Certification details not provided.
Publications
PREPRINT
Seyed Mahmoud Sajjadi Mohammadabadi, Burak Cem Kara, Can Eyupoglu, Oktay Karakus (2026). A Survey on Hallucination in Large Language Models: Definitions, Detection, and Mitigation .
Seyed Mahmoud Sajjadi Mohammadabadi, Burak Cem Kara, Can Eyupoglu, Oktay Karakus (2025). A Survey on Hallucination in Large Language Models: Definitions, Detection, and Mitigation .
Seyed Mahmoud Sajjadi Mohammadabadi, Burak Cem Kara, Can Eyupoglu, Can Uzay, Mehmet Serkan Tosun, Oktay Karakus (2025). A Survey of Large Language Models: Evolution, Architectures, Adaptation, Benchmarking, Applications, Challenges, and Societal Implications .
Akshay Dagadu Yewle, Oktay Karakus (2024). Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction .
Sudharshan Balaji, Oktay Karakus (2024). Unsupervised Structural Damage Assessment from Space using the Segment Anything Model (USDA-SAM): A Case Study of the 2023 Türkiye Earthquake .
Wanli Ma, Oktay Karakus, Paul L. Rosin (2022). AMM-FuseNet: Attention based Multi Modal Image Fusion Network for Land Cover Mapping .
Oktay Karaku{\c{s}} and Ercan E Kuruoglu and Alin Achim(2020). A Modification of Rician Distribution for SAR Image Modelling . {MDPI} {AG}
(2020). Representation Learning via Cauchy Convolutional Sparse Coding .
(2020). A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling .
(2020). Line Artefact Quantification in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularisation .
(2020). On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty .
(2020). Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties .
(2020). Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation .
JOURNAL ARTICLE
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Oktay Karakus (2026). A Classification-Aware Superresolution Framework for Ship Targets in SAR Imagery . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Rayan Binlajdam, Dulani Meedeniya, Charuni Kosala, Oktay Karakus, Omer Rana, Pablo Ter Wengel, Benoit Goossens, Adisorn Lertsinsrubtavee, Preechai Mekbungwan, Deepak Mishra, et al. (2025). Review on Sustainable Forestry with Artificial Intelligence . ACM Journal on Computing and Sustainable Societies.
Oktay Karakuş, Seyed Mahmoud Sajjadi Mohammadabadi, Burak Cem Kara, Can Eyupoglu, Can Uzay, Mehmet Serkan Tosun (2025). A Survey of Large Language Models: Evolution, Architectures, Adaptation, Benchmarking, Applications, Challenges, and Societal Implications . Electronics.
Oktay Karakuş, Seyed Mahmoud Sajjadi Mohammadabadi, Burak Cem Kara, Can Eyupoglu, Can Uzay, Mehmet Serkan Tosun (2025). A Survey of Large Language Models: Evolution, Architectures, Adaptation, Benchmarking, Applications, Challenges, and Societal Implications . Electronics.
Oktay Karakuş, Alessio Guerra, Marcello Lepre (2025). Quantifying extreme opinions on Reddit amidst the 2023 Israeli–Palestinian conflict . Natural Language Processing Journal.
RKFNet @article{ff134bfc89574a83aceaf92767d4ec04, title = "RKFNet: A novel neural network aided robust Kalman filter", abstract = "Driven by the filtering challenges in linear systems disturbed by non-Gaussian heavy-tailed noise, robust Kalman filters (RKFs) leveraging diverse heavy-tailed distributions have been introduced. However, the RKFs rely on precise noise models, and large model errors can degrade their filtering performance. Also, the posterior approximation by the employed variational Bayesian (VB) method can further decrease the estimation precision. Here, we introduce an innovative RKF method, the RKFNet, which combines the heavy-tailed-distribution-based RKF framework with the deep learning technique and eliminates the need for the precise parameter estimation of the heavy-tailed distributions. To reduce the VB approximation error, the mixing-parameter-based function and the scale matrix are estimated by the incorporated neural network structures. Also, the stable training process is achieved by our proposed unsupervised scheduled sampling (USS) method, where a loss function based on the Student{\textquoteright}s t (ST) distribution is utilised to overcome the disturbance of the noise outliers and the filtering results of the traditional RKFs are employed as reference sequences. Furthermore, the RKFNet is evaluated against various RKFs and recurrent neural networks (RNNs) under three kinds of heavy-tailed measurement noises, and the simulation results showcase its efficacy in terms of estimation accuracy and efficiency.", author = "Pengcheng Hao and Oktay Karakus and Alin Achim", note = "Publisher Copyright: {\textcopyright} 2024 The Authors. Published by Elsevier B.V.", year = "2025", month = may, day = "1", doi = "10.1016/j.sigpro.2024.109856", language = "English", volume = "230", journal = "Signal Processing", issn = "0165-1684", publisher = "Elsevier B.V.", } . Signal Processing.
Oktay Karakuş, Akshay Dagadu Yewle, Laman Mirzayeva (2025). Multi-modal data fusion and deep ensemble learning for accurate crop yield prediction . Remote Sensing Applications: Society and Environment.
Oktay Karakuş, Burak Cem Kara, Can Eyupoglu (2025). (r, k, ε)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, k-Anonymity, and ε-Differential Privacy . IEEE Access.
Oktay Karakuş, Wanli Ma, Paul L. Rosin (2025). DiverseNet: Decision Diversified Semi-Supervised Semantic Segmentation Networks for Remote Sensing Imagery . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Robust Kalman filters based on the sub-Gaussian α-stable distribution @article{bbbb42e73a6b455a86a0a529e6323953, title = "Robust Kalman filters based on the sub-Gaussian α-stable distribution", abstract = "Motivated by filtering tasks under a linear system with non-Gaussian heavy-tailed noise, various robust Kalman filters (RKFs) based on different heavy-tailed distributions have been proposed. Although the sub-Gaussian α-stable (SGαS) distribution captures heavy tails well and is applicable in various scenarios, its potential has not yet been explored for RKFs. The main hindrance is that there is no closed-form expression of its mixing density. This paper proposes a novel RKF framework, RKF-SGαS, where the process noise is assumed to be Gaussian and the heavy-tailed measurement noise is modelled by the SGαS distribution. The corresponding joint posterior distribution of the state vector and auxiliary random variables is approximated by the Variational Bayesian approach. Also, four different minimum mean square error (MMSE) estimators of the scale function are presented. The first two methods are based on the Importance Sampling (IS) and Gauss–Laguerre quadrature (GLQ), respectively. In contrast, the last two estimators combine a proposed Gamma series (GS) based method with the IS and GLQ estimators and hence are called GSIS and GSGL. Besides, the RKF-SGαS is compared with the state-of-the-art RKFs under three kinds of heavy-tailed measurement noises, and the simulation results demonstrate its estimation accuracy and efficiency.", author = "Pengcheng Hao and Oktay Karaku{\c s} and Alin Achim", note = "Publisher Copyright: {\textcopyright} 2024 Published by Elsevier B.V.", year = "2024", month = dec, day = "8", doi = "10.1016/j.sigpro.2024.109574", language = "English", volume = "224", journal = "Signal Processing", issn = "0165-1684", publisher = "Elsevier B.V.", } . Signal Processing.
Oktay Karakuş, Seyda Eyupoglu, Can Eyupoglu, Nigar Merdan (2024). Investigation of Dyeing Characteristics of Merino Wool Fiber Dyed with Sustainable Natural Dye Extracted from Aesculus hippocastanum . Sustainability.
Oktay Karakuş, Seyda Eyupoglu, Can Eyupoglu, Nigar Merdan (2024). Investigation of Dyeing Characteristics of Merino Wool Fiber Dyed with Sustainable Natural Dye Extracted from <i>Aesculus hippocastanum</i> . Sustainability.
Oktay Karakuş, Pengcheng Hao, Alin Achim (2024). Robust Kalman filters based on the sub-Gaussian α-stable distribution . Signal Processing.
Oktay Karakuş, Alec Cook (2024). LLM-Commentator: Novel fine-tuning strategies of large language models for automatic commentary generation using football event data . Knowledge-Based Systems.
Oktay Karakuş, Can Eyupoglu (2024). Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques . Journal of Clinical Medicine.
Oktay Karakuş, Can Eyupoglu (2024). Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques . Journal of Clinical Medicine.
Oktay Karakuş, Abdulkerim Duman, Xianfang Sun, Solly Thomas, James Powell, Emiliano Spezi (2023). RFS+: A Clinically Adaptable and Computationally Efficient Strategy for Enhanced Brain Tumor Segmentation . Cancers.
Oktay Karakuş, Abdulkerim Duman, Xianfang Sun, Solly Thomas, James Powell, Emiliano Spezi (2023). RFS+: A Clinically Adaptable and Computationally Efficient Strategy for Enhanced Brain Tumor Segmentation . Cancers.
Oktay Karakuş, James H. Hewitt (2023). A machine learning approach for player and position adjusted expected goals in football (soccer) . Franklin Open.
Oktay Karakuş, Andrew Rowley (2023). Predicting air quality via multimodal AI and satellite imagery . Remote Sensing of Environment.
A hybrid particle-stochastic map filter @article{544ff7a971714ab4b8a5cab4c346701f, title = "A hybrid particle-stochastic map filter", abstract = "Filtering in nonlinear state-space models is known to be a challenging task due to the posterior distribution being either intractable or expressed in a complex form. One of the most successful methods, particle filtering (PF), although generally outperforming traditional filters, suffers from sample degeneracy. Drawing from optimal transport theory, the stochastic map filter (SMF) accommodates a solution to this problem, but its performance is influenced by the limited flexibility of nonlinear map parameterisation. To alleviate these drawbacks, we propose a hybrid filter which combines the PF and SMF, and hence call it PSMF. Specifically, the PSMF splits the likelihood into two parts, which are then updated by PF and SMF, respectively. The proposed approach adopts systematic resampling and smoothing to break the particle degeneracy caused by the PF. To investigate the influence of the nonlinearity of transport maps, we introduce two variants of the proposed filter, the PSMF-L and PSMF-NL, which are based on linear and nonlinear maps, respectively. The PSMF is tested on various nonlinear state-space models and a nonlinear non-Gaussian target tracking model. The proposed linear PSMF-L outperforms all the reference models for medium-to-large numbers of particles, whilst the PSMF-NL shows better resilience to parameter changes.", author = "Pengcheng Hao and Oktay Karaku{\c s} and Alin Achim", note = "Publisher Copyright: Crown Copyright {\textcopyright} 2023 Published by Elsevier B.V.", year = "2023", month = jun, day = "1", doi = "10.1016/j.sigpro.2023.108969", language = "English", volume = "207", journal = "Signal Processing", issn = "0165-1684", publisher = "Elsevier B.V.", } . Signal Processing.
Oktay Karakuş, Pengcheng Hao, Alin Achim(2023). A hybrid particle-stochastic map filter . Signal Processing. 207. p. 108969. Elsevier {BV}
Oktay Karakuş, Henry Booth, Wanli Ma(2023). High-precision density mapping of marine debris and floating plastics via satellite imagery . Scientific Reports. 13. (1). Springer Science and Business Media {LLC}
Oktay Karakuş, Alessio Guerra(2023). Sentiment analysis for measuring hope and fear from Reddit posts during the 2022 Russo-Ukrainian conflict . Frontiers in Artificial Intelligence. 6. Frontiers Media {SA}
Current Advances in Computational Lung Ultrasound Imaging: A Review @article{07eb0150138a4fbcafad3f89de5490fc, title = "Current Advances in Computational Lung Ultrasound Imaging: A Review", abstract = "In the field of biomedical imaging, ultrasonography has become common practice, and used as an important auxiliary diagnostic tool with unique advantages, such as being non-ionising and often portable. This article reviews the state of the art in medical ultrasound image processing and in particular its applications in the examination of the lungs. First, we briefly introduce the basis of lung ultrasound examination. We focus on (i) the characteristics of lung ultrasonography, and (ii) its ability to detect a variety of diseases through the identification of various artefacts exhibiting on lung ultrasound images. We group medical ultrasound image computing methods into two categories: (1) model-based methods, and (2) data-driven methods. We particularly discuss inverse problem-based methods exploited in ultrasound image despeckling, deconvolution, and line artefacts detection for the former, whilst we exemplify various works based on deep/machine learning, which exploit various network architectures through supervised, weakly supervised, and unsupervised learning for the data-driven approaches.", author = "Tianqi Yang and Oktay Karakus and Nantheera Anantrasirichai and Alin Achim", note = "Publisher Copyright: {\textcopyright} 1986-2012 IEEE.", year = "2023", month = jan, day = "1", doi = "10.1109/TUFFC.2022.3221682", language = "English", volume = "70", pages = "2--15", journal = "IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control", issn = "0885-3010", publisher = "Institute of Electrical and Electronics Engineers (IEEE)", number = "1", } . IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.
Tianqi Yang, Oktay Karakus, Nantheera Anantrasirichai, Alin Achim (2023). Current Advances in Computational Lung Ultrasound Imaging: A Review . IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.
Oktay Karakuş, Wanli Ma, Paul L. Rosin (2022). AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping . Remote Sensing.
Oktay Karakuş, Wanli Ma, Paul L. Rosin (2022). AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping . Remote Sensing.
Modeling and SAR Imaging of the Sea Surface @article{131291a4f5d54744ad88df725c52e7f8, title = "Modeling and SAR Imaging of the Sea Surface: a Review of the State-of-the-Art with Simulations", abstract = "Among other remote sensing technologies, synthetic aperture radar (SAR) has become firmly established in the practice of oceanographic research. Despite solid experience in this field, comprehensive knowledge and interpretation of ocean/sea and vessel wave signatures on radar images are still very challenging. This is not only due to the complex mechanisms involved in the SAR imaging of moving waves: Many technical parameters and scanning conditions vary for different SAR platforms, which also imposes some restrictions on the cross-analysis of their respective images. Numerical simulation of SAR images, on the other hand, allows the analysis of many radar imaging parameters including environmental, ship, or platform related. In this paper, we present a universal simulation framework for SAR imagery of the sea surface, which includes the superposition of sea-ship waves. This paper is the first attempt to cover exhaustively all SAR imaging effects for the sea waves and ship Kelvin wakes scene. The study is based on well proven concepts: the linear theory of sea surface modeling, Michell thin-ship theory for Kelvin wake modeling, and ocean SAR imaging theory. We demonstrate the role of two main factors that affect imaging of both types of waves: (i) SAR parameters and (ii) Hydrodynamic related parameters such as wind state and Froude number. The SAR parameters include frequency (X, C, and L-band), signal polarization (VV, HH), mean incidence angle, image resolution (2.5, 5 and 10 m), variation by scanning platform (airborne or spaceborne) of the range-to-velocity (R/V) ratio, and velocity bunching with associated shifting, smearing and azimuthal cutoff effects. We perform modeling for five wave frequency spectra and four ship models. We also compare spectra in two aspects: with Cox and Munk{\textquoteright}s probability density function (PDF), and with a novel proposed evaluation of ship wake detectability. The simulation results agree well with SAR imaging theory and the example of a real SAR image. The study gives a fuller understanding of radar imaging mechanisms for sea waves and ship wakes.", keywords = "physics.ao-ph", author = "Rizaev, \{Igor G\} and O Karakus and Hogan, \{S J\} and Alin Achim", year = "2022", month = may, day = "1", doi = "10.1016/j.isprsjprs.2022.02.017", language = "English", volume = "187", pages = "120--140", journal = "ISPRS Journal of Photogrammetry and Remote Sensing", issn = "0924-2716", publisher = "Elsevier B.V.", } . ISPRS Journal of Photogrammetry and Remote Sensing.
Oktay Karakus, Ercan E. Kuruoglu, Alin Achim (2022). A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling . IEEE Transactions on Geoscience and Remote Sensing.
Oktay Karakus, Ercan E. Kuruoglu, Alin Achim, Mustafa A. Altinkaya (2022). Cauchy–Rician Model for Backscattering in Urban SAR Images . IEEE Geoscience and Remote Sensing Letters.
Oktay Karakus, Alin Achim (2021). On Solving SAR Imaging Inverse Problems Using Nonconvex Regularization With a Cauchy-Based Penalty . IEEE Transactions on Geoscience and Remote Sensing.
Karakus, O., Kuruoglu, E.E., Achim, A.(2021). A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling . IEEE Transactions on Geoscience and Remote Sensing.
Mayo, P., Karakus, O., Holmes, R., Achim, A.(2021). Representation Learning via Cauchy Convolutional Sparse Coding . IEEE Access. 9.
A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface @article{30d7fdfcbb474569a9a88559b600ef30, title = "A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface", abstract = "The analysis of ocean surface is widely performed using synthetic aperture radar (SAR) imagery as it yields information for wide areas under challenging weather conditions, during day or night, etc. Speckle noise constitutes however the main reason for reduced performance in applications such as classification, ship detection, target tracking and so on. This paper presents an investigation into the despeckling of SAR images of the ocean that include ship wake structures, via sparse regularisation using the Cauchy proximal operator. We propose a closed-form expression for calculating the proximal operator for the Cauchy prior, which makes it applicable in generic proximal splitting algorithms. In our experiments, we simulate SAR images of moving vessels and their wakes. The performance of the proposed method is evaluated in comparison to the L1 and TV norm regularisation functions. The results show a superior performance of the proposed method for all the utilised images generated. ", keywords = "Cauchy proximal operator, Simulated SAR images, Ship wakes, Despeckling", author = "Oktay Karaku{\c s} and Igor Rizaev and Alin Achim", year = "2020", month = dec, day = "11", language = "English", journal = "arXiv", publisher = "Cornell University", } . arXiv.
A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface @article{30d7fdfcbb474569a9a88559b600ef30, title = "A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface", abstract = "The analysis of ocean surface is widely performed using synthetic aperture radar (SAR) imagery as it yields information for wide areas under challenging weather conditions, during day or night, etc. Speckle noise constitutes however the main reason for reduced performance in applications such as classification, ship detection, target tracking and so on. This paper presents an investigation into the despeckling of SAR images of the ocean that include ship wake structures, via sparse regularisation using the Cauchy proximal operator. We propose a closed-form expression for calculating the proximal operator for the Cauchy prior, which makes it applicable in generic proximal splitting algorithms. In our experiments, we simulate SAR images of moving vessels and their wakes. The performance of the proposed method is evaluated in comparison to the L1 and TV norm regularisation functions. The results show a superior performance of the proposed method for all the utilised images generated. ", keywords = "Cauchy proximal operator, Simulated SAR images, Ship wakes, Despeckling", author = "Oktay Karaku{\c s} and Igor Rizaev and Alin Achim", year = "2020", month = dec, day = "11", language = "English", journal = "arXiv", publisher = "Cornell University", } . arXiv.
Oktay Karakus and Nantheera Anantrasirichai and Adrian Basarab and Alin Achim(2020). A non-convex regularization based line artefact quantification method in lung ultrasound imagery for pulmonary disease evaluation . The Journal of the Acoustical Society of America. 148. (4). p. 2735--2735. Acoustical Society of America ({ASA})
Oktay Karakus, Nantheera Anantrasirichai, Amazigh Aguersif, Stein Silva, Adrian Basarab, Alin Achim (2020). Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Patients Via Nonconvex Regularization . IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.
Oktay Karakuş, Ercan E. Kuruoğlu, Mustafa A. Altınkaya (2020). Modelling impulsive noise in indoor powerline communication systems . Signal, Image and Video Processing.
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties @article{8a0e6982a9c3432eb079f567e21fd34c, title = "Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties", abstract = "In this paper, we propose a convex proximal splitting methodology with a non-convex penalty function based on the heavy-tailed Cauchy distribution. We first suggest a closed-form expression for calculating the proximal operator of the Cauchy prior, which then makes it applicable in generic proximal splitting algorithms. We further derive the required condition for minimisation problems with the Cauchy based penalty function that guarantees convergence to the global minimum even though it is non-convex. Setting the system parameters by satisfying the proposed condition keeps the overall cost function convex and it can be minimised via the forward-backward (FB) algorithm. The proposed method based on Cauchy regularisation is evaluated by solving two generic signal processing examples, i.e. 1D signal denoising in the frequency domain and two image reconstruction tasks including de-blurring and denoising. We experimentally verify the proposed convexity conditions for various cases, and show the effectiveness of the proposed Cauchy based non-convex penalt y function over state-of-the-art penalty functions such as L1 and total variation (TV) norms.", keywords = "Non-convex regularisation, Convex optimisation, Cauchy proximal operator, Inverse problems, Denoising, Image reconstruction", author = "Oktay Karakus and \{Mayo Diaz De Leon\}, \{Perla Jazmin\} and Achim, \{Alin M\}", year = "2020", month = oct, day = "21", doi = "10.1109/TSP.2020.3032231", language = "English", volume = "68", journal = "IEEE Transactions on Signal Processing", issn = "1053-587X", publisher = "Institute of Electrical and Electronics Engineers (IEEE)", number = "1", } . IEEE Transactions on Signal Processing.
Oktay Karakus and Perla Mayo and Student Member and Alin Achim(2020). Convergence Guarantees for Non-Convex Optimisation With Cauchy-Based Penalties . IEEE Transactions on Signal Processing. 68. p. 6159--6170. Institute of Electrical and Electronics Engineers ({IEEE})
Oktay Karakus, Igor Rizaev, Alin Achim (2020). Correction to “Ship Wake Detection in SAR Images via Sparse Regularization” . IEEE Transactions on Geoscience and Remote Sensing.
Oktay Karakus and Nantheera Anantrasirichai and Amazigh Aguersif and Stein Silva and Adrian Basarab and Alin Achim(2020). Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Patients Via Nonconvex Regularization . IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 67. (11). p. 2218--2229. Institute of Electrical and Electronics Engineers ({IEEE})
Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization @article{682881bac8cb420aa1ecc78ce3755a5b, title = "Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization", abstract = " In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Despite being non-convex, the proposed technique is guaranteed to convergence through our proposed Cauchy proximal splitting (CPS) method and accurately identifies both horizontal and vertical line artefacts in LUS images. In order to reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method and show a considerable performance gain with 87\% correctly detected B-lines in LUS images of nine COVID-19 patients. In addition, owing to its fast convergence, our proposed method is readily applicable for processing LUS image sequences. ", keywords = "Lung Ultrasound, COVID-19, Line Artefacts, Radon Transform, Cauchy-based penalty", author = "Oktay Karaku{\c s} and Nantheera Anantrasirichai and Amazigh Aguersif and Stein Silva and Adrian Basarab and Alin Achim", note = "Provisional acceptance date added, based on publication information. ", year = "2020", month = aug, day = "12", doi = "10.1109/TUFFC.2020.3016092", language = "English", volume = "67", pages = "2218 -- 2229", journal = "IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control", issn = "0885-3010", publisher = "Institute of Electrical and Electronics Engineers (IEEE)", number = "11", } . IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.
Oktay Karakus and Alin Achim(2020). On Solving SAR Imaging Inverse Problems Using Nonconvex Regularization With a Cauchy-Based Penalty . IEEE Transactions on Geoscience and Remote Sensing. p. 1--13. Institute of Electrical and Electronics Engineers ({IEEE})
(2020). Modelling impulsive noise in indoor powerline communication systems . Signal, Image and Video Processing.
Oktay Karakus, Igor Rizaev, Alin Achim (2020). Ship Wake Detection in SAR Images via Sparse Regularization . IEEE Transactions on Geoscience and Remote Sensing.
Karakuş, O., Kuruoğlu, E.E., Altınkaya, M.A.(2020). Modelling impulsive noise in indoor powerline communication systems . Signal, Image and Video Processing. 14. (8). p. 1655-1661.
Karakus, O., Anantrasirichai, N., Aguersif, A., Silva, S., Basarab, A., Achim, A.(2020). Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Patients Via Nonconvex Regularization . IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 67. (11). p. 2218-2229.
Karakuş, O., Mayo, P., Achim, A.(2020). Convergence guarantees for non-convex optimisation with Cauchy-based penalties . IEEE Transactions on Signal Processing. 68. p. 6159-6170.
Oktay Karakus, Perla Mayo, Alin Achim (2020). Convergence Guarantees for Non-Convex Optimisation With Cauchy-Based Penalties . IEEE Transactions on Signal Processing.
Karakuş, O., Rizaev, I., Achim, A.(2020). Ship Wake Detection in SAR Images via Sparse Regularization . IEEE Transactions on Geoscience and Remote Sensing. 58. (3). p. 1665-1677.
Karakus, O., Rizaev, I., Achim, A.(2020). Erratum: Ship wake detection in sar images via sparse regularization (IEEE Trans. Geosci. Remote Sens. (2020) 58: 3 (1665-1677) DOI: 10.1109/TGRS.2019.2947360) . IEEE Transactions on Geoscience and Remote Sensing. 58. (9). p. 6122-6123.
Ship Wake Detection in SAR Images via Sparse Regularization @article{3f42734df89741288cf59ceb037d3422, title = "Ship Wake Detection in SAR Images via Sparse Regularization", abstract = "In order to analyze synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this article, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e., the generalized minimax concave (GMC) function. Despite being a nonconvex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using a maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and Advanced Land Observing Satellite 2 (ALOS2). The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L₁, Lₚ, nuclear, and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80% success rate.", author = "Oktay Karakus and Rizaev, {Igor G} and Achim, {Alin M}", year = "2019", month = nov, day = "5", doi = "10.1109/TGRS.2019.2947360", language = "English", journal = "IEEE Transactions on Geoscience and Remote Sensing", issn = "0196-2892", publisher = "IEEE Computer Society", } . IEEE Transactions on Geoscience and Remote Sensing.
Ship Wake Detection in SAR Images via Sparse Regularization @article{3f42734df89741288cf59ceb037d3422, title = "Ship Wake Detection in SAR Images via Sparse Regularization", abstract = "In order to analyze synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this article, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e., the generalized minimax concave (GMC) function. Despite being a nonconvex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using a maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and Advanced Land Observing Satellite 2 (ALOS2). The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L₁, Lₚ, nuclear, and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80\% success rate.", author = "Oktay Karakus and Rizaev, \{Igor G\} and Achim, \{Alin M\}", year = "2019", month = nov, day = "5", doi = "10.1109/TGRS.2019.2947360", language = "English", volume = "58", pages = "1665 -- 1677", journal = "IEEE Transactions on Geoscience and Remote Sensing", issn = "0196-2892", publisher = "Institute of Electrical and Electronics Engineers (IEEE)", number = "3", } . IEEE Transactions on Geoscience and Remote Sensing.
Generalized Bayesian model selection for speckle on remote sensing images @article{200e40683e874c8cac76577f7a3d6315, title = "Generalized Bayesian model selection for speckle on remote sensing images", abstract = "Synthetic aperture radar (SAR) and ultrasound (US) are two important active imaging techniques for remote sensing, both of which are subject to speckle noise caused by coherent summation of back-scattered waves and subsequent nonlinear envelope transformations. Estimating the characteristics of this multiplicative noise is crucial to develop denoising methods and to improve statistical inference from remote sensing images. In this paper, reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with a wider interpretation and a recently proposed RJMCMC-based Bayesian approach, trans-space RJMCMC, has been utilized. The proposed method provides an automatic model class selection mechanism for remote sensing images of SAR and US where the model class space consists of popular envelope distribution families. The proposed method estimates the correct distribution family, as well as the shape and the scale parameters, avoiding performing an exhaustive search. For the experimental analysis, different SAR images of urban, forest and agricultural scenes, and two different US images of a human heart have been used. Simulation results show the efficiency of the proposed method in finding statistical models for speckle.", keywords = "envelope distributions, generalized (heavy-tailed) Rayleigh distribution, Reversible jump MCMC, SAR imagery, speckle noise modeling, ultrasound imagery", author = "Oktay Karakus and Kuruoglu, \{Ercan E.\} and Altinkaya, \{Mustafa A.\}", year = "2019", month = apr, day = "1", doi = "10.1109/TIP.2018.2878322", language = "English", volume = "28", pages = "1748--1758", journal = "IEEE Transactions on Image Processing", issn = "1057-7149", publisher = "Institute of Electrical and Electronics Engineers (IEEE)", number = "4", } . IEEE Transactions on Image Processing.
Oktay Karakus, Ercan E. Kuruoglu, Mustafa A. Altinkaya (2019). Generalized Bayesian Model Selection for Speckle on Remote Sensing Images . IEEE Transactions on Image Processing.
Karakus, O., Kuruoglu, E.E., Altinkaya, M.A.(2019). Generalized Bayesian model selection for speckle on remote sensing images . IEEE Transactions on Image Processing. 28. (4). p. 1748-1758.
O. Karakuş, E.E. Kuruoğlu, M.A. Altınkaya (2018). Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling . Signal Processing.
Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling @article{269d89ab772f4080969f3b236ebeb22f, title = "Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling", abstract = "Reversible jump Markov chain Monte Carlo (RJMCMC) is a Bayesian model estimation method, which has been generally used for trans-dimensional sampling and model order selection studies in the literature. In this study, we draw attention to unexplored potentials of RJMCMC beyond trans-dimensional sampling. the proposed usage, which we call trans-space RJMCMC exploits the original formulation to explore spaces of different classes or structures. This provides flexibility in using different types of candidate classes in the combined model space such as spaces of linear and nonlinear models or of various distribution families. As an application, we looked into a special case of trans-space sampling, namely trans-distributional RJMCMC in impulsive data modeling. In many areas such as seismology, radar, image, using Gaussian models is a common practice due to analytical ease. However, many noise processes do not follow a Gaussian character and generally exhibit events too impulsive to be successfully described by the Gaussian model. We test the proposed usage of RJMCMC to choose between various impulsive distribution families to model both synthetically generated noise processes and real-life measurements on power line communications impulsive noises and 2-D discrete wavelet transform coefficients.", author = "Oktay Karakus and Kuruoglu, \{Ercan E.\} and Altinkaya, \{Mustafa A.\}", year = "2018", month = dec, language = "English", volume = "153", pages = "396--410", journal = "Signal Processing", issn = "0165-1684", publisher = "Elsevier B.V.", } . Signal Processing.
Karakuş, O., Kuruoğlu, E.E., Altınkaya, M.A.(2018). Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling . Signal Processing. 153. p. 396-410.
Bayesian Volterra system identification using reversible jump MCMC algorithm @article{6d27bc7d2417432488e050ce5d745f4c, title = "Bayesian Volterra system identification using reversible jump MCMC algorithm", abstract = "Volterra systems have had significant success in modelling nonlinear systems in various real-world applications. However, it is generally assumed that the nonlinearity degree of the system is known beforehand. In this paper, we contribute to the literature on Volterra system identification (VSI) with a numerical Bayesian approach which identifies model coefficients and the nonlinearity degree concurrently. Although this numerical Bayesian method, namely reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with success in various model selection problems, our use is in a novel context in the sense that both memory size and nonlinearity degree are estimated. The aforementioned study ensures an anomalous approach to RJMCMC and provides a new understanding on its flexible use which enables trans-structural transitions between different classes of models in addition to transdimensional transitions for which it is classically used. We study the performance of the method on synthetically generated data including OFDM communications over a nonlinear channel.", author = "Oktay Karakus and Kuruoglu, \{Ercan E.\} and Altinkaya, \{Mustafa A.\}", year = "2017", month = dec, day = "1", doi = "10.1016/j.sigpro.2017.05.031", language = "English", volume = "141", pages = "125--136", journal = "Signal Processing", issn = "0165-1684", publisher = "Elsevier B.V.", } . Signal Processing.
One-day ahead wind speed/power prediction based on polynomial autoregressive model @article{9dc078fb19344ca8b73662c0f7d99c36, title = "One-day ahead wind speed/power prediction based on polynomial autoregressive model", abstract = "Wind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of {\c C}e{\c s}me and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h.", author = "Oktay Karakus and Kuruoglu, \{Ercan E.\} and Altinkaya, \{Mustafa A.\}", year = "2017", month = sep, day = "13", language = "English", volume = "11", pages = "1430--1439", journal = "IET Renewable Power Generation", issn = "1752-1416", publisher = "John Wiley \& Sons, Inc", number = "11", } . IET Renewable Power Generation.
Karakuş, O., Kuruoğlu, E.E., Altınkaya, M.A.(2017). Bayesian Volterra system identification using reversible jump MCMC algorithm . Signal Processing. 141. p. 125-136.
Karakuş, O., Kuruoǧlu, E.E., Altinkaya, M.A.(2017). One-day ahead wind speed/power prediction based on polynomial autoregressive model . IET Renewable Power Generation. 11. (11). p. 1430-1439.
Karakuş, O., Kuruoǧlu, E.E., Altinkaya, M.A.(2017). One-day ahead wind speed/power prediction based on polynomial autoregressive model . IET Renewable Power Generation. 11. (11). p. 1430-1439.
(2014). Perceptual quality evaluation of asymmetric stereo video coding for efficient 3D rate scaling . Turkish Journal of Electrical Engineering & Computer Sciences.
&#214;zbek, N., Ertan, G., Karakuş, O.(2014). Perceptual quality evaluation of asymmetric stereo video coding for efficient 3D rate scaling . Turkish Journal of Electrical Engineering and Computer Sciences. 22. (3). p. 663-678.
BOOK CHAPTER
Oktay Karakuş, Hasan Arkadaş (2026). Through the Gaps: Uncovering Tactical Line-Breaking Passes with Clustering .
(2013). The Performance Comparison of European DTTV Standards with LDPC-Encoded-DVB-T Standard under AWGN Channel . Advances in Intelligent Systems and Applications - Volume 1.
CONFERENCE PAPER
Dubline @inproceedings{c2e13fa035bf4834b9aadd586b027d4e, title = "Dubline: a Deep Unfolding Network for B-Line Detection in Lung Ultrasound Images", abstract = "In the context of lung ultrasound, the identification of B-lines, which serve as indicators of interstitial lung disease and pulmonary edema, holds immense significance in clinical diagnosis. Presently, although vision-based automatic B-line detection techniques have emerged, their performance remains suboptimal. This paper introduces a novel approach, framing B-line detection as an inverse problem through the deep unfolding of the Alternating Direction Method of Multipliers. By leveraging the capabilities of deep neural networks and model-based methods, this methodology addresses the challenges associated with data labeling and model training in lung ultrasound image analysis. Our primary aim is to significantly augment diagnostic precision while maintaining efficient real-time capabilities. The experiment on 34 patients demonstrates that the proposed method outperforms traditional model-based approaches, achieving a 10.6\% higher F1 score and running over 90 times faster, underscoring its potential for real-time clinical utility.", keywords = "ADMM, deep unfolding, inverse problem, line detection, lung ultrasound", author = "Tianqi Yang and Nantheera Anantrasirichai and Oktay Karakus and Marco Allinovi and Koydemir, \{Hatice Ceylan\} and Alin Achim", note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 ; Conference date: 27-05-2024 Through 30-05-2024", year = "2024", month = aug, day = "22", doi = "10.1109/ISBI56570.2024.10635643", language = "English", isbn = "9798350313345", series = "Proceedings - International Symposium on Biomedical Imaging", publisher = "IEEE Computer Society", booktitle = "IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings", address = "United States", url = "https://biomedicalimaging.org/2024/", } . IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings.
Karakus, O., Kuruoglu, E.E., Achim, A.(2021). A modification of Rician distribution for SAR image modelling . Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. 2021-March. p. 587-592.
Igor Rizaev and Oktay Karakus and S. John Hogan and Alin Achim(2020). The Effect Of Sea State On Ship Wake Detectability In Simulated Sar Imagery . 2020 IEEE International Conference on Image Processing (ICIP). {IEEE}
Tianqi Yang and Oktay Karakus and Alin Achim(2020). Detection Of Ship Wakes In Sar Imagery Using Cauchy Regularisation . 2020 IEEE International Conference on Image Processing (ICIP). {IEEE}
(2020). Modelling Sea Clutter In Sar Images Using Laplace-Rician Distribution . ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Rizaev, I., Karakus, O., Hogan, S.J., Achim, A.(2020). The Effect of Sea State on Ship Wake Detectability in Simulated Sar Imagery . Proceedings - International Conference on Image Processing, ICIP. 2020-October. p. 3478-3482.
Yang, T., Karakus, O., Achim, A.(2020). Detection of Ship Wakes in Sar Imagery Using Cauchy Regularisation . Proceedings - International Conference on Image Processing, ICIP. 2020-October. p. 3473-3477.
Karakus, O., Kuruoglu, E.E., Achim, A.(2020). Modelling Sea Clutter in Sar Images Using Laplace-Rician Distribution . ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2020-May. p. 1454-1458.
Karakus, O., Rizaev, I., Achim, A.(2020). A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface . International Geoscience and Remote Sensing Symposium (IGARSS). p. 1568-1571.
Ship Wake Detection in X-band SAR Images Using Sparse GMC Regularization @inproceedings{c082dc09b8e24ab29dffb4e5e973d604, title = "Ship Wake Detection in X-band SAR Images Using Sparse GMC Regularization", abstract = "Ship wakes have crucial importance in the analysis of SAR images of the sea surface due to the information they carry about vessels. Since ship wakes mostly appear as lines in SAR images, line detection methods have been widely used for their identification. In the literature, common practice for detecting ship wakes is to use Hough and Radon transforms in which bright (dark) lines appear as peaks (troughs) points. In this paper, the ship wake detection problem is addressed as a Radon transform based inverse problem with a sparse non-convex generalized minimax concave (GMC) regularization. Despite being a non-convex regularizer, the GMC penalty enforces the cost function to be convex. The solution to this convex cost function optimisation is obtained in a Bayesian formulation and the lines are recovered as maximum a posteriori (MAP) point estimates with a sparse GMC based prior. The detection procedure consists of a restricted area search in the Radon domain and the validation of candidate wakes. The performance of the proposed method is demonstrated in TerraSAR-X images of five different ships and with a total of 19 visible ship wakes. The results show a successful detection performance of up to 84\% for the utilised images.", keywords = "GMC Regularization, Inverse Problem, MAP Estimation, Ship Wake Detection", author = "Oktay Karakus and Alin Achim", year = "2019", month = apr, day = "16", doi = "10.1109/ICASSP.2019.8683489", language = "English", series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings", publisher = "Institute of Electrical and Electronics Engineers (IEEE)", pages = "2182--2186", booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings", address = "United States", note = "44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019", } . 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings.
Karakus, O., Achim, A.(2019). Ship Wake Detection in X-band SAR Images Using Sparse GMC Regularization . ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2019-May. p. 2182-2186.
(2017). Nonlinear model selection for PARMA processes using RJMCMC . 2017 25th European Signal Processing Conference (EUSIPCO).
Karakus, O., Kuruoglu, E.E., Alt&#253;nkaya, M.A.(2017). Nonlinear model selection for PARMA processes using RJMCMC . 25th European Signal Processing Conference, EUSIPCO 2017. 2017-January. p. 2056-2060.
(2016). Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity . 2016 24th European Signal Processing Conference (EUSIPCO).
Karakuş, O., Kuruoǧlu, E.E., Altinkaya, M.A.(2016). Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity . European Signal Processing Conference. 2016-November. p. 1543-1547.
(2015). Estimation of the nonlinearity degree for polynomial autoregressive processes with RJMCMC . 2015 23rd European Signal Processing Conference (EUSIPCO).
(2015). Long term wind speed prediction with polynomial autoregressive model . 2015 23nd Signal Processing and Communications Applications Conference (SIU).
Karakuş, O., Kuruoʇlu, E.E., Altinkaya, M.A.(2015). Long term wind speed prediction with polynomial autoregressive model,Polinom Özbaʇlanimli Model ile Uzun Süreli Rüzgar Hizi Öngörüsü . 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings. p. 645-648.
Karakus, O., Kuruoglu, E.E., Altinkaya, M.A.(2015). Estimation of the nonlinearity degree for polynomial autoregressiv processes with RJMCMC . 2015 23rd European Signal Processing Conference, EUSIPCO 2015. p. 953-957.
(2013). The effect of convolutional encoder memory on the sphere decoding search radius in MIMO systems . 2013 21st Signal Processing and Communications Applications Conference (SIU).
Karakus, O., Altinkaya, M.A., Kili&#231;aslan, K.(2013). The effect of convolutional encoder memory on the sphere decoding search radius in MIMO systems,MIMO sistemlerinde evrisimli kodlayici hafizasinin küresel kodçözücü arama yariçapina etkisi . 2013 21st Signal Processing and Communications Applications Conference, SIU 2013.
(2012). European Terrestrial Digital Television standards performance comparison under AWGN channel . 2012 20th Signal Processing and Communications Applications Conference (SIU).
Karakuş, O., &#214;zen, S.(2012). European terrestrial digital television standards performance comparison under AWGN channel,Avrupa karasal sayisal televi̇zyon standartlarinin TBGG kanali etki̇si̇ndeki̇ performans karşilaştirmasi . 2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings.
(2011). Interactive quality assessment for asymmetric coding of 3D video . 2011 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON).
Ozbek, N., Ertan, G., Karakus, O.(2011). Interactive quality assessment for asymmetric coding of 3D video . 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2011 - Proceedings.
OTHER
Yang, T., Karakuş, O., Anantrasirichai, N., Achim, A.(2021). Current advances in computational lung ultrasound imaging: A review . arXiv.
Rizaev, I.G., Karakuş, O., Hogan, S.J., Achim, A.(2021). Modeling and SAR imaging of the sea surface: A review of the state-of-the-art with simulations . arXiv.
Yang, T., Karakuş, O., Achim, A.(2020). Detection of ship wakes in SAR imagery using cauchy regularisation . arXiv.
Karakuş, O., Achim, A.(2020). On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty . arXiv.
Karakuş, O., Anantrasirichai, N., Aguersif, A., Silva, S., Basarab, A., Achim, A.(2020). Line artefact quantification in lung ultrasound images of covid-19 patients via non-convex regularisation . arXiv.
Karakuş, O., Mayo, P., Achim, A.(2020). Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties . arXiv.
Karakuş, O., Kuruoglu, E.E., Achim, A.(2020). A generalized Gaussian extension to the Rician distribution for SAR image modeling . arXiv.
Ma, W., Achim, A., Karakuş, O.(2020). Exploiting the dual-tree complex wavelet transform for ship wake detection in SAR imagery . arXiv.
Karakuş, O., Rizaev, I., Achim, A.(2020). A simulation study to evaluate the performance of the cauchy proximal operator in despeckling SAR images of the sea surface . arXiv.
Karakuş, O., Rizaev, I., Achim, A.(2019). Ship Wake Detection in SAR Images via Sparse Regularisation . arXiv.
Karakuş, O., Kuruoǧlu, E.E., Altinkaya, M.A.(2017). Beyond trans-dimensional RJMCMC: Application to impulsive data modeling . arXiv.
Karakuş, O., Kuruoǧlu, E.E., Altinkaya, M.A.(2017). Beyond trans-dimensional RJMCMC: Application to impulsive data modeling . arXiv.
BOOK
Karakuş, O.(2013). The performance comparison of european DTTV standards with LDPC-encoded-DVB-T standard under AWGN channel . Smart Innovation, Systems and Technologies. 20. p. 683-691.