Scholarly Publications - Electrical and Electronics Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11693/115599
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Item Open Access Detection and classification architecture for sdr based radar electronic support measure systems(IEEE, 2024-06-23) Yavuz, Göktuğ Sami; Saygılı, Berkay; Aydınlı, Yasin; Dalkıran, Rıfat; Esin, Irem; Uluçay, Merit; Uykulu, Batuhan; Kıyma, Sümeyye Sena; Arıkan, Orhan; Yıldız, Ayberk YarkınElectronic Support Measures (ESM) devices are key to situational awareness of the electromagnetic environment in the field. However, the current ESM systems tend to be physically large and cumbersome. To mitigate this problem, a portable ESM device is proposed. In this work, a compact single-board computer (SBC) is coupled with a Software Defined Radio (SDR) to create such a device. Signals received by the SDR are sampled within the SDR and sent to the SBC. Those signals are then processed with various signal processing and machine learning algorithms to perform detection, measurement, and classification tasks. Later, these results are reported to the user.Item Open Access Modeling interfering sources in shared queues for timely computations in edge computing systems(Association for Computing Machinery, 2024-10-17) Akar, Nail; Baştopçu, Melih; Ulukus, Sennur; Başar, TamerMost existing stochastic models on age of information (AoI) focus on a single shared server serving status update packets from N > 1 sources where each packet update stream is Poisson, i.e., single-hop scenario. In the current work, we study a two-hop edge computing system for which status updates from the information sources are still Poisson but they are not immediately available at the shared edge server, but instead they need to first receive service from a transmission server dedicated to each source. For exponentially distributed and heterogeneous service times for both the dedicated servers and the edge server, and bufferless preemptive resource management, we develop an analytical model using absorbing Markov chains (AMC) for obtaining the distribution of AoI for any source in the system. Moreover, for a given tagged source, the traffic arriving at the shared server from the N − 1 un-tagged sources, namely the interference traffic, is not Poisson any more, but is instead a Markov modulated Poisson process (MMPP) whose state space grows exponentially with N. Therefore, we propose to employ a model reduction technique that approximates the behavior of the MMPP interference traffic with two states only, making it possible to approximately obtain the AoI statistics even for a very large number of sources. Numerical examples are presented to validate the proposed exact and approximate models.Item Open Access Finite dimensional stabilizing controllers for a class of distributed parameter systems this work was supported in part by TUBITAK project no 123E233(Institute of Electrical and Electronics Engineers Inc., 2024-07-12) Yeğin, Mustafa Oğuz; Özbay, HitayThis paper considers finite dimensional controller design problem for a class of distributed parameter systems. It is assumed that the transfer function of the plant can be written in terms of coprime factors as P=MN/D where M is inner, N is outer and D is rational stable. The proposed controller design can be outlined as follows. First, consider an approximation Nn of the outer part N and design a low order stabilizing controller Kn for Pon=Nn/D. Next, construct a predictor-like internal feedback around Kn; and finally perform rational H∞- approximation of the local predictor feedback in the controller for a finite dimensional implementation. The main idea behind this approach is that it is relatively easy to design simple controllers for rational transfer functions in the form Pon. The inner factor M (which is infinite dimensional) can be treated as a 'time delay', hence the predictor structure. The modeling and controller design steps analyzed here are illustrated on a flexible beam model.Item Open Access Reduction in temperature-dependent fiber-optic gyroscope bias drift by using multifunctional integrated optical chip fabricated on pre-annealed linbo3(MDPI AG, 2024-12-11) Karagoz, Ercan; Aşık, Fatma Yasemin; Gokkavas, Mutlu; Akbaş, Erkut Emin; Yertutanol, Aylin; Ozbay, Ekmel; Ozcan, SadanThe refractive index change obtained after annealed proton exchange (APE) in lithium niobate (LiNbO3) crystals depends on both the proton exchange process carried out in hot acid and the structure of the crystals. In devices produced by the APE method, dislocations and lattice defects within the crystal structure are considered to be primary contributors to refractive index discontinuities and waveguide instability. In this study, the effects of pre-annealing LiNbO3 crystals at 500 degrees C on multifunctional integrated optical chips (MIOCs) were investigated through interferometric fiber-optic gyroscope (IFOG) system-level tests. It was observed that the pre-annealing process resulted in an improvement in the optical throughput of MIOCs (from %34 to %51) and the temperature-dependent bias drift stability of the IFOG (from 0.031-0.038 degrees/h to 0.012-0.019 degrees/h). The angle random walk (ARW) was measured as 0.0056 deg/root h.Item Open Access Robust brain tumor segmentation with deep residual supervision and mixed precision training(IEEE, 2024-06-23) Arslan, Fuat; Yılmaz, Melih Berk; Çukur, TolgaSegmentation of brain tumors from MRI data is an application of great clinical importance in diagnostic evaluation, treatment and operational planning processes. In recently proposed deep learning techniques, supervision is commonly applied to network output segmentation maps, which may lead to deficiencies in learning features in early network stages. In addition, early termination of training or restricting the number of model parameters in order to limit the computational load caused by three-dimensional architectures that process volumetric MRI data may cause performance losses. The novel segmentation method proposed in this study enhanced sensitivity to information in MR images by applying deep residual supervision on feature maps in decoder stages of the neural network. Additionally, it reduces computational complexity by using mixed precision training algorithms, thus providing effective training in short run times. Experiments on the BraTS dataset show that the proposed model yields higher performance than reference techniques while improving computational efficiency.Item Open Access Cumulative regret analysis of the piyavskii–shubert algorithm and its variants for global optimization(Association for the Advancement of Artificial Intelligence, 2024-02-27) Gokcesu, Hakan; Gokcesu, KaanWe study the problem of global optimization, where we analyze the performance of the Piyavskii–Shubert algorithm and its variants. For any given time duration T, instead of the extensively studied simple regret (which is the difference of the losses between the best estimate up to T and the global minimum), we study the cumulative regret up to time T. For L-Lipschitz continuous functions, we show that the cumulative regret is O(Llog T). For H-Lipschitz smooth functions, we show that the cumulative regret is O(H). We analytically extend our results for functions with Hölder continuous derivatives, which cover both the Lipschitz continuous and the Lipschitz smooth functions, individually. We further show that a simpler variant of the Piyavskii–Shubert algorithm performs just as well as the traditional variants for the Lipschitz continuous or the Lipschitz smooth functions. We further extend our results to broader classes of functions, and show that, our algorithm efficiently determines its queries; and achieves nearly minimax optimal (up to log factors) cumulative regret, for general convex or even concave regularity conditions on the extrema of the objective (which encompasses many preceding regularities). We consider further extensions by investigating the performance of the Piyavskii-iShubert variants in the scenarios with unknown regularity, noisy evaluation and multivariate domain. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Item Open Access Generalizable deep mri reconstruction with cross-site data synthesis(IEEE, 2024-06-23) Nezhad, Valiyeh Ansarian; Elmas, Gökberk; Arslan, Fuat; Kabas, Bilal; Çukur, TolgaDeep learning techniques have enabled leaps in MRI reconstruction from undersampled acquisitions. While they yields high performance when tested on data from sites that the training data originates, they suffer from performance losses when tested on separate sites. In this work, we proposed a novel learning technique to improve generalization in deep MRI reconstruction. The proposed method employs cross-site data synthesis to benefit from multi-site data without introducing patient privacy risks. First, MRI priors are captured via generative adversarial models trained at each site independently. These priors are shared across sites, and then used to synthesize data from multiple sites. Afterwards, MRI reconstruction models are trained using these synthetic data. Experiments indicate that the proposed method attains higher generalization against single-site models, and higher site-specific performance against site-average models.Item Open Access Comparison of performances for computerized ionospheric tomography methods(2024-05-12) Yenen, Sinem Deniz; Arıkan, Feza; Arıkan, OrhanSolving the 3-D ionosphere electron density reconstruction prolem is of great importance for studying the effects of the ionosphere on signals. In this study, electron density tomography is performed for a midlatitude region using function-based methods available in the literature. The reconstructed electron density profiles are compared with the vertical electron density profiles of the ionosonde using comparison methods. The method with the most similar results to the ionosonde and the lowest computational complexity is decided.Item Open Access Super resolution mri via upscaling diffusion bridges(2024-06-23) Mirza, Muhammad Usama; Arslan, Fuat; Çukur, TolgaMagnetic Resonance Imaging (MRI) is a powerful medical imaging modality that provides high-resolution anatomical information about tissues. However, the intrinsic trade-off between acquisition time and image quality poses challenges in obtaining high-resolution images within a clinically feasible timeframe. This study introduces a novel approach to acquire high-resolution images in short scan times based on Super-Resolution Diffusion Bridges (SRDB). The proposed method leverages advanced machine learning techniques based on diffusion models to upscale MR images. The While standard diffusion models learn a mapping from Gausssian distributed noise images to target images, SRDB instead learns a mapping from low-resolution MR images to high-resolution images. Unlike the task-independent learning in standard diffusion model, SRDB thus performs task-based learning to improve structural consistency and better preservation of anatomical features. In this way, the trained models help capture fine details that may be missed in standard low-resolution MRI acquisitions.Item Open Access Maximally selective fractional fourier pooling(IEEE, 2024-06-23) Koç, Emirhan; Ekiz, Yunus Emre; Özaktaş, Haldun; Koç, AykutIn traditional image classification models, global average pooling is typically employed in the final layer to mitigate model complexity. However, this approach is prone to loss of information while reducing the complexity. Recent studies have proposed alternatives, replacing this layer by propagating information in various domains. In our work, we propose replacing this conventional pooling layer with a fractional Fourier transform (FrFT) based pooling layer. We first transform the feature of the last convolutional layer to the FrFT domain and transfer only the k-largest coefficients to the following layer in each channel, thereby enhancing efficiency by preserving only the essential information. To support our proposal, we conducted experiments on two datasets using various image classification models. Our results show that the integration of the FrFT as a pooling layer not only improves model performances but also does not add significant computational burden to model complexity.Item Open Access Parameter-Free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy Gradients(Springer, 2024-03-02) Sağlam, Baturay; Mutlu, Furkan Burak; Çiçek, Doğan Can; Kozat, Süleyman SerdarApproximation of the value functions in value-based deep reinforcement learning induces overestimation bias, resulting in suboptimal policies. We show that when the reinforcement signals received by the agents have a high variance, deep actor-critic approaches that overcome the overestimation bias lead to a substantial underestimation bias. We first address the detrimental issues in the existing approaches that aim to overcome such underestimation error. Then, through extensive statistical analysis, we introduce a novel, parameter-free Deep Q-learning variant to reduce this underestimation bias in deterministic policy gradients. By sampling the weights of a linear combination of two approximate critics from a highly shrunk estimation bias interval, our Q-value update rule is not affected by the variance of the rewards received by the agents throughout learning. We test the performance of the introduced improvement on a set of MuJoCo and Box2D continuous control tasks and demonstrate that it outperforms the existing approaches and improves the baseline actor-critic algorithm in most of the environments tested.Item Open Access Binary feature mask optimization for feature selection(Springer UK, 2025) Lorasdağı,Mehmet Efe; Turalı,Mehmet Yiğit; Kozat,Süleyman SerdarWe investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly share the implementation or our code to encourage further research in this area.Item Open Access Gate leakage reduction in AlGaN/GaN HEMTs using in situ ion treatment(IOP Publishing Ltd., 2024-09-23) Nawaz, Muhammad Imran; Gürbüz, Abdulkadir; Salkım, Gurur; Zafar, Salahuddin; Çankaya Akoğlu, Büşra Çankaya; Bek, Alpan; Özbay, EkmelA new in situ treatment method is proposed to reduce the gate leakage in normally-on AlGaN/GaN HEMTs. It consists of O2-Ar ion bombardment before the gate metalization. Ion treatment is found to improve the quality of gate metal and semiconductor interfaces. This process reduces the gate leakage current by around 25 times. The process is validated for wafer level uniformity and temperature dependency against the traditional NH4OH treatment. Ion treated HEMT devices are found to possess two orders of magnitude smaller standard deviations in gate leakage distribution across the wafer. The gate leakage is found to be less dependent on temperature comparatively. The trap energy level of the HEMTs treated using the proposed method is found to be higher than the traditional ones as extracted from Poole-Frenkel electron emission analysis. The new method results in a 0.13 dB improvement in the minimum noise figure of the HEMT on average from DC—16 GHz.Item Open Access Optical signatures of lattice strain in chemically doped colloidal quantum wells(NATURE PORTFOLIO, 2025-01-18) Yu, Junhong; Demir, Hilmi Volkan; Sharma, ManojLattice strain plays a vital role in tailoring the optoelectronic performance of colloidal nanocrystals (NCs) with exotic geometries. Although optical identifications of lattice strain in irregular-shaped NCs or hetero-structured NCs have been well documented, less is known about optical signatures of the sparsely distributed lattice mismatch in chemically-doped NCs. Here, we show that coherent acoustic phonons (CAPs) following bandgap optical excitations in Cu-doped CdSe colloidal quantum wells (CQWs) offer a unique platform for indirectly measuring the dopant-induced lattice strain. By comparing the behavior of CAPs in Cu-doped and undoped CQWs (i.e., vibrational phase/lifetime/amplitude), we have revealed the driving force of CAPs related to the optical screening of lattice strain-induced piezoelectric fields, which thus allows to determine the strain-induced piezoelectric field of similar to 10(2) V/m in Cu-doped CdSe CQWs. This work may facilitate a detailed understanding of lattice strain in chemically-doped colloidal NCs, which is a prerequisite for the design of favorable doped colloids in optoelectronics.Item Open Access Multi-contrast mr image synthesis with a brownian diffusion model(IEEE, 2024-12-05) Kabaş, Bilal; Arslan, Fuat; Nezhad, Valiyeh Ansarian; Çukur, TolgaMagnetic Resonance Imaging (MRI) plays a significant role in medical diagnostics. However, prolonged scan times may hinder its widespread applicability in clinical settings. To mitigate this challenge, certain contrasts within multi-contrast MRI protocols can be excluded, and these target contrasts can then be synthesized from the acquired set of source contrasts retrospectively. Recently introduced generative adversarial and diffusion based MRI synthesis models yield enhanced performance against classical methods, yet there can still benefit from technical improvements. In this study, we propose a Brownian diffusion-based multi-contrast MR image synthesis model. Existing diffusion models synthesize images starting from a Gaussian noise sample, so guidance from the source contrast images are weakened. Conditional denoising diffusion models employs a weak conditioning during reverse process within the denoising network that may result in suboptimal sample generation due to poor convergence to target distribution. Capitalizing Brownian diffusion, the proposed model instead incorporates stronger guidance toward the target contrast distribution via a refined diffusion process. Experimental results suggest that our method attains higher performance in noise reduction and capture of tissue structural details over existing methods.Item Open Access RIS-Aided NLoS monostatic sensing under mobility and angle-doppler coupling(IEEE, 2024-06-03) Ercan, Mahmut Kemal; Keskin, Musa Furkan; Gezici, Sinan; Wymeersch, HenkWe investigate the problem of reconfigurable intelligent surface (RIS)-aided monostatic sensing of a mobile target under line-of-sight (LoS) blockage considering a single-antenna, full-duplex, and dual-functional radar-communications base station (BS). For the purpose of target detection and delay/Doppler/angle estimation, we derive a detector based on the generalized likelihood ratio test (GLRT), which entails a high-dimensional parameter search and leads to angle-Doppler coupling. To tackle these challenges, we propose a two-step algorithm for solving the GLRT detector/estimator in a low-complexity manner, accompanied by a RIS phase profile design tailored to circumvent the angle-Doppler coupling effect. Simulation results verify the effectiveness of the proposed algorithm, demonstrating its convergence to theoretical bounds and its superiority over state-of-the-art mobility-agnostic benchmarks.Item Open Access Improving the temperature stability of mems gyroscope bias with on-chip stress sensors(IEEE, 2024-04-23) Erkan, Derin; Tatar, ErdinçTemperature calibration is commonly used to suppress the bias drift of MEMS inertial sensors. Temperature compensation reduces the bias drift but cannot eliminate it. We report a compensation technique for temperature- induced drifts by incorporating temperature and on-chip stress, for the first time. Adding on-chip stress to the temperature captures the offset behavior with hysteresis more accurately. Our open and closed-loop sense mode temperature sweep results demonstrate almost three-fold offset stability improvement over only temperature calibration for a wide (65 degrees C) temperature range. Temperature and stress sensors provide data about thermal stress and stress mismatches in the sensor stack, respectively. We validate the calibration concept with a MEMS ring gyroscope integrated with eight capacitive stress sensors. We perform the temperature tests with an on-PCB heater that only heats the MEMS die and front-end amplifiers.Item Open Access On temperature effects in a mems ring gyroscope(2024-04-23) Hosseini-Pishrobat, Mehran; Tatar, ErdinçWe report on experimental and analytical investigation of temperature effects in a 3.2mm-diameter, 57kHz ring gyroscope equipped with 16 capacitive stress sensors. According to the well-known ~-60ppm/°C temperature dependency of Young’s modulus of silicon, the temperature coefficient of frequency (TCF) is expected to be ~-30ppm/°C. Our experimentally observed TCFs, however, tend to be ~-14ppm/°C, pointing to thermal stresses as the countering factor. To find the root cause of the measured TCFs, we develop an analytical framework that enables us to calculate the temperature-induced stiffness variations, considering both thermal and mechanical strains. The model successfully predicts changes and hysteretic behavior of frequency over temperature using the measured stress and temperature data.Item Open Access An unrestricted Arnold’s cat map transformation(Springer New York LLC, 2024-02-06) Turan, Mehmet; Gökçay, Erhan; Tora, HakanThe Arnold's Cat Map (ACM) is one of the chaotic transformations, which is utilized by numerous scrambling and encryption algorithms in Information Security. Traditionally, the ACM is used in image scrambling whereby repeated application of the ACM matrix, any image can be scrambled. The transformation obtained by the ACM matrix is periodic; therefore, the original image can be reconstructed using the scrambled image whenever the elements of the matrix, hence the key, is known. The transformation matrices in all the chaotic maps employing ACM has limitations on the choice of the free parameters which generally require the area-preserving property of the matrix used in transformation, that is, the determinant of the transformation matrix to be +/- 1.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm 1.$$\end{document} This reduces the number of possible set of keys which leads to discovering the ACM matrix in encryption algorithms using the brute-force method. Additionally, the period obtained is small which also causes the faster discovery of the original image by repeated application of the matrix. These two parameters are important in a brute-force attack to find out the original image from a scrambled one. The objective of the present study is to increase the key space of the ACM matrix, hence increase the security of the scrambling process and make a brute-force attack more difficult. It is proved mathematically that area-preserving property of the traditional matrix is not required for the matrix to be used in scrambling process. Removing the restriction enlarges the maximum possible key space and, in many cases, increases the period as well. Additionally, it is supplied experimentally that, in scrambling images, the new ACM matrix is equivalent or better compared to the traditional one with longer periods. Consequently, the encryption techniques with ACM become more robust compared to the traditional ones. The new ACM matrix is compatible with all algorithms that utilized the original matrix. In this novel contribution, we proved that the traditional enforcement of the determinant of the ACM matrix to be one is redundant and can be removed.Item Open Access Federated multi-armed bandits under Byzantine attacks(IEEE, 2025) Saday, Artun; Demirel, İlker; Yıldırım, Yiğit; Tekin, CemMulti-armed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward. Federated multi-armed bandits (FMAB) is an emerging framework where a cohort of learners with heterogeneous local models play a MAB game and communicate their aggregated feedback to a server to learn a globally optimal arm. Two key hurdles in FMAB are communication-efficient learning and resilience to adversarial attacks. To address these issues, we study the FMAB problem in the presence of Byzantine clients who can send false model updates threatening the learning process. We analyze the sample complexity and the regret of β-optimal arm identification. We borrow tools from robust statistics and propose a median-of-means (MoM)-based online algorithm, Fed-MoM-UCB, to cope with Byzantine clients. In particular, we show that if the Byzantine clients constitute less than half of the cohort, the cumulative regret with respect to β-optimal arms is bounded over time with high probability, showcasing both communication efficiency and Byzantine resilience. We analyze the interplay between the algorithm parameters, a discernibility margin, regret, communication cost, and the arms’ suboptimality gaps. We demonstrate Fed-MoM-UCB’s effectiveness against the baselines in the presence of Byzantine attacks via experiments.