Dept. of Electrical and Electronics Engineering - Ph.D. / Sc.D.

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Now showing 1 - 20 of 175
  • ItemOpen Access
    Federated learning and distributed inference over wireless channels
    (Bilkent University, 2023-11) Tegin, Büşra; Duman, Tolga Mete
    In an era marked by massive connectivity and a growing number of connected devices, we have gained unprecedented access to a wealth of information, enhancing the reliability and precision of intelligent systems and enabling the de-velopment of learning algorithms that are more capable than ever. However, this proliferation of data also introduces new challenges for centralized learning algorithms for the training and inference processes of these intelligent systems due to increased traffic loads and the necessity of substantial computational resources. Consequently, the introduction of federated learning (FL) and distributed inference systems has become essential. Both FL and distributed inference necessitate communication within the network, specifically, the transmission of model updates and intermediate features. This has led to a significant emphasis on their utilization over wireless channels, underscoring the pivotal role of wireless communications in this context. In pursuit of a practical implementation of federated learning over wireless fading channels, we direct our focus towards cost-effective solutions, accounting for hardware-induced distortions. We consider a blind transmitter scenario, wherein distributed workers operate without access to channel state information (CSI). Meanwhile, the parameter server (PS) employs multiple antennas to align received signals. To mitigate the increased power consumption and hardware cost, we leverage complex-valued, low-resolution digital-to-analog converters (DACs) at the transmitter and analog-to-digital converters (ADCs) at the PS. Through a combination of theoretical analysis and numerical demonstrations, we establish that federated learning systems can effectively operate over fading channels, even in the presence of low-resolution ADCs and DACs. As another aspect of practical implementation, we investigate federated learning with over-the-air aggregation over time-varying wireless channels. In this scenario, workers transmit their local gradients over channels that undergo time variations, stemming from factors such as worker or PS mobility and other transmission medium fluctuations. These channel variations introduce inter-carrier interference (ICI), which can notably degrade the system performance, particularly in cases of rapidly varying channels. We examine the effects of the channel time variations on FL with over-the-air aggregation, and show that the resulting undesired interference terms have only limited destructive effects, which do not prevent the convergence of the distributed learning algorithm. Focusing on the distributed inference concept, we also consider a multi-sensor wireless inference system. In this configuration, several sensors with constrained computational capacities observe common phenomena and engage in collaborative inference efforts alongside a central device. Given the inherent limitations on the computational capabilities of the sensors, the features extracted from the front part of the network are transmitted to an edge device, which necessitates sensor fusion for the intermediate features. We propose Lp-norm inspired and LogSumExp approximations for the maximum operation as a sensor fusion method, resulting in the acquisition of transformation-invariant features that also enable bandwidth-efficient feature transmission. As a further enhancement of the proposed method, we introduce a learnable sensor fusion technique inspired by the Lp-norm. This technique incorporates a trainable parameter, providing the flexibility to customize the sensor fusion according to the unique network and sensor distribution characteristics. We show that by encompassing a spectrum of behaviors, this approach enhances the adaptability of the system and contributes to its overall performance improvement.
  • ItemEmbargo
    Artificial intelligence-based hybrid anomaly detection and clinical decision support techniques for automated detection of cardiovascular diseases and Covid-19
    (Bilkent University, 2023-10) Terzi, Merve Begüm; Arıkan, Orhan
    Coronary artery diseases are the leading cause of death worldwide, and early diagnosis is crucial for timely treatment. To address this, we present a novel automated arti cial intelligence-based hybrid anomaly detection technique com posed of various signal processing, feature extraction, supervised, and unsuper vised machine learning methods. By jointly and simultaneously analyzing 12-lead electrocardiogram (ECG) and cardiac sympathetic nerve activity (CSNA) data, the automated arti cial intelligence-based hybrid anomaly detection technique performs fast, early, and accurate diagnosis of coronary artery diseases. To develop and evaluate the proposed automated arti cial intelligence-based hybrid anomaly detection technique, we utilized the fully labeled STAFF III and PTBD databases, which contain 12-lead wideband raw recordings non invasively acquired from 260 subjects. Using the wideband raw recordings in these databases, we developed a signal processing technique that simultaneously detects the 12-lead ECG and CSNA signals of all subjects. Subsequently, using the pre-processed 12-lead ECG and CSNA signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of coronary artery diseases. Using the extracted discriminative features, we developed a supervised classi cation technique based on arti cial neural networks that simultaneously detects anomalies in the 12-lead ECG and CSNA data. Furthermore, we developed an unsupervised clustering technique based on the Gaussian mixture model and Neyman-Pearson criterion that performs robust detection of the outliers corresponding to coronary artery diseases. By using the automated arti cial intelligence-based hybrid anomaly detection technique, we have demonstrated a signi cant association between the increase in the amplitude of CSNA signal and anomalies in ECG signal during coronary artery diseases. The automated arti cial intelligence-based hybrid anomaly de tection technique performed highly reliable detection of coronary artery diseases with a sensitivity of 98.48%, speci city of 97.73%, accuracy of 98.11%, positive predictive value (PPV) of 97.74%, negative predictive value (NPV) of 98.47%, and F1-score of 98.11%. Hence, the arti cial intelligence-based hybrid anomaly detection technique has superior performance compared to the gold standard diagnostic test ECG in diagnosing coronary artery diseases. Additionally, it out performed other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it signi cantly increases the detec tion performance of coronary artery diseases by taking advantage of the diversity in di erent data types and leveraging their strengths. Furthermore, its perfor mance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or clas sify coronary artery diseases. It also has a very short implementation time, which is highly desirable for real-time detection of coronary artery diseases in clinical practice. The proposed automated arti cial intelligence-based hybrid anomaly detection technique may serve as an e cient decision-support system to increase physicians' success in achieving fast, early, and accurate diagnosis of coronary artery diseases. It may be highly bene cial and valuable, particularly for asymptomatic coronary artery disease patients, for whom the diagnostic information provided by ECG alone is not su cient to reliably diagnose the disease. Hence, it may signi cantly improve patient outcomes, enable timely treatments, and reduce the mortality associated with cardiovascular diseases. Secondly, we propose a new automated arti cial intelligence-based hybrid clinical decision support technique that jointly analyzes reverse transcriptase polymerase chain reaction (RT-PCR) curves, thorax computed tomography im ages, and laboratory data to perform fast and accurate diagnosis of Coronavirus disease 2019 (COVID-19). For this purpose, we retrospectively created the fully labeled Ankara University Faculty of Medicine COVID-19 (AUFM-CoV) database, which contains a wide variety of medical data, including RT-PCR curves, thorax computed tomogra phy images, and laboratory data. The AUFM-CoV is the most comprehensive database that includes thorax computed tomography images of COVID-19 pneu monia (CVP), other viral and bacterial pneumonias (VBP), and parenchymal lung diseases (PLD), all of which present signi cant challenges for di erential diagnosis. We developed a new automated arti cial intelligence-based hybrid clinical de cision support technique, which is an ensemble learning technique consisting of two preprocessing methods, long short-term memory network-based deep learning method, convolutional neural network-based deep learning method, and arti cial neural network-based machine learning method. By jointly analyzing RT-PCR curves, thorax computed tomography images, and laboratory data, the proposed automated arti cial intelligence-based hybrid clinical decision support technique bene ts from the diversity in di erent data types that are critical for the reliable detection of COVID-19 and leverages their strengths. The multi-class classi cation performance results of the proposed convolu tional neural network-based deep learning method on the AUFM-CoV database showed that it achieved highly reliable detection of COVID-19 with a sensitivity of 91.9%, speci city of 92.5%, precision of 80.4%, and F1-score of 86%. There fore, it outperformed thorax computed tomography in terms of the speci city of COVID-19 diagnosis. Moreover, the convolutional neural network-based deep learning method has been shown to very successfully distinguish COVID-19 pneumonia (CVP) from other viral and bacterial pneumonias (VBP) and parenchymal lung diseases (PLD), which exhibit very similar radiological ndings. Therefore, it has great potential to be successfully used in the di erential diagnosis of pulmonary dis eases containing ground-glass opacities. The binary classi cation performance results of the proposed convolutional neural network-based deep learning method showed that it achieved a sensitivity of 91.5%, speci city of 94.8%, precision of 85.6%, and F1-score of 88.4% in diagnosing COVID-19. Hence, it has compara ble sensitivity to thorax computed tomography in diagnosing COVID-19. Additionally, the binary classi cation performance results of the proposed long short-term memory network-based deep learning method on the AUFM-CoV database showed that it performed highly reliable detection of COVID-19 with a sensitivity of 96.6%, speci city of 99.2%, precision of 98.1%, and F1-score of 97.3%. Thus, it outperformed the gold standard RT-PCR test in terms of the sensitivity of COVID-19 diagnosis Furthermore, the multi-class classi cation performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database showed that it diagnosed COVID-19 with a sen sitivity of 66.3%, speci city of 94.9%, precision of 80%, and F1-score of 73%. Hence, it has been shown to very successfully perform the di erential diagnosis of COVID-19 pneumonia (CVP) and other pneumonias. The binary classi cation performance results of the automated arti cial intelligence-based hybrid clinical decision support technique revealed that it diagnosed COVID-19 with a sensi tivity of 90%, speci city of 92.8%, precision of 91.8%, and F1-score of 90.9%. Therefore, it exhibits superior sensitivity and speci city compared to laboratory data in COVID-19 diagnosis. The performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database demon strate its ability to provide highly reliable diagnosis of COVID-19 by jointly ana lyzing RT-PCR data, thorax computed tomography images, and laboratory data. Consequently, it may signi cantly increase the success of physicians in diagnosing COVID-19, assist them in rapidly isolating and treating COVID-19 patients, and reduce their workload in daily clinical practice.
  • ItemOpen Access
    Timely throughput maximization using multiple access channel
    (Bilkent University, 2023-10) Gamgam, Onur Berkay; Karaşan, Ezhan
    Latency and reliability capabilities of currently available fourth-generation (4G) wireless networks paved the path towards massively connected devices requiring much lower latency and much higher reliability. In the fifth-generation (5G) wireless networks, the concept of ultra-reliable and low-latency communications (URLLC) is introduced to fulfill these demands. URLLC aims to deliver short packets with 1 ms latency with a reliability rate of 99.999%. The cellular Internet of Things (IoT) is a framework for conceptualizing such massive connectivity while addressing fundamental challenges such as the ever-increasing number of interconnected devices, latency constraints, and high-throughput demands. One of the challenging tasks for cellular IoT applications is the delivery of deadline-constrained information to densely deployed IoT devices. Increasing demand for delivering timing-critical information in cellular IoT networks poses a URLLC-oriented challenge for both academia and industry. With this motivation, this thesis aims to develop techniques for reliably transferring short packets to densely deployed devices within a given deadline. In this thesis, we address the problem of latency-constrained communications with strict deadlines under average power constraint using Multiple Access (MA) schemes. The first MA scheme considered in the thesis is Hybrid MA, which consists of both Orthogonal MA (OMA) and power domain Non-Orthogonal MA (NOMA) as transmission scheme options. The second MA scheme studied in the thesis is Rate-Splitting Multiple Access (RSMA), which generalizes OMA, NOMA and Space-Division MA (SDMA) schemes. We maximize the timely throughput, which represents the average number of successfully transmitted packets before deadline expiration, where expired packets are dropped from the buffer. We use Lyapunov stochastic optimization methods to develop a dynamic power assignment algorithm for minimizing the packet drop rate while satisfying time average power constraints. Moreover, we propose a flexible packet dropping mechanism called Early Packet Dropping (EPD) to detect likely to become expired packets and drop them proactively. Finally, we propose a simple heuristic to reduce the computational load of the proposed algorithm. Numerical results show that Hybrid MA improves the timely throughput compared to conventional OMA by up to 46% and on average by more than 21%. With EPD, these timely throughput gains improve to 53% and 24.5%, respectively. Utilization of RSMA with EPD further improves timely throughput by up to 5.95% and on the average by about 3.12% compared to Hybrid MA with EPD. Simulation results indicate that the proposed heuristic significantly reduces the computational load at the cost of a small loss in the timely throughput performance.
  • ItemOpen Access
    Scheduling and queue management for information freshness in multi-source status update systems
    (Bilkent University, 2023-09) Gamgam, Ege Orkun; Akar, Nail
    Timely delivery of information to its intended destination is essential in many ex-isting and emerging time-sensitive applications. While conventional performance metrics like delay, throughput, or loss have been extensively studied in the literature, research concerning the management of age-sensitive traffic is relatively immature. Recently, a number of information freshness metrics have been intro-duced for quantifying the timeliness of information in networked systems carrying age-sensitive traffic, primarily the Age of Information (AoI) and peak AoI (PAoI) metrics as well as their alternatives including Age of Synchronization (AoS), ver-sion age, binary freshness, etc. The focus of this thesis is the development and performance modeling of age-agnostic scheduling and queue management policies in various multi-source status update systems carrying age-sensitive traffic, using the recently introduced information freshness metrics. In this thesis, first, the exact distributions of the AoI and PAoI for the probabilistic Generate-At-Will (GAW) and Random Arrival with Single Buffer (RA-SB) servers are studied with general number of heterogeneous information sources with phase-type (PH-type) service time distributions for which an absorbing Continuous-Time Markov Chains (CTMC) based analytical modeling method, namely AMC (Absorbing Markov Chains) method, is proposed. Secondly, a homogeneous multi-source status update system with Poisson information packet arrivals and exponentially distributed service times is studied for which the server is equipped with a queue holding the freshest packet from each source referred to as Single Buffer Per-Source Queueing (SBPSQ). For this case, two SBPSQ-based scheduling policies are studied, namely First Source First Serve (FSFS) and the Earliest Served First Serve (ESFS) policies, using the AMC method, and it is shown that ESFS presents a promising scheduler for this special setting. Third, a general status update system with two heterogeneous information sources is studied, i.e., sources have different priorities and generally distributed service times, for Deterministic GAW (D-GAW) and Deterministic RA-SB (D-RA-SB) servers. The aim in both servers is to minimize the system AoI/AoS that is time-averaged and weighted across the two sources. For the D-GAW server, the optimal update policy is obtained in closed form. A packet replacement policy, referred to as Pattern-based Replacement (PR) policy, is then proposed for the D-RA-SB server based on the optimal policy structure of the D-GAW server. Finally, scheduling in a cache update system is investigated where a remote server delivers time-varying contents of multiple items with heterogeneous popularities and service times to a local cache so as to maximize the weighted sum binary freshness of the system, and the server is equipped with a queue that holds the most up-to-date content for each item. A Water-filling based Scheduling (WFS) policy and its extension, namely Extended WFS (E-WFS) policy, are proposed based on convex optimization applied to a relaxation of the original system, with low computational complexity and near optimal weighted sum binary freshness performance.
  • ItemEmbargo
    Visible light positioning in presence of malicious LED transmitters or intelligent reflecting surfaces
    (Bilkent University, 2023-09) Kökdoğan, Furkan; Gezici, Sinan
    Visible light positioning (VLP) is a recent solution to the localization problem in indoor environments which involves the use of light emitting diodes (LEDs) as transmitters and photodetectors (PDs) as receivers. VLP systems have in-creasingly been popular as LEDs are employed for illumination purposes over conventional light bulbs nowadays due to their various advantages. In this the-sis, we develop VLP algorithms for two main scenarios. In the first scenario, we assume that the system is not completely secure, meaning that the transmit power of some LEDs can be controlled by a third unknown party, i.e., hijacked, to degrade the positioning accuracy. In the second scenario, we assume the de-ployment of intelligent reflecting surfaces (IRSs) into the system to improve the positioning accuracy in the absence of line-of-sight (LOS) signals from of a subset of LED transmitters. First, we consider a VLP system in which a receiver performs position estimation based on signals emitted from a number of LED transmitters. Each LED transmitter can be malicious and transmit at an unknown power level with a certain probability. A maximum likelihood (ML) position estimator is derived based on the knowledge of probabilities that LED transmitters can be malicious. In addition, in the presence of training measurements, decision rules are designed for detection of malicious LED transmitters, and based on detection results, various ML based location estimators are proposed. To evaluate the performance of the proposed estimators, Cram´er-Rao lower bounds (CRLBs) are derived for position estimation in scenarios with and without a training phase. Moreover, an ML estimator is derived when the probabilities that the LED transmitters can be malicious are unknown. The performances of all the proposed estimators are evaluated via numerical examples and compared against the CRLBs. Second, we formulate and analyze a received power based position estimation problem for VLP systems in the presence of IRSs. In the proposed problem formulation, a visible light communication (VLC) receiver collects signals from a number of LED transmitters via LOS paths and/or via reflections from IRSs. We derive the CRLB expression and the ML estimator for generic three-dimensional positioning in the presence of IRSs with arbitrary configurations. In addition, we consider the problem of optimizing the orientations of IRSs when LOS paths are blocked, and propose an optimal adjustment approach for maximizing the received powers from IRSs based on analytic expressions, which can be solved in closed form or numerically. Since the optimal IRS orientations depend on the actual position of the VLC receiver, an N-step localization algorithm is proposed to perform adjustment of IRS orientations in the absence of any prior knowledge about the position of the VLC receiver. Performance of the proposed approach is evaluated via simulations and compared against the CRLB. It is deduced that although IRSs do no provide critical improvements in positioning accuracy in the presence of LOS signals from a sufficient number of LED transmitters, they can be very important in achieving accurate positioning when all or most of LOS paths are blocked.
  • ItemOpen Access
    Deep-learning for communication systems: new channel estimation, equalization, and secure transmission solutions
    (2023-08) Gümüş, Mücahit; Duman, Tolga Mete
    Traditional communication system design takes a model-based approach that aims to optimize relevant performance metrics using somewhat simple and tractable channel and signal models. For instance, channel codes are designed for simple additive white Gaussian or fading channel models, channel equalization algorithms are based on mathematical models for inter-symbol interference (ISI), and channel estimation techniques are developed with the underlying channel statistics and characterizations in mind. Through utilizing superior mathematical models and expert knowledge in signal processing and information theory, the model-based approach has been highly successful and has enabled development of many communication systems until now. On the other hand, beyond 5G wireless communication systems will further exploit the massive number of antennas, higher bandwidths, and more advanced multiple access technologies. As communication systems become more and more complicated, it is becoming increasingly important to go beyond the limits of the model-based approach. Noting that there have been tremendous advancements in learning from data over the past decades, a major research question is whether machine learning based approaches can be used to develop new communication technologies. With the above motivation, this thesis deals with the development of deep neural network (DNN) solutions to address various challenges in wireless communications. We first consider orthogonal frequency division multiplexing (OFDM) over rapidly time-varying multipath channels, for which the performance of standard channel estimation and equalization techniques degrades dramatically due to inter-carrier in-terference (ICI). We focus on improving the overall system performance by designing DNN architectures for both channel estimation and data demodulation. In addition, we study OFDM over frequency-selective channels without cyclic prefix insertion in an effort to improve the overall throughputs. Specifically, we design a recurrent neu-ral network to mitigate the effects of ISI and ICI for improved symbol detection. Furthermore, we explore secure transmission over multi-input multi-output multi-antenna eavesdropper wiretap channels with finite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which benefits from the generalized singular value decomposition to obtain independent streams and exploits function approximation abilities of DNNs for solving the required power allocation problem. We also propose a DNN technique to jointly optimize the data precoder and the power allocation for artificial noise. We use extensive numerical examples and computational complexity analyses to demonstrate the effectiveness of the proposed solutions.
  • ItemEmbargo
    Essays on gene regulatory network models and their stability analysis
    (Bilkent University, 2023-07) Şener, Dilan Öztürk; Özbay, Hitay
    Gene expression is one of the core areas in comprehending and assessing how biological cells work. Gene regulatory networks (GRNs), representing the intri-cate mechanism between genes and their regulatory modules, are instrumental in controlling gene expression and cell functions. These models shed light on how transcription factors interact with their regulatory modules within a cell. Despite the multitude of studies focusing on the analysis and enhancement of GRNs, there is still room for contributions. This thesis investigates a novel framework inspired by the gene networks constructed using synthetic biology, and presents stability analyses of the nonlinear infinite dimensional dynamical system models arising in this framework. In the first part of the thesis, we extend a previously studied benchmark GRN model including time delay, and present an analysis of the extended frame-work. We utilize unmodeled dynamics and possibly ignored interactions, including higher-order dynamics, in our system design. The stability of the extended system is analyzed by considering various nonlinearity functions and design pa-rameters, and the results are compared with those of the benchmark original model. In the second part, we employ an extension of a gene network model using a multiplicative perturbation of the dynamical system. Each cascaded subsystem in this extended framework has an additional block, including a multiplicative term with a high-pass filter, and the effect of additional parameters on the robustness and delay margin of the system is investigated. Experiments with various design parameters yield that the stability characteristics of GRNs can be improved using the model pertaining to the extension under specific perturbations. Finally, the third part covers the analysis of nonlinear dynamics and chaos in GRNs, particularly focusing on the two-gene original and extended gene net-works. Chaotic dynamics depend strongly on the inclusion of time delays, but the circuit motifs that show chaos differ when both original and extended models are considered. Our results suggest that for a particular higher-order extension of the gene network, it is possible to observe the chaotic dynamics in a two-gene system without adding any self-inhibition. This finding can be explained as a result of the modification of the original benchmark model induced by unmodeled dynamics. We argue that regulatory gene circuit models with additional parameters demonstrate non-periodic dynamics much more easily.
  • ItemOpen Access
    Technical innovations in gradient array systems for MRI applications
    (Bilkent University, 2023-02) Babaloo, Reza; Atalar, Ergin
    In Magnetic Resonance Imaging, gradient array coils have lately been employed in a variety of applications, such as field profiling. This capability of array technology can be used to minimize electric fields induced by gradient waveforms. For this purpose, a whole-body gradient array with all three gradients is being investigated. Gradient current amplitudes are optimized to produce a target magnetic field within a desired region of linearity (ROL) while minimizing induced electric fields. By reducing the diameter of ROL, generating a target gradient within a slice, and relaxing the linearity error, array coil electric fields are significantly reduced compared to a conventional coil. When a linear gradient is required in a small region, higher gradient strengths and slew rates can be achieved without exceeding peripheral nerve stimulation thresholds. Because of a high number of channels in the array design, feedback controllers significantly raise the system cost due to the expensive current sensors used for gradient current measurements. Thus, a nonlinear second-order feed-forward controller is introduced for the gradient array chain. The feed-forward controller is then modified to update the controller coefficients based on thermal behavior prediction to deal with time-varying parameters caused by temperature-dependent resistances. Gradient current measurements and MRI experiments are conducted to show the effectiveness of the proposed method. In the scope of this thesis, novel applications and hardware solutions are proposed to make array technology valuable and feasible.
  • ItemOpen Access
    Learning-based reconstruction methods for magnetic particle imaging
    (Bilkent University, 2023-01) Güngör, Alper; Çukur, Tolga
    Magnetic particle imaging (MPI) is a novel modality for imaging of magnetic nanoparticles (MNP) with high resolution, contrast and frame rate. An inverse problem is usually cased for reconstruction, which requires a time-consuming calibration scan for measuring a system matrix (SM). Previous calibration procedures involve scanning an MNP filled sample with a size that matches desired resolution through field of view. This time-consuming calibration scan which accounts for both system and MNP response imperfections is a critical factor prohibiting its practical use. Moreover, the quality of the reconstructed images heavily depend on the prior information about the MNP distribution as well as the specific re-construction algorithm, since the inverse problem is highly ill-posed. Previous approaches commonly solve an optimization problem based on the measurement model that iteratively estimates the image while enforcing data consistency in an interleaved fashion. However, while conventional hand-crated priors do not fully capture the underlying complex features of MPI images, recently proposed learned priors suffer from limited generalization performance. To tackle these issues, we first propose a deep learning based technique for accelerated MPI calibration. The technique utilizes transformers for SM super-resolution (TranSMS) for accelerated calibration of SMs with high signal-to-noise-ratio. For signal-to-noise-ratio efficiency, we propose scanning a low resolution SM with larger MNP sample size. For improved SM estimation, TranSMS leverages the vision trans-former to capture global contextual information while utilizing the convolutional module for local high-resolution features. Finally, a novel data-consistency module enforces measurement fidelity. TranSMS is shown to outperform competing methods significantly in terms of both SM recovery and image reconstruction performance. Next, to improve image reconstruction quality, we propose a novel physics-driven deep equilibrium based technique with learned consistency block for MPI (DEQ-MPI). DEQ-MPI embeds deep network operators into iterative optimization procedures for improved modeling of image statistics. Moreover, DEQ-MPI utilizes learned consistency to better capture the data statistics which helps improve the overall image reconstruction performance. Finally, compared to previous unrolling-based techniques, DEQ-MPI leverages implicit layers which enables training on the converged output. Demonstrations on both simulated and experimental data show that DEQ-MPI significantly improves image quality and reconstruction time over state-of-the-art reconstructions based on hand-crafted or learned priors.
  • ItemEmbargo
    Wideband distributed choke inductor for distributed power amplifiers
    (Bilkent University, 2023-01) Ballı, Çağdaş; Atalar, Abdullah
    A method to design a wideband, low-loss, and high-current choke inductor suitable for use in a distributed power amplifier (DPA) is presented. The choke inductor is composed of several parallelled low-current inductors with small parasitics placed in a distributed manner. High-frequency gain-limiting parasitic shunt capacitors of these inductors are absorbed by the series inductors already present between the transistor drain terminals of the distributed amplifier. We also provide an analytical procedure to determine the widths of stage transistors of a non-uniform distributed power amplifier (NDPA). Several example designs are presented to demonstrate the use of our method. The measurement results of a 2–18 GHz NDPA designed using a distributed choke inductor with an equivalent DC resistance of 0.32 Ω are given. The MMIC has fully integrated with the on-chip DC blocking capacitors and bias inductors. The results of the CW mode measurement for the MMIC at 28 V supply voltage show greater than 10 dB of small signal gain, 6 W to 12 W output power, and 15% to 28% power-added efficiency in the bandwidth.
  • ItemOpen Access
    Design and development of X-band GaN-based low-noise amplifiers
    (Bilkent University, 2022-12) Zafar, Salahuddin; Özbay, Ekmel
    Gallium nitride (GaN) high electron mobility transistor (HEMT) technology emerged as a preferable candidate for high-power applications. GaN HEMTs on silicon carbide (SiC) substrate provide the best combination of speed and power due to high power density, escalated saturated carrier velocity, high efficiency, enhanced electrical breakdown, and superior thermal conductivity. Over the years, GaN technology also started to take its place in low-noise applications due to built-in power handling capability at the receive end of transceivers for compact designs and high linearity. For GaN-based low-noise amplifiers (LNAs), improving the noise figure (NF) and getting it close to other competitive technologies is always challenging. More-over, further improvement in the robustness of GaN-based LNAs in terms of survivability and reverse recovery time (RRT) is needed. For this purpose, NAN-OTAM’s 0.15 µm GaN on SiC HEMT process is used to realize LNAs, one with survivability as high as 42 dBm and the other having NF as low as 1.2 dB. Survivability is investigated in terms of gain compression and forward gate current, while RRT is explored in detail with respect to the RC time constant of transistor and trap phenomenon. In the LNA design, the significance of inductive source degenerated HEMT, and the role of stability networks towards NF improvement are discussed in detail. Furthermore, thermal simulations and infrared (IR) thermographic measurements of the LNA monolithic microwave integrated circuit are correlated to unveil the maximum channel temperature buried inside the two-dimensional electron gas of HEMT.
  • ItemOpen Access
    Rapid relaxation-based color magnetic particle imaging
    (Bilkent University, 2022-09) Arslan, Musa Tunç; Çukur, Emine Ülkü Sarıtaş
    Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the capability to distinguish different MNPs and/or MNP environments. The system function approach for color MPI relies on extensive calibrations that capture the differences in the harmonic responses of the MNPs. An alternative calibration-free x-space-based method called TAURUS (TAU estimation via Recovery of Underlying mirror Symmetry) estimates a map of the relaxation time constant, τ, by recovering the underlying mirror symmetry in the MPI signal. However, TAURUS requires a back and forth scanning of a given region, restricting its usage to slow trajectories with constant or piecewise constant focus fields (FFs). In this thesis, a novel technique is proposed to increase the performance of TAURUS and enable τ map estimation for rapid and multi-dimensional trajectories. The proposed technique is based on correcting the distortions on mirror symmetry induced by time-varying FFs. Simulations and experiments in an in-house MPI scanner demonstrates that the proposed method successfully estimates high-fidelity τ maps for rapid trajectories that provide orders of magnitude reduction in scanning time (over 300 fold for simulations and over 8 fold for experiments) while preserving the calibration-free property of TAURUS. Additionally, the proposed method can successfully map the effective relaxation time constants in a relatively wide field-of-view at frame rates exceeding 5 frames-per-second. This thesis presents the first simulation results demonstrating that the proposed method is capable of generating high fidelity and high frame-rate color MPI images in real time.
  • ItemOpen Access
    Maximizing the signal-to-noise ratio of diverging ultrasonic waves in multiple scattering, attenuating, and diffracting media
    (Bilkent University, 2022-09) Kumru, Yasin; Köymen, Hayrettin
    Diverging wave imaging is an unfocused imaging method in which a diverging beam is transmitted to insonify the entire region of interest. This diverging beam is formed by applying appropriate time delays to each transducer array element. It provides a higher data acquisition rate and thus a higher temporal resolution, quantified as a higher frame rate. Therefore, diverging wave imaging is widely used in fast ultrasound imaging applications where rates above 1000 frames per second are required. Diverging wave imaging is generally implemented with phased array transducers having a smaller aperture than their counterparts to increase the field of view. Although diverging wave imaging allows for a high frame rate, it has a decreased spatial resolution and limited SNR due to the broader unfocused beam transmission compared to conventional focused imaging techniques. Conventional focused imaging techniques employ focused narrow beam transmissions for every image line resulting in a higher spatial resolution and SNR in the focal region. However, it offers approximately 30 frames per second, and thus it is not used in fast ultrasound imaging applications. There is a trade-off between frame rate, image quality, and SNR in diverging wave imaging. Therefore, fast imaging with high SNR and resolution while maintaining a high frame rate remains a practical problem in medical ultrasound. This thesis focuses on SNR maximization of diverging waves in weakly and multiple scattering, attenuating, and diffracting media. The primary outcome is that the SNR improves at deeper regions if the transmitted burst duration or the chip signal duration in the case of coded transmission is decreased when diverging waves are used. The maximum SNR is obtained in diverging wave transmission when the transmitted burst or the chip signal is as short-duration as the array permits. This result does not comply with the expectation implying that more transmitted energy results in higher SNR. The analytical foundation for diverging wave propagation in weakly and multiple scattering media is not sufficient at the level required to derive analytical results. In order to understand this counter-intuitive result, either finite element analysis (FEA) or semi-analytical simulation tools can be utilized. FEA can predict this counter-intuitive result, but detailed modeling of the medium is quite involved and results in very long simulation times, which renders the use of FEA impossible. Unlike the other imaging modalities, the wavelength is on the order of hundred micrometers in medical ultrasound imaging; thus, the simulation of a reasonable tissue volume is impossible. Semi-analytical simulation tools based on linear spatial impulse response produce erroneous results because scatterers are modeled as monopole sources, and multiple scattering is not modeled. As there is no analytical and simulation-based solution for this problem, the experimental verification of the results is presented. The transmitted ultrasound energy spreads over a broader region in diverging wave imaging. The energy spreading further aggravates due to diffraction and multiple scattering, which may cause energy loss. Keeping the transmitted ultrasound energy within the region of interest prevents this energy loss in diverging wave imaging. Therefore, we determined the optimum diverging wave profile to confine the transmitted ultrasound energy in the imaging sector. Using this optimized profile contributes to the SNR maximization. Complementary Golay sequences and Binary Phase Shift Keying modulation are used to code the transmitted signal. We used an ultrasound research scanner, a tissue-mimicking phantom, and a 128-element phased array transducer with 70% bandwidth at 7.5 MHz center frequency for data acquisition. The SNR in speckle and pin targets is maximized with respect to chip signal length and code length. The SNR performances of the optimized coded diverging wave and conventional single-focused phased array imaging are compared on a single frame basis. The focal region in the focused scheme is used as a reference. For the 90° imaging sector, the SNR of an 8-bit coded signal is maximum when the chip signal duration is one cycle of the center frequency. The SNR of the optimized coded diverging wave is higher than that of the conventional single-focused phased array imaging at all depths and regions. One frame of diverging wave data is acquired in 200 microseconds, equivalent to 5000 frames/s, whereas the time required for single-focused phased array imaging is 181 times more.
  • ItemOpen Access
    Hardware implementation of Fano Decoder for polarization-adjusted convolutional (PAC) codes
    (Bilkent University, 2022-06) Hokmabadi, Amir Mozammel; Arıkan, Erdal
    Polarization-adjusted convolutional (PAC) codes are a new class of error-correcting codes that have been shown to achieve near-optimum performance. By combining ideas from channel polarization and convolutional coding, PAC codes create an overall encoding transform that achieves a performance near the information-theoretic limits at short block lengths. In this thesis we propose a hardware implementation architecture for Fano decoding of PAC codes. First, we introduce a new variant of Fano algorithm for decoding PAC codes which is suitable for hardware implementation. Then we provide the hardware diagrams of the sub-blocks of the proposed PAC Fano decoder and an estimate of their hardware complexity and propagation delay. We also introduce a novel branch metric unit for sequential decoding of PAC codes which is capable of calculating the current and previous branch metric values online, without requiring any storage element or comparator. We evaluate the error-correction performance of the proposed decoder on FPGA and its hardware characteristics on ASIC with TSMC 28 nm 0.72 V library. We show that, for a block length of 128 and a message length of 64, the proposed decoder can be clocked at 500 MHz and achieve approximately 38.1 Mb/s information throughput at 3.5 dB signal-to-noise ratio with a power consumption of 3.85 mW.
  • ItemOpen Access
    Estimation theoretic analyses of location secrecy and ris-aided localization under hardware impairments
    (Bilkent University, 2022-06) Öztürk, Cüneyd; Gezici, Sinan
    In this thesis, we present estimation theoretic analyses of location secrecy and reconfigurable intelligent surface (RIS) aided localization under hardware impairments. First, we consider a wireless source localization network in which a target node emits localization signals that are used by anchor nodes to estimate the target node position. In addition to target and anchor nodes, there can also exist eavesdropper nodes and jammer nodes which aim to estimate the position of the target node and to degrade the accuracy of localization, respectively. We propose the problem of eavesdropper selection with the goal of optimally placing a given number of eavesdropper nodes to a subset of possible positions in the network to estimate the target node position as accurately as possible. As the performance metric, the Cramér-Rao lower bound (CRLB) related to the estimation of the target node position by eavesdropper nodes is derived, and its convexity and monotonicity properties are investigated. By relaxing the integer constraints, the eavesdropper selection problem is approximated by a convex optimization problem and algorithms are proposed for eavesdropper selection. Moreover, in the presence of parameter uncertainty, a robust version of the eavesdropper selection problem is developed. Then, the problem of jammer selection is proposed where the aim is to optimally place a given number of jammer nodes to a subset of possible positions for degrading the localization accuracy of the network as much as possible. A CRLB expression from the literature is used as the performance metric, and its concavity and monotonicity properties are derived. Also, a convex optimization problem and its robust version are derived after relaxation. Moreover, the joint eavesdropper and jammer selection problem is proposed with the goal of placing certain numbers of eavesdropper and jammer nodes to a subset of possible positions. Simulation results are presented to illustrate performance of the proposed algorithms. Second, a wireless source localization network consisting of synchronized target and anchor nodes is considered. An anchor placement problem is formulated to minimize the CRLB on estimation of target node positions by anchor nodes. It is shown that the anchor placement problem can be approximated as a minimization problem of the ratio of two supermodular functions. Due to the lack of a polynomial time algorithm for such problems, an anchor selection problem is proposed to solve the anchor placement problem. Via relaxation of integer constraints, the anchor selection problem is approximated by a convex optimization problem, which is used to propose two algorithms for anchor selection. Furthermore, extensions to quasi-synchronous wireless localization networks are discussed. To examine the performance of the proposed algorithms, various simulation results are presented. Third, we investigate the problem of RIS-aided near-field localization of a user equipment (UE) served by a base station (BS) under phase-dependent amplitude variations at each RIS element. Through a misspecified Cramér -Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér -Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location. Fourth, we consider RIS-aided localization scenarios with RIS pixel failures, where individual RIS elements can become faulty due to hardware imperfections. We explore the impact of such failures on the localization performance. To that aim, an MCRB analysis is conducted and numerical results indicate that performance loss for estimating the UE position can be significant in the presence of pixel failures. To remedy this issue, we develop two different diagnosis strategies to determine which pixels are failing, and design robust methods to perform localization in the presence of faulty elements. One strategy is based on the l_1-regularization method, the second one employs a successive approach. Both methods significantly reduce the performance loss due to pixel failures. The successive one performs very close to the theoretical bounds at high SNRs even though it has a higher computational cost than the l_1-regularization based method. In the final part of the dissertation, the optimal encoding strategy of a scalar parameter is performed in the presence of jamming based on an estimation theoretic criterion. Namely, the aim is to obtain the optimal encoding function at the transmitter that minimizes the expectation of the conditional Cramér -Rao bound (ECRB) at the receiver when the jammer has access to the parameter and alters the received signal by sending an encoded version of the parameter. Via calculus of variations, the optimal encoding function at the transmitter is characterized explicitly, and an algorithm is proposed to calculate it. Numerical examples demonstrate benefits of the proposed optimal encoding approach.
  • ItemOpen Access
    Performance and computational analysis of polarization-adjusted convolutional (PAC) codes
    (Bilkent University, 2022-06) Moradi, Mohsen; Arıkan, Erdal
    We study the performance of sequential decoding of polarization-adjusted con- volutional (PAC) codes. We present a metric function that employs bit-channel mutual information and cutoff rate values as the bias values and significantly re- duces the computational complexity while retaining the excellent error-correction performance of PAC codes. With the proposed metric function, the computa- tional complexity of sequential decoding of PAC codes is equivalent to that of conventional convolutional codes. Our results indicate that the upper bound on the sequential decoding compu- tational complexity of PAC codes follows a Pareto distribution. We also employ guessing technique to derive a lower bound on the computational complexity of sequential decoding of PAC codes. To reduce the PAC sequential decoder’s worst-case latency, we restrict the number of searches executed by the sequential decoder. We introduce an improvement to the successive-cancellation list (SCL) decod- ing for polarized channels that reduces the number of sorting operations without degrading the code’s error-correction performance. In an SCL decoding with an optimum metric function, we show that, on average, the correct branch’s bit- metric value must be equal to the bit-channel capacity. On the other hand, the average bit-metric value of a wrong branch can be at most 0. This implies that a wrong path’s partial path metric value deviates from the bit-channel capacity’s partial summation. This enables the decoder to identify incorrect branches and exclude them from the list of metrics to be sorted. We employ a similar technique to the stack algorithm, resulting in a considerable reduction in the stack size. Additionally, we propose a technique for constructing a rate profile for PAC codes of arbitrary length and rate which is capable of balancing the error- correction performance and decoding complexity of PAC codes. For signal-to- noise ratio (SNR) values larger than a target SNR value, the proposed approach can significantly enhance the error-correction performance of PAC codes while retaining a low mean sequential decoding complexity. Finally, we examine the weight distribution of PAC codes with the goal of providing a new demonstration that PAC codes surpass polar codes in terms of weight distribution.
  • ItemOpen Access
    Control and system identification of legged locomotion with recurrent neural networks
    (Bilkent University, 2022-06) Çatalbaş, Bahadır; Morgül, Ömer
    In recent years, robotic systems have gained massive popularity in the industry, military, and daily use for various purposes, thanks to advancements in artificial intelligence and control theory. As an exciting sub-branch of robotics with their differences and opportunities, legged robots have the potential to diversify and spread the use of robotic systems to new fields. Especially, legged locomotion is a desirable ability for mechanical systems where agile mobility and a wide range of motions are required to fulfill the designated task. On the other hand, unlike wheeled robots, legged robot platforms have a hybrid dynamical structure consisting of the flight and contact phases of the legs. Since the hybrid dynamical structure and nonlinear dynamics in the robot model make it challenging to apply control and perform system identification for them, various methods are proposed to solve these problems in the literature. This thesis focuses on developing new neural network-based techniques to apply control and system identification to legged locomotion so that robotic platforms can be designed to move efficiently as animal counterparts do in nature. In the first part of this thesis, we present our works on neural network-based controller development and evaluation studies for bipedal locomotion. In detail, neural controllers, in which long short-term memory (LSTM) type of neuron models are employed at recurrent layers, are utilized in the feedback and feedforward paths. Supervised learning data sets are produced using a biped robot platform controlled by a central pattern generator to train these neural networks. Then, the ability of the neural networks to perform stable gait by controlling the robot platform is assessed under various ground conditions in the simulation environment. After that, the stable walking generation capacity of the neural networks and the central pattern generators are compared with each other. It is shown that the proposed neural networks are more successful gait controllers than the central pattern generator, which is employed to generate data sets used in training. In the second part, we present our studies on the end-to-end usage of neural networks in system identification for bipedal locomotion. To this end, supervised learning data sets are produced using a biped robot model controlled by a central pattern generator. After that, neural networks are trained under series-parallel and parallel system identification schemes to approximate the input-output relations of the biped robot model. In detail, different neural models and neural network architectures are trained and tested in an end-to-end manner. Among neuron models, LeakyReLU and LSTM are found as the most suitable feedforward and recurrent neuron types for system identification, respectively. Moreover, neural network architecture consisting of recurrent and feedforward layers is found to be efficient in terms of learnable parameter numbers for system identification of the biped robot model. The last part discusses the results obtained in the control and system identification studies using neural networks. In the light of acquired results, neural networks with recurrent layers can apply control and systems identification in an end-to-end manner. Finally, the thesis is completed by discussing possible future research directions with the obtained results.
  • ItemOpen Access
    Optimizing doherty power amplifier output networks for maximized bandwidth
    (Bilkent University, 2022-06) Alemdar, Sinan; Atalar, Abdullah
    A method is presented to optimize the combining network and the post match-ing network of a Doherty power amplifier for maximizing the bandwidth. For widely applicable results, RF power transistors are approximated in the large-signal regime using a simple analytical model with a few parameters. A defini-tion of bandwidth of Doherty power amplifier is given, which involves gain and efficiency at full-power and 6 dB backoff. Different combining network topologies are compared in terms of this bandwidth definition. The element values are op-timized using two factors, one to scale the combining node impedance and the other to scale the impedance seen by the transistors. For each optimized topol-ogy, explicit formulas are given resulting in the element values in terms of the optimized values and a few transistor parameters. The method presented also leads to a proper selection of the post-matching network.
  • ItemOpen Access
    Anomaly detection in diverse sensor networks using machine learning
    (Bilkent University, 2022-01) Akyol, Ali Alp; Arıkan, Orhan
    Earthquake precursor detection is one of the oldest research areas that has the potential of saving human lives. Recent studies have enlightened the fact that strong seismic activities and earthquakes affect the electron distribution of the ionosphere. These effects are clearly observable on the ionospheric Total Electron Content (TEC) that shall be measured by using the satellite position data of the Global Navigation Satellite System (GNSS). In this dissertation, several earthquake precursor detection techniques are proposed and their precursor detection performances are investigated on TEC data obtained from different sensor networks. First, a model based earthquake precursor detection technique is proposed to detect precursors of the earthquakes with magnitudes greater than 5 in the vicinity of Turkey. Precursor detection and TEC reliability signals are generated by using ionospheric TEC variations. These signals are thresholded to obtain earthquake precursor decisions. Earthquake precursor detections are made by using Particle Swarm Optimization (PSO) technique on these precursor decisions. Performance evaluations show that the proposed technique is able to detect 14 out of 23 earthquake precursors of magnitude larger than 5 in Richter scale while generating 8 false precursor decisions. Second, a machine learning based earthquake precursor detection technique, EQ-PD is proposed to detect precursors of the earthquakes with magnitudes greater than 4 in the vicinity of Italy. Spatial and spatio-temporal anomaly detection thresholds are obtained by using the statistics of TEC variation during seismically active times and applied on TEC variation based anomaly detection signal to form precursor decisions. Resulting spatial and spatio-temporal anomaly decisions are fed to a Support Vector Machine (SVM) classifier to generate earthquake precursor detections. When the precursor detection performance of the EQ-PD is investigated, it is observed that the technique is able to detect 22 out of 24 earthquake precursors while generating 13 false precursor decisions during 147 days of no-seismic activity. Last, a deep learning based earthquake precursor detection technique, DLPD is proposed to detect precursors of the earthquakes with magnitudes greater than 5.4 in the vicinity Anatolia region. The DL-PD technique utilizes a deep neural network with spatio-temporal Global Ionospheric Map (GIM)-TEC data estimation capabilities. GIM-TEC anomaly score is obtained by comparing GIMTEC estimates with GIM-TEC recordings. Earthquake precursor detections are generated by thresholding the GIM-TEC anomaly scores. Precursor detection performance evaluations show that DL-PD shall detect 5 out of 7 earthquake precursors while generating 1 false precursor decision during 416 days of noseismic activity.
  • ItemOpen Access
    Strong light-matter interaction in lithography-free perfect absorbers for photoconversion, photodetection, light emission, sensing, and filtering applications
    (Bilkent University, 2022-01) Ghobadi, Amir; Özbay, Ekmel
    The efficient harvesting of electromagnetic (EM) waves by subwavelength nanostructures can result in perfect light absorption in the narrow or broad frequency range. These metamaterial based perfect light absorbers are of particular interest in many applications, including thermal photovoltaics, photovoltaics, emission, sensing, filtering, and photodetection applications. Although advances in nanofabrication have provided the opportunity to observe strong light-matter interaction in various optical nanostructures, the repeatability and upscaling of these nano units have remained a challenge for their use in large-scale applications. Thus, in recent years, the concept of lithography-free metamaterial absorbers (LFMAs) has attracted much attention in different parts of the EM spectrum, owing to their ease of fabrication and high functionality. In this thesis, the unprecedented potential of these LFMAs will be explored. This thesis explores the material and architecture requirements for the realization of a LFMA from ultraviolet (UV) to far-infrared (FIR) wavelength regimes. For this aim, we theoretically investigate the required conditions to realize an ideal perfect absorber. Then, based on the operation wavelength and application, the proper material and design architecture is defined. Later, to experimentally realize these ideal LFMAs, lithography-free large-scale compatible routes are developed to generate nanostructures in centimeter scales. Finally, the application of these LFMAs has been demonstrated in various fields including filtering, sensing, emission, photodetection, and photoelectrochemical water splitting. This thesis study demonstrates that, by the use of proper material and design configuration, it is possible to realize these LFMAs in every portion of the EM spectrum with a vast variety of potential applications. This, in turn, opens up the opportunity of the practical application of these perfect absorbers in large-scale dimensions. In the last section of the thesis, we discuss the progress, challenges, and outlook of this field to outline its future direction.