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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Event-triggered reinforcement learning based joint resource allocation for ultra-reliable low-latency V2X communications(Institute of Electrical and Electronics Engineers Inc., 2024) Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Khan, Nasir; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and EngineeringFuture 6G-enabled vehicular networks face the challenge of ensuring ultra-reliable low-latency communication (URLLC) for delivering safety-critical information in a timely manner. Existing resource allocation schemes for vehicle-toeverything (V2X) communication systems primarily rely on traditional optimization-based algorithms. However, these methods often fail to guarantee the strict reliability and latency requirements of URLLC applications in dynamic vehicular environments due to the high complexity and communication overhead of the solution methodologies. This paper proposes a novel deep reinforcement learning (DRL) based framework for the joint power and block length allocation to minimize the worst-case decoding-error probability in the finite block length (FBL) regime for a URLLC-based downlink V2X communication system. The problem is formulated as a non-convex mixed-integer nonlinear programming problem (MINLP). Initially, an algorithm grounded in optimization theory is developed based on deriving the joint convexity of the decoding error probability in the block length and transmit power variables within the region of interest. Subsequently, an efficient event-triggered DRL based algorithm is proposed to solve the joint optimization problem. Incorporating event-triggered learning into the DRL framework enables assessing whether to initiate the DRL process, thereby reducing the number of DRL process executions while maintaining reasonable reliability performance. The DRL framework consists of a twolayered structure. In the first layer, multiple deep Q-networks (DQNs) are established at the central trainer for block length optimization. The second layer involves an actor-critic network and utilizes the deep deterministic policy-gradient (DDPG)-based algorithm to optimize the power allocation. Simulation results demonstrate that the proposed event-triggered DRL scheme can achieve 95% of the performance of the joint optimization scheme while reducing the DRL executions by up to 24% for different network settings.Publication Metadata only Contrast improvement through a Generative Adversarial Network (GAN) by utilizing a dataset obtained from a line-scanning confocal microscope(SPIE, 2024) Department of Physics; Kiraz, Alper; Morova, Berna; Bavili, Nima; Ketabchi, Amir Mohammad; Department of Physics; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Sciences; Graduate School of Sciences and EngineeringConfocal microscopy offers enhanced image contrast and signal-to-noise ratio compared to wide-field illumination microscopy, achieved by effectively eliminating out-of-focus background noise. In our study, we initially showcase the functionality of a line-scanning confocal microscope aligned through the utilization of a Digital Light Projector (DLP) and a rolling shutter CMOS camera. In this technique, a sequence of illumination lines is projected onto a sample using a DLP and focusing objective (50X, NA=0.55). The reflected light is imaged with the camera. Line-scanning confocal imaging is accomplished by synchronizing the illumination lines with the rolling shutter of the sensor, leading to a substantial enhancement of approximately 50% in image contrast. Subsequently, this setup is employed to create a dataset comprising 500 pairs of images of paper tissue. This dataset is employed for training a Generative Adversarial Network (cGAN). Roughly 45% contrast improvement was measured in the test images for the trained network, in comparison to the ground-truth images.Publication Metadata only Multi-scale deformable alignment and content-adaptive inference for flexible-rate bi-directional video compression(IEEE Computer Society, 2023) Department of Electrical and Electronics Engineering; Yılmaz, Mustafa Akın; Ulaş, Ökkeş Uğur; Tekalp, Ahmet Murat; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringThe lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adaptive motion-compensation model for end-to-end rate-distortion optimized hierarchical bi-directional video compression. In particular, we propose two novelties: i) a multi-scale deformable alignment scheme at the feature level combined with multi-scale conditional coding, ii) motion-content adaptive inference. In addition, we employ a gain unit, which enables a single model to operate at multiple rate-distortion operating points. We also exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding1.Publication Metadata only Ris-aided angular-based hybrid beamforming design in mmwave massive mimo systems(IEEE, 2022) Koc, Asil; Tho Le-Ngoc; Department of Electrical and Electronics Engineering; Yıldırım, İbrahim; Başar, Ertuğrul; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringThis paper proposes a reconfigurable intelligent surface (RIS)-aided and angular-based hybrid beamforming (AB-HBF) technique for the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The proposed RIS-AB-HBF architecture consists of three stages: (i) RF beam-former, (ii) baseband (BB) precoder/combiner, and (iii) RIS phase shift design. First, in order to reduce the number of RF chains and the channel estimation overhead, RF beamformers are designed based on the 3D geometry-based mmWave channel model using slow time-varying angular parameters of the channel. Second, a BB precoder/combiner is designed by exploiting the reduced-size effective channel seen from the BB stages. Then, the phase shifts of the RIS are adjusted to maximize the achievable rate of the system via the nature-inspired particle swarm optimization (PSO) algorithm. Illustrative simulation results demonstrate that the use of RISs in the AB-HBF systems has the potential to provide more promising advantages in terms of reliability and flexibility in system design.Publication Metadata only A wideband silicon photonic duplexer constructed from a deep photonic network of custom Mach-Zehnder interferometers(Society of Photographic Instrumentation Engineers (SPIE), 2024) Department of Electrical and Electronics Engineering; Amiri, Ali Najjar; Görgülü, Kazım; Mağden, Emir Salih; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringUsing a highly-scalable and physics-informed design platform with custom Mach-Zehnder interferometers (MZIs), we design and experimentally demonstrate a 1 x 2 wideband duplexer on silicon operating within 1450-1630 nm. The device is constructed from six layers of cascaded MZIs whose geometries are optimized using an equivalent artificial neural network, in a total timeframe of 75 seconds. Experimental results show below 0.72 dB deviation from the arbitrarily-specified target response, and less than 0.66 dB insertion loss. Demonstrated capabilities and the computational efficiency of our design framework pave the way towards the scalable deployment of custom MZI networks in communications, sensing, and computation applications.Publication Metadata only Experimental demonstration of a broadband, ultra-compact, and fabrication-tolerant silicon photonic 10% power tap(Society of Photographic Instrumentation Engineers (SPIE), 2024) Department of Electrical and Electronics Engineering; Daşdemir, Ahmet Onur; Mağden, Emir Salih; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringInverse design approaches with topology optimization can yield in highly efficient devices; however designing fabrication-compatible, broadband, yet simultaneously fabrication-tolerant devices still widely remains a challenge. Here, we design a broadband and fabrication-tolerant 10% silicon-based power tap using 3D-FDTD simulations and topology optimization, and demonstrate its experimental performance. The power tap has a compact footprint of 7.0 mu mx3.1 mu m, and achieves a broadband and spectrally flat operation from 1500 nm to 1600 nm. The device was specifically built to be fabrication-tolerant using an approach that maintains high performance under over-etch and under-etch scenarios by maximizing the contiguous area of the silicon layer in the final device. This tolerance was verified with 3D-FDTD simulations with 15 nm over-etch and under-etch modifications, demonstrating a change of less than 0.64 dB at either output port compared to the original device response at 1550 nm. The designed power tap was fabricated using a standard 220 nm thick silicon-on-insulator platform. The experimental measurements match closely with the design target and 3D-FDTD results, achieving state-of-the-art performance with excess losses as low as 0.23 dB and broadband operation. The output ports of the device also exhibit extremely flat spectra, where the transmission remains between 0.86 and 0.92 for the through port, and between 0.06 and 0.14 for the tap port throughout the 1500-1600 nm spectral range. These results represent the state-of-the-art experimental performance in compact power taps, and prove the effectiveness of fabrication-tolerant optimization.Publication Metadata only Effect of finger moisture on tactile perception of electroadhesion(Institute of Electrical and Electronics Engineers, 2024) Lefevre, Philippe; Martinsen, Orjan Grottem; Department of Mechanical Engineering; Aliabbasi, Easa; Muzammil, Muhammad; Şirin, Ömer; Başdoğan, Çağatay; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of EngineeringWe investigate the effect of finger moisture on the tactile perception of electroadhesion with 10 participants. Participants with moist fingers exhibited markedly higher threshold levels. Our electrical impedance measurements show a substantial reduction in impedance magnitude when sweat is present at the finger-touchscreen interface, indicating increased conductivity. Supporting this, our mechanical friction measurements show that the relative increase in electrostatic force due to electroadhesion is lower for a moist finger.Publication Metadata only ML-augmented bayesian optimization of pain induced by microneedles(Wiley, 2024) Department of Mechanical Engineering; Choukri, Abdullah Ahmed; Taşoğlu, Savaş; Department of Mechanical Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Sciences and Engineering; College of EngineeringMicroneedles (MNs) have emerged as a promising solution for drug delivery and extraction of body fluids. Pain is an important physiological attribute to be examined when designing MNs. There is no known representation of pain with geometric features of a MN despite the focus on experimental work. This study focuses on optimizing MN designs with the aim of minimizing pain through means of machine learning, finite element analysis, and optimization tools. Three distinct approaches are proposed. The first approach involves training multiple regression models on data obtained through finite element analysis in COMSOL. The second approach uses COMSOL's built-in nonlinear optimization solver. Finally, the third approach utilizes the LiveLink interface between COMSOL and MATLAB, combined with Bayesian optimization. Each approach presents unique strengths and challenges, with the third approach demonstrating significant promise due to its efficiency, practicality, and time-saving. A machine learning (ML)-augmented Bayesian framework is described in the article number by Ahmed Choukri Abdullah and Savas Tasoglu to optimize and minimize pain induced by microneedles. Introduction of ML-based optimization frameworks into microfabrication processes can pave the way for a much more effective and customized designs of minimally invasive microneedles.Publication Metadata only Fundamentals and applications of heat currents in quantum systems(Springer Science and Business Media Deutschland GmbH, 2024) Department of Physics; Naseem, Muhammad Tahir; Müstecaplıoğlu, Özgür Esat; Department of Physics; College of Sciences; Graduate School of Sciences and EngineeringThe growing field of quantum thermodynamics has attracted much attention in the last two decades. The possibility of exploiting quantum features in thermal machines led to exciting avenues both from fundamental and application perspectives. For instance, in the presence of non-thermal baths, a quantum heat engine may surpass the classical Carnot limit. On the other hand, heat flow puts severe restrictions on the miniaturization of technologies based on quantum features. It is of paramount importance to look for efficient methods of heat management in the quantum system. One promising direction can be employing heat for powering these devices rather than considering the heat flow as noise. In this chapter, we briefly overview such strategies proposed for efficient heat flow management in the recent past. In particular, we present some of the developments in quantum thermal diodes, thermal transistors, and quantum thermal entanglement machines. In addition, some discussion on the particular models of quantum heat engines and quantum absorption refrigerators is presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Publication Metadata only Optimizing photonic devices under fabrication variations with deep photonic networks(SPIE-Int Soc Optical Engineering, 2024) Department of Electrical and Electronics Engineering; Görgülü, Kazım; Vit, Aycan Deniz; Amiri, Ali Najjar; Mağden, Emir Salih; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of EngineeringWe propose a deep photonic interferometer network architecture for designing fabrication-tolerant photonic devices. Our framework incorporates layers of variation-aware, custom-designed Mach-Zehnder interferometers and virtual wafer maps to optimize broadband power splitters under fabrication variations. Specifically, we demonstrate 50/50 splitters with below 1% deviation from the desired 50/50 ratio, even with up to 15 nm over-etch and under-etch variations. The significantly improved device performance under fabrication-induced changes demonstrates the effectiveness of the deep photonic network architecture in designing fabrication-tolerant photonic devices and showcases the potential for improving circuit performance by optimizing for expected variations in waveguide width.