Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 5Citation - Scopus: 7Adaptive Resizer-Based Transfer Learning Framework for the Diagnosis of Breast Cancer Using Histopathology Images(Springer, 2023) Düzyel, Okan; Çatal, Mehmet Sergen; Kayan, Ceyhun Efe; Sevinç, Arda; Gümüş, AbdurrahmanBreast cancer is a major global health concern, and early and accurate diagnosis is crucial for effective treatment. Recent advancements in computer-assisted prediction models have facilitated diagnosis and prognosis using high-resolution histopathology images, which provide detailed information on cancerous tissue. However, these high-resolution images often require resizing, leading to potential data loss. In this study, we demonstrate the effect of a learnable adaptive resizer for breast cancer classification using the BreakHis dataset. Our approach incorporates the adaptive resizer with various convolutional neural network models, including VGG16, VGG19, MobileNetV2, InceptionResnetV2, DenseNet121, DenseNet201, and EfficientNetB0. Despite producing visually less appealing images, the learnable resizer effectively improves classification performance. DenseNet201, when jointly trained with the adaptive resizer, achieves the highest accuracy of 98.96% for input images of 448x448 resolution. Our experimental results demonstrate that the adaptive resizer performs better at a magnification factor of 40x compared to higher magnifications. While its effectiveness becomes less pronounced as image resolution increases to 100x, 200x, and 400x, the adaptive resizer still outperforms bilinear interpolation. In conclusion, this study highlights the potential of adaptive resizers in enhancing performance for medical image classification. By outperforming traditional image resizing methods, our work contributes to the advancement of deep neural networks in the field of breast cancer diagnostics.Article Citation - WoS: 2Citation - Scopus: 2Subwavelength Thickness Characterization of Curved Dielectric Films Exploiting Spatially Structured Entangled Photons(Optica Publishing Group, 2023) Ataç, Enes; Dinleyici, Mehmet SalihPrecise determination of thin dielectric film optical properties is a critical issue for fiber optic sensor technologies. However, conventional methods for the optical characterization of these films not only are generally complex and tedious processes on curved surfaces but also require well-calibrated and overly sophisticated devices. We, on the other hand, propose a novel and practical quantum-based phase diffraction scheme to characterize the thickness of ultra-thin transparent dielectric films coated on an optical fiber beyond the classical diffraction limits in this paper. The approach is implemented by evaluating the effect of thickness variations on the highly visible two-photon diffraction pattern's zero crossings and amplitudes. The mathematical model and numerical simulations con-tribute to a better understanding of how the spatially structured entangled photons improve thickness precision with the help of intensity correlations and a confocal aperture. To prove the impact of the proposed system, it is compared with the classical phase diffraction method in the literature via simulations. According to the results, the thickness of the transparent dielectric films can be accurately estimated below one-twentieth of the wavelength of interest. & COPY; 2023 Optica Publishing GroupArticle Citation - WoS: 3Citation - Scopus: 4Cost-Effective Experiments With Additively Manufactured Waveguide and Cavities in the S-Band(Iop Publishing Ltd, 2023) Karatay, Anıl; Yilmaz, Hasan Önder; Özkal, Ceren; Yaman, FatihThis study demonstrates the applicability of additively manufactured components that are metalized with conductive tape for two different microwave experiments. We focus on dielectric measurements and prototyping elliptical accelerator cavities at a low power regime for 2.45 GHz. To illustrate the accuracy of our results for the commonly used solid/liquid materials in engineering and to compare the fundamental accelerator cavity parameters with previous research rectangular and elliptic 3D-printed cavities coated with aluminum-type tape were employed in the experiments. Results reported for the complex-valued permittivities and specific design parameters for the cavity prototype are consistent with the literature. Various approaches to obtain the conductivity value of the tape and the effect of the roughness/thickness of the coating on the reflection parameter are discussed in detail. We confirm the effectiveness of the proposed approach, which reduces costs and provides a high degree of accuracy for investigated applications.Article Citation - WoS: 4Citation - Scopus: 5Toward Safe and High-Performance Human-Robot Collaboration Via Implementation of Redundancy and Understanding the Effects of Admittance Term Parameters(Cambridge University Press, 2022) Kanık, Mert; Ayit, Orhan; Dede, Mehmet İsmet Can; Tatlıcıoğlu, EnverSummary Today, demandsin industrial manufacturing mandate humans to work with large-scale industrial robots, and this collaboration may result in dangerous conditions for humans. To deal with this situation, this work proposes a novel approach for redundant large-scale industrial robots. In the proposed approach, an admittance controller is designed to regulate the interaction between the end effector of the robot and the human. Additionally, an obstacle avoidance algorithm is implemented in the null space of the robot to prevent any possible unexpected collision between the human and the links of the robot. After safety performance of this approach is verified via simulations and experimental studies, the effect of the parameters of the admittance controller on the performance of collaboration in terms of both accuracy and total human effort is investigated. This investigation is carried out via 8 experiments by the participation of 10 test subjects in which the effect of different admittance controller parameters such as mass and damper are compared. As a result of this investigation, tuning insights for such parameters are revealed.Article Citation - WoS: 2Citation - Scopus: 2Deep Learning Based Adaptive Bit Allocation for Heterogeneous Interference Channels(Elsevier, 2021) Aycan, Esra; Özbek, Berna; Le Ruyet, DidierThis paper proposes an adaptive bit allocation scheme by using a fully connected (FC) deep neural network (DNN) considering imperfect channel state information (CSI) for heterogeneous networks. Achieving an accurate CSI has a crucial role on the system performance of the heterogeneous networks. Different quantization techniques have been employed to reduce the feedback overhead. However, the system performance cannot increase linearly with the number of bits increasing exponentially. Since optimizing the total number of bits is too complex for the entire network, an initial step is performed to distribute the bits to each cell in the conventional method. Then, the distributed bits are further allocated to each channel optimally. In order to enable direct allocation for the entire network, a FC-DNN based method is presented in this study. The optimized number of bits can be directly obtained for a different number of bits and scenarios by the proposed approach. The simulations are performed by using various scenarios with different allocation schemes. The performance results show that the DNN based method achieves a closer performance to the conventional approach. (C) 2021 Elsevier B.V. All rights reserved.Article Time-Efficient Evaluation of Adaptation Algorithms for Dash With Svc: Dataset, Throughput Generation and Stream Simulator(Springer, 2021) Çalı, Mehmet; Özbek, NükhetBitrate adaptation algorithms have received considerable attention recently. In order to evaluate these algorithms objectively, multiple DASH datasets have been proposed. However, only few of them are compatible to SVC-based adaptation algorithms. Apart from the dataset, to fully implement and evaluate an adaptation algorithm, many time-consuming steps are required such as MPD parser design, adaptation logic design and network environment setup. In this paper, a dash simulator which assesses the performance of SVC-based adaptation algorithms without the requirement of any additional implementation steps is proposed. Also, an SVC dataset that includes both CBR and VBR encoded videos is designed. Demonstration is performed as evaluation of an SVC-based adaptation algorithm under several throughput scenarios using the designed dataset. Results show that the proposed system considerably reduces time requirement compared to real-time assessment. Dataset, throughput generation tool and simulator are all publicly available so that the researchers can test their implementation and compare with the results presented in this paper.Article Citation - WoS: 4Citation - Scopus: 5Hybrid Beamforming Strategies for Secure Multicell Multiuser Mmwave Mimo Communications(Elsevier, 2021) Özbek, Berna; Erdoğan, Oğulcan; Busari, Sherif A.; Gonzalez, JonathanOver the last decade, many advancements have been made in the field of wireless communications. Among the major technology enablers being explored for the beyond fifth-generation (B5G) networks at the physical layer (PHY), a great deal of attention has been focused on millimeter-wave (mmWave) communications, massive multiple-input multiple-output (MIMO) antenna systems and beamforming techniques. These enablers bring to the forefront great opportunities for enhancing the performance of B5G networks, concerning spectral efficiency, energy efficiency, latency, and reliability. The wireless communication is prone to information leakage to the unintended nodes due to its open nature. Hence, the secure communication is becoming more critical in the wireless networks. To address this challenge, the concept of Physical Layer Security (PLS) is explored in the literature. In this paper, we examine the mmWave transmission through linear beamforming techniques for PLS based systems. We propose the secure multiuser (MU) MIMO mmWave communications by employing hybrid beamforming at the base stations (BSs), legitimate users and eavesdroppers. Using three Dimensional (3D) mmWave channel model for each node, we utilize the artificial noise (AN) beamforming to jam the transmission of eavesdropper and to enhance the secrecy rate. The secrecy performance on multicell mmWave MU-MIMO downlink communications is demonstrated to reveal the key points directly related to the system security for B5G wireless systems. (C) 2021 Elsevier B.V. All rights reserved.Article Citation - WoS: 4Ramcess 2.x Framework-Expressive Voice Analysis for Realtime and Accurate Synthesis of Singing(Springer Verlag, 2008) d'Alessandro, Nicolas; Babacan, Onur; Bozkurt, Barış; Dubuisson, Thomas; Holzapfel, Andre; Kessous, Loic; Vlieghe, MaximeIn this paper we present the work that has been achieved in the context of the second version of the RAMCESS singing synthesis framework. The main improvement of this study is the integration of new algorithms for expressive voice analysis, especially the separation of the glottal source and the vocal tract. Realtime synthesis modules have also been refined. These elements have been integrated in an existing digital instrument: the HANDSKETCH 1.X, a bimanual controller. Moreover this digital instrument is compared to existing systems.Article Citation - WoS: 5Citation - Scopus: 6An Extended Jacobian-Based Formulation for Operational Space Control of Kinematically Redundant Robot Manipulators With Multiple Subtask Objectives: An Adaptive Control Approach(The American Society of Mechanical Engineers(ASME), 2019) Çetin, Kamil; Tatlıcıoğlu, Enver; Zergeroğlu, ErkanIn this study, an extended Jacobian matrix formulation is proposed for the operational space tracking control of kinematically redundant robot manipulators with multiple subtask objectives. Furthermore, to compensate the structured uncertainties related to the robot dynamics, an adaptive operational space controller is designed, and then, the corresponding stability analysis is presented for kinematically redundant robot manipulators. Specifically, the proposed method is concerned with not only the stability of operational space objective but also the stability of multiple subtask objectives. The combined stability analysis of the operational space objective and the subtask objectives are obtained via Lyapunov based arguments. Experimental and simulation studies are presented to illustrate the performance of the proposed method.Article Citation - WoS: 1Citation - Scopus: 2Blind Recognition of Alpha-Stable Random Carrier Signals by an Eavesdropper in Random Communication Systems(Institution of Engineering and Technology, 2019) Ahmed, Areeb; Savacı, Ferit AcarInvisibility of alpha-stable (alpha -stable) noise as carrier signals, in the presence of additive white Gaussian noise (AWGN) as channel noise, is a key factor to ensure covert transmission by employing random communication systems (RCSs). This study introduces a novel blind recognition method for an eavesdropper to detect the presence of real-valued symmetric and skewed alpha -stable random carrier signals in the presence of AWGN. The introduced method is based on the proposed random carrier signal recogniser (RCSR), which consists of fractional lower-order auto-covariance block, threshold control block and the random carrier signal recognition indicator. The proposed RCSR first detects the possible presence of alpha -stable random carrier signals and then recognises the impulsiveness and skewness parameters, exploited by the transmitter and the intended receiver, to extract covertly conveyed binary information. However, the determined covert range can be adopted to perform secure transmission by RCSs. The simulation results reflect the simplicity of the proposed method as it is capable of performing effectively in real time by utilising extremely less number of observed samples.