Image processing methods for computer-aided interpretation of microscopic images

Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical refences.Image processing algorithms for automated analysis of microscopic images have become increasingly popular in the last decade with the remarkable growth in computational power. The advent of high-throughput scanning devices allows for computer-assisted evaluation of microscopic images, resulting in a quick and unbiased image interpretation that will facilitate the clinical decision-making process. In this thesis, new methods are proposed to provide solution to two image analysis problems in biology and histopathology. The first problem is the classification of human carcinoma cell line images. Cancer cell lines are widely used for research purposes in laboratories all over the world. In molecular biology studies, researchers deal with a large number of specimens whose identity have to be checked at various points in time. A novel computerized method is presented for cancer cell line image classification. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DTCWT) coefficients as pixel features is computed. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. For 14 different classes, we achieve an overall accuracy of 98%, which outperforms the classical covariance based methods. Histopathological image analysis problem is related to the grading of follicular lymphoma (FL) disease. FL is one of the commonly encountered cancer types in the lymph system. FL grading is based on histological examination of hematoxilin and eosin (H&E) stained tissue sections by pathologists who make clinical decisions by manually counting the malignant centroblast (CB) cells. This grading method is subject to substantial inter- and intra-reader variability and sampling bias. A computer-assisted method is presented for detection of CB cells in H&Estained FL tissue samples. The proposed algorithm takes advantage of the scalespace representation of FL images to detect blob-like cell regions which reside in the scale-space extrema of the difference-of-Gaussian images. Multi-stage false positive elimination strategy is employed with some statistical region properties and textural features such as gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM) and Scale Invariant Feature Transform (SIFT). The algorithm is evaluated on 30 images and 90% CB detection accuracy is obtained, which outperforms the average accuracy of expert hematopathologists.Keskin, Musa FurkanM.S

On the Impact of Phase Noise on Monostatic Sensing in OFDM ISAC Systems

Phase noise (PN) can become a major bottleneck for integrated sensing and communications (ISAC) systems towards 6G wireless networks. In this paper, we consider an OFDM ISAC system with oscillator imperfections and investigate the impact of PN on monostatic sensing performance by performing a misspecified Cram\'er-Rao bound (MCRB) analysis. Simulations are carried out under a wide variety of operating conditions with regard to SNR, oscillator type (free-running oscillators (FROs) and phase-locked loops (PLLs)), 3-dB bandwidth of the oscillator spectrum, PLL loop bandwidth and target range. The results provide valuable insights on when PN leads to a significant degradation in range and/or velocity accuracy, establishing important guidelines for hardware and algorithm design in 6G ISAC systems

Multi-RIS-Enabled 3D Sidelink Positioning

Positioning is expected to be a core function in intelligent transportation systems (ITSs) to support communication and location-based services, such as autonomous driving, traffic control, etc. With the advent of low-cost reflective reconfigurable intelligent surfaces (RISs) to be deployed in beyond 5G/6G networks, extra anchors with high angular resolutions can boost signal quality and makes high-precision positioning with extended coverage possible in ITS scenarios. However, the passive nature of the RIS requires a signal source such as a base station (BS), which limits the positioning service in extreme situations, such as tunnels or dense urban areas, where 5G/6G BSs are not accessible. In this work, we show that with the assistance of (at least) two RISs and sidelink communication between two user equipments (UEs), these UEs can be localized even without any BSs involvement. A two-stage 3D sidelink positioning algorithm is proposed, benchmarked by the derived Cram\'er-Rao bounds. The effects of multipath and RIS profile designs on positioning performance are evaluated, and several scenarios with different RIS and UE locations are discussed for localizability analysis. Simulation results demonstrate the promising positioning accuracy of the proposed BS-free sidelink communication system in challenging ITS scenarios. Additionally, we propose and evaluate several solutions to eliminate potential blind areas where positioning performance is poor, such as removing clock offset via round-trip communication, adding geometrical prior or constraints, as well as introducing more RISs

V2X Sidelink Positioning in FR1: Scenarios, Algorithms, and Performance Evaluation

In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in 5G vehicular networks through a comprehensive end-to-end methodology encompassing ray-tracing-based channel modeling, novel theoretical performance bounds, high-resolution channel parameter estimation, and geometric positioning using a round-trip-time (RTT) protocol. We first derive a novel, approximate Cram\'er-Rao bound (CRB) on the connected road user (CRU) position, explicitly taking into account multipath interference, path merging, and the RTT protocol. Capitalizing on tensor decomposition and ESPRIT methods, we propose high-resolution channel parameter estimation algorithms specifically tailored to dense multipath V2X sidelink environments, designed to detect multipath components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using realistic ray-tracing data and antenna patterns, comprehensive simulations are conducted to evaluate channel estimation and positioning performance, indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the proposed algorithms

Analysis of V2X Sidelink Positioning in sub-6 GHz

Radio positioning is an important part of joint communication and sensing in beyond 5G communication systems. Existing works mainly focus on the mmWave bands and under-utilize the sub-6 GHz bands, even though it is promising for accurate positioning, especially when the multipath is uncomplicated, and meaningful in several important use cases. In this paper, we analyze V2X sidelink positioning and propose a new performance bound that can predict the positioning performance in the presence of severe multipath. Simulation results using ray-tracing data demonstrate the possibility of sidelink positioning, and the efficacy of the new performance bound and its relation with the complexity of the multipath

Model-Driven End-to-End Learning for Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G. However, 6G is also expected to be severely affected by hardware impairments. Under such impairments, standard model-based approaches might fail if they do not capture the underlying reality. To this end, data-driven methods are an alternative to deal with cases where imperfections cannot be easily modeled. In this paper, we propose a model-driven learning architecture for joint single- target multi-input multi-output (MIMO) sensing and multi-input single-output (MISO) communication. We compare it with a standard neural network approach under complexity constraints. Results show that under hardware impairments, both learning methods yield better results than the model-based standard baseline. If complexity constraints are further introduced, model- driven learning outperforms the neural-network-based approach. Model-driven learning also shows better generalization performance for new unseen testing scenario