Stepped-Carrier OFDM V2V Resource Allocation for Sensing and Communication Convergence

Stepped-carrier orthogonal frequency division multiplexing (OFDM) radar is a promising low-cost alternative to conventional OFDM radar for automotive applications due to its capability to provide high resolution with low-rate analog to-digital converters (ADCs). In this paper, we investigate centralized time-frequency resource allocation strategies in vehicular networks for vehicle-to-vehicle (V2V) sidelinks employing stepped-carrier OFDM waveform for joint radar sensing and communications. To quantify radar-communication performance trade-offs, we formulate a nonlinear integer programming problem for weighted optimization of radar accuracy and communication spectral efficiency, and perform Boolean relaxation to obtain an efficiently solvable convex program. Simulation results demonstrate radar-optimal and communication-optimal operation regimes, providing insights into time-frequency weightings along the trade-off curve

ICI-aware parameter estimation for MIMO-OFDM radar via APES spatial filtering

We propose a novel three-stage delay-Doppler-angle estimation algorithm for a MIMO-OFDM radar in the presence of inter-carrier interference (ICI). First, leveraging the observation that spatial covariance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form. In the final stage, delay and Doppler of each target can be recovered from target-specific channel estimates over time and frequency. Simulation results illustrate the superior performance of the proposed algorithm in high-mobility scenarios

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

SISO RIS-Enabled Joint 3D Downlink Localization and Synchronization

We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) multi-carrier system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array. First, we derive the Cram\ue9r-Rao bounds (CRBs) on the estimation error of the channel parameters, namely, the angle-of-departure (AOD), composed of azimuth and elevation, from RIS to the user equipment (UE) and times-of-arrival (TOAs) for the path from the base station (BS) to UE and BS-RISUE reflection. In order to avoid high-dimensional search over the parameter space, we devise a low-complexity estimation algorithm that performs two 1D searches over the TOAs and one 2D search over the AODs. Simulation results demonstrate that the considered RIS-aided wireless system can provide submeter-level positioning and synchronization accuracy, materializing the positioning capability of Beyond 5G networks even with single-antenna BS and UE. Furthermore, the proposed estimator is shown to attain the CRB at a wide interval of distances between UE and RIS. Finally, we also investigate the scaling of the position error bound with the number of RIS elements

Semi-Passive 3D Positioning of Multiple RIS-Enabled Users

Reconfigurable intelligent surfaces (RISs) are set to be a revolutionary technology in the 6th generation of wireless systems. In this work, we study the application of RIS in a multi-user passive localization scenario, where we have one transmitter (TX) and multiple asynchronous receivers (RXs) with known locations. Classical approaches fail in this scenario due to lack of synchronization and lack of data association between multi-static measurements and users. To resolve this, we consider each user to be equipped with an RIS, and show that we can avoid the data association problem and estimate users\u27 3D position with submeter accuracy in a large area around the transmitter, using time-of-arrival measurements at the RXs. We develop a low-complexity estimator that attains the corresponding Cram\\u27er-Rao bound as well as a novel RIS phase profile design to remove inter-path interference

RIS-Enabled SISO Localization under User Mobility and Spatial-Wideband Effects

Reconfigurable intelligent surface (RIS) is a promising technological enabler for the 6th generation (6G) of wireless systems with applications in localization and communication. In this paper, we consider the problem of positioning a single-antenna user in 3D space based on the received signal from a single-antenna base station and reflected signal from an RIS by taking into account the mobility of the user and spatial-wideband (WB) effects. To do so, we first derive the spatial-WB channel model under the far-field assumption, for orthogonal frequency-division multiplexing signal transmission with the user having a constant velocity. We derive the Cram\\u27er Rao bounds to serve as a benchmark. Furthermore, we devise a low-complexity estimator that attains the bounds in high signal-to-noise ratios. Our estimator neglects the spatial-WB effects and deals with the user mobility by estimating the radial velocities and compensating for their effects in an iterative fashion. We show that the spatial-WB effects can degrade the localization accuracy for large RIS sizes and large signal bandwidths as the direction of arrival or departure deviate from the RIS normal. In particular, for a 64 $\times$ 64 RIS, the proposed estimator is resilient against the spatial-WB effects up to 140 MHz bandwidth. Regarding user mobility, our results suggest that the velocity of the user influences neither the bounds nor the accuracy of our estimator. Specifically, we observe that the state of the user with a high speed (42 m/s) can be estimated virtually with the same accuracy as a static user