Waveform Design Considerations for 5G Wireless Networks

In this chapter, we first introduce new requirements of 5G wireless network and its differences from past generations. The question “Why do we need new waveforms?” is answered in these respects. In the following sections, time‐frequency (TF) lattice structure, pulse shaping, and multicarrier schemes are discussed in detail. TF lattice structures give information about TF localization of the pulse shape of employed filters. The structures are examined for multicarrier, single‐carrier, time‐division, and frequency‐division multiplexing schemes, comparatively. Dispersion on time and frequency response of these filters may cause interference among symbols and carriers. Thus, effects of different pulse shapes, their corresponding transceiver structures, and trade‐offs are given. Finally, performance evaluations of the selected waveform structures for 5G wireless communication systems are discussed

Enhanced physical layer security by OFDM signal transmission in fractional Fourier domains

The main idea of physical layer security for wireless communications is making the transmitted signal as meaningless for eavesdroppers. It can be achieved by signal processing techniques. In this study, fractional Fourier transform is used for secure communication. Transmitted signal is divided to equal and random intervals, then each interval was taken fractional Fourier transform with four degrees. In this way, signals can be transmitted with an angle between time and frequency domain. Receiver needs to know which angular parameters are used in each interval for obtaining the signal correctly. It is difficult to obtain signals by eavesdropper without any parameter knowledge. In this study, the bit error rate performances of legitimate users and eavesdropper are compared and the bit error rate performance of eavesdropper becomes close to 0,5

FMCW Signal Detection and Parameter Extraction by Cross Wigner–Hough Transform

The article of record as published may be found at http://dx.doi.org/10.1109/TAES.2017.2650518Published in: IEEE Transactions on Aerospace and Electronic Systems (Volume: 53 , Issue: 1 , Feb. 2017)The combination of Wigner-Ville distribution (WVD) and Hough transform (HT) has been successfully used in detection and parameter extraction of frequency modulated continuous waveform (FMCW) signals. In this paper, a combination of Cross-Wigner-Ville and HT [(Cross Wigner-Hough transform (XWHT)] is proposed for detection and parameter extraction of FMCW signals with a novel methodology. The XWHT method makes use of the cross-terms created by WVD instead of trying to suppress them. Utilization of the properties of the cross-terms to detect and unveil the parameters of FMCW signals on HT space is a new approach. The performance of the method is compared with other Wigner-Hough transform-based methods in terms of transform speed, parameter extraction, and detection performance. As a result, this study proposes that the XWHT is a candidate method to be used in digital electronic support receivers' automatic signal detection and analysis capabilities due to its speed and performance.Scientific and Technological Research Council of Turkey, TUBITAKGrant Project 113E11

A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability

he cervical cancer developing from the precancerous lesions caused by the human papillomavirus (HPV) has been one of the preventable cancers with the help of periodic screening. Cervical intraepithelial neoplasia (CIN) and squamous intraepithelial lesion (SIL) are two types of grading conventions widely accepted by pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis. In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is created and the “Uterine Cervical Cancer Database” introduced in this article is the first publicly available cervical tissue microscopy image dataset. In addition, a morphological feature representing the angle between the basal membrane (BM) and the major axis of each nucleus in the tissue is proposed. The presence of papillae of the cervical epithelium and overlapping cell problems are also discussed. Besides that, the inter-observer variability is also evaluated by thorough comparisons among decisions of pathologists, as well as the final diagnosis. [Figure not available: see fulltext.].Istanbul Technical University ; Yildiz Technical Universit

A multi-level thresholding based segmentation method for microscopic fluorescence in situ hybridization (FISH) images

Kanser tanısında yaygın olarak kullanılan Floresan In Situ Hibridizasyon (FISH) tekniği, kromozom bölgelerinin özel boyalarla boyanarak FISH sinyalleri hâlinde görüntülenmesine dayanır. Bu çalışmada, FISH tekniğiyle elde edilmiş görüntüler üzerinde, çoklu seviye eşiklemeye dayanan yeni bir FISH sinyali bölütleme yöntemi önerilmiştir. Floresan mikroskop ile yüksek büyütmede elde edilmiş görüntüler üzerinde hücre çekirdeklerinin bölütlenmesi için uyarlamalı eşikleme, uzaklık dönüşümü ve “su bölümü çizgisi” (watershed) yöntemleri kullanılmıştır. Geliştirilen özgün bölütleme yöntemiyle, hücre sınırları içine düşen bölgelerdeki FISH sinyalleri, çoklu seviye eşiklemeye ve morfolojik art işlemlere tabi tutularak belirlenmektedir. Önerilen yöntemin gerçek hastalardan alınmış 49 adet FISH görüntüsü üzerinde ölçülen tespit başarımının, literatürde yaygın olarak kullanılan diğer yöntemlere göre daha yüksek olduğu gözlenmiştir.Fluorescence in situ hybridization (FISH) technique widely used in cancer diagnosis is based on displaying chromosomal regions as FISH signals by staining with specific dyes. In this study, a new multi-level thresholding based FISH signal segmentation method is proposed for images produced by FISH technique. Cell nuclei are segmented on images, that are grabbed from fluorescence microcopes at high resolution, with adaptive thresholding, distance transform and watershed methods. FISH signals falling in cell boundaries are detected by applying multi-level thresholding and morphological post processes thanks to proposed segmentation method. It is observed that the detection rate of the proposed method on 49 FISH images taken from real patients, are higher than other widely used techniques in the literature.IEEEBülent Ecevit Üniversites

Classification of cervical precursor lesions via local histogram and cell morphometric features

Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public dataset.The method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.Scientific Research Projects Coordination Department (BAP), Istanbul Technical University ; Yildiz Technical Universit

Segmentation of precursor lesions in cervical cancer using convolutional neural networks

Ülkemizde ve dünyada en sık görülen kanser tiplerinden olan rahim ağzı (serviks) kanseri, kanser öncüsü lezyonlarından gelişmektedir. Kanser öncüsü bu lezyonların saptanması, hastanın kanser olmadan tedavi olmasına olanak sağladığiçin önemlidir ve analizleri yapan patologlar tarafından tanısı konmaktadır. Bu çalışmada evrişimsel sinir ağları (ESA) yöntemi kullanılarak kanser öncüsü lezyonların otomatik tespitini gerçekletiren bir sistem tasarlanmıştır. Eğitim aşamasında sistemin görüntülerden lezyonları tanıma başarımı %92 olarak elde edilmektedir. Eğitim aşamasından sonra bütün görüntüler 60×60 boyutlarında bir pencere ile evriştirilerek bölütlenmektedir. İlgili lezyonların Dice katsayısına göre %81.71 başarı ile bölütlendiği bir model oluşturulmuştur.Cervical carcinoma is one of the frequently seen cancers in the world and in our country, develops from precursor lesions. These precursor lesions are analyzed by pathologists so that the diagnosis of the disease can be made. In this study, a system that performs automatic detection of pre-cancerous lesions was performed using the convolutional neural networks (CNNs). In the training phase, lesion recognition performance of the proposed system has reached 92%. Thereafter, whole image was segmented by using 60 × 60 pixel tiles during the training phase. After all, the precursor lesions were segmented with 81.71% Dice coefficient