Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.

Several super-resolution (SR) techniques are introduced in the literature, including traditional and machine learning-based algorithms. Especially, deep learning-based SR approaches emerge with demands for better quality images providing deeper subpixel enhancement. Dealing with the image enhancement task in the satellite images domain, a new SR method for single image SR, namely Enhanced Deep Pyramidal Residual Networks, is introduced in this study. The proposed method overcomes the potential instability problem of Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) approach by gradually increasing the feature maps depending upon Pyramidal Residual Networks architecture. The EDSR itself is a good algorithm in the SR domain. However, it has a strict structure for increasing the block size. To overcome this problem with the aim of increasing the algorithm’s performance, the pyramidal residual networks gradually increasing hypothesis is utilized in the proposed approach, which is the main contribution and novelty of this study. Besides, by using the pyramidal residual networks gradually increasing hypothesis in the proposed approach, the parameter size of the models is also reduced, which affects the computational time. Two different models are proposed by considering addition and multiplication manners, and the proposed models are evaluated using well-known remote sensing datasets NWPU-RESISC45 and UC Merced. The results obtained by the proposed model are compared with the results of traditional image enhancement algorithms together with the EDSR itself, EDSR with deeper structure, Super-Resolution Generative Adversarial Networks approach, and Residual Local Feature Networks approach in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) metrics and showed that the proposed models present better quality images. Moreover, considering the computational time and complexity, it is shown that some proposed models achieve approximately 27% less output parameter having similar PSNR and SSIM values and computational time for EDSR itself and 65% less output parameter having better PSNR and SSIM values and 16% lower computational time for EDSR with deeper structure.

With the increasing size of datasets in data mining applications, feature selection has become critical for enhancing classification accuracy and reducing computational complexity. In this study, a novel binary feature selection algorithm, called bWSO-log, is proposed based on the White Shark Optimizer (WSO). Unlike the commonly used S-shaped and V-shaped transfer functions in the literature, the WSO algorithm is converted into a binary form for the first time using a logarithmic transfer function. The performance of the proposed method was tested on nineteen benchmark datasets and compared with eight widely used metaheuristic algorithms. The results show that the bWSO-log algorithm demonstrates superior or competitive performance in terms of classification accuracy and the number of selected features. These findings reveal the effectiveness of the proposed logarithmic function and highlight the potential of WSO-based binary optimization in feature selection problems.

Nesnelerin İnterneti (IoT) kapsamında çok sayıda veri üretici sistem belirli bir ağ üzerinde veri alışverişinde bulunurlar. Veri transferi için çeşitli avantajları bulunan DDS (Data Distribution Service) ara katman mimarisi veri merkezli ağ haberleşmesi için sıklıkla kullanılmaktadır. Ölçeklendirme, yönetim ve izleme amaçlarına yönelik olarak kullanılan ara katman mimarisinin sağladığı birçok servis kalitesi (QoS) özelliği ile güvenilir veri aktarımı gerçekleştirilir. Bununla birlikte, olası ağ kesintileri, yavaşlama ya da veri kaybı oluşturabilecek senaryolar için yazılım geliştirme aşamasında ağ benzetimi yapılması, olası hataların erken tespiti ve düzeltilmesi maliyet ve zaman açısından faydalı olacaktır. Bu çalışmada DDS ara katman mimarisine yönelik ağ kesintisi, bant genişliği daralması, paket kaybı ve ağ topolojisine yönelik olası incelemeler için bir benzetim modeli ve yazılım mimarisi önerilmiştir. Buna göre, veri iletim ağının belirli noktalarının davranışı değiştirilerek ağ gecikmesi, paket kaybı ya da servis kesintisi durumlarında yazılım davranışlarının incelenebilmesi sağlanmıştır. Ağ benzetimi ve test sistemi için bir arayüz yazılımı geliştirilerek ağ bağlantısının farklı durumlar için benzetimi amaçlanmıştır.

In this study, data obtained from a 360-degree (or 2D) Light Detection and Ranging sensor, which is commonly used for Simultaneous Localization and Mapping, were converted into an image/frame matrix to apply optical flow methods. Key features on the resulting image matrix were identified as corners using the Shi-Tomasi method. The positional changes of these key features during each complete measurement cycle of the LiDAR sensor were calculated using the Lucas-Kanade method, an optical flow technique. These positional changes were analyzed to estimate the vehicle’s displacement within the environment. The developed method was tested in a simulation environment with different object densities, moving linearly at different speeds, both constant and variable.