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.

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.

Meta-heuristic approaches are used as a powerful tool for solving numeric optimization problems. Since these problems are deeply concerned with their diversified characteristics, investigation of the utilization of algorithms is significant for the researchers. Whale optimization algorithm (WOA) is one of the novel meta-heuristic algorithms employed for solving numeric optimization problems. WOA deals with exploitation and exploration of the search space in three stages, and in every stage, all dimensions of the candidate solutions are updated. The drawback of this update scheme is to lead the convergence of the algorithm to stack. Some known meta-heuristic approaches treat this issue by updating one or a predetermined number of dimensions in their update scheme. To improve the exploitation behavior of WOA, a fuzzy logic controller (FLC) based adaptive WOA (FAWOA) is suggested in this study. An FLC realizes the update scheme of WOA, and the proposed FLC determines the rate of the change in terms of dimension. The suggested FAWOA is evaluated using 23 well-known benchmark problems and compared with some other meta-heuristic approaches. Considering the benchmark problems, FAWOA achieves best results on 11 problem and only differential evaluation algorithm achieve best results on 10 problems. The rest of the algorithms couldn’t achieve the best results on not more than 5 problems. Besides, according to the Friedman and average ranking tests, FAWOA is the first ranked algorithm for solving the benchmark problems. Evaluation results show that the suggested FAWOA approach outperforms the other algorithms as well as the WOA in most of the benchmark problems.

Bilgi güvenliğinin oldukça hassas olduğu kritik bilgiler içeren siber sistemlerin ve ağların yetkisiz erişim ve dış müdahalelerden korunması oldukça önemlidir. Ağ güvenliğinin sağlanması ve fiziksel olarak tek yönlü güvenli veri aktarımının yapılması için son yıllarda veri diyotları olarak isimlendirilen sistemler kullanılmaktadır. Tek yönlü veri aktarımı için veri merkezli bir ara katman mimarisi olan Data Distribution Service (DDS) gerek güvenli veri aktarımı özelliği gerekse barındırdığı yönlendirme, filtreleme ve izleme özellikleri ile oldukça uygun bir teknolojidir. Bu çalışmada DDS ara katman mimarisini kullanan tek yönlü güvenli veri aktarım sistemi önerilmiş ve performansı incelenmiştir. Buna göre kabul edilebilir performans kaybı olsa dahi kritik bilgiler içeren ağ sistemleri için DDS mimarisindeki tek yönlü iletim sisteminin uygun bir çözüm olabileceği, siber güvenlik sistemleri için birçok avantajı barındıran bir seçenek olacağı değerlendirilmiştir.

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.

Recently, delayed reinforcement learning (RL) has been proposed as a strong method for learning in multi-agent systems (MASs). In this method, agents are concerned with the problem of discovering an optimal policy, a function mapping states to actions. The most popular RL technique, Q-learning, has been proven to produce an optimal policy under certain conditions. In this paper, we consider a multi-agent cooperation problem, and propose a multiagent reinforcement learning method based on the other agents’ actions. In our learning method, the agent under consideration observes other agents’ action, and uses the minimax Q-learning using fuzzy state and fuzzy goal representation for updating fuzzy Q values.

Recently, delayed reinforcement learning (RL) has been proposed as a strong method for learning in multi-agent systems (MASs). In this method, agents are concerned with the problem of discovering an optimal policy, a function mapping states to actions. The most popular RL technique, Q-learning, has been proven to produce an optimal policy under certain conditions. In this paper, we present a new method called FQ-learning for more quickly learning agents acting in MAS. In experimental results done on the pursuit domain, we demonstrate the superiority of the proposed algorithm over standard Q-learning method in terms of convergence speed and number of convergence steps.