This study presents a novel ultra-thin dual band frequency selective surface (FSS) for X band applications. The proposed single layer FSS operates as a band reject filter at 8.47 GHz and 10.45 GHz. The ratio of these two operating bands has small value such as 1.23 and it points out that the proposed FSS has closely spaced characteristic. In addition, the designed dual-band single-layer FSS has an ultra-thin thickness of 0.021* $\lambda_{l}$ , where $\lambda_{l}$ is the wavelength of the lower operational frequency. The stable frequency responses are sufficiently achieved for the TE (transverse electric) and TM (transverse magnetic) polarizations by using the proposed FSS. The validity of FSS is obtained by comparing measurements and simulations.

Technological developments and transformations have rapidly risen since the Fourth Industrial Revolution. The prevalence of industrial devices interconnected over the wireless sensor networks and the provision of a sustainable data flow reveal the importance of the Industrial Internet of Things (IIoT). In the manufacturing industry, supervisory control and data acquisition (SCADA) systems are used to control IIoT for critical infrastructure. A cyberattack on the network-based communication structure embedded into the architecture of industrial equipment can significantly disrupt/sabotage product manufacturing and other industrial operations. The digitization of industrial control systems can expose the systems to malicious actors and therefore requires additional security solutions, such as intrusion detection systems (IDSs). Increasing sophistication of cyberattacks, industrial companies need to adopt innovative solutions like artificial intelligence (AI)-based attack detection to protect their valuable assets. In addition, AI-based approaches are more effective as they analyze network traffic, identify threats, and adapt to new attack techniques. This study aims to develop an AI-based IDS with high accuracy for SCADA security. In the study, cyberattacks that may occur against SCADA systems are examined. AI methods (including K-nearest neighbor, quadratic discriminant analysis, adaptive boosting, gradient boosting, and random forest) in different categories are used and AI models with various parameters are built. To improve the detection performance of the models, comprehensive experiments are carried out on two different SCADA data sets. As a result of experiments, the test accuracy rates exceeding 96.82% are achieved by all models: on the WUSTL-IIOT-2021 data set, the XGB model has outperformed with an accuracy of 99.99%.

Home automation based on the Internet of Things (IoT) includes various components such as lighting, security, and remote control. Smart Home (SH) components should be varied and customised according to the user's specific needs. Therefore, in this study, a SH system needed by users with specials is designed and implemented on a model. The NodeMCU microcontroller with ESP8266 Wi-Fi module, Radio Frequency Identification (RFID) tags, temperature-humidity, motion detection, gas and moisture sensors and several actuators are used to build the system. Thanks to If This Then That (IFTTT), Google Assistant, and Blynk, the SH components can be managed remotely and with voice commands via a user-friendly Android-based mobile interface. The multiple control system and the ability to control the home components with different methods make this study comprehensive. In particular, the barrier-free stairs design has added innovation to the SH system for disabled people. Thus, the accessibility, security, and comfort requirements of disabled people are met, and their quality of life is improved to live independently.

Şehir sakinleri için daha sağlıklı bir yaşam ortamının sağlaması ve onların hava kirliliğine maruziyetinin en aza indirilmesi oldukça önemlidir. Bunun için yapılabilecek çalışmalar arasında kirletici parametrelerine ilişkin ölçümlerin düzenli olarak yapılması ve izlenmesi, hava kirliliğinin tahmin edilmesi ve insan sağlığı üzerindeki etkilerinin erken değerlendirilmesi yer almaktadır. Bu çalışmada, makine öğrenmesi yöntemleri kullanılarak hava kirliliği tahmini gerçekleştirilmiştir. İstanbul ili Başakşehir ilçesinde 2016-2021 yılları arasında ölçülen çeşitli hava kirleticilerine ilişkin günlük ortalama konsantrasyonları ile rüzgar yönü, rüzgar hızı ve hava basıncı değerleri kullanılmıştır. PM10, CO, SO2, O2 ve O3 kirleticilerin konsantrasyonları, Çoklu Doğrusal Regresyon, Destek Vektör Makinaları, K En Yakın Komşu, Karar Ağaçları, Rastgele Orman ve Çok Katmanlı Algılayıcı Sinir Ağı yöntemleri ile tahmin edilmiştir. Elde edilen sonuçlar karşılaştırıldığında PM10, CO, SO2 ve O3 konsantrasyonları tahmininde Rastgele Orman yöntemi, NO2 tahmininde ise Çoklu Doğrusal Regresyon, en iyi sonuçları sunmuştur.

This study presents a transmitarray antenna design operating at a center frequency of 9 GHz, addressing the need for high-gain and broad bandwidth antennas in modern data communication. The proposed design is structured as a quad-layer configuration using FR-4 dielectric substrates. The transmitarray’s phase profile is tailored to deliver a wideband flat response with low Side Lobe Level (SLL) and stable aperture efficiency across the operating band. It achieves a 1-dB gain bandwidth of 12.12% (from 8.56 GHz to 9.67 GHz) and a 3-dB gain bandwidth of 49.43% (from 8.45 GHz to 13.46 GHz) with an aperture efficiency of 21.3%. A prototype of the proposed design with 11 × 11 elements was fabricated and measured, and its measurement results closely aligned with simulation results, validating its performance. The proposed simple design realizes reduced complexity and fabrication costs while expanding operational gain bandwidth, thereby demonstrating substantial promise for next-generation X-band communication systems.