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.

Cloud computing has emerged as a fundamental pillar of modern technology, enabling large-scale data management, computational efficiency, and operational flexibility. However, critical challenges persist, particularly concerning security and performance. DDoS attacks severely impact cloud infrastructure by degrading system performance and causing service disruptions. These persistent threats raise concerns about cloud system reliability and underscore the necessity for advanced security measures. This study investigates the cloud computing task scheduling problem, recognized as NP-hard, and explores the impact of adversarial conditions such as DDoS attacks on system performance. To address this challenge, metaheuristic algorithms are employed. The research evaluates the effectiveness of traditional approaches, including genetic algorithms (GAs), particle swarm optimization (PSO), and artificial bee colony (ABC), while also introducing a GA–PSO algorithm designed to enhance task scheduling efficiency. The proposed method aims to minimize makespan by optimizing task allocation across virtual machines (VMs) within cloud environments. A comparative analysis of scheduling performance under both normal and DDoS-affected conditions reveals that metaheuristic techniques contribute significantly to system resilience. Furthermore, the GA–PSO algorithm demonstrates notable improvements at specific iteration levels. The findings underscore the potential of advanced scheduling methods to enhance cloud computing sustainability while offering practical solutions to mitigate real-world security threats.

Task scheduling in cloud computing becomes significantly more challenging under Distributed Denial-of-Service (DDoS) attacks, as malicious workload injection disrupts resource availability and degrades Quality of Service (QoS). To address this issue, this study proposes an improved Wild Horse Optimizer (A-WHO) that incorporates a stagnation detection mechanism and a stagnation-driven adaptive leader perturbation strategy. The proposed mechanism dynamically applies a noise-guided perturbation into the stallion position only when no improvement is observed over a predefined threshold, enabling A-WHO to escape local optima without modifying the standard behavior of WHO in normal iterations. In addition, a DDoS-aware CloudSim environment is developed by generating attacker virtual machines and high-MI malicious cloudlets to emulate realistic resource exhaustion scenarios. A-WHO’s performance is assessed using makespan, SLA violation rate, each of the QoS metrics, and energy consumption on normal and DDoS conditions. The experimental results indicate that A-WHO achieves the best absolute makespan and QoS metrics during an attack and competitive results under normal conditions. In comparison with the WHO, PSO, ABC, GA, SCA, and CSOA, the proposed approach demonstrates improved robustness and greater resilience to resource degradation attacks. These findings indicate that integrating stagnation-aware diversification into metaheuristic schedulers represents a promising direction for securing cloud task scheduling frameworks.

This study was planned to assess the impact of magnification and fluorescence-guidance on the performance of undergraduate and specialist program students