Radio-frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary data set and the target data set often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary data sets, in this article, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN.” First, we analyze the causes of RFF, and model the RFF identification problem with augmented data set. A nonauxiliary data augmentation method is proposed to acquire an extended data set, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio-frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has a greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

Wi-Fi-based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we first propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as convolutional neural network (CNN)-ABLSTM, a combination of CNNs and attention-based bi-directional long short-term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Second, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Third, we introduce an attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM, and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.

The communications between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference in the region through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep Q-network reinforcement learning (RL) with low-dimensional fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. In addition, we extend the amount of transmitted data in our work, while adding scenarios where spectrum resources are relatively scarce, i.e. the number of V2V links is significantly larger than the amount of spectrum, to compensate for some of the shortcomings in existing literature studies. We demonstrate that the proposed D3QN-LS algorithm leads to a further improvement in the total capacity of V2I links and the success rate of periodic secure message transmission in V2V links.

Alpine skiing is a sport and recreational physical activity which requires fine postural control to maintain balance in challenging conditions. Theoretically, balance dominates in alpine skiing, but coordinated action of the whole body of the skiers is equally important. The aim of this research was to determine the effects of experimental short-term program of intensive training of alpine skiing techniques to postural stability (on Biodex Balance System) of students. The sample is divided into an experimental (31 students, age 21.4±1.0 and body height 180.7±6.3 cm) and control group (34 students, age 20.6±0.8 and body height 180.3±6.8 cm). The results of ANCOVA within variables for the evaluation of postural stability show statistically significant effects of the applying experimental program in all applied variables at the level of significance p=.000. From the mean value results (M) it is obvious that the experimental group achieved better results compared to the identical tests applied to the control group. The results of this research show that learning to ski can improve the ability to maintain balance, especially if it is conducted under the expert supervision of a ski instructor, which can have the effect of reducing the risk of injury.

We propose a novel strategy to construct optimal controllers for continuous-time nonlinear systems by means of linear-like techniques, provided that the optimal value function is differentiable and quadratic-like. This assumption covers a wide range of cases and holds locally around an equilibrium under mild assumptions. The proposed strategy does not require solving the Hamilton–Jacobi–Bellman equation, i.e., a nonlinear partial differential equation, which is known to be hard or impossible to solve. Instead, the Hamilton–Jacobi–Bellman equation is replaced with an easy-solvable state-dependent Lyapunov matrix equation. We exploit a linear-like factorization of the underlying nonlinear system and a policy-iteration algorithm to yield a linear-like policy-iteration for nonlinear systems. The proposed control strategy solves optimal nonlinear control problems in an asymptotically exact, yet still linear-like manner. We prove optimality of the resulting solution and illustrate the results via four examples.

Abstract Congenital malformations are defined as structural or functional anomalies that occur in utero or at birth and can be detected at an early age. They are also known as birth defects, disabilities or congenital malformations. Congenital malformations are accompanied by hereditary or developmental disabilities or disease. From the establishment of the registry in early 2019 until the end of 2021, the total number of reported congenital malformations is 449. According to available data from EUROCAT (European network of population-based registries for the epidemiological surveillance of congenital anomalies), the average rate of congenital malformations in the countries of the European Union (EU) is 262/per 10,000 live births, while the registered rate of congenital malformations in the Federation of Bosnia and Herzegovina is 261/per 10,000 live births. In the Federation of Bosnia and Herzegovina, the highest incidence rate was registered in Sarajevo Canton (175 cases with a rate of 416/10,000 live births) and Tuzla Canton (122 cases with a rate of 356/10,000 live births). The most common congenital malformations are heart defects, cleft lip and palate, musculoskeletal deformities and Down syndrome. In the Federation of Bosnia and Herzegovina (FBiH) in 2020, 135 children under the age of 5 died, among which 18 children (13.3%) died from congenital malformations, deformations and chromosomal abnormalities (Q00-Q99). Congenital malformations can lead to chronic diseases and disabilities, death of infants and children up to five years of age. Congenital malformations represent a significant public health problem, given that they lead to disability, incapacity and pressure on the health system, as well as the problem of social integration of patients. Key messages • The registered rate of congenital malformations in the Federation of Bosnia and Herzegovina is 261/per 10,000 live births. • Congenital malformations can lead to chronic diseases and disabilities, death of infants and children up to five years of age.

Attack graphs are a tool for analyzing security vulnerabilities that capture different and prospective attacks on a system. As a threat modeling tool, it shows possible paths that an attacker can exploit to achieve a particular goal. However, due to the large number of vulnerabilities that are published on a daily basis, they have the potential to rapidly expand in size. Consequently, this necessitates a significant amount of resources to generate attack graphs. In addition, generating composited attack models for complex systems such as self-adaptive or AI is very difficult due to their nature to continuously change. In this paper, we present a novel fragment-based attack graph generation approach that utilizes information from publicly available information security databases. Furthermore, we also propose a domain-specific language for attack modeling, which we employ in the proposed attack graph generation approach. Finally, we present a demonstrator example showcasing the attack generator's capability to replicate a verified attack chain, as previously confirmed by security experts.

Magnetic nanoparticles can be electrostatically assembled around sperm cells to form biohybrid micro robots. These biohybrid microrobots possess sufficient magnetic material to potentially allow for pulse-echo localization and wireless actuation. Alternatively, magnetic excitation of these nanoparticles can be used for localization based on Faraday's law of induction using a detection coil. Here, we investigate the influence of the electrostatic attraction between positively charged nanoparticles and negatively charged sperm cells on the activation of the nanoparticles during nonlinear differential magnetometry and wireless magnetic actuation. Activation of clusters of free nanoparticles and nanoparticles bound to the body of sperm cells is achieved by a combination of a high- frequency alternating field and a pulsating static field. The nonlinear response in both cases indicates that constraining the nanoparticles is likely to yield significant decreases in the magnetometry sensitivity. While the attachment of particles to the cells enables wireless actuation (rolling locomotion), the rate of change of the magnetization of the nanoparticles decreases one order of magnitude compared to free nanoparticles.

Technoeconomic, environmental and safety criteria generally affect the management of metallic and non-metallic mining operations. The first basic question that needs to be addressed when planning ore mining is which methods are adequate and what is the optimal mining technology? Due to the complex geologic framework of ore deposits, geological exploration has rendered synonymous the inherent uncertainties, vagueness, and inaccuracies. As a result, subjective evaluation by engineers and expert experience have become increasingly important. Given that the natural language used by miners and geologists is most suited for relaying knowledge and expressing opinions, the paper tests a fuzzy optimization methodology that uses linguistic variables. Consequently, extent analysis is applied to fuzzy AHP by means of triangular fuzzy numbers to arrive at a decision about the optimal mining technology. The entire procedure constitutes an integrated mine management system, which will contribute to sustainable production in the future. A case study to which the model was applied is presented in the paper.