In the face of current modular robots that rely on manual coding to generate deformation instructions and are limited by binary instructions, this research proposes a Cognitive Modular Control (CMC) architecture inspired by ACT-R theory. Build simulation tool ReoForm to conduct experiments through simulation-based experimental design. The data results show that each module of the architecture demonstrates feasibility and universality in terms of time-to-completion, energy consumption, manipulability index and robustness indicators.

Current probes have long been used for Electromagnetic Compatibility (EMC) pre-compliance and diagnostic purposes. Dipole and transmission line-based models are often used to relate Common-Mode (CM) current levels to potential Radiated Emission (RE) levels. This relationship enables RE estimates to be performed without the use of accepted certification methodologies – reducing project costs and timelines. These models are based on a set of assumptions which cannot fully match real-world scenarios. This article expands the discussion by examining the relationship between the CM current and RE of a representative metallic structure using both measurements and a Finite Element Method (FEM) code. The relationship is shown to be frequency dependent, resulting in both over and under prediction of the RE levels. A practical recommendation is made on how to exploit the equivalence of CM current-based estimations of system RE.

The radiation pattern and efficiency of a leaky-wave antenna is dependent on its aperture field distribution. The required field magnitude is achieved by modulating the leakage factor along the antenna, for which an approximate formula derived in the last century continues to be widely used nowadays. This expression assumes the field transverse profile inside the guiding structure does not change along its length. This contribution revisits this well-known formula in order to obtain it from a rigorous approach assuming a leaky rectangular waveguide. Thus, the origin of the limitation of the maximum leakage factor value is explained, and its influence is quantified by establishing a numerical criterion. Finally, some practical examples are discussed, showing how the approximate leakage factor differs from the exact numerically-computed one in limit cases, and how the radiation patterns are affected.

Abnormal joint rigidity is a common symptom of many neurological disorders like Parkinson's disease and stroke. Joint rigidity is clinically described as increased resistance during passive joint movement and is quantitatively related to the static passive component of joint impedance, i.e., passive stiffness. Here, we introduce a novel approach to estimate the passive stiffness of the wrist in humans across two coupled Degrees of Freedom (DoFs): Flexo-Extension (FE) and Radio-Ulnar Deviation (RUD). This method employs a subject-specific kinematic model of the human wrist, treated as a universal joint with two skew-oblique axes (FE and RUD), not intersecting and not necessarily perpendicular one to each other. We tested this methodology on ten healthy volunteers using infrared cameras and a hand-held device equipped with a 6-axis load cell for manual wrist perturbations. We used motion and force/torque data to determine angles and torques at the wrist DoFs, and applied multiple linear regression to calculate the 2-by-2 stiffness matrix. Our findings align with existing literature in terms of stiffness behavior, showing stiffness anisotropy with the highest value predominantly along the RUD direction. However, compared to a simplified wrist model with intersecting, orthogonal axes and to prior studies assuming alignment between human joints and robotic ones, our method demonstrates significant differences, particularly in the magnitude of the stiffness ellipse. By providing a more anatomically plausible representation of wrist kinematics through relaxing the constraints of simplified methods, our results show, for the first time, an accurate subject-specific estimation of wrist stiffness.

Graph neural networks (GNN) have become a powerful framework for analyzing structured data in the form of graphs, with applications spanning diverse fields such as social networks, biology, recommender systems, etc. This survey explores methodology, development, and advances in GNN architectures. We methodically survey the major classes of GNNs, including Convolutional GNNs (ConvGNNs), Spatial-Temporal Graph Neural Networks (STGNNs), Recurrent-based GNNs (RecGNNs), and Graph Autoencoders (GAEs). Every model is discussed in terms of underlying mathematical formulations, design principles, and practical applications. This survey goals to supply a comprehensive conception of GNNs for practitioners and researchers alike, highlighting their versatility and potential for future innovations in graph neural networks. We will furthermore discuss applications of graph neural networks across different fields and epitomize open-source codes, benchmark datasets, and model valuation for graph neural networks. In the end, this survey specifies existing challenges in interpretability, generalization, and scalability and proposes possible future research trends to further promote the performance of GNNs across various graph-based learning missions.

The integration of artificial intelligence (AI) in next generation cloud-native mobile networks has transformed network and service orchestration, driving the evolution towards fully automated zero-touch operations. As the AI in telecommunications market rapidly expands, the demand for transparency in AI decision-making, known as eXplainable AI (XAI), becomes of paramount importance. XAI mitigates the "black box" nature of AI models, ensuring transparency, crucial for regulatory compliance, user acceptance and system improvement. In this article, motivated by the diversity of data types in cloudnative orchestration scenarios (i.e., tabular, time-series and raw text), we explore the impact that the different data types may have on the selection of the appropriate XAI technique. In addition, we classify and discuss XAI techniques suitable for each data type, outlining a series of use cases in edge/cloud orchestration. Finally, we present two real-world proof-of-concept use cases, demonstrating the benefits that XAI can bring to cloudnative networks. Our contributions aim to facilitate the effective integration of AI in dynamic environments, bridging the gap between AI model interpretability and practical deployment in next generation cloud-native networks.

Efficient resource allocation is essential for optimizing various tasks in wireless networks, which are usually formulated as generalized assignment problems (GAP). GAP, as a generalized version of the linear sum assignment problem, involves both equality and inequality constraints that add computational challenges. In this work, we present a novel Conditional Value at Risk (CVaR)-based Variational Quantum Eigensolver (VQE) framework to address GAP in vehicular networks (VNets). Our approach leverages a hybrid quantum-classical structure, integrating a tailored cost function that balances both objective and constraint-specific penalties to improve solution quality and stability. Using the CVaR-VQE model, we handle the GAP efficiently by focusing optimization on the lower tail of the solution space, enhancing both convergence and resilience on noisy intermediate-scale quantum (NISQ) devices. We apply this framework to a user-association problem in VNets, where our method achieves 23.5% improvement compared to the deep neural network (DNN) approach.

In recent years, deep learning-based joint source-channel coding (DJSCC) has gained significant attention for its impressive performance in image transmission. Unlike traditional separate source-channel coding (SSCC) methods, DJSCC performs particularly well in low signal-to-noise ratio (SNR) and limited bandwidth environments. However, ensuring the security of private information during transmission remains a critical concern. A notable limitation of DJSCC is its incompatibility with traditional encryption methods used for secure communications, making it vulnerable to eavesdropping attacks. To address this issue, we propose integrating a chaotic map encryption method into the DJSCC framework for secure wireless image transmission. This approach leverages chaotic sequence to shuffle the position of the elements in latent space without altering the values of the latent tensor. This allows the encryption process to be designed independently of DJSCC, eliminating the need for retraining the end-to-end model. Our proposed method preserves DJSCC's superior transmission characteristics, ensuring high-quality image reconstruction at the receiver, while effectively ensuring the security against deep learning-based known plaintext attacks (Deep KPA).

Mathematical modelling of visual perception in two dimensions is described. Although perception is ubiquitously mentioned in literature and it is used in various contexts, its explicit measurable presentation has not yet been given beyond verbal descriptions. In this seminal work the visual perception is explicitly defined for clarity. Based on this definition, it is treated as a mathematical entity in the form of probability in a particular circular region. Theoretically, human vision is expressed in advanced probabilistic terms and the enigmatic relation between vision and perception is mathematically formulated. Here, the perception concerns a special range of interest, coined as vision circle, where percepts describe the vision act of human as the retinal ophthalmic sensing. Perceptual Intelligence Quotient (PIQ) is introduced, that it is subject to measurement. The results of the theoretical considerations are shown to be closely matching the common visual perception experiences as well as the reported visual perception studies, including the variation of peripheral acuity. Two eye-tracking experiments demonstrate that saccadic movement behavior of human is reasonably matching the prediction based on the novel perception theory. The work demystifies the visual perception and understanding its role in sciences. As to the cybernetics, perception plays an important role in the control of complex, integrated processing systems, which are interfaced by indispensable man-machine control systems. In these systems, the role of perception is critical, referring to the hazard situations. The implications of the study are investigated and explained in two such cybernetics applications, too.

This Technical paper presents an innovative approach to enhancing cybersecurity by leveraging advanced Network Address Allocation (NAA) techniques. Using dynamic address assignment and automated management systems, the paper demonstrates how organizations can proactively reduce vulnerabilities, enhance network visibility, and improve overall security. The inclusion of automated solutions for identifying available IP addresses in real-time further optimizes the NAA process. This approach has been shown to significantly reduce attack surfaces, improve monitoring, and ensure more resilient industrial networks. By adopting these innovations, industries can stay ahead of evolving cyber threats, ensuring secure and uninterrupted operations in the digital age.

Constrained environments, such as indoor and urban settings, present a significant challenge for accurate moving object positioning due to the diminished line-of-sight (LoS) communication with the wireless anchor used for positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave) spectrum promises high multipath resolvability in the time and angle domains, enabling the utilization of multipath signals for such problems rather than mitigating their effects. This paper investigates the benefits of integrating multipath signals into 5G LoS-based positioning systems with onboard motion sensors (OBMS). We provide a comprehensive analysis of the positioning system’s performance in various conditions of erroneous 5G measurements and outage scenarios, which offers insights into the system’s behavior in challenging environments. To validate our approach, we conducted a road test in downtown Toronto, utilizing actual OBMS measurements gathered from sensors installed in the test vehicle. The results indicate that utilization of multipath signals for wireless positioning operating in multipath-rich environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus enhancing the reliability and accuracy of the positioning solution. The redundant measurements obtained from the multipath signals can enhance the system’s robustness, particularly when low-cost 5G receivers with a limited angle or range measurements are present. This holds true even when only considering the utilization of single-bounce reflections (SBRs).

Observations made using antenna arrays are often limited by the noise coming from the antenna system itself and from the environment. In demanding applications such as radioastronomy, the thermal noise emitted by the multilayered substrate below the antenna array may become significant and needs to be properly characterized. Both the intensity of the noise at each port and the correlation of the noise at pairs of ports need to be quantified. In this paper, we provide a spectral formulation to calculate the noise correlation matrix of antenna arrays. The method is based on the propagative and evanescent plane wave spectrum emitted by each pair of active antenna ports into the lossy layered medium, and can thus be implemented as a post-processing step combined with any full-wave numerical solver. This formulation allows us to account for layered media with different physical temperatures at each layer. Numerical examples are provided using Square Kilometer Array (SKA) stations lying on a finite ground plane, itself lying on a layered semi-infinite soil. The behaviours of noise power and noise correlation coefficients are studied for different positions of the antennas on the ground plane. This is done considering a multilayered soil with different moisture levels. A very good agreement with the commercial software FEKO is observed regarding the evaluation of the noise correlation coefficients. Moreover, the efficiency of the algorithm in terms of computational time is shown by comparison with FEKO. The direction-dependent system noise temperature picked up by a full SKA station in presence of the soil is also given.

To address the rapidly increasing demand for advanced communication services, spectrum sharing technologies have become essential in mitigating the limited availability of radio spectrum resources. Understanding and modeling spectrum activity across various frequency ranges, times, locations, and altitudes are critical for realizing these technologies effectively. In this paper, we first present a comparative survey of the existing works on spectrum activity measurements in sub-6 GHz bands, including locations, frequency ranges, measurement times/durations, and various other characteristics of the datasets. Subsequently, we focus on a comparative analysis of altitudedependent spectrum activity in sub-6 GHz bands based on our measurement campaigns between 2022 and 2024. To do the measurements, we use an aerial platform equipped with a software-defined radio (SDR) that flies up to an altitude of 300 m in urban and rural areas of Raleigh, NC, USA. Based on the collected data, we analyze the spectrum occupancy characteristics and trends of long-term evolution (LTE) and fifth generation (5G) new radio (NR) cellular networks, as well as the industrial, scientific, and medical (ISM) bands in the United States. To better understand altitude effects, we examine the measurements in the frequency modulation (FM) radio band as a case study and propose an altitude-dependent analytical path loss model to characterize the spectrum measurements. Our observations from four dominant FM radio stations in Raleigh indicate that the received signal power gradually increases with altitude up to a critical threshold. Beyond this threshold, the signal power plateaus despite further increases in the aerial platform's altitude, attributed to enhanced line-of-sight conditions and the accumulation of signal sources in the urban environment. These findings highlight the importance of incorporating altitude as an additional dimension in spectrum usage analysis, which can significantly enhance the effectiveness of spectrum sharing strategies in the future.

Digital leisure activities—online games, short videos, and extracurricular reading—are popular among elementary school students. These activities can extend learning beyond the classroom but may also compete for time and energy, potentially impacting academic performance, especially for students with less self-control. This study investigates the relationship between these activities and exam scores, using advanced analytical methods. The findings reveal a significant correlation between learning outcomes and the frequency, duration, and fatigue associated with these leisure activities. Specifically, the frequency of extracurricular reading negatively correlates with science performance (effect coefficient -0.242), while the duration positively correlates (effect coefficient 0.440). An intriguing ”hourglass effect” was observed, where exam scores are heavily influenced by the time and energy students with low self-control spend on recreational activities. These results provide valuable insights into balancing digital leisure with academic responsibilities, offering practical guidance for educators in planning school content and developing educational strategies. By understanding the impact of digital leisure on learning, this research supports the development of more effective learning technologies to enhance academic outcomes in elementary education.

Supervised machine learning classifiers often encounter challenges related to performance, accuracy, and overfitting. This paper introduces the Artificial Liver Classifier (ALC), a novel supervised learning classifier inspired by the human liver's detoxification function. The ALC is characterized by its simplicity, speed, hyperparameters-free, ability to reduce overfitting, and effectiveness in addressing multi-classification problems through straightforward mathematical operations. To optimize the ALC's parameters, an improved FOX optimization algorithm (IFOX) is employed as the training method. The proposed ALC was evaluated on five benchmark machine learning datasets: Iris Flower, Breast Cancer Wisconsin, Wine, Voice Gender, and MNIST. The results demonstrated competitive performance, with the ALC achieving 100% accuracy on the Iris dataset, surpassing logistic regression, multilayer perceptron, and support vector machine. Similarly, on the Breast Cancer dataset, it achieved 99.12% accuracy, outperforming XGBoost and logistic regression. Across all datasets, the ALC consistently exhibited lower overfitting gaps and loss compared to conventional classifiers. These findings highlight the potential of leveraging biological process simulations to develop efficient machine learning models and open new avenues for innovation in the field.

The rapid expansion of Internet of Things (IoT) ecosystems has revolutionized industries by enabling real-time data exchange and interconnected operations. However, this growth presents significant security challenges, including scalability, resource constraints, and the need for real-time adaptability to evolving cyber threats. To address these issues, this study proposes an innovative, AI-driven framework that integrates lightweight intrusion detection systems (IDS), blockchain-based authentication, and edge computing. This qualitative research synthesizes peer-reviewed literature to identify existing gaps and design a multi-layered security solution tailored for resource-constrained IoT environments. The proposed framework enhances scalability by leveraging decentralized blockchain systems and edge computing for distributed data processing. Lightweight AI algorithms are employed to optimize resource efficiency and ensure real-time adaptability. Analytical comparisons with traditional security models demonstrate the framework's superiority in mitigating IoT vulnerabilities while maintaining computational feasibility. This study contributes to the theoretical and practical understanding of IoT security, offering a scalable, resource-efficient, and adaptive solution for emerging threats.

The work considers the N-server distributed computing scenario with K users requesting functions that are linearly-decomposable over an arbitrary basis of L real (potentially non-linear) subfunctions, and the aim is to receive the function outputs with zero error, reduced computing cost (γ; the fraction of subfunctions each server must compute), and reduced communication cost (δ; the fraction of users each server must connect to). For a matrix F ∈ R K×L representing the linearly-decomposable form of the K requested functions, our problem is made equivalent to the open problem of zeroerror sparse matrix factorization that seeks F = DE over a special subset of γ-sparse and δ-sparse E ∈ R N ×L and D ∈ R K×N matrices that respectively define which servers compute each subfunction, and which users connect to each server. We here design an achievable scheme designing E, D by utilizing a fixed-support SVD-based matrix factorization method that first splits F into properly sized and carefully positioned submatrices, and then decomposes these into properly designed submatrices of D and E. For the zero-error case and under basic dimensionality and single-shot assumptions, this work reveals that the optimal number of servers can be upper-bounded as Nopt ≤ min(∆, Γ)⌊K/∆⌋⌊L/Γ⌋ + min(mod(K, ∆), Γ)⌊L/Γ⌋ + min(mod(L, Γ), ∆)⌊K/∆⌋ + min(mod(K, ∆), mod(L, Γ)). In the special case, it reveals that the maximum possible ratio K/N or the zero-error capacity of this system for any feasible single-shot scheme satisfying δ −1 , γ −1 ∈ N is C = max(K/Lδ, γ).

In this work, we investigate the underlying physical mechanism for electric-field induced resistive switching in Au/MoS 2 /Au based memristive devices by combining reactive Molecular Dynamics (MD) and ab-initio quantum transport calculations. Using MD with Au/Mo/S ReaxFF potential, we observe the formation of realistic conductive filament consisting of gold atoms through monolayer MoS 2 layer when sufficient electric field is applied. We furthermore instigate the rupture of the gold atom filament when a sufficiently large electric field is applied in the opposite direction. To calculate the conductance of the obtained structures and identify the High Resistance (HR) and Low Resistance (LR) states, we employ the ab-initio electron transport calculations by importing the atomic structures from MD calculations. For single-defect MoS 2 memristors, the obtained LRS, HRS current densities are in order of 10 7 A/cm 2 which agrees reasonably well with the reported experiments.