Network slicing is a key enabler for 5G and beyond networks that permits operators to provide scalable, flexible, and dedicated networks over a common physical infrastructure. To cope with the rising demand for Ultra-Reliable and Low-Latency Communication (URLLC) in beyond 5G networks, the provision of dedicated secure networks closer to the users is essential. Multi-access Edge Computing (MEC) is a promising technology that provides data and computational resources closer to mobile users. However, MEC servers are resource-constrained, and offering dedicated service-specific network slices at the edge in a highly dynamic and mobile environment is challenging. Network slicing and MEC are being evolved by two different standardization bodies that limit their integration and raise mobility challenges that deserve more attention. We propose a cloud-native microservices architecture for network slice mobility management in MEC that permits each MEC slice to be distributed as stateless and independently deployable microservices. The proposal separates the MEC slice operational data and the user context, as each network function in an MEC slice stores the context in a separate shared database. The proposed architecture leverages new SDN extended federation modules in compliance with the ETSI requirements for inter-MEC system coordination. The federation modules support a more flexible and scalable creation of network slices at MEC servers, efficient resource utilization, and mobility of network slices across MEC servers. The simulation results show that our proposed architecture outperforms the existing SDN-based approaches for network slicing in MEC by achieving high slice acceptance rates and reduced slice migration delay.

This paper presents a novel approach to video super-resolution (VSR) by focusing on the selection of input frames, a process critical to VSR. VSR methods typically rely on deep learning techniques, those that are able to learn features from a large dataset of low-resolution (LR) and corresponding high-resolution (HR) videos and generate high-quality HR frames from any new LR input frames using the learned features. However, these methods often use as input the immediate neighbouring frames to a given target frame without considering the importance and dynamics of the frames across the temporal dimension of a video. This work aims to address the limitations of the conventional sliding-window mechanisms by developing input frame selection algorithms. By dynamically selecting the most representative neighbouring frames based on content-aware selection measures, our proposed algorithms enable VSR models to extract more informative and accurate features that are better aligned with the target frame, leading to improved performance and higher-quality HR frames. Through an empirical study, we demonstrate that the proposed dynamic content-aware selection mechanism improves super-resolution results without any additional architectural overhead, offering a counter-intuitive yet effective alternative to the long-established trend of increasing architectural complexity to improve VSR results.

The small-scale prosumers’ decentralized cooperation and aggregation in energy communities are essential to increase renewable energy penetration and to ensure a successful energy transition. Despite their potential small-scale prosumers are not motivated to participate in local energy value chains due to a lack of trust and decentralized cooperation models for capturing local sustainability preferences, most innovation efforts are focused on financial incentives that are anyway very low. In this paper, we propose a solution for prosumers’ decentralized coordination using a cooperative game on top of a blockchain overlay by considering their complementary energy-related features and flexibility for self-sufficient energy communities. We define a governance model based on cooperative games and blockchain-sharing mechanisms reinforce the prosumers’ trust to support the decentralized self-organization in coalitions to balance the renewable generation and demand. Self-enforcing smart contracts are used for the blockchain integration of the governance model to allow the decentralized creation and management of prosumers’ coalitions for optimized payoff distribution towards self-sufficiency and using tokens capturing the beyond-financial community level exchanges. The evaluation results show our solution’s effectiveness in enabling prosumers to cooperate and create self-sufficient coalitions in a decentralized fashion with low gas consumption and transactional time overhead.

Interest in the mmWave and sub-THz bands for next-generation communication and radar systems is increasingly growing due to the available electromagnetic spectrum. This has led to an urgent need to develop low-loss and low-fabrication-cost technologies that perform well at these frequencies. The Gap Waveguide (GW) technology is an emerging and viable contender for the development of radio frequency passive components and antenna systems. The GW technology is based on the parallel-plate waveguide principle. According to Maxwell’s equations, ideally, no wave propagates within a structure if the top plate consists of a perfect electric conductor (PEC) while the bottom plate is a perfect magnetic conductor (PMC), and the distance between the plates is less than a quarter wavelength. This PMC structure creates high surface impedance characteristics which provide cutoff to all propagating modes within the airgap. In practice, these magnetic conductors can be created artificially using an Electromagnetic Band Gap (EBG) structure. Based on this simple principle, considerable research and development of novel components with high performance has seen the light in the past decade. The purpose of this paper is to present an overview of current advancements in the design and technical implementations of GW-based antenna systems and components. Practical applications and the industry interest in the developments of the GW technology for mmWave and sub-THz applications have also been scrutinized.

Upper-limb prosthesis users continue to reject devices despite continued research efforts. Today, the passive topology of body-powered prehensors, which physically transmits grasp force and position data between user and device, results in improved performance over myoelectric alternatives. However, the loads and postures on the user’s body also result in discomfort, fatigue, and worsened grasp force control. Despite the long history and everyday adoption of body-powered prehensors in society, the measurement of how specific body loads and postures affect grasp performance and user experience has yet to be systematically studied. In this work, we present a body-powered prosthesis emulator to independently change required input forces and motions to study the positive and negative effects provided by the inherent haptic feedback. Using a simulated grasping task, we collect functional and qualitative data from 15 participants using a shoulder harness interface. Outcomes show that reducing input loads and excursions to below 24 N and 45 mm, respectively, significantly reduces negative outcomes, but further reductions beyond those thresholds produce limited benefit. Coupling load and excursion, as is the case in real passive body-powered transmissions, demonstrates the importance of the force-motion tradeoff within the tested range of transmission ratios from 0.5:1 to 2:1. The purpose of this study is to inform future prehensor designs that leverage the transparency of body-power to deliver high functionality while mitigating user discomfort.

Koh Block cognitive test indicates that adding sound transparency to an IV A space suit is correlated with higher cognitive performance in a simulated astronaut environment.

This article discusses the possibilities and limitations of using large language models (LLMs) in software development with applications in computational electromagnetics. Three tasks are discussed: code translation, code generation, and code description. The tests showed that LLMs are generally very useful. Even when errors occurred, they can provide useful hints to find a solution.

This is a WIP (Work in progress) article to determine hate speech in social network using ML

In this work, we propose a 2TnC ferroelectric random access memory (FeRAM) cell design to realize the quasi- nondestructive readout (QNRO) of ferroelectric polarization (PFE) in a capacitor, which can relax the endurance requirement of the ferroelectric thin film and exploits the benefits of both FeRAM and ferroelectric FET (FeFET). We demonstrate that: i) QNRO sensing of PFE is conducted successfully in experiment with a ON/OFF ratio (ION/IOFF)>103, ION>10 μA, and read endurance>106 cycles, which can relax the FeRAM endurance requirement by 106x; ii) optimization of the cell performance can be realized by tuning the metal-ferroelectric-metal capacitor (MFM) capacitor to read transistor area ratio and read transistor threshold voltage (V TH); iii) the proposed cell structure is 3D-compatible, enabling integration of highly dense memory solution; iv) the proposed cell structure also enables compute-in-memory (CIM) applications of FeRAM, which has not been widely explored. With this technology, storage and memory-centric computing can be enable

Optimizing the emission properties of materials in ultraviolet and deep blue (UV-DB) is interesting in development of new scintillator devices for the detection of X-ray, γ-ray and radiation particles as those materials can be strong candidates for high light yield and fast scintillators. While their intrinsic material properties are already well studied, photonic enhancement generated through optical confinement could significantly improve their emission characteristics, however one needs to overcome the problem of relatively low refractive indices contrast resulting in poor confinement of UV-DB light. This motivates the search for resonator structures built from readily accessible materials that can boast strong confinement in this spectral regime. Here, we present such a structure, leveraging bound states in the continuum (BICs) to realize large-area confinement of UV-DB light with ultra-high quality factors up to Q ∼107. These ultra-high Q-factors in turn result in strong enhancements in light emission via the Purcell effect. We demonstrate the operation of such a design by simulating the mode shape, Q factor and emission behaviour in organic, hybrid perovskite and III-V scintillating materials. By tailoring the structure geometry, it can be robustly tuned to match the emission characteristics of chosen materials. We start with considering ideal infinite structure supporting perfect BIC and we extend our model on finite sized structures and we discuss the limitations associated with the self-absorption and thickness of the structure. Our findings pave the way to cost-effective and efficient designs for scintillators in the UV-DB regime (submitted to IEEE Transactions on Nuclear Sciences).

We investigate transceiver design and digital signal processing for spatially multiplexed transmission over multimode fibers. In conventional architectures, the full spatial domain of the transmission fiber has to be detected and processed such that the modal walk-off and mixture can be estimated and equalized. These architectures scale poorly with the number of modes supported, besides the sparsity of the fiber transfer matrix is not fully exploited. Instead, here we aim to employ selective mode vector launch and detection in order to minimize the number of optical front-ends required. In this case, an ideal basis for multiplexing is offered by principal modes, that to first order are frequency independent. We show that such mode vector basis can be used for full baud rate transmission over inter-data center distances despite limited coherence bandwidth and vulnerability to environmental-induced drift of the optical channel. It is shown that crosstalk at the receiver front-end can be significantly suppressed, critically reducing the number of coherent receiver front-ends to that of spatial tributaries aimed for data transmission -- as opposed to the total number of fiber modes. Residual crosstalk can still be experienced due to environmental-induced channel drift and loss of orthogonality in presence of mode dependent loss. Multiple-input single-output digital signal processing is shown to be effective in this case, with the required equalizer array size scaling sub-linearly with the number of tributaries. A multimode fiber with 156 spatial and polarization modes and optimized for low modal dispersion is considered.

Land-reflected signals from a geosynchronous communication satellite broadcasting in P-band (367.5 MHz) were captured in low Earth orbit using a simple dipole antenna. A delay-Doppler map (DDM) was generated through autocorrelation. Estimates of the specular point delay were obtained from the lag of the second peak in the DDM with a bias of 239.4 m and a standard deviation of 44 m (12 m over a frozen lake) with respect to a predicted orbit model. Relative magnitudes of the first and second DDM peaks fell within the range of values predicted using dielectric models for the frozen ground and lake. Lastly, retrievals of surface reflectivity were generated using a range of realistic values for the transmitter link budget G/T, these also fell within the range of possible values for the antenna gain pattern. Given the lack of calibration and the large uncertainties in the receiver orbit and attitude, this agreement is sufficient to conclude a successful demonstration of the fundamental principle of single-antenna reflectometry in P-band. P-band reflectometry may offer a new approach to remote sensing of sub-canopy and root-zone soil moisture.

This is research about how reinforcement learning benefits autonomous drone navigation and control

Post-processing techniques have been shown to improve the quality of the decision stream generated by classifiers used in pattern-recognition-based myoelectric control. However, these techniques have largely been tested individually and on well-behaved, stationary data, failing to fully evaluate their trade-offs between smoothing and latency during dynamic use. Correspondingly, in this work, we survey and compare 8 different post-processing and decision stream improvement schemes in the context of continuous and dynamic class transitions: majority vote, Bayesian fusion, onset locking, outlier detection, confidence-based rejection, confidence scaling, prior adjustment, and adaptive windowing. We then propose two new temporally aware post-processing schemes that use changes in the decision and confidence streams to better reject uncertain decisions. Our decision-change informed rejection (DCIR) approach outperforms existing schemes during both steady-state and transitions based on error rates and decision stream volatility whether using conventional or deep classifiers. These results suggest that added robustness can be gained by appropriately leveraging temporal context in myoelectric control.

We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks. Inspired by the weighted minimum mean squared error (WMMSE) method, a classical approach to solving this problem, and the principle of algorithm unfolding, we present unfolded WMMSE (UWMMSE) for MU-MIMO. This method learns a parameterized functional transformation of key WMMSE parameters using graph neural networks (GNNs), where the channel and interference components of a wireless network constitute the underlying graph. These GNNs are trained through gradient descent on a network utility metric using multiple instances of the beamforming problem. Comprehensive experimental analyses illustrate the superiority of UWMMSE over the classical WMMSE and state-of-the-art learning-based methods in terms of performance, generalizability, and robustness.

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation–dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1 years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant’s class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1–score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross–movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross–movement analysis and the artificial generation of larger amounts of data.

This letter addresses the challenge of determining the maximum sum rate achievable in high mobility scenarios with time and frequency selectivity in 5G V2X communications, particularly in the context of IoT/sensor networks. We derive a closed-form expression for the maximum sum rate of a multicarrier non-orthogonal multiple access (NOMA) system with 𝑛 users, and validate our theoretical results through simulations by comparing them with traditional orthogonal schemes. Our work provides valuable insights into the potential of NOMA for high-speed V2X communications, and offers a novel contribution to the existing literature by presenting a closed-form expression allowing the development of efficient and reliable 5G V2X systems.

Owing to the principle of relativity, the present state of knowledge explicitly allows Maxwell's equations to be solved not only in the rest frame of an electromagnetic transmitter but also directly in the rest frame of the receiver without use of the Lorentz transformation and the Lorentz force. Recently, such a calculation was first performed for the Hertzian dipole. The analysis of the resulting formula breaks new scientific ground and indicates that Maxwell's equations predict that electromagnetic waves in vacuum propagate at the speed of light, notably for each receiver, even when these receivers have relative velocities with respect to each other. Although this paradoxical phenomenon was expected, the finding that Maxwell's equations nevertheless predict a classical Doppler effect was unexpected and indicates inconsistent or not yet fully understood aspects of canonical Lorentz-Einstein electrodynamics consisting of Maxwell's equations, Lorentz force and Lorentz transformation.