A novel antenna technology referred to as dynamic metasurface antennas (DMA) that employs reconfigurable metamaterials as its radiating/receiving elements has emerged as a potential alternative to traditional antennas. It specifically helps to simplify the hardware complexities of transceiver structures by reducing the number of radio frequency (RF) chains, thus lowering the operational cost of multiple-input multiple-output (MIMO) systems. To assess its usefulness, this study analyzes the capacity characterization of different DMA-based multiuser MIMO systems by considering uplink and downlink communications when DMA is deployed at the base station or the users. In each case, we formulate a capacity maximization problem. Further, using either reduced minimum mean square error or iterative waterfilling techniques, we propose efficient algorithms to optimize transmit precoders and DMA configurable weights alternately. The solution for each optimizable variable is designed to attain fast convergence that ensures the overall low complexity of the proposed scheme. The simulation results demonstrate the effectiveness of the proposed algorithms in achieving performance comparable to conventional MIMO while significantly reducing the number of RF chains.

In this paper, we present a novel algorithm for multipath channel estimation for a known preamble sequence in the presence of significant phase noise (PHN) that affects both transmitter and receiver oscillators. The PHN exists in both the receiver and the transmitter and is assumed to vary significantly over the channel length, so it is not possible to approximate the PHN to appear only in one of them. The nonlinearity induced by PHN presents obstacles for conventional linear channel estimation algorithms such as least squares, linear MMSE, or Kalman filters. To address this challenge, we introduce an algorithm based on sequential Monte Carlo methods, also known as Particle Filter. The algorithm’s initial condition is derived using a novel low-complexity windowing method. In addition, we suggest a specially designed preamble sequence that improve further the performance.

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.

Generative modeling has seen significant advancements with the rise of diffusion models, which leverage iterative denoising processes to generate high-quality data. Originally developed for image synthesis, these models have now been extended to a wide range of domains, including text, audio, and video. Beyond their generative capabilities, diffusion models have also demonstrated strong potential for representation learning, enabling the extraction of meaningful features useful for downstream tasks. This survey provides a comprehensive review of diffusion models in the context of representation learning. We first introduce the theoretical foundations and key architectural advancements, such as improved noise schedules, latent diffusion, and conditional modeling. Next, we examine their role in self-supervised and contrastive learning, highlighting how the denoising process facilitates structured feature representations. We also explore the integration of diffusion models with other generative frameworks, including VAEs and GANs, to enhance efficiency and representation quality. Despite their growing success, diffusion models face several challenges, including high computational costs, slow inference, and limited theoretical understanding. We discuss ongoing efforts to address these limitations, including accelerated sampling techniques, model compression strategies, and the development of more interpretable representations. Additionally, we highlight ethical concerns, such as bias, robustness, and the potential misuse of generated content. Finally, we outline promising research directions, including multimodal and crossdomain learning, improved integration with large-scale language models, and real-world deployment considerations. By synthesizing recent developments, this survey aims to provide a structured perspective on the evolving role of diffusion models in both generative modeling and representation learning.

Gig economy platforms depend significantly on effective data processing to manage the requirements of changing environments. Nonetheless, conventional data pipelines often face obstructions and delays, which restrict overall operational efficiency. In this research, we present a novel approach aimed at enhancing data pipelines tailored for gig economy platforms. Our system integrates sophisticated design concepts with responsive algorithms to improve data flow and minimize latency. By analyzing current challenges, we intentionally distribute resources based on real-time data needs. We evaluate the effects of our optimizations through precise performance metrics that gauge data throughput and processing speed. Experimental findings show that our method greatly improves the efficiency of data operations, establishing a basis for expanding gig economy platforms while upholding service quality. This study provides important perspectives on the complex dynamics of data management within fast-evolving business environments, promoting additional investigation into effective and scalable strategies in the gig economy.

Enterprises face increasing challenges in managing authentication across thousands of applications while ensuring security and compliance. This article explores a scalable authentication framework that integrates modern identity providers with internal systems, enabling seamless transitions from legacy authentication. By leveraging centralized access control and adaptive authentication, organizations can enhance security while maintaining operational efficiency. This article shares key lessons from implementing this architecture in largescale enterprises and provides practical strategies for security teams to modernize authentication without disrupting workflows.

This manuscript investigates the network traffic prediction problem, which is to predict network traffic on a network function virtualization (NFV) enabled and digital twin (DT) assisted physical network for network service providers and network resource provider. It faces several key challenges like Data Privacy, different variation patterns of network traffic for multiple service function chain (SFC) requests, and few existing works have comprehensively considered or solved these challenges. In view of this, we address the network traffic prediction problem by jointly considering the above key challenges in this manuscript. Specifically, we formulate the virtual network function (VNF) migration problem as integer linear programming (ILP) that aim to maximize acceptance ratios, minimize network resource costs, and minimize migration cost. Then, we define the Markov Decision Process (MDP) for the network traffic prediction problem, and propose the novel model and algorithm, Transfer Learning and Deep Deterministic Policy Gradient (TL_DDPG), to solve the problem. Simulation results demonstrate that our scheme achieves better performance.

As extreme weather events become more frequent and intense, energy demand during peak hours rises and the use of variable energy sources such as wind and solar expands. Scarcity pricing events have likewise surged in frequency and severity. Scarcity events often occur during periods of extreme weather and generation constraints. For instance, wind resources tend to underperform during heatwaves and cold snaps, while electricity prices spike as the sun sets, prompting dispatchable generators to respond. Gas plants can face supply limitations or be outpriced due to high gas heating demand.Hydroelectric resources, with their long-duration energy storage, flexibility, and low-carbon generation, will become increasingly valuable in balancing the growing share of variable energy resources as these forces drive increasing constraints in the future grid. This is especially true as policy shifts reduce reliance on fossil-fueled flexible generation and as extreme weather events and electricity market scarcity events intensify. This paper reports results from a generalized framework for historical operations and statistical analysis of critical generation assets during scarcity events, in this case during the Martin Luther King Jr. 2024 weekend polar vortex in the Pacific Northwest.

This paper presents a novel image guide crossover design leveraging the uniaxial anisotropic characteristics of subwavelength dielectric gratings for millimeter-wave (mmWave) applications. The structure moreover, exploits the engineered polarization differences of the dominant mode in orthogonal channels for the designed image guides, which employ anisotropic features to achieve a compact, high-isolation, and wideband crossover. Results with optimized parameters demonstrate competitive performances for the compact structure, including an ultra-wide 30 dB isolation bandwidth from 22 GHz to over 50 GHz, insertion loss values around 0.5 dB, and group delay variations within 0.04 ns. To the best of the authors' knowledge, this is the first design achieving such performances for a mmWave crossover. Additionally, the proposed structure and design methodology is versatile, facilitating adaptation to other frequency ranges and different waveguide technologies. A fabricated prototype also shows agreement with analytical studies using the finite difference frequency domain (FDFD) method and full-wave simulations completed by a commercial solver. All in all, the combination of high isolation, low insertion loss, compact size, minimal group delay variations, and wide bandwidth operation positions the design as a promising solution for mmWave crossover circuit systems and other high density transmission line scenarios.

Recent advancements in Artificial Intelligence (AI) and immersive technologies, such as Extended Reality (XR), coupled with complementary innovations like 5G/6G wireless communications, are paving the way for fully realized AI-XR metaverses. AI will play a pivotal role in this transformation, enabling the seamless convergence of virtual and physical worlds. Among the various AI applications, Generative AI (GenAI) stands out for creating rich, dynamic, and interactive virtual environments-essential elements for sustained metaverse growth and user engagement. To facilitate a deeper understanding of how GenAI will be integrated and utilized within the metaverse, we provide a comprehensive overview of GenAI and Artificial Intelligence-Generated Content (AIGC). Specifically, we examine the potential influence of GenAI's on future metaverses, exploring both the opportunities it offers and the major challenges associated with its deployment. Additionally, we investigate the robustness of AIGC detection techniques against adversarial attacks, highlighting less-explored risks posed by adversarial examples. Finally, we highlight various open research issues related to GenAI, AIGC, the metaverse, and responsible innovation that merit further exploration.

k-means is a clustering algorithm used to group observations into clusters. Due to the multidimensionality of datasets, interpreting clustering results has become increasingly challenging. In response, sparse clustering variants have emerged, allowing each feature to be weighted. In the sparse k-means algorithm, feature weights are computed based on the values of their associated observations. However, the sparse k-means algorithm is known to be sensitive to outliers. Hence, robust sparse k-means variants have emerged, performing sparse kmeans while detecting outliers. In numerous real-world cases, data entry or measurement errors can lead to poorly collected values for a feature, making them significantly different from other values in that feature. Due to dataset multidimensionality, these observations are often not detected as outliers by existing robust approaches. This negatively impacts the evaluation of feature weights, biases the interpretability of results, and leads to poor clustering quality. To fill this gap, this paper introduces a new robust clustering framework. This framework integrates robust initialization and detection techniques for such observations. The results demonstrate that robustness, interpretability, clustering quality are enhanced across several real and synthetic datasets. Impact Statement-Clustering algorithms are fundamental to understand datasets and also serve as a preprocessing step for many algorithms. In particular, sparse k-means builds on the interpretability of k-means and further improves it based on feature selection. Our method will help to robustify sparse kmeans with respect to abnormal feature values that may impede the estimation of feature weights. The applicability of sparse kmeans will be enhanced in more challenging, noisy settings.

Inspired by human workers who perform complicated sewing tasks by repeating relatively simple operations, this paper proposes a fixture-free automated sewing system using a dual-arm manipulator and an ordinary sewing machine to sew two aligned fabrics along the edges, a common task in garment production. The proposed sewing system has a five-layer architecture: perception, dual-arm sewing Petri net, fundamental operations, control primitives, and hardware layers. This architecture decomposes various complex sewing tasks into sequences of fundamental operations. To meet the real-time requirement of automated sewing, a High-speed Fabric Edge Detection System (Hi-FEDS) is further proposed for the perception layer, which formulates the fabric edge detection problem for sewing as a classification problem of predefined distributed anchors. The anchor distribution is modeled by the Gaussian Uniform Mixture Model (GUMM). The proposed method achieves high-speed fabric edge detection at an average of 120 FPS, with an average error of about one pixel. Finally, an experimental robotic sewing platform is developed and the sewing results show that the proposed system achieves high-quality sewing across fabrics of various shapes and materials.

This paper addresses the optimization of the radiation pattern of surface-wave (SW) based metasurface (MTS) antennas. Those antennas are considered as a promising alternative to parabolic reflectors, and phased arrays due to their extremely low profile and their ability to provide high gain, shaped beams and multibeams. However, pattern synthesis with MTS antennas is very challenging because of the single active control point and the need to control surface and leaky-waves through the MTS. An accurate optimization of the radiation pattern, along with the sidelobe level requires a full-wave modelling of the feeding structure, including its coupling with the MTS. MTS synthesis methods existing in the literature usually approximate the feeder model, and neglect its coupling with the MTS. Such approximation may lead to more than 1 dB error in the predicted antenna directivity. This paper presents a technique for optimization of the far-field pattern, built on a Method of Moments (MoM) analysis tool in which the MTS coupling with the feeder, a coax probe, is fully considered. The MTS is modeled as an arbitrarily shaped, spatially modulated electric sheet impedance in a layered medium. At each optimization iteration, the complexity of the underlying analysis is O(N log N) owing to the use of a FFT based acceleration. The effectiveness of the method is demonstrated through the optimization of MTSs radiating a pencil beam and a conical beam with orbital angular momentum (OAM).

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form of X (formerly Twitter) posts. Human evaluators found that 87.43% of the sampled messages (n = 2880) sufficiently met the intentions of their structured prompts based on their interpretations of the prompt and the corresponding AI-generated message. To automate the identification of valid and invalid messages, we train and validate nine Machine Learning models (ML) on the classified datasets. Leveraging an ensemble of these nine models, we extend our analysis to predict the classifications of the remaining 21,120 untagged messages. Finally, we manually tag 1% of the messages with predicted classifications and attain 90.57% accuracy (sd = 29.3%, n = 212) across the four life event types. The ML models excelled at classifying valid messages as valid, but experienced challenges at simultaneously classifying invalid messages as invalid. Our findings advance the study of LLM capabilities, limitations, and validity while offering practical insights for message generation and natural language processing applications.

Ensuring the reliability and security of file transfer systems is essential for modern enterprises as data volumes and complexities continue to grow. Traditional monitoring techniques often fail to identify subtle anomalies indicative of potential system failures, inefficiencies, or security breaches. This paper proposes a novel machine learning-based anomaly detection framework that leverages autoencoder models implemented with Deeplearning4j. By learning typical file transfer behaviors, the framework effectively identifies deviations such as irregular file sizes, prolonged transfer times, and unexpected errors. Designed with scalability and integration in mind, the proposed framework is adaptable to real-world enterprise environments. It incorporates advanced preprocessing techniques, robust model training, and dynamic anomaly thresholding to achieve high precision in anomaly detection. By proactively flagging potential issues, the system minimizes operational risks, enables timely intervention, and ensures uninterrupted data transfer processes. This research bridges the gap between traditional rule-based monitoring systems and adaptive AI-driven solutions. It not only demonstrates the transformative potential of machine learning in enterprise applications but also lays a solid foundation for future advancements in secure and efficient file transfer systems. The insights presented here will benefit researchers and practitioners aiming to enhance enterprise resilience and operational efficiency.

As minimum area SRAM bit-cells are obtained when using cell ratio and pull-up ratio of 1, we analyze the possibility of decreasing the cell ratio from the conventional values comprised between 1.5-2.5 to 1. The impact of this option on area, power, performance and stability is analyzed showing that the most affected parameter is read stability, although this impact can be overcome using some of the read assist circuits proposed in the literature. The main benefits are layout regularity enhancement, with its consequent higher tolerance to variability, cell area reduction by 25% (with respect to a cell having a cell ratio of 2), leakage current improvement by a 35%, as well as energy dissipation reduction and a soft error rate per bit improvement of around 30%.

The inherent hysteresis nonlinearity of piezoactuated positioning stages (piezo-stages) is very difficult to deal with due to the amplitude-and frequency-dependent characteristics, which severely limits the tracking accuracy for high speed trajectories. In this work, we first develop a deep serial model to describe the dynamics of the piezo-stage by using historical voltage-displacement data over a period of time. It achieves relative prediction errors less than 0.16% on sinusoidal trajectories with frequencies greater than 72% of the resonance frequency of the piezo-stage through an elaborately designed network structure that includes direct connections between the input layer and the output layer. Then, we design an integral model predictive control (iMPC) and build a feedforward neural network (FNN) to learn its optimal solution offline. This forms the proposed FNN-iMPC and ensures the feasibility of evaluating the control law within the sampling time of 0.1ms. It achieves a maximum positioning error of 0.02µm for a ±32µm staircase reference signal and a maximum tracking error of 0.19µm for a sinusoidal reference signal with a range of 10µm and a frequency of 500Hz in real experiments.

True time delay circuits are generally realized using variable delay all-pass filters due to their flat delay-frequency response. However, they are power hungry and practically, variations in transistor characteristics and parasitic impedances result in delay variations with frequency. This paper discusses a first-order RC-low pass filter as a TTD circuit, which leverages the fact that the phase response starts changing linearly 1 decade before the pole location. The variable delay is accomplished by changing the circuit's effective impedance using the Miller effect obviating the need for varactors or variable resistors. A bandwidth extension of the delay cell is achieved using a delay correction circuit. A 4-bit TTD circuit realized by cascading 15 stages of variable delay LPFs has been designed in TSMC 65 nm CMOS technology and post-layout simulation results show that the TTD has a power consumption of 36 mW and a delay range of 400 ps with a delay variation of ±5 ps across a bandwidth of 1 GHz.