Grid-forming technology has a crucial role in achieving the future all renewable power grid. Among different types of grid-forming controllers, Virtual Oscillator (VO) based Controllers (VOCs) are the most advanced. VOCs outperform the conventional droop-based grid-forming controllers in terms of dynamic performance and synchronization stability by adapting time-domain-based implementation. However, because of the time-domain-based implementation, the same Fault Ride-through (FRT) techniques for droop-based controllers are incompatible with VOCs. Existing literature has successfully incorporated current limiting techniques in VOCs to protect the converters during severe transient conditions. Nevertheless, some very important aspects of FRT requirements are not attended to by the existing literature on VOCs, such as maintaining synchronization with the network during a fault, minimizing power oscillation during a fault, and at the fault clearance. First, this article introduces a unique analytical approach for quantifying the underlying dynamics of VOCs during faults. Next, using the mentioned analysis and in-depth reasoning, the systematic development of a unique FRT control architecture for VOCs is presented. The proposed FRT technique has unified both current and voltage synchronization in the same architecture to work successfully under three-phase and unbalanced faults. The performance of the proposed FRT technique is thoroughly validated using simulation studies.

We present a bilayer metasurface design for efficient polarization conversion of linearly polarized electromagnetic waves operating at the center frequency of 280 GHz. The metasurface unit cell consists of a substrate sandwiched by two identical metallic double-split ring resonators (DSRRs). Through careful selection of split opening angle and angular offset between the two rings, a phase shifts up to 2π with more than 80% polarization conversion efficiency across a broad bandwidth is achievable. The wide bandwidth and high efficiency performance are attributed to the Fabry-Perot-like effect i.e. a cavity is formed between the two DSRRs where multiple reflections occur, leading enhanced transmission. A semi-empirical equation to calculate the total transmission has been derived and validated through fullwave numerical simulations. In addition, we demonstrate a lowprofile polarization conversion lens using the proposed meta cells. Experimental and simulation results show that the lens has a gain enhancement exceeding 16 dB with one full 2π phase coverage, operating at the frequency of 280 GHz. The proposed metasurfaces are anticipated to inspire new designs and studies on intricate wave-manipulating devices, finding applications in wireless communications, radar, imaging, and spectroscopy.

The proliferation of large multi-antenna configurations operating in high frequency bands has recently challenged the conventional far-field, rich-scattering paradigm of wireless channels. Extra large antenna arrays must usually work in the near field and in the absence of multipath, which are far from traditional assumptions in conventional wireless communication systems. The present study proposes to analyze the spatial multiplexing capabilities of large multi-antenna configurations under line-of-sight, near field conditions by considering the use of multiple orthogonal diversities at both transmitter and receiver. The analysis is carried out using a holographic approximation to the problem, whereby the number of radiating elements is assumed to become large while their separation becomes asymptotically negligible. This emulates the operation of a continuous aperture of infinitesimal radiating elements, also recently known as holographic surfaces. The present study characterizes the asymptotic MIMO channel as seen by extra large uniform linear and planar arrays, as well as their associated achievable rates assuming access to perfect channel state information (CSI). It is shown, in particular, that for a given distance between the receiver and the center of the array and a given signal quality, there exists an optimum dimension of the multi-antenna surface that maximizes the spectral efficiency.

Nowadays, commercial off-the-shelf (COTS) processors are increasingly popular in embedded systems for their affordability and suitability in industrial use. However, technology downsizing has made them more susceptible to bit flips-unintended memory changes in CPU registers that can cause silent data corruption (SDC) by disrupting data and control flow. These errors can cause unpredictable behavior and reduce system reliability. In this study, we evaluate the resilience of an Autonomous Mobile Robot (AMR) by injecting bit flips into its Intel-based embedded computer. The experimental results revealed a notable vulnerability of the AMR to bit flips, reaching an SDC as high as 39.71%. While many existing methods for detecting and recovering from bit flips are effective, they often impose considerable resource demands and performance overhead, making them impractical for use in resource-constrained COTS processors. To tackle this issue, our research introduces a novel selective protection approach designed for AMR. Our methodology involves two phases. Initially, we analyzed the AMR's components based on their vulnerability to fault injection and their interaction with memory. We then applied the S-SWIFT-R protection technique to the most vulnerable components, gradually increasing the number of protected elements. The results show that selectively protecting critical components reduced SDC occurrences to below 3.1%, with a code size overhead of approximately 1.71X.

This paper delves into the inherent graphtopological structure utilized by algorithms such as LandMark in the context of RFID indoor positioning. We uncover the essence of these algorithms, which leverage the implicit topological relationships within signal features for message passing and positioning accuracy. Despite the theoretical advantages, practical applications of the LandMark algorithm have been hindered by issues related to signal propagation, the limitations of topological structures, neighbor tag selection, and simplistic weight distribution methods. To address these limitations, we propose a series of innovative improvements. Our approach includes data preprocessing techniques like B-spline interpolation and normalization to mitigate environmental noise and enhance signal integrity. We introduce the concept of spatiotemporal graphs that map signals into a high-dimensional space, allowing for the construction of dynamic graph structures that more accurately capture the temporal dynamics of signals. Furthermore, we employ the PNAConv algorithm, a graph neural network technique, to refine the message passing and feature aggregation process, optimizing the selection of neighboring tags. Our experiments, conducted across various datasets, demonstrate that our model maintains low error rates, showcasing its high precision and robustness in diverse environments. The results not only validate the effectiveness of our improved algorithm but also highlight the importance of understanding and exploiting the graph-topological structure inherent in signal-based positioning systems.

In this paper, we present a novel approach for managing the file system in Linux using a voice assistant. Our system allows users to perform file system operations such as creating directories, renaming files, and deleting files by issuing voice commands. We develop a voice assistant using Python libraries and integrate it with the file system in Linux. The voice assistant is capable of understanding natural language and executing commands based on the user's voice inputs. We conduct experiments to evaluate the performance of the system and demonstrate that our approach is effective and efficient in managing the file system using voice commands. Our system can enhance the accessibility and usability of the file system in Linux for individuals with disabilities or those who prefer a hands-free approach to file management.

Signal transform and new time series representations are crucial for advancing signal processing techniques. This letter introduces the consensus-based signal transform (CBST), a method to represent a time series as a weighted undirected graph, a linear combination of base graphs. Within CBST, each graph's topology is determined by maximizing the collective frequency response, measured by the rate of change in the state variables of all nodes in response to an input signal. The state variables are updated using a linear distributed consensus protocol, which filters different input time series frequency components. The resulting graph topology resonates with specific frequencies. Base graphs are produced for each dominant frequency, and their weighted sum forms the graph representation of the input time series. Mathematical analysis and examples demonstrate the CBST's validity and potential applications, offering a new perspective on signal processing.

Recently, 3D occupancy prediction, a camera-only perception task, has garnered significant attention for addressing key limitations in traditional 3D object detection, such as overlooking uncommon categories and failing to capture complex geometric shapes. However, current methods for occupancy prediction face two major challenges. First, they require high computational resources, making deployment on non-GPU devices impractical. Second, height estimation is often inaccurate due to the overlooked imbalance in height distribution within datasets. To address these limitations, we propose two innovative strategies. First, we eliminate the use of computationally expensive components such as transformer operators, depth estimation modules, and 3D convolutions. Second, we introduce a focused weighting mechanism to improve height-related accuracy. Building on these strategies, we introduce ConvOcc, an efficient and deployment-friendly framework composed entirely of 2D convolutions. ConvOcc features: (1) Feature Fuse Module for enhanced multi-scale 2D feature fusion, (2) Voxel-to-Image View Transformation for rapid conversion of 2D image features to 3D voxel space, (3) Squash and Stretch Module to simplify complex 3D voxel computations into a more efficient 2D BEV form, (4) Height-Attention Multi-Scale BEV Fusion Module for dynamic reweighting of BEV features based on height, and (5) Multi-Frame Temporal Fusion Strategy for denser voxel feature extraction. Extensive ablation studies validate the effectiveness and efficiency of our approach. ConvOcc achieves 2× FPS with a mean IoU of 36.1 on the Occ3D-nuScenes dataset, all while maintaining deployment-friendly requirements. This work challenges the conventional reliance on 3D methods for occupancy prediction, demonstrating that it can be effectively and efficiently addressed using a 2D-based approach.

The cascading of power modules can generate multilevel voltage output to enhance power transmission capacity and quality. However, conventional cascaded converters need excessively high numbers of modules for high quality due to the only linear increase of levels with modules. Modules whose voltages following a decreasing sequence, e.g., a binary geometric progression (1, ½, ¼, …), imitate analog-digital conversion and generate very fine output shapes through a linear combination of modules with finer and finer steps. However, the use of modules with decreasing voltages rapidly reduces the energy content per module and typically requires dedicated module designs for every module voltage level. Thus, the large voltage differences thwart the initial goal of a uniform, modular, and readily scalable circuit. Furthermore, keeping this balance with very different voltages and energy contents in the same string becomes challenging, while modules with smaller voltages need to switch by orders of magnitude faster than larger ones. Active or diode-enabled parallel modes that simplified balancing and increased utilization in conventional cascaded circuits appear not helpful across the large voltage differences. This paper proposes a novel asymmetric converter design featuring a very different sequence of module voltages, an optimal low-cost control scheme, and accurate sensorless module-voltage regulation. The circuit does not operate any module below half the highest-voltage module (dc link voltage). Compared to previously suggested asymmetric cascaded circuits, modules will have significantly closer rated voltages, yet their voltages never decay to negligible values at higher voltage levels. The proposed tighter rated voltage range allows for a uniform module design with fully interchangeable modules. The reduced voltage difference between neighboring modules exponentially reduces the total size and dimensions of the inductors required to limit the balancing current. Moreover, we introduce an optional pseudo-parallel mode for efficient module-to-module energy exchange. Simulations using MATLAB/Simulink and a hardware prototype confirm the effectiveness of this approach.

This brief presents a linear high-resolution time-todigital converter (TDC) that employs a pulse-shrinking ring comprised of an even number of alternating delay elements along with a pulse arbiter. This design takes advantage of the linearity of the pulse-shrinking delay ring and the highresolution features of the pulse arbiter, leading to a TDC with good linearity and accuracy. Since arbiters are used to determine the precise location where a circulating pulse dissipates within the delay line, additional bits of resolution (LSBs) are generated and the accumulated nonlinearity remains small. Operating at a resolution of 4 ps, this 13-bit TDC functions effectively in a standard 0.18 μm CMOS technology with a Full-Scale Range (FSR) of 36 ns.

In practical engineering tasks, multi-objective topology optimization (TO) problems often arise, thus requiring the introduction of the concept of Pareto-optimal solutions. In this article, a novel gradient-informed Pareto-based multi-objective TO algorithm with binary decision variables, called GPBTO, is proposed. Without loss of generality, GPBTO is tailored to solving constrained bi-objective optimization problems (CBOPs), which represents a standard case in many engineering applications. The binary space of the decision variables together with a linearization step of objectives and constraints allows efficient integer linear programming (ILP) techniques to be used for the evolution of the decision vector. The proposed algorithm represents an effective tool for solving the TO task with traditional gradient-based formulations bypassing the limitations of weighted sum (WS) approaches usually adopted in this context. Thanks to the simple implementation, the proposed GPBTO algorithm can be readily adopted for solving multi-objective TO problems involving binary decision variables. While WS is an out-of-date technology for solving multi-objective optimization problems due to its limitations, it is well-known that in the framework of TO is still the state of the art. To the author's knowledge, this is the first time that an attempt to solve Pareto-based TO with binary variables and gradients has been proposed.

This study examines how well SSL/TLS and blockchain work to secure online transactions, especially purchase orders. Reviewing literature from 2013 to 2024 shows each method's key themes, benefits, and weaknesses. SSL/TLS is known for its strong encryption and solid framework but often has issues like centralization and threats from different attacks, leading to transaction delays and bottlenecks. On the other hand, blockchain technology uses decentralized protocols and features such as zero-knowledge proofs, promising better scalability and security, allowing for smooth and safe transactions without traditional middlemen. This comparison clarifies how effective each method is. It highlights the rise of blockchain as a viable option to the limits of SSL/TLS, deserving more study in e-commerce security.

A novel approach to deriving physical bounds on the time-domain (TD) response of a class of linear time-invariant systems is described. The approach is illustrated on determining the (worst-case) maximum voltage induced along a loaded transmission line (TL), to which previously established TD bounds are not directly applicable. Illustrative numerical are presented.

The complexity of the power system with high shares of renewable energy, driven by the uneven distribution of renewable resources and diverse regional energy demands, poses significant challenges for traditional planning methods. These approaches often struggle to capture the interregional dynamics and meet operational constraints needed for effective energy distribution. To address this, we propose the Constraint-Adaptive Graph-Convolution (CAG-C) Reinforcement Learning (RL) model. First, we decouple the large-scale planning problem using an RL-based framework that leverages the state transition process to break down interregional power flow optimization. This approach allows each region to be optimized independently. Graph Convolutional Networks (GCNs) are employed to extract region-specific data, where each node represents resource endowments and each edge represents power flow between regions, enabling the RL model to capture both regional and interregional dynamics. Finally, we apply a constraint-adaptive architecture that reduces the number of RL decision variables while ensuring all operational constraints are met. It works by adjusting each region's power input and output within feasible ranges. This ensures balanced power flows between regions while automatically satisfying requirements. Numerical results show that CAG-C can reduce planning costs and improve coordination across regions, offering a scalable solution for future power system planning.

IntroductionWith no doubt is the prevalence of dementia skyrocketing, with Alzheimer’s, Frontotemporal, and many more which for the patients, the incidence of dementia doubles every 5 years from the ages 65-90 (Corrada et al., 2010). Dementia is also an incredibly difficult condition to manage, with caretakers found to have higher rates of burden and mental health issues, poor physical health, financial problems, and social isolation (Brodaty and Donkin, 2009). Dementia by itself is also characterized by neurological deterioration over time, with a great deal of cognitive functioning being lost in the process. There is no doubt that most caretakers are generally less experienced than traditional hospital staff in managing those with dementia, and so this leaves them open minded to new approaches to help treat their patients (Ma et al., 2024).

The circulatory system ensures continuous blood flow to all body tissues. During gestation, progressive changes in the female body and immune system lead to homeodynamic alterations, characterized by increased venous return and cardiac output, enhancing blood flow to meet tissue metabolic demands. This paper presents the Homeodynamic Maternal-Placental Molecular Communication Model (HMP-MC) in the cardiovascular network to accurately represent circulatory components and interactions. The model also analyzes the dynamics of exosomes carrying Zika virus particles throughout pregnancy, integrating new parameters from gestational physiological changes. We quantify the risk of pregnancy over different stages accounting the impact of infection on the placenta viral. This interdisciplinary approach explores cardiovascular interactions during pregnancy analysis over time with the quantification of pregnancy risk.

In this research work, a new approach to compensate for both the response In this paper, we highlight the crucial role of substation digitalization in identifying the origins of harmonic disturbances within iron and steel plants connected to the point of common coupling (PCC). Our method capitalizes on the benefits of a digital substation infrastructure by utilizing merging units that operate at a 28.8 kHz sampling rate, in line with the IEC 61850-9-2 standard. This setup allows for the real-time measurement of voltage and current from all feeders, streaming these high-resolution data directly to substation servers. This digital approach not only facilitates precise and timely data acquisition but also enhances the overall reliability and efficiency of the monitoring process. By digitalizing the substation, we enable a more dynamic response to power quality issues and improve the capacity for preventive maintenance. Integrating our method into existing digital substations supports a robust framework for managing and mitigating the effects of current harmonics and interharmonics, showcasing the practical benefits of advanced digital infrastructure in critical utility operations. We propose attention based long short-term memory as a novel method for determining the harmonic responsibility of each plant without the need for instantaneous impedance measurements. This method relies on the correlation of time-synchronized voltage and current measurements taken from all feeders at the PCC. The approach is first validated using synthetic data generated in a PSCAD/EMTDC simulation environment, as well as real-world field data. Further verification is conducted through measurements of voltage and current waveforms using mobile Power Quality (PQ) measurement systems at one of the PCCs in the electricity transmission system. Our results demonstrate that the harmonic responsibility of each IS plant can be accurately determined for each harmonic order. Unlike existing methods, our proposed approach eliminates the need for online system impedance measurements or in-plant measurements.

The increasing complexity of mobile networks as they evolve towards 6 th Generation (6G), coupled with the importance of providing fast responses to performance degradation issues, requires the adoption of cost-effective Trouble-Ticket (TT) management systems by Mobile Network Operators (MNOs). These systems are pivotal in ensuring compliance with Service Level Agreements (SLAs) and maintaining Quality of Service (QoS) standards. In this context, the introduction of a new Smart Operations (SO) framework referred to as Smart Trouble Ticket Management (STTM), promotes the integration of Artificial Intelligence (AI)/Machine Learning (ML) to leverage performance data, aiming at early performance degradation detection and an efficient handling of TTs. This paper presents a novel methodology for STTM that relies on a ML clustering algorithm to identify performance degradation instances when cells transition to lower-ranked clusters. The benefits of this dynamic approach are twofold. Firstly, it offers an automated and adaptable process capable of accommodating diverse Key Performance Indicators (KPIs). Secondly, it enhances the reliability of TT opening and closure compared to conventional approaches, particularly those reliant on fixed threshold values. The proposed methodology, tested on live network data, achieved an improvement of 5.53% in TT Opening Accuracy Rate (OAR), and reduced the TT Repeat Call Rate (RCR) by 17.57%.