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.

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.

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.

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).

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.

To support the promise of personalized augmented reality (AR) scaffolding mechanisms that adapt in real-time to the user's demonstrated proficiency, we must be able to first assess individual skill levels during task performance. This study introduces a novel data-driven approach for the multidimensional assessment of expertise in AR-guided psychomotor tasks. We evaluate users' performance against standard benchmarks by employing pairwise correlation and principal component analysis (PCA) on multimodal data collected from AR devices and wearable sensors-including hand tracking, galvanic skin response (GSR), and gaze information. The results demonstrate that objective measures-such as visual scanning efficiency, stress indicators, inspection accuracy, and hand dexterity-provide valuable insights into multiple indicators of skill acquisition. These findings validate the potential for AR systems to adapt dynamically to users' expertise levels based on observable data. This approach offers a promising direction for enhancing the effectiveness of AR-based training systems in high-stakes fields such as manufacturing, aviation, and surgery, where precise skill acquisition is critical.

We introduce a guard time-free approach for simultaneous channel estimation and equalization, addressing both inter-carrier interference (ICI) and inter-symbol interference (ISI) in multi-carrier communication systems. The proposed method utilizes two maximum a-posteriori (MAP) equalizers based on the BCJR algorithm, operating in the time-frequency domain. These equalizers share soft-bit data among themselves and with a channel decoder. We derive the a-posteriori probabilities (APPs) and show that the equalizers exploit Dopplertime diversity. We also derive maximum-likelihood (ML) bounds on the system's performance, which also exhibit diversity gains. We initially simulate on Rayleigh fading doubly-dispersive (DD) channels with perfect receiver channel state information (CSI). We compare with an existing iterative maximum likelihood equalizer (IMLE) from the literature and show that the proposed system outperforms it. Next, we focus on an underwater acoustic channel (UAC), and develop a joint channel estimationequalization architecture. To estimate this sparse channel, we take a compressed sensing (CS) approach using the orthogonal matching pursuit (OMP) algorithm together with an overcomplete dictionary set. Compared to 1D equalization with full guard time, the guard time-free estimation-equalization system achieves bit error rate (BER) reductions between 24% and 78% for high-spread channels that can surpass the orthogonal timefrequency space (OTFS) crystallization condition.

This report presents the development and evaluation of a machine learning model for identifying vulnerable C code. Using an AI-generated dataset of both vulnerable and non-vulnerable C code snippets, we explore various methodologies including Bag of Words (BOW), Logistic Regression, word embeddings, and Recurrent Neural Networks (RNNs) to build an effective classification model.

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.

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.

Dynamic wireless charging (DWC) of electric vehicles (EVs) can greatly alleviate "short driving range" concern, making it a highly feasible prospect for electrified transportation. To achieve maximum power transfer in DWC, it is essential that the primary coil (under the road) and secondary coil (below the EV) are fully aligned. Lateral misalignment (LTM) occurs when the coils fail to align properly. The energy losses caused by LTM can be compensated using conventional controllers, however, they cannot perform well in higher misalignment scenarios and exhibit overshooting, and steady-state error (SSE). In this paper, a model prediction-based approach is implemented to design a controller that nullifies the LTM effect and compensates for the reduced power transfer to the EV. This work presents a comparative study between the model predictive controller (MPC) and conventional Proportional-Integral (PI) controller in terms of performance and suitability. The MPC controller uses an optimization algorithm to select the optimal control action over the prediction horizon, minimizing SSE and reducing overshooting in the system, thereby performing exceptionally well across all degrees of LTMs. The effectiveness of the proposed MPC has been evaluated through simulations in MATLAB/Simulink and experimental tests. The proposed MPC demonstrates superior performance compared to the PI controller in both simulation and experimental evaluations.

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.

Virtualized RAN (vRAN) matches O-RAN Alliance specifications while transitioning towards virtualized functions on general-purpose computing platforms. However, the energy consumption of these systems remains a major concern. Although this issue has been addressed in the literature, previous works oversimplify routing decisions, overlook the benefits of flexible split choices, or neglect the energy consumption of the transport network. Additionally, most studies employing optimal solutions exhibit very limited scalability due to their high computational time. In this work, we present a comprehensive and efficient Mixed Integer Linear Programming model to minimize the energy consumption of O-RAN systems, addressing the limitations of current approaches. We also design and implement a synthetic data generator to evaluate our model across various network usage profiles, topologies, and reasonable-size networks. We achieved valuable insights and promising results in our evaluation. For example, our results show that when devices require high throughput, the transport network incurs significant energy costs and reduces the centralization rate. We also observed that hierarchical RAN topologies can achieve greater energy efficiency than ring topologies, with our approach enabling up to 15% more centralization while saving around 28% of energy and consuming at least one order of magnitude less time than other strategies.

This paper presents an advanced hierarchical detection and real-time text recognition system tailored for UAE traffic signs. Our system is designed to support critical applications in autonomous vehicle navigation, assistance for visually impaired individuals, and automated traffic sign maintenance. Emphasizing sensor-based applications our approach uniquely incorporates text recognition within the traffic sign analysis pipeline, addressing the complex challenge of bilingual (Arabic-English) signs-a capability not previously explored in the literature.The system employs a three-stage pipeline, combining object detection, symbol detection, and advanced text recognition, all optimized for high-speed, realworld conditions. Trained on DoTaS, a custom dataset with 1,500 UAE traffic sign images, the system achieves a mean Average Precision (mAP) of 0.9124 using YOLOv8. For English text recognition, PARSeq achieves 89.3% word accuracy, setting a new benchmark for real-time, high-accuracy sign interpretation. Operating at around 250 ms per inference (around 4 FPS), our framework enhances safety, accessibility, and efficiency in urban environments.

As humanity advances toward permanent lunar exploration and habitation, the establishment of reliable, autonomous power systems becomes a cornerstone for the success of lunar missions. Lunar microgrids, which are responsible for supplying power to habitats and critical infrastructure on the Moon, must operate with a high degree of autonomy due to the unique challenges of the lunar environment. One critical aspect of lunar power system operation is frequency regulation. Unlike Earth’s power grids, which benefit from a wide range of dynamic grid support mechanisms, lunar microgrids must manage frequency regulation autonomously due to the absence of external support and the extreme conditions on the Moon. This paper explores the importance of autonomous frequency regulation in lunar microgrids, discussing the challenges of maintaining grid stability, ensuring energy efficiency, and mitigating power fluctuations in a microgrid system. Additionally, the role of autonomous control systems, energy storage technologies, and advanced predictive algorithms in achieving effective frequency regulation on the Moon is analyzed. By examining the operational and technical requirements for lunar energy systems, this paper highlights how autonomous frequency regulation is crucial for ensuring reliable and continuous power supply for long-term lunar habitation and exploration. Through a combination of AI-driven optimization techniques, real-time monitoring, and predictive maintenance, lunar microgrids can achieve the resilience and stability required to meet the energy demands of lunar habitats, rovers, and other essential systems.

In this work, the concept of test-time learning is presented, wherein Machine-Learning (ML) models are constructed by involving unlabeled test samples. Based on this concept, we propose an unsupervised method called Local Augment (LA) designed to improve the performance of trained outlier detectors at the prediction stage without altering the trained models or accessing the training data. LA operates under the only assumption that the model should produce similar outputs for similar inputs, implying that the prediction of a given sample can be enhanced by the predictions for its similar samples. Specifically, LA boosts outlier detection performance during prediction by fusing the outlier score of a given sample with the scores of synthetically neighboring samples generated by adding random perturbations to the given sample. This simple method demonstrates an average improvement of +0.04 Area Under the Receiver Operating Characteristic curve (AUROC) across 22 real-world datasets for all 11 tested detectors. Notably, this represents the pioneering work of enhancing ML models during the prediction stage without the need to modify the trained models or access the training dataset. This work opens up new possibilities for addressing existing bottleneck problems in various ML tasks beyond outlier detection in diverse domains.