This letter, based on measuring the admittance of a black-boxed grid-following inverter (GFLI) at seven distinct operating points to synthesize an operating-point-parameterized state-space model (OPP SSM), presents an efficient method for analytical prediction of the maximum transferable active power (MTAP) injected by the black-boxed GFLI into the grid under any arbitrary grid condition and reactive power injection level. The proposed method obviates the need for repetitive electromagnetic transient time-domain simulations or frequency-domain impedance criteria by gradually increasing active power output until the MTAP is reached. The feasibility of the synthesized OPP SSM for MTAP prediction has been experimentally validated on a power-controlled GFLI, demonstrating its potential for aiding the integration of inverter-based resources in modern power systems.

While innovation can lead to groundbreaking products, it also typically entails higher costs due to the need for advanced design, production processes, and licensing fees for critical technologies. Therefore, RISC-V is crucial for cost-effective applications as a practical and robust instruction set architecture (ISA). RISC-V is a free-available and open-source instruction set architecture, allowing you to create CPU architectures that are not subject to any intellectual property rights. Due to the growing demand for IoT devices, it is more practical to create boards RISC-V specifically designed for IoT applications. Although significant progress has been made in creating open source CPU cores, more emphasis must still be placed on special abilities for IoT applications. Most open-source cores have inadequate documentation or are designed for specific purposes. This analysis focuses on multiple open-source CPUs and System-on-Chip (SoC) designs that leverage the RISC-V architecture, examining their relevance and effectiveness in IoT environments. The study further outlines a RISC-V SoC specifically designed for IoT applications and investigates the primary features of a suitable open-source IoT node, also identifying the need for ongoing R&D to support the development of specialized protocols.

A document by Dylan Wheeler. Click on the document to view its contents.

Despite extensive research on integrating Substrate Integrated Waveguide (SIW) technologies with conventional transmission technologies such as Microstrip Transmission Line (MSTL), Co-planar Waveguide (CPW), Rectangular Waveguide (RWG), and Coaxial lines, a comprehensive evaluation and design framework has yet to be explored. This review addresses this critical gap by providing a detailed examination of recent advancements in interconnect technologies at millimeter-wave (mmWave) frequencies. The paper identifies unresolved challenges and introduces a novel Figure-of-Merit (F oM) designed to evaluate and decide the state-of-the-art in interconnect technologies. Furthermore, it proposes a novel design flow that aids interconnect designers by integrating reported advances into a well-defined process. As such, the study provides a basis for further developments in this area and uses the advantages of existing technologies and SIW to ease the integration on mmWave systems.

Grid-forming (GFM) inverters, like synchronous generators (SGs), form voltage at their terminals and are considered crucial for enabling high renewable energy integration. Most GFM controllers mimic the power-synchronization characteristics of the SG rotor's swing dynamics to synchronize with the system. However, GFM inverters differ significantly from SGs in both design and control. This paper systematically explores these differences. The analysis reveals that the coupling between steady-state and transient performance in traditional GFM controllers, both influenced by the active power-frequency droop gain, along with the absence of features like large stator inductance, damper windings, and power system stabilizers (PSS), may result in SGs having superior disturbance rejection in the small-signal domain. Building on these insights, a novel GFM control technique with improved disturbance rejection performance is proposed. A comparative stability analysis is performed to evaluate the robustness of the proposed technique against conventional GFM controls under various grid conditions. The effectiveness of the proposed control method is further validated through MATLAB/Simulink simulations and real-time hardware-in-the-loop testing in diverse power system scenarios.

This paper presents a novel data-driven approach to nonlinear system control using a behavioral systems framework. A Dynamic Latent Variable Autoencoder (DLVAE) is proposed to project the nonlinear physical variable space onto a linear latent variable space. A data-predictive control approach is developed to control the physical process variables through the latent variables. Based on the behavioral systems theory, the proposed data-driven control framework does not require the knowledge on the causality of the latent variables. The stability of the controlled system is ensured by utilizing the concept of trajectory-based dissipativity. The robustness of this control approach is achieved by incorporating the Lipschitz bounds between the latent and physical variable under dissipativity conditions.

This work presents the complete design process of a 3D fully-metallic reflectarray. The structure is composed of two unit cells with resonators in z direction that are phase-shifted 180 • and distributed in the x-y direction. Here will be shown how those unit cells have been designed and how we determine their distribution. Furthermore, a tolerance analysis is carried out with the aim of obtaining a prototype that is robust to manufacturing imperfections. Lastly, that protoype is measured to verify the results.

As RISC-V processors become more widely distributed, security issues arise in them. To this end, security verification techniques for processors should be incorporated into the design process to ensure the processor specifications, security requirements, and its actual implementation are consistent. Among the verification techniques, assertionbased verification has emerged as one of the most promising techniques. Although assertions are widely used for functional verification, there is limited effort in applying assertions for security verification. Thus, in this work, a novel security assertionbased technique is introduced for verifying the invulnerability of the processors against attacks. The method works in three main steps, first, collecting the security properties related to RISC-V, second, systematically identifying characteristics of instruction set architecture for each security property. Third, with the guide of input from the previous step, a security-based assertion miner is proposed to automatically extract a set of security assertions for the RISC-V. The experiments show that the proposed method can automatically generate security assertions in a very short amount of time and detect all the inserted Hardware Trojans in the processor, thereby accurately verifying the security of the processor.

The state of power (SOP) refers to the maximum available power from a battery. The SOP is a function of the open circuit voltage and internal resistance of the battery, both of which change with the state of charge (SOC). As such, the SOP is a more informative metric of battery state than the SOC and is crucial for the continued operation of an electric vehicle. The existing approaches employ non-linear system identification techniques that are sensitive to changes along temperature and SOC. In this paper, a novel approach is proposed to estimate the state of power in real time. The proposed approach does not require non-linear approaches for both system identification and filtering. Instead, a linear least squares estimation technique is utilized to estimate the parameters needed for SOP computation. The proposed approach utilizes a mechanism to account for the change in OCV in the observation window and employs a simple yet robust technique to account for the relaxation effect of the battery. The SOP estimates computed through the proposed approach are tested using realistic battery data collected from three battery cells at the following eight different temperatures: −25◦C, −15◦C, −5 ◦C, 5 ◦C, 15◦C, 25◦C, 35◦C, and 45◦C.

A document by BigDD. Click on the document to view its contents.

This work presents a novel signal design based on time-domain interleaving (TDI) of orthogonal frequency division multiplexing (OFDM). Unlike conventional OFDM-TDI that introduces a one-dimensional frequency diversity, the proposed design introduces a two-dimensional frequency diversity leading to a superior symbol error rate (SER) performance for various channel conditions. The performance of the proposed signal design is evaluated using zero-forcing (ZF) and minimum-mean-square-error (MMSE) equalizers where the signal-to-noise ratio (SNR) and signal-to-interference-plus-noise ratio (SINR) are derived and used to evaluate the SER, outage probability (OP), and diversity order. The analytical SER results obtained, corroborated by Monte Carlo simulation, demonstrate that the proposed signal model with MMSE equalizer achieves ∼40 dB gain over OFDM and ∼7 dB over conventional TDI in Rayleigh fading channels, and it is only 2 dB from the Gaussian channel SER in Rician channels. The proposed scheme also outperforms other state-of-the-art techniques such as interleave frequency division multiplexing (IFDM) and orthogonal time frequency space (OTFS). The proposed system architecture generally follows the conventional OFDM system model, which emphasizes its compatibility and low complexity.

Quantum approximate optimization algorithm (QAOA) with layer depth p is promising near-optimum performance and low complexity for NP-hard maximum-likelihood (ML) detection in n × n multi-input multi-output (MIMO) systems. Experimental challenges for ML detection on Noisy Intermediate-Scale Quantum (NISQ) computers arise from accumulated errors with large p and n. Recursive QAOA (RQAOA) is promising with small p by reducing complexity over n steps. In this article, we modify RQAOA for p n with cost sorting and post-selection in m n steps, and then integrate it with majority voting (MV) and successive interference cancellation (SIC) into the QAOA-MVSIC algorithm to tackle experimental challenges. We truncate QAOA circuits to further improve experimental feasibility. Simulations with n = 24 and 12 for BPSK and QPSK modulations, respectively, show near-optimum bit-error rate (BER) with p = 1 and m ≤ 4. Truncated version requires O(m n p) quantum and O(m n2 ) classical operations with low complexity. We experimentally implement QAOA combined with MV (QAOA-MV) for n ∈ [17, 64] in IBM Eagle processor by observing superior performance of QAOA-MV over QAOA and reducing problem dimensions by at least n/4. We generalize QAOA as cost-restricted uniform sampling (CRUS) oracle and approximately simulate for n ≤ 128 to obtain comparison benchmark for future QAOA experiments.

Large language models (LLMs) increasingly serve as interactive healthcare resources, yet user acceptance remains underexplored. This study examines how ease of use, perceived usefulness, trust, and risk perception interact to shape intentions to adopt DeepSeek, an emerging LLM-based platform, for healthcare purposes. A cross-sectional survey of 556 participants from India, the United Kingdom, and the United States was conducted to measure perceptions and usage patterns. Structural equation modeling assessed both direct and indirect effects, including potential quadratic relationships. Results revealed that trust plays a pivotal mediating role: ease of use exerts a significant indirect effect on usage intentions through trust, while perceived usefulness contributes to both trust development and direct adoption. By contrast, risk perception negatively affects usage intent, emphasizing the importance of robust data governance and transparency. Notably, significant non-linear paths were observed for ease of use and risk, indicating threshold or plateau effects. The measurement model demonstrated strong reliability and validity, supported by high composite reliabilities, average variance extracted, and discriminant validity measures. These findings extend technology acceptance and health informatics research by illuminating the multifaceted nature of user adoption in sensitive domains. Stakeholders should invest in trust-building strategies, user-centric design, and risk mitigation measures to encourage sustained and safe uptake of LLMs in healthcare. Future work can employ longitudinal designs or examine culture-specific variables to further clarify how user perceptions evolve over time and across different regulatory environments. Such insights are critical for harnessing AI to enhance outcomes.

Neuromorphic computing, inspired by the processing capabilities of the brain, aims to overcome the limitations of conventional computing architectures. Valence change memory (VCM), along with other emerging resistive random access memory (ReRAM) devices, is a promising candidate for this endeavor due to its features, including fast write times. Moreover, VCM devices are also suitable for neuromorphic applications such as long-term potentiation (LTP), short-term potentiation (STP), and gradual switching. However, evaluating these schemes on a timescale down to sub-100 ps is challenging. This is because the capacitive current dominates the device current on shorter time scales. Therefore, new methods are required to analyze the response of device current to the application of short voltage pulses with short delays. To address the challenge of dominating capacitive current, this study proposes two methods: the integration method and the reference method. The integration method integrates the current and eliminates capacitive charges across a full charge/discharge cycle, proving to be more reliable for longer pulses and delays exceeding 200 ps. On the other hand, the reference method determines the device current by subtracting the capacitive current from the measured current, using a reference measurement. This method is particularly adept at analyzing shorter pulses and delays below 200 ps. Both methods offer distinct advantages in solving the capacitive current challenge in evaluating VCM devices for neuromorphic applications. This research contributes to a better understanding of the behavior of ReRAM cells under ultra-short pulses and delays.

It is commonly known that both different brain regions and brainwaves are associated with different activities. This paper attempts to determine and analyze the differences in region activation and brainwave manifestation between a subject with Autism Spectrum Disorder (ASD) and a neurotypical subject regarding the working memory for spatial information. I hypothesized that variances in temporal and frontal lobe activation as well as beta and gamma wave activation. Data from a spatial memory test was captured, purified, plotted, and analyzed through Muse, MATLAB, and EEGLAB. I found inverse activation patterns between the frontal and temporal regions of the neurotypical and ASD brain. The ASD brain presented cumulatively lower-level delta, theta, alpha, and gamma waves. The neurotypical brain featured significantly more powerful beta waves. Increasing the subject pool and the number of electrodes would better reinforce the results of this study.

In this paper, we introduce a framework for detecting changes in strategy of an adversarial cognitive radar in the inverse learning context. We model the cognitive radar as a constrained utility maximizer and formulate the problem of detection of change in strategy as a changepoint detection problem in the revealed preference setting. We address the revealed preference strategy changepoint detection problem in a Bayesian setting, wherein we model the utility of cognitive radar as a random direction vector, that follows the von Mises-Fisher distribution with unknown parameters. The main contribution of the paper is the development of Bayesian changepoint detection algorithm that detects the changes in strategy of an adversarial cognitive radar, in an inverse learning context under stochastic revealed preference framework. The main advantages of the proposed method are (i) The proposed method uses a Hamiltonian Monte Carlo sampling algorithm that exploits the Afriat's theorem as well as a subsetting structure that arises in the posterior, and hence is devoid of the computational burden of solving optimization problems in existing techniques. (ii) Numerical results demonstrate the ability to determine the changes in the beam allocation strategy of a cognitive radar in noise free and noisy adversarial settings. The proposed approach inherently resists observation noise compared to the existing naive approaches. (iii) The proposed changepoint detection performs, on an average, five times faster compared to a naive, Generalized Likelihood Ratio approach for changepoint detection. Further, we also demonstrate that the proposed method generalizes existing changepoint detection under revealed preference setting and can be used for applications such as social media and finance. The changepoint detection framework, in the inverse learning context, is useful in the design of both electronic counter measures and electronic counter-counter measures.

The flashover in vacuum is a rapid interfacial discharge across the insulator surface when subjected to high applied voltage. Here a theoretical model covering above-surface processes is introduced. The model calculates the flashover threshold in vacuum, with revised secondary electron emission avalanche theory and improved desorbed neutral transport model. The model serves as the first part of an integrated flashover model, aiming for consistent treatment of both above-surface and subsurface processes during the entire flashover development. The flashover threshold is obtained by combining the desorbed neutral pressure at given applied voltage and the gas breakdown criterion dictated by the Paschenâ\euro™s law. An analytical formula for threshold estimation containing physical parameters, and a simplified formula consisting of empirical coefficients are introduced, catering for both conceptual understanding and practical application. The derived formulae are validated by experimental data for a variety of insulator materials. The theory generalization for nonuniform electric field distribution is further discussed.

This paper presents a novel Graph-Convolutional-Network Based Deep-Learning (GCDL) approach to effectively determine estrogen-receptor-status (ERS) in patients with invasive breast cancer, from their Hematoxylin-and-Eosin (H&E) stained Tissue Microarray (TMA) images. Exploring previously overlooked correlation between ERS and nuclei spatial properties from H&E images, we use a set of novel methods to 1) construct two level cell-graphs from breast cancer H&E-stained tissue images and a novel graph node pooling, 2) extract two-level graph-metrics, 3) find the feature distribution statistics on low-level graph, 4) combine the features from 2) and 3) with conventional nuclei morphometric features to form composite node embeddings on high-level subgraph, and 5) associate a single-layer graph convolution with a stack of several nonlinear dense layers to create a GCDL classifier for enhanced performance. The proposed GCDL approach is tested and compared with a popular residual based Convolutional-Neural-Network (ResNet) method on a dataset of 960 patients with their ERSs diagnosed by pathologists. The test results show that the GCDL approach outperforms ResNet, achieving the best accuracy by far in ERS classification using a single H&E-stained TMA image per patient. Moreover, the GCDL approach requires much less training data and training time to achieve competitive results.