The numerical integration of expressions containing strong singularities or strong near-singularities has long been a challenging problem in the electromagnetics community. Much attention has been paid to this problem, as strong 𝟏𝟏/𝑹𝑹𝟐𝟐 singularities routinely appear when implementing electromagnetic simulation techniques like the Method of Moments (MoM). To date, several techniques, from singularity extraction to singularity cancellation, have been employed to deal with problems that require the evaluation of 2D strongly-singular integrals. However, no single technique has been proposed that can deal with both strong singularities and strong near-singularities in a fully-numerical manner for arbitrary 2D domains. Moreover, it has been claimed that the Helmholtz-type strongly singular integral found in the MoM is convergent in a principal value sense, but this convergence value has yet to be proven mathematically. In this work, we will conduct the convergence proof and introduce a “polar scaling” change of variables method that may be used to evaluate Helmholtz integrals with both strong and weak singularities/near-singularities. The technique is fully-numerical and can in principle be applied to any planar or curved polygon and any non-singular basis function. We will also provide numerical results showing useful convergence behavior for integrals involving both exact and near-singularities.

A document by Zeynep Kumralbaş. Click on the document to view its contents.

Integrating inverter-based resources into power systems increases their complexity, and makes their operation more challenging. The conventional approach for simulating power systems neglects transients of the power grid, using steady-state phasors and considering only fundamental frequency values. However, to accurately predict the dynamic behavior of systems with high shares of inverter based resources, electromagnetic transient simulations (EMTSs) are necessary. The primary limitation of classical time-domain EMTSs is their frequent timeconsuming nature, making them impractical for larger systems. In this work, we explore the use of dynamic phasors (DPs) as a technique to accelerate EMTSs. For this purpose, we first adapt the power grid and inverter model to DP simulation. Subsequently, we assess the effectiveness of this approach through three numerical examples, demonstrating a reduction in the simulation times by a factor of 4 to 40.

We propose a machine learning (ML) modeling methodology to predict the propagation mode number of electromagnetic (EM) fields inside a metallic rectangular waveguide based on the field configuration in the waveguide cross-section, in the presence of noise. We consider the Transverse Electric (TEmn) modes and assume m and n in the range of 0 to 2 inside the waveguides, where the magnitude and phase of the noiseless field configurations are obtained from the analytical solution of the electric vector field E⃗ . We generate training/testing datasets that includes 64,000 plots of the magnitude and phase of E⃗ over the waveguide cross-section, spanning various TE modes in the frequency range of 13-17 GHz. Our methodology for training and evaluation is based on the classification model, and relies primarily on Stochastic Gradient Descent (SGD) and k-Nearest Neighbors. For real-world scenarios which include noise, we introduce two random distributions in the datasets; specifically, the exponential and the Gaussian distributions are added onto the computed E-fields to further challenge the ML model. We discuss the limitations of the proposed ML modeling approach and the challenges in finding the optimal ML model for these types of problems. The proposed methodology may be generalized to predict both the TE and Transverse Magnetic (TMmn) mode numbers with a wide ranges of m and n, as well as for other types of waveguides; e.g., circular, elliptical, etc.

A document by Neha Sharma. Click on the document to view its contents.

The primary focus of Research VLSI Technology is to optimize power and Delay, so that a low power and High Speed Designs are obtained. The MTCMOS is one of the Such Promising CMOS Chip technology that uses transistors with multiple threshold voltages to optimize power and Delay. Subthreshold Leakage has brought down and achieved power optimization. In this article Multi threshold CMOS based Data Flip-Flop is Designed, analyzed and Validated on 90 nm Technology Node Using EDA Tools.

The impact of climate change, arguably the global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices.he impact of climate change, arguably the global warming and resulting drought, is one of the most escalating agricultural challenges affecting crop productivity. Therefore, effective water management is critical in agricultural practices.T The analysis of plant leaves presents an opportunity to gauge irrigation status through automated solutions to encourage broader adoption among farmers. Currently, there is a notable absence of AI methods in the literature for detecting tomato plant irrigation status through leaf analysis. Addressing this gap, we propose a novel end-to-end deep learning (DL)-based method, inspired by the ResNet-50 model. Our model trims unnecessary blocks and reduces larger kernels, significantly streamlining the model to better fit with the leaf image dataset related to the tomato irrigation status. We evaluate our method using a newly developed dataset and find outstanding performance (Precision: 99.05%, Recall: 99.01%, F1-score: 99.01%, mean-average F1: 98.98%, weighted-average F1: 98.95%, Kappa: 98.61%, accuracy: 98.90%) while comparing with the pre-trained DL models. Additionally, our model has fewer parameters and lower floating-point operations (FLOPs), enhancing its efficiency and suggesting its potential for more cost-effective and productive irrigation management practices.

A modular voltage equalizer can be used in a wide variety of applications without changing the module design. However, the modularization of an equalizer topology often involves slow inter-module equalization, additional energy loss, and cost. A study on available modularization techniques shows that the transformer-based equalizers offer simple and low-cost modularization methods. Conversely, the transformer-less equalizers can offer more compact design, but their modularization methods suffer from either high complexity and loss, or slow inter-module equalization speed. In this work, one two-winding transformer is introduced into a transformer-less equalizer module to leverage the modularization benefits of transformer isolation while still achieving a more compact design than transformer-based equalizers. The proposed direct modularization method achieves fast inter-module equalization and fixed design for a variety of applications with a small increase in cost and size. The proposed method is implemented on a transformer-less equalizer and its effectiveness is validated experimentally with a three-module prototype.

This paper develops a gain randomization framework against inference attacks on feedback control systems where an adversary with access to the states of the system attempts to infer the system model. To prevent the inference attack, the control gain of the system at each time step is randomly selected from a predefined set of control gains. We cast the gain selection problem as an optimal control problem where a gain selection policy at each time step selects a control gain according to a probability distribution such that (i) quadratic control cost is minimized and (ii) the uncertainty level of the adversary about selected control gain is maximized. In our formulation, the gain selection policy is allowed to depend on the entire history of the state measurements and the uncertainty level of the adversary about the control gain is captured by the Kullback-Leibler (KL) divergence between a uniform distribution and the posterior distribution of the feedback gains given the history of the system states. We first derive the backward Bellman optimality equation for the gain selection problem and study the structural properties of the optimal gain selection policy. Our results show that the optimal gain selection policy only depends on the current state of the system, rather than the entire history of the states, which renders the optimal gain selection problem to a non-linear Markov decision process. We next derive a policy gradient theorem for the gain selection problem which provides an expression for the gradient of the objective function of the gain selection problem with respect to the parameter of a stationary (time-invariant) policy. The policy gradient theorem allows us to develop a stochastic gradient descent algorithm for computing an optimal policy. We finally demonstrate the effectiveness of our results using a numerical example.

Embedding a programming language in a QR code is a new and extremely promising opportunity, as it makes devices and objects smarter without necessarily requiring an Internet connection. In this paper, all the steps needed to translate a program written in a high-level programming language to its binary representation encoded in a QR code, and the opposite process that, starting from the QR code, executes it by means of a virtual machine, have been carefully detailed. The proposed programming language was named QRscript, and can be easily extended so as to integrate new features. One of the main design goals was to produce a very compact target binary code. In particular, in this work we propose a specific sub-language (a dialect) that is aimed at encoding decision trees. Besides industrial scenarios, this is useful in many other application fields. The reported example, related to the configuration of an industrial networked device, highlights the potential of the proposed technology, and permits to better understand all the translation steps.

This work presents the design of a broadband frequencyselective surface absorber (FSSA) using laser-induced graphene (LIG). This type of resistive material composed of porous graphene has been analyzed, and its performance has been modeled through different cases of FSS based on LIG patches. The results of this analysis, both simulated and experimental, show the existence of periodic frequency bands of absorption ranging from a few GHz to 50 GHz. Taking advantage of this absorbing behavior, a single-layer FSSA, including a circular LIG patch at the center of the unit cell, is proposed. A circuit model for this FSSA unit cell is introduced, which allows us to efficiently improve the absorption over a large bandwidth by including an ad-hoc dielectric layer. The FSSA is fabricated and measured: the experimental results show a good agreement with the simulated ones, providing absorption of more than 0.9 from 14 GHz to 48 GHz (109.7% relative bandwidth). In addition, the proposed design exhibits robust behavior to both TE and TM oblique incidence up to 45º.

This work explores the principle of utilizing GalliumNitride devices as a gate driver for Silicon Carbide power devices. As silicon has long reached its performance limits, Wide Bandgap semiconductors such as Gallium Nitride and Silicon Carbide have emerged as promising alternatives due to their superior characteristics. However, few publications suggest using a Gallium-Nitride-based gate driver for Silicon Carbide, highvoltage power devices. This paper proposes a novel resonant gate driver topology using Gallium Nitride transistors as a gate-drive circuit for Silicon Carbide power switching. The driver receives a single input supply and pulsed width modulation signal, producing a high current bi-polar gate driving signal. This innovation recovers energy and provides a higher efficiency driver by reducing the gate charge losses by 25%. Experimental results validate the effectiveness of the proposed gate driver in driving a quasi-resonant boost converter, showcasing high efficiency (>97%) and power delivery (>700 Watts) in varying voltages and loads at 2.5MHz switching frequency.

Point cloud video streaming over networks is challenging because of the extremely high data rate of uncompressed point cloud data. Adaptive point cloud video streaming has been proposed to deal with this challenge. However, temporal quality variation and stalling might occur under unstable network conditions, potentially degrading users' Quality of Experience (QoE). This paper aims to evaluate and model the impacts of temporal quality variations and stalling on users' QoE in adaptive point cloud video streaming. We first conduct a largescale subjective study to construct a QoE database. Then, based on the constructed database, the effects of individual factors are analyzed, and two novel QoE prediction models are presented. Experiment results show that the proposed QoE models achieve high prediction performance in terms of PLCC, SROCC, and RMSE across various point cloud videos.

We study burst erasure correcting streaming codes for three-node relay networks, where there is a source-relay link and a relay-destination link. These codes guarantee that all message packets are recovered within a delay of τ time slots, given that a single burst erasure of length at most b packets occurs in both links. Leveraging previously known techniques in the streaming code literature, we first provide a simple upper bound on the rate of burst erasure correcting streaming codes for three-node relay networks. Our main result is a coding scheme that achieves rates arbitrarily close to the rate upper bound, as message size increases.

Modern fifth-generation (5G) networks are increasingly moving towards architectures characterized by softwarization and virtualization. This paper addresses the complexities and challenges in deploying applications and services in the emerging multi-tiered 5G network architecture, particularly in the context of microservices-based applications. These applications, characterized by their structure as directed graphs of interdependent functions, are sensitive to the deployment tiers and resource allocation strategies, which can result in performance degradation and susceptibility to failures. Additionally, the threat of deploying potentially malicious applications exacerbates resource allocation inefficiencies. To address these issues, we propose a novel optimization framework that incorporates a probabilistic approach for assessing the risk of malicious applications, leading to a more resilient resource allocation strategy. Our framework dynamically optimizes both computational and networking resources across various tiers, aiming to enhance key performance metrics such as latency, accuracy, and resource utilization. Through detailed simulations, we demonstrate that our framework not only satisfies strict performance requirements but also surpasses existing methods in efficiency and security.

The advent of 6G wireless networks has the potential to unlock diverse applications of scalable autonomy. By advantageously coupling the individual and aggregated attributes of diverse multi-UAV fleets, a range of high-value applications such as logistics, enhanced disaster response, urban navigation, and surveillance can be significantly improved. However, enabling effective communication for knowledge fusion necessitates the intrinsic optimization of performance metrics like energy consumption, resource allocation, latency, and computational overheads to enhance autonomous efficiency. Furthermore, designing robust security features is essential to safeguarding privacy, control, and operational integrity. This paper explores a novel collaborative knowledge-sharing (KS) framework that leverages 6G and edge-computing capabilities to facilitate the cooperative training of decentralized machine learning models among multiple UAVs, without the need to transmit raw data. This framework aims to enhance the learning experience and operational efficiency of autonomous vehicles. The DECKS (distributed edge-based collaborative knowledge-sharing) architecture enables Federated Learning (FL) within UAV networks, allowing local models to be trained and shared among neighboring UAVs for creating global models. This promotes intelligent knowledge aggregation without a central entity, enhancing collaborative capabilities among autonomous vehicles. The DECKS architecture efficiently extracts and distributes collaborative shared experience to ground vehicles through edge and direct inference, reducing energy consumption, latency, and computational overhead. Our simulation analysis demonstrates that the DECKS architecture has the potential to reduce energy consumption by 70% in sensorless vehicles and improve autonomous vehicle learning performance by 15% compared to centralized approaches in a distributed environment. This improvement is achieved by comparing the efficiency of systems with and without aggregated knowledge.

The sintered Nd-Fe-B magnets with various rare earth (RE) contents were prepared using strip casting technique, and the dependence of the microstructures and coercivity on rare earth content was investigated. It is found that an appropriate higher rare earth content in magnets is beneficial to the formation of uniform distribution of the RE-rich phases in grain boundaries, which leads to a rapid growth in the coercivity of magnets with increasing the rare earth content from the range of 29 wt.% to 32 wt.%. Moreover, continuous thin layers of RE-rich phase were confirmed by scanning electron microscopy in details. We further demonstrate that the coercivity of magnets becomes stronger with an appropriate higher RE content using micromagnetic simulation.

The use of Internet-of-Things (IoT) technology in electric power distribution is growing, specially with smart meters, automated controls, demand response (DR), and renewable distributed energy resource (DER) integration. These advancements have made it easier to monitor, control, and optimize DER-rich cyber-physical distribution systems (CPDS). However, as IoT devices become more widespread, they also increase the risk of cyber-attacks, which makes ensuring system resiliency critical. This work presents a novel resiliency-driven load restoration technique leveraging demand response through IoT devices to enhance the resiliency of a three-phase unbalanced power distribution systems. A hierarchical optimization framework is proposed, featuring a continuous linear program for primary optimization and a binary linear program for secondary optimization, aimed at achieving load-source energy balance and connection of secondary-level house/building loads based on their criticality. To address the limitations of traditional theoretical methods in analyzing distribution system losses across multiple devices and configurations, this paper proposes a novel deep neural network-based approach for system loss calculation. Validation of the proposed methodology is conducted using the IEEE 123-node system modeled in the HELICS co-simulation testbed, to demonstrate optimally configuring switch status, supplying critical loads, balancing load generation, and maintaining feeder voltages.