The use of multi-band matching for rectifiers leads to design complexity. Instead, recent advancements suggested self-matched branches combined in parallel to enable multiband operation. However, this method controls only the imaginary part. In this letter, we propose an efficient dual-band rectifier with compact realization. The rectifier consists of two self-matched parallel branches. Each branch provides comprehensive impedance control over real and imaginary parts in the corresponding band independent of the design frequency. The branch impedance matching is analyzed theoretically, and design equations are presented. To verify the proposed theory, a compact dual-band rectifier was fabricated with a compact area of only 0.42 cm2 after excluding the area required for the RF connector. The measured RF-DC power conversion efficiency (PCE) was > 50% for input power (𝑷𝒊𝒏) ranging from -5.5 dBm to 11 dBm at 390 MHz with a peak of 69%. Also, the PCE was > 50% for 𝑷𝒊𝒏 ranging from -4 dBm to 12 dBm at 690 MHz with a peak of 68%. The fabricated rectifier operates with a wide load range from 0.5 KΩ to 3 KΩ with PCE > 50% at both bands when 𝑷𝒊𝒏 = 𝟓 dBm.

Although digitalization of future power grids offer several coordination incentives, the reliability and security of information and communication technologies (ICT) hinders its overall performance. In this paper, we introduce a novel architecture Spike Talk via a unified representation of power and information as a means of data normalization using spikes for coordinated control of microgrids. This grid-edge technology allows each distributed energy resource (DER) to execute decentralized secondary control philosophy independently by interacting among each other using power flow along the tie-lines. Inspired from the field of computational neuroscience, Spike Talk basically builds on a fine-grained parallelism on the information transfer theory in our brains, particularly when neurons (modeled as DERs) transmit information (inferred from power streams measurable at each DER) through synapses (modeled as tie-lines). Not only does Spike Talk simplify and address the current bottlenecks of the cyber-physical architectural operation by dismissing the ICT layer, it provides intrinsic operational and cost-effective opportunities in terms of infrastructure development, computations and modeling. Hence, this paper provides a pedagogic illustration of the key concepts and design theories. Since we focus on coordinated control of microgrids in this paper, the signaling accuracy and system performance is studied for several neural coding schemes responsible for converting the real-valued local measurements into spikes.

Brain tumor is a serious disease that can lead to fatal consequences. Moreover, there are different types of brain tumor with different progression rate and severeness. Thus, brain tumor classification is an essential task in medical diagnosis. Convolutional Neural Network (CNN) based deep learning methods shown great potential brain tumor classification using Magnetic Resonance Imaging (MRI) scans of brain. Recent development in transformer based deep learning models have shown promising outcome in image classification task. However, these models have not yet much explored in the application of brain tumor classification. In this paper, we employ seven advanced models which utilizes transformers or mimics the selfattention operation of transformers through their design, for brain tumor classification. To assess their performance for this particular task, five conventional CNN based methods have also been applied and compared with these models. To make our assessment more coherent and comprehensive we perform the comparison for four different datasets. The results indicate that transformer-based advanced models do not provide a distinct advantage over conventional CNN-based models. The CNN-based ResNet-50 performs well overall, especially with smaller datasets. For larger datasets, transformer-based models generally perform better than CNN-based models, although the difference is not significant.

The electrochemistry dependent variable voltage of the battery, the volume and weight limitations in electric vehicles (EVs) pose a few challenges regarding higher power density, higher efficiency, wider and higher voltage range, and bidirectional capability of the auxiliary power modules (APM). The APM enables power transfer from the high voltage (HV) traction battery to the low voltage (LV) loads. Furthermore, two-winding based converter topologies lead to high current stress on the LV side. To address the aforementioned issues, this paper firstly proposes a multiwinding CLLC converter topology using GaN HEMT on the HV side and Si MOSFET on the LV side, and secondly considers the variation of battery charge with respect to voltage in the design optimization. Utilizing a multiwinding transformer with parallel outputs distributes the current stress and improve thermal management on the LV side, while increasing power density and efficiency. Optimization for the multiwinding transformer and converter will be carried out using the new approach that considers the electrochemistry of the battery to weight the operation points of the converter. Experiments verify the proposed multiwinding CLLC converter and an efficiency of 96.6 % and power density of 1.36 kW/L including cold plate, microcontroller and protection circuit are achieved.

Grid codes require High Voltage Direct Current (HVDC) converter stations to have AC Fault Ride-Through (AC-FRT) capability, which prevents disconnection during AC grid faults and requires them to support the faulty grid. A major challenge during AC-FRT operation is the faulty converter's reduced power capacity, which can lead to over-energizing of the Multi-Terminal Direct Current (MTDC) grid if said converter was exporting power from the MTDC grid prior to the fault. This paper presents an innovative strategy for dealing with DC grid over-energization during AC-FRT operation by utilizing the existing Metal Oxide Varistor (MOV) banks within DC Circuit Breakers (DCCBs) through a slight and economical modification to the DCCB architecture. By adopting this strategy, it is possible to reduce the size or number of Dynamic Break System (DBS) used in the MTDC grid. The proposed strategy is able to prevent overvoltage by clamping the DC voltage throughout the MTDC grid. Furthermore, it is able to attain a uniform distribution of energy dissipation in DCCBs throughout the MTDC grid without the need for communication and coordination and achieves a smooth transition between normal and AC-FRT operation and vice versa.

This paper deals with the design, analysis and experimental characterization of a planar inductor realized on FR4-PCB (Printed Circuit Board) for a DC-DC converter equipped with GaN (Gallium Nitride) based power devices. In the past, such inductors were conceived for radio-frequency applications; now, thanks to fast switching frequencies allowed by WBG (Wide BandGap) power devices, they can be considered for power conversion applications. A planar inductor has been designed for a synchronous GaNbased buck converter working at 1 MHz switching frequency; the inductor design starts from the analytical equations describing radiofrequency integrated inductors; then, its performance has been verified firstly by FEM (Finite Element Method) software and then by experimental measurements. Finally, the realized inductor has been tested in different working conditions in the power converter. To highlight the benefits and drawbacks, a comparison with ferrite-based inductors has also been proposed. Planar coreless inductors exhibit constant inductance when the current varies, and their lack of saturation reduces current distortion and harmonic pollution; however, size and weight are larger than with ferrite-based inductors.

A document by Alessandro Carollo. Click on the document to view its contents.

This paper investigates beamforming design with partial channel state information (CSI) feedback aimed at maximizing the energy efficiency (EE) of a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multiuser system. By leveraging the spatial reciprocity, which means that the path angle information (PAI) and power angular spectrum (PAS) of sparse paths are similar in the uplink and downlink channels, we can acquire the downlink PAI and PAS at the base station (BS) via uplink estimation. Subsequently, we select several dominant paths that contribute to EE maximization among all the cascaded paths. Consequently, only the small-scale fading coefficients (i.e., the normalized path gain information, NPGI) of these selected paths need to be fed back from the user equipments (UEs), thereby significantly reducing the feedback overhead. Moreover, we update the active BS beamformer and passive RIS beamformer by using the feedback of partial NPGI to further improve the EE. Numerical results demonstrate the superiority of our proposed algorithms over conventional schemes.

Knowledge tracing (KT), which involves the use of students' problem-solving histories to estimate their current level of knowledge, has attracted significant interest from researchers. Modeling learners' knowledge state can be used to recommend appropriate learning content; consequently, it can serve as a foundational technology to support personalized learning. Nevertheless, using only student problem-solving data is subject to certain limitations when attempting to model their knowledge states. Recent studies have attempted to address this issue by incorporating various side information in terms of questions, knowledge components, and students. However, the majority of the side information used in prior studies was constrained to the process of problem solving, thereby lacking the ability to adequately capture information from the students' side. In the real world, students engage in various learning activities that significantly impact the changes in their knowledge states. Therefore, students' learning behaviors (SLBs) during the online learning process can be useful in estimating their knowledge states. In this study, we propose SLB-KT that uses SLB data. We collected learning behavior and problem-solving data from the same students from an e-learning platform. Following a literature review, learning styles and interaction types were adopted as the background theories for this study. Student behavior modeling was then performed in two dimensions: contextual information based on learning style and interaction type, and behavior pattern. Subsequently, we demonstrated the performance of the SLB-KT models in many aspects using various baseline models. The experiment showed that the SLB-KT models outperformed the baseline models in terms of prediction performance, alleviated the cold-start issue, and improved reliability.

Contributions: An adoption framework to include GenAI in the university curriculum. It identifies and highlights the role of different stakeholders (university management, students, staff, etc.) during the adoption process. It also proposes an objective approach based upon an evaluation matrix to assess the success and outcome of the GenAI adoption.Background: Universities worldwide are debating and struggling with the adoption of GenAI in their curriculum. GenAI has impacted our perspective on traditional methods of academic integrity and the scholarship of teaching, learning, and research. Both the faculty and students are unsure about the approach in the absence of clear guidelines through the administration and regulators. This requires an established framework to define a process and articulate the roles and responsibilities of each stakeholder involved.Research Questions: Whether the academic ecosystem requires a methodology to adopt GenAI into its curriculum? A systematic approach for the academic staff to ensure the students' learning outcomes are met with the adoption of GenAI. How to measure and communicate the adoption of GenAI in the university setup? Methodology: The methodology employed in this study focuses on examining the university education system and assessing the opportunities and challenges related to incorporating GenAI in teaching and learning. Additionally, it identifies a gap and the absence of a comprehensive framework that obstructs the effective integration of GenAI within the academic environment. Findings: The literature survey results indicate the limited or no adoption of GenAI by the university, which further reflects the dilemma in the minds of different stakeholders. For the successful adoption of GenAI, a standard framework is proposed i) for effective redesign of the course curriculum, ii) for enabling staff and students, iii) to define an evaluation matrix to measure the effectiveness and success of the adoption process.

Phase aberration in ultrasound images is caused by inaccurate information in the sound speed distribution in the medium and can result in image distortion, such as shape change and position shifting of the imaged objects. Various methods, including cross-correlation-based methods, have been applied to the distorted images to estimate phase aberration. In this paper, we first propose that the position shifting induced by the phase aberration causes the estimated phase aberration to be inaccurate. Then, we propose an equation relating the estimated phase aberration to the true one and the equations to predict position shifting. Finally, we present a forward model for estimating phase aberration. Considering phase aberration as a function of the array element position, the theory shows that both the constant term and linear term of the true phase aberration will be cancelled in the estimated phase aberration as they result solely in predictable position shifting. Field II simulations and data from tissue-mimicking phantom were used to validate the proposed theory. The theory was also applied to improve the estimation of the initial delay of ultrasound probes in both Field II simulations and experimental phantom study. Other potential applications of the proposed theory were also discussed.

Eye movement signals are becoming an increasingly common data source due to its growing integration into mainstream consumer devices. Although eye tracking enables advancements in several vital applications, the growing accessibility and quality of the data captured by consumer-grade devices raises questions as to whether eye tracking poses risks to user privacy. To proactively address these concerns, it is necessary to identify and meaningfully characterize the privacy implications of eye tracking in consumer devices. Toward this end, we present a literature survey that summarizes the circumstances under which sensitive user characteristics---such as identity, gender, emotion, personality, cognitive load, skills, and fatigue---can be inferred from eye movement data. For each of these potentially sensitive user attributes that are encoded in eye movement, we summarize peer-reviewed methodologies that analyze eye movement signals to reveal them. From this review, we discuss the potential for eye tracking data to be used to enable privacy violations in practical settings, considering factors such as eye tracking signal quality and the availability of other data. In doing so, we identify the most promising avenues for future work in practical privacy enhancement for eye tracking data.

This paper explores the benefits of utilizing multistage cascaded (CC) behavioral models for digital predistortion (DPD) linearization of wideband high-efficiency power amplifiers (PAs). To reduce the computational complexity of these multistage CC behavioral models, a model-order reduction technique based on a greedy algorithm is proposed. The advantages of employing CC DPD models with gradient descent parameter identification, as opposed to single-stage DPD models with least squares parameter identification, are extensively demonstrated and analyzed. The trade-off among linearity, power efficiency and computational complexity is evaluated considering the linearization of a high-efficiency pseudo-Doherty load-modulated balanced amplifier (PD-LMBA). Using the proposed pruning strategy for CC DPD models, we demonstrate a significant reduction in the number of parameters needed to linearize the PD-LMBA. The PA operates at an RF frequency of 2 GHz and delivers a mean output power of 40 dBm with an approximately 50% power efficiency when driven by 5G new radio signals with up to 200 MHz bandwidth and an 8 dB peak-to-average power ratio.

In today's fast-paced world, efficient route experiences are crucial for individuals, businesses, and transportation agencies. Whether routing goods, managing logistics operations, or planning supply chain strategies, the goal is to minimize time, reduce costs, and enhance satisfaction. This project addresses these challenges by leveraging data, intelligent analysis, and innovative technologies to optimize routes. By personalizing services, improving efficiency, and driving affordability, the initiative aims to enrich the route experience for all stakeholders. Key algorithms like Dijkstra's algorithm, K-means, VRP, and TSP will play significant roles. The project also focuses on integrating emerging technologies and predictive analytics to enhance precision and efficiency. Through collaboration with various stakeholders, it aims to create practical, efficient, environmentally friendly, and socially just routes.

The article provides an introduction to the basics of breast cancer in terms of radiotherapy. The techniques which are used for the radiation-based therapy of breast cancer are 3D conformal radiotherapy (3DCRT) and intensity-modulated radiation therapy (IMRT) along with technical terms for early career physicists. Also, the article provides information about the comparative analysis applied to these techniques. Overall, the treatment of breast cancer from the perspective of radiotherapy is discussed.

This paper presents a feature selection technique based on ℓ1 regularization to select the most relevant weights of artificial neural networks (ANNs) for digital predistortion (DPD) linearization of wideband radio-frequency (RF) power amplifiers (PAs). The proposed pruning method is applied to the first hidden layer of a feed-forward real-valued time-delay neural network, commonly used for DPD purposes. In addition, this paper presents the ANN-based DPD implementation using a graphic processing unit (GPU) with compute unified device architecture (CUDA) units. Thanks to the proposed pruning strategy, it is possible to reduce the ANN complexity significantly, thereby achieving a higher data throughput with the GPUbased implementation. The trade-off among RF performance metrics, number of model parameters and throughput of the GPU implementation is evaluated considering the linearization of a high-efficiency pseudo-Doherty load modulated balanced amplifier (LMBA). The linearized PA operating at 2 GHz RF frequency delivers a mean output power of 40 dBm with around 50% power efficiency when being excited with 5G new radio signals with up to 200 MHz bandwidth and 8 dB peak-to-average power ratio (PAPR). The real-time GPU implementation of the ANN-based DPD can meet the linearity specifications with a throughput circa 1 GSa/s.

In this paper, we present a versatile and novel framework for antenna array synthesis and optimization by leveraging simulated bifurcations of nonlinear oscillators. The desired antenna radiation profile is recast into an Ising Hamiltonian form, where the optimal antenna configuration is found by solving the ground-state solution of the Hamiltonian function. At the core of this framework is the quantum-inspired simulated bifurcation algorithm, which enables the efficient computation of ground-state solutions. We demonstrate the performance of the proposed work across a wide range of antenna synthesis scenarios, including array thinning, super-resolution beamforming, weighted beamforming, and beam shaping. The results highlight its potential as a powerful tool for addressing complex optimization problems in the field of antenna arrays.

This letter analyses the achievable performance of Low Earth Orbit (LEO) Position-Navigation-Timing (PNT) using the novel Orthogonal Time Frequency Space (OTFS) modulation. While originally designed for communication purposes, we show the advantages this modulation may bring to the PNT domain, in particular for doubly-selective channels exhibiting both delay and Doppler spreads. We show how the LEO channel fits this description due to its fast dynamics, and we present the improvements of using OTFS with respect to the standard Binary Phase Shift Keying (BPSK) modulation in terms of delay-Doppler estimation accuracy, in addition to a comparison of their respective Multipath Error Envelopes.