Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband Modal Solution of the Full-Wave Method of Moments Based on a Quasi-Static Eigenvalue Approach

A novel modal solution of the Full-Wave Method of Moments (MoM) based on a quasi-static eigenvalue problem is presented. The resulting modal current solutions have to be determined only once and serve as global basis and test functions. By extracting the static modes from the full-wave system, the system order can be significantly reduced. Furthermore, the modal solution provides also a physical insight into the system behaviour and can therefore, similar to the Characteristic Mode Analysis, be used for EMC optimizations tasks, whereby the eigenvalue problem has to be solved only once. The method is validated by comparison with the conventional Full-Wave MoM as reference.
Flexible Fault-Tolerant Multi-Die FPGA-based Architectures for Varying Space Environments

Electronic components in spacecrafts operate in very harsh environments. Furthermore, radiation levels may change when moving from one orbit to another. SRAM-based FPGA systems are affected by radiation which induce soft and hard errors. This paper proposes multi-die FPGAs with dice of different technology nodes to take advantage of the higher reliability of older nodes. Several fault-tolerant architectures are analyzed, and it is shown how to choose the appropriate one with modules on one or both dice. Biased voters are designed to increase architecture reliability. A case study illustrates the use of this technique on a Xilinx FPGA.
A 680 pJ / task Processor for Sensor Network Applications in 130 nm CMOS

This paper presents a low power processor designed specifically to address event driven computation and long idle times that characterize wireless sensor network workloads. The system employs application specific hardware accelerators and fine-grained VDD-gating. We present active, idle, and gated power measurements of our 130 nm prototype system across voltage and frequencies. The system consumes 680pJ for a typical WSN task at 550mV and 12.5 MHz and for low-throughput workloads VDD-gating reduces energy consumption by 9x.
Beam-Steering Antennas Using Semi-Periodic Metasurfaces

Metasurfaces advance the technology to develop high-performance antenna systems due to their unique electromagnetic (EM) wave manipulation capabilities. This article describes the antenna beam-steering mechanism using semiperiodic metasurfaces (locally aperiodic metasurfaces). Metasurfaces are synthesized based on the near-field phase transformation principle and placed atop the fixed-beam antenna to steer the beam dynamically, following Risley prism techniques. We discuss several advances that have been made since the technology was first introduced.
(2021). Modal analysis of a rotating pre-twisted beam axially loaded by an internally guided tendon. Journal of Sound and Vibration, 498,

A guided tendon concept in the context of rotating helicopter blades is proposed to modify their dynamic characteristics, so that the rotorcraft can operate under a wide range of working conditions. In this paper, a rotating pre-twisted beam with bending-bending-torsion coupling axially loaded by a tendon is used to model the proposed system. An internally guided tendon is attached to the beam at several spanwise locations to avoid the resonant vibrations due to insufficient separation between the rotor harmonics and natural frequencies of the beam. A set of orthogonal polynomials that satisfy the geometric boundary conditions is used to represent the deflections of the beam and tendon. The equations of motion are derived by means of the Lagrange’s equation of the second kind combined with the appropriate energy expressions. In vacuo modal analysis is then carried out after the modal properties of the rotating pre-twisted beam without the tendon are validated against the reference. The effect of the attachments and rotation on the free vibration of the beam-tendon system is studied. It is found that the attachments and rotation can improve the structural stability of the system significantly, so as to allow a significantly larger axial force to be applied. The natural frequencies of all studied modes increase with rotation due to the centrifugal stiffening effect. The tendon-guiding attachments cause an increase in the natural frequencies of the tendon-dominated modes because of their reduced effective length but decrease the frequency reduction rate of the beam-dominated modes with the applied axial force. The beam part of the mode shapes of the beam-dominated modes is not significantly sensitive to the varying axial force, rotor speed or number of attachments expect when the studied modes enter the modal veering regions with other neighbouring modes.
Introducing a Smart Learning Assistant on a MOOC Platform: Enhancing Personalized Learning Experiences

This paper presents the Smart Learning Assistant on the German MOOC platform AI-Campus. It is an AI-powered, dialog-based system that provides learners with personalized learning support and features for enhanced learning. The paper presents the original motivation and planned features of the Smart Learning Assistant as well as its technical implementation, architecture, and status quo. An initial evaluation was conducted using event logs from the dialog system and an integrated feedback loop. It shows that learners start to use the learning assistant and are also engaged in long conversations. Frequent topics of conversation are related to the user's profile, more information about AI-Campus, information about AI, or reporting an error. However, the initial analysis has also shown that users are not yet satisfied with the first assistant and that the list of topics and functions as well as the conversation guidance need to be expanded. The paper concludes with a discussion of future directions for AI development and integration in MOOC platforms, including the need for continued evaluation and refinement of AI-enabled educational technologies. In addition, an overview of future functionalities and opportunities for improvement is provided. Overall, this paper contributes to the understanding of the potential benefits and challenges of AI in education and sheds light on the role of a Smart Learning Assistant in enhancing personalized learning experiences on MOOC platforms.
Array Analysis by Iterative Localised Solutions with Improved Convergence

The method of moments (MoM) is well-suited to radiation and scattering analysis of array structures. Domain decomposition strategies for array analysis, where only successive localised solutions are required to converge to the global solution, can yield high computational efficiency. Recently, such an iterative scheme has been proposed, which is denoted the direct coupling technique (DCT). In this paper, an extension to the DCT scheme is discussed, which is shown to improve its convergence. The scheme is suitable for large-scale parallelisation.
A Cognitive Automation Solution for Responding to Service Desk Requests

We consider the problem of automating responses to service desk requests in the IT department of a company. Employees get support, when issues arise, by sending an email to a Service Desk team. Some of these requests are forwarded to relevant parties, including a software development team for second-tier support. Providing this function takes developers away from their project development tasks and this tends to delay responses and hence problem resolution. We use cognitive automation to determine the nature of email requests and respond to them thus reducing the time developers spend on troubleshooting and support. A Machine Learning classification model, trained on a labelled set of emails, is used to perform this task. We focused on two approaches, Support Vector Machines (SVM) and Multi-Layer Perceptrons (MLP). The SVM model achieved an 86% accuracy score and a 92% micro F1 score. This solution speeds up the process significantly and we show that this translates to a potential time saving of 8 weeks of developer man-hours and 70 weeks of additional user man-hours per year.
Bi-static Visible Light Backscatter Modelling and Tag Localisation

Backscattering visible light communication (VLC) is an emerging technology in the field of indoor communication and the internet-of-things. Here, bi-static topology has gained recent interest in radio-frequency (RF) backscatter communication systems due its longer range and higher scalability. However, the difference between VLC and RF systems raises the demand for new bi-static VLC backscatter models to accurately describe the characteristics. In this paper, we present two novel models designed with specular and diffuse reflectors respectively. We also present the global optimal tag location that maximises signal-to-noise ratio (SNR) for both models. Numerical simulations validated the analytical claims and demonstrate that the specular model outperforms the diffuse model in SNR despite extra effort required for alignment, and the relationship between optimal tag location and the system parameters in the specular model is different compared to the one in the diffuse model.
Artificial Intelligence-Based Chatbotc Model Providing Expert Advice to Potato Farmers in Kenya

This research paper presents a solution to the declining yield in potato farming in Kenya, which has been attributed to a limited supply of quality seeds and access to expert advice. The study used IBM Watson Assistant, an AI-based chatbot framework, and GIS to offer expert advice and link farmers to quality seed producers. The paper first introduces potato farming in Kenya and provides a brief history of conversation agents before delving into the theory behind chatbots, including their classifications and general architecture. The methodology section outlines the five significant steps taken in the research, including data collection, implementation, testing and training, and evaluation. The evaluation phase used performance indicators which are presented in detail. The results demonstrate that this potato farming chatbot model had a score of 97.7% in terms of message coverage, a score of 78.4% in terms of conversation containment, and was 88.05% effective, users were 60% satisfied with the model and the likelihood of use of the model in the future was at 80%. The study concludes that this integrated potato farming chatbot model is a practical solution for farmers to improve their yield, and the recommendations made based on user feedback and expert input could improve the model further. Overall, the study presents a promising location-based approach to addressing food security challenges in Kenya through technology-driven solutions.
Efficient Terahertz Generation via Optical Rectification in Halide Perovskites

THz emission from the all-organic, halide perovskite N-methyl-1,4-diazabicyclo [2.2. 2] octan-1-ium (methyl-DABCO) ammonium iodide (MDNI) has been demonstrated for the first time. The generation mechanism was shown to be optical rectification, and a peak field strength of up to 0.8 kV/cm was achieved, demonstrating the promise of this class of materials as efficient THz emitters.
Development Of Undergraduate Students Course Recommender System

Recommender systems can be utilized to their full potential by suggesting to users the information they need, tailored to their interests and personality. However, researchers in the past have faced a common challenge when developing a recommender system: the “Cold-Start” problem. This issue arises when the system cannot suggest any information to the user due to a lack of data from that user. Individual interests and personalities are strongly influenced by the choices made regarding future careers, including course selection prior to entering university. To address the “Cold-Start” problem, a Course Recommender system has been proposed. The collection of data from users before developing this system is vital to ensure its efficient and effective function. The Agile methodology has been employed in the development of the Course Recommender system, encompassing the discovery of requirements, design to fulfill those requirements, prototyping, and evaluation. Holland's RIASEC personality test is utilized to gather users' interests and personality traits, which then serve as a guide to determine the courses that suit the users best. In a sample of 15 respondents chosen from 105, nine claimed that they were matched with a course of interest, while six did not feel the same way. Hence, the incorporation of Holland's RIASEC personality traits has indeed proven beneficial in selecting courses that best suit the users, thereby enhancing the effectiveness and efficiency of the developed Course Recommender system.
System Modelling for Microwave Photonic Generation Based on Period-One Dynamics in a Laser with Optical Feedback

Microwave photonic (MWP) sensing and measurement are envisioned to be a promising alternate to the conventional pure electronic or optical solutions. By using a semiconductor laser (SL) subjected to external optical feedback (EOF) operating at period-one (P1) dynamic state, an MWP signal can be generated for various applications. To assist the design of such MWP sensing system, the paper proposes to use single hidden layer feedforward networks (SLFN) for modeling a laser with optical feedback to generate P-l based MWP signals. The proposed modeling approach can solve the problem of high computational cost in solving the Lang and Kobayashi (L-K) equations to describe SL with EOF system and quickly determine optimal system parameters according to the system performance desired.
Grid Search Based Hyperparameter Optimization for Machine Learning Based Non-Intrusive Load Monitoring

This paper solves the Non-Intrusive Load Monitoring problem by using two machine learning based models: Xgboost and Recurrent Neural Network. We utilize and develop models using a publicly available dataset. To improve the performance we have implemented hyperparameter optimization using grid search. The numerical simulation results show that proposed Xgboost model outperforms the RNN based model. With the implementation of hyperparameter optimization an improved numerical accuracy is obtained.
Facial Emotion Recognition in Smart Education Systems: A Review

Facial Emotion Recognition (FER) plays a pivotal role in the realm of Smart Education Systems (SES), catering to the growing need for personalized and adaptive learning experiences. This paper presents a comprehensive review of the current state-of-the-art techniques and methodologies employed in FER in general and within the context of SES in particular. The primary objective of this review is to explore the FER technology for the enhancement of educational environments. The review begins by providing an insightful overview of the significance of emotions in education, leveraging deep learning approaches to elucidate the impact of emotional states on cognitive performance and academic outcomes. Subsequently, it explores the key components and working principles of FER systems, encompassing the extraction, representation, and classification of facial emotions. Furthermore, this paper highlights the updated literature in the field of FER applied to SES, ranging from real-time emotion analysis during classroom interactions to adapting instructional content based on learners' emotional states. Furthermore, the review critically analyzes the existing facial emotion recognition datasets utilized in facial emotion recognition research. The dataset is subcategorized in to two parts, images and videos, providing readers with convenience in understanding the various data sources employed in FER model training and evaluation.
Design of a Hybrid Wireless Powered Communication Network with Power Control for Self-Powered Devices

A wireless-powered communication network (WPCN) is a widely studied technology in many fields and applications. These studies use two models regarding the locations of the power transmitter or energy transmitter and the information receiver. The WPCN with colocated energy transmitter and information receiver model suffers from the doubly near-far problem, where a user that is far away from its associated hybrid access point (HAP) harvests less energy but may consume more energy to transmit data than a user nearer to the HAP. While the WPCN with a separated energy transmitter and information receiver model provides better user fairness among users, but it degrades the total system throughput. However, because these models rely on fixed resource allocation, they are unsuitable for achieving user fairness and the system's maximum data rate. In this paper, a hybrid architecture is designed and developed with power control between the HAP and power beacon (PB) to provide the best exchange between the system data rate and the user's fairness. Particularly, the power control factor is optimized dynamically to ensure maximum network performance at all times. This strategy enables maximum utilization of network performance at all times, and is very useful and effective with mobile devices. Where the largest part of the energy is transferred to the devices through HAP, if most devices are close to HAP, and the largest part of the energy is transferred to the devices through PB, if most devices are close to PB. In addition, another scenario from the WPCN separated model is considered when locating the PB at the center of the distance between the HAP and cell edge. This scenario provides a data rate very close to that is provided by the WPCN colocated model and user's fairness close to that is provided by the WPCN separated model.
Texture Segmentation in 2D vs. 3D: Did 3D Developmentally Precede 2D?

Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct surfaces, thus, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this paper, we investigated the relative difficulty of learning to segment textures in 2D vs. 3D configurations. It turns out that learning is faster and more accurate in 3D, very much in line with our expectation. Furthermore, we have shown that the learned ability to segment texture in 3D transfers well into 2D texture segmentation, bolstering our initial hypothesis, and providing a possible explanation for the developmental origin of 2D texture segmentation function in human vision.
Solar Tracking Systems for Photovoltaic Panel Made for Educational Purposes

Increasing the efficiency of photovoltaic panels using solar tracking systems is a current topic. The paper presents two types of solar tracking systems, one based on astronomical data, and the other based on tracking the point of maximum illumination. Both systems are made for educational purposes for the analysis of methods, principles and particularities in order to use them in practical applications.
Switching loss comparison between a tapped- and conventional boost converter

Power semiconductors have made enormous progress in recent years and the performance of power electronic converters has increased enormously. This is a necessary advance, especially for the electrification of vehicles. With WBG semiconductors, now it is possible to increase switching frequencies of the systems significantly and thus also reduce the volume of passive components. However high switching frequencies cause switching losses in the semiconductors, especially in hard-switching topologies, which reduce the performance of the overall system. In this paper, a switching loss reduction for a boost converter is discussed. In addition, high-gain conversion is possible with the topology discussed here. Numerous publications have discussed these issues already but switching usually focus on additional resonant networks that often shift the losses to another component. Topologies that produce a higher turns ratio with a lower duty cycle are also found in numerous designs. Here, both approaches are combined and the novel concept is analyzed by simulation and measurement. A significant reduction of turn-on losses is shown, followed by new challenges.
Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping

Prostate cancer is the most frequently diagnosed cancer in males in developed countries. To identify common prostate cancer susceptibility alleles, we genotyped 211,155 SNPs on a custom Illumina array (iCOGS) in blood DNA from 25,074 prostate cancer cases and 24,272 controls from the international PRACTICAL Consortium. Twenty-three new prostate cancer susceptibility loci were identified at genome-wide significance (P < 5 × 10−8). More than 70 prostate cancer susceptibility loci, explaining ~30% of the familial risk for this disease, have now been identified. On the basis of combined risks conferred by the new and previously known risk loci, the top 1% of the risk distribution has a 4.7-fold higher risk than the average of the population being profiled. These results will facilitate population risk stratification for clinical studies.
Security and Efficiency Enhancement for Split Learning: A Machine Learning based Malicious Clients Detection Approach

Ensuring data privacy and mitigating the potential impact of malicious clients are crucial considerations in split learning frameworks. To improve the security and efficiency of split learning, we propose a novel approach that integrates the hashcash algorithm with a deep neural network based malicious client detection mechanism. Specifically, we first design a supervised deep neural network based algorithm to detect potentially malicious clients by analyzing an array of client attributes, such as IP address, region, and behavioral patterns. This serves as a risk ranking system to evaluate each client, indicating the likelihood of malicious behavior. Subsequently, we design a hashcash based algorithm to verify the clients’ legitimacy and computing capabilities for completing the split learning task, using puzzles with different difficulty levels. By using open source datasets and machine learning models, the simulation results demonstrate the effectiveness of the proposed method in identifying and mitigating the impact of malicious clients while enhancing the overall security and efficiency of split learning scenarios.
Internet of Things Security in Cloud: A Review on Fog Layer Security

Cloud computing in IoT systems enables flexible design with distributed data, infrastructure, and resources accessible from diverse industrial settings. The tremendous rise of the Internet of Things (IoT) has posed numerous issues to the centralized cloud computing architecture which are solved by fog computing. A passive rogue fog node acting as a man-in-the-middle attack poses a significant security vulnerability in the cloud fog layer, compromising data confidentiality and making identification difficult. This survey paper proposes an Intrusion Detection System (IDS) to protect the fog layer from the Man-in-the-Middle Attack (MitM/MITM/MiTM) which is present in the rogue node. Literature review methodology is employed to study various scientific articles providing a comprehensive survey of the existing security and privacy concerns in cloud computing.
Investigation of Fast Frequency Selective Qualitative Terahertz Spectroscopy

We present a new method for ultrafast qualitative terahertz (THz) spectroscopy using a combination of broadband THz pulses and a Schottky energy sensor. By integrating a mechanical chopper composed of frequency selective surfaces in front of the detector, samples are identified by combining unique spectral signatures normalized to each filter transmission. This simple scheme will have a significant impact on the advanced detection of THz solutions for real-world applications and provide a new playground for predictive modelling of the THz signal.