Miracle-dz/UniFAR-FTUs huggingface dataset:
GaN FET-Based, Scalable DCDC Converter Development for Space and Stratospheric Applications

The GRASSHOPPER (GaN-based Resonant Aerospace Switching-mode Supply, for High Output Power with Promising Efficiency and Reliability) has the potential to bring highly efficient power conversion into space applications (e.g. from MPPTs to regulated buses) in a robust, failure-tolerant way. Since C3S has extensive experience in traditional, Metal Oxide Semiconductor Field Effect Transistor (MOSFET)-based power supplies in both the Nanosat and the High-ReI industry, we have looked for a technology to increase both power and efficiency levels in our converters. In the GRASSHOPPER project, we implemented a 750W Switching-Mode Power Supply with 2 parallel Phase-Shifted Full Bridge cores to achieve these goals, and to bring the advantages of GaN FETs into both space and stratospheric applications. There is an example of a similar converter being designed previously [1]. Our design tackles an extended input voltage range and power needs with scalability and a higher efficiency target in mind, as requested by the proj ect needs incorporating this converter.
Transitioning to Chisel in University Education: Experiences and Lessons Learned

Hardware description languages are essential for specifying, designing, and verifying digital systems. VHDL and Verilog have been the predominant hardware description languages, but they present educational challenges due to their steep learning curve and low-level nature. Conversely, Chisel, a language embedded in Scala, offers a high-level, software-like approach to hardware design. This paper presents our experience at the Technical University of Denmark (DTU) transitioning from VHDL to Chisel for selected courses and projects. We discuss the challenges faced in using VHDL and Verilog as entry-level languages, and we highlight the benefits and lessons learned from the transition to Chisel, including improved accessibility and efficiency in learning hardware design. This paper contributes to the ongoing discussion on languages for hardware description in education and provides insights into the advantages of adopting Chisel as an alternative language. The transition resulted in overwhelmingly positive outcomes, with improved student understanding, productivity, satisfaction, and performance.
Increasing the Motivation to Train Through Haptic Social Interaction – Pilot study

Motivation is crucial in stroke rehabilitation, as it enhances patient engagement, adherence, and recovery. Robots can be employed to improve motivation through multiplayer rehabilitation games, which allow patients to collaborate and interact in a virtual environment through multimodal sensory cues. This social interaction can provide social support and increase motivation, resulting in better therapy engagement. A hand rehabilitation robot (PLUTO) was used to investigate the potential of social interaction to implement haptic multiplayer games. Twelve unimpaired participants (6 dyads) played in solo, collaborative, and competitive game modes. Surprisingly, no difference was found in self-reported engagement, tension, or competence between solo and multiplayer games. However, the IMI scale indicated that engagement for multiplayer games was rated higher than for solo games. The collaborative game was preferred by 10 out of 12 participants, highlighting its potential for promoting behavioural involvement and engagement. This study indicates that using PLUTO with multiplayer game modes can enhance therapy engagement. This can potentially improve rehabilitation outcomes if translated to the patient population.
Turbulence development assessment in a LES simulation

: This proposal aims to analyze conditions that allow the development of turbulence in a LES simulation with uniform velocity and temperature initial and boundary conditions first and with conditions obtained from WRF simulations ultimately. According to recent studies and considering the underlined governing equations of the simulation, a transition zone should occur over which resolved-scale turbulence is generated within the flow. This transition zone is referred to as fetch which consists of the distance into the LES domain from the inflow boundary over which the turbulence motions approaches an equilibrium. Here, we assess the different fetches that arise and the characteristics turbulent motion that develops under different conditions and configurations.
Wireless Intrusion Detection Using Shallow Neural Network Models

Wireless systems, due to their nature lack the security features against various attacks such as jamming and eavesdropping. In order to successfully detect such attacks that occur in a wireless network, Artificial Intelligence (AI) models which continuously monitor wireless statistic records are used. On this context, this paper proposes an artificial neural networks based Wireless Intrusion Detection System (WIDS) for 802.11 (Wi-Fi) wireless networks, where the model is trained with public Aegean Wi-Fi Intrusion Dataset (AWID-2).
Visually-Assisted Decomposition of Monoliths to Microservices

The architectural style of microservices has received much attention from both business and academia and converting a monolithic application into a microservice-based one has become a regular practice. However, companies struggle with migrating their existing monolithic applications to microservices and software engineers frequently face challenges due to a lack of awareness of alternative migration methodologies, making the migration process even harder. In this paper, we present a framework to help software engineers during the migration process by addressing gaps in understanding various migration tools and approaches, allowing for easy comparison between multiple options. Our tool combines multiple existing approaches into one platform, allowing a comprehensive visualization of migration proposals and comparing different options offered by already existing approaches.
Low Complexity Online RL Enabled UAV Trajectory Planning Considering Connectivity and Obstacle Avoidance Constraints

In recent years, we have witnessed a significant proliferation of UAV s to service variety of verticals, including last-mile delivery, medical assistance, crop monitoring etc. The true utility of these UAV s lies in beyond-line-of-sight (BLoS) operations which require wide-area connectivity. The increased densification of the cellular networks under 5G networks is ideally suited for this purpose. However, traditional cellular networks are not optimised for servicing aerial clients. In this paper, we propose a novel approach to improve connectivity for delivery UAV s with the ground base stations using low complexity online reinforcement learning (RL) and multiple Q-learning algorithms. We demonstrate that by training multiple Q-learning models, we can accurately predict optimal trajectory for UAV s flight. The optimality is in the sense of its shortest flight path while ensuring continuity in connectivity with the ground base station. Our modeling explicitly incorporates dynamic changes in the environment which may not be observed during the previous missions by UAV. We implement our proposed methodology using OpenAI Gym and demonstrate that the proposed approach allows perpetual connectivity for UAV s while generating obstacle-free flight trajectories.
A mm-Wave Differential-to-Quadrature Frequency Tripler with Automatic Locking and Quadrature Correction

This paper presents an injection-locked frequency tripler for 28-GHz direct-conversion transceivers, which takes a differential input and outputs a quadrature signal. A feedback system is added to ensure that lock occurs and to keep the quadrature error below 1° across operating frequencies and process corners. Simulations show that the tripler can cover the entire 24-30 GHz band across all corners, while consuming 32 mW from a 0.8 V supply. The circuit is designed and simulated in a 22nm FD-SOI CMOS process and occupies 0.2 mm2 active chip area.
Comparing Foraging Behavior Across Code Hosting and Q&A Platforms Through a Gender Lens

This study compares the information foraging behavior of developers on two prominent platforms, StackOverflow and GitHub, which are widely used for code hosting and question and answer purposes. Understanding how developers seek and retrieve information is crucial for designing effective interfaces. In a gender and expertise-balanced study involving 12 developers, we utilized Information Foraging Theory to analyze their foraging behavior. Our findings revealed contrasting patterns, with women spending 30% more time and utilizing 21.24% more cues on GitHub, while men utilized 55% more time and 19.7% more cues on StackOverflow. These insights have significant implications for optimizing website design and information presentation to enhance the efficiency and effectiveness of developers' information seeking processes.
Acoustic Devices (PAW, SAW, and BAW) using Wafer Bonding Technology

By combining different materials with piezoelectric thin plates, acoustic wave devices with novel characteristics have been realized. The bonding technique with high bonding strength enabled the precise polishing of piezoelectric substrates into ultra-thin plates. By bonding ultra-thin piezoelectric thin plates and different substrates, we have developed the following acoustic wave devices with various merits: (1) A high frequency 5.8 GHz A1 mode Lamb wave resonator and an ultrawide 0-th shear horizontal (SH0) mode plate wave devices using LiNbO3 (LN) thin plates. (2) A solidly mounted (SM) type SH surface acoustic wave (SAW) resonator with an ultrawide and high impedance ratio using LN thin film, multi-layer films, and a glass substrate. (3) Hetero Acoustic Layer (HAL) SAW resonator combining a LiTaO3 (LT) thin plate and a quartz substrate, exhibiting zero temperature coefficient of frequency (TCF), high Q, and spurious-free characteristics. (4) various SM type bulk acoustic wave resonators (SM-BAWRs) with low TCF, wideband, or high-frequency using LT and LN. (5) SM-BAWR exciting an overtone frequency of 9.5 GHz, which is three times of the fundamental frequency, by combining LN, an average acoustic film reduced to 1/5 of its conventional thickness, and a support substrate.
Critical Nodes Evaluation in Multiplex Heterogeneous Network Based on Gravity Model

Identifying critical nodes in networks is one of the important ways to explore the structural and dynamic characteristics of networks. The existing evaluation of key nodes in multiple heterogeneous networks often ignores the importance of different relationships. Based on this issue, this study proposes a method for evaluating critical nodes in multiplex heterogeneous networks, called the Gravity Centrality measure based on the Network Embedding (NEGC). The method calculates the importance of different link relationships and constructs a weighted network based on the multiplex heterogeneous networks. It uses graph neural network to extract node features and obtain embedding vectors. Based on the embedding vectors, it calculates the effective distance between nodes and improves the gravity model with the effective distance to complete the evaluation of critical nodes. Experimental results on two real network datasets using the Susceptible Infected Recovered(SIR) model show that this method can effectively and comprehensively evaluate critical nodes in multiplex heterogeneous networks.
Flexible Base Power and Isolation Unit for Robotic Payloads

This paper presents the proposed system design of a flexible Base Power and Isolation Unit (BPIU) for robotic payloads. A robotic arm as a payload can be used to assemble, manipulate, repair or service existing systems remotely. The BPIU ensures the safe and reliable operation of the overall robotic payload. This includes the requirement to operate in harsh environment, including wide temperature ranges and the exposure to radiation. Based on the power interface and power demand of the robotic system as well as on the satellite bus power distribution and levels, a flexible BPIU design is required to harmonize both systems. The Compliant Assistance and Exploration SpAce Robot (CEASAR) arm, developed by the Institute of Robotics and Mechatronics at the German Aerospace Center serves as an example of a robotic payload in this paper. CAESAR is used within the European Robotic Orbital Support Services In Orbit Demonstrator (EROSS IOD) mission. The power, grounding and Electro-Magnetic Compliance (EMC) requirements of the robotic arm and the spacecraft have to be fulfilled by the BPIU design. Possible solutions for the individual BPIU subsystems are discussed based on the requirements of the spacecraft system and of the robotic payload.
NeurOPar, A Neural Network-Driven EDP Optimization Strategy for Parallel Workloads

The pursuit of energy efficiency has been driving the development of techniques to optimize hardware resource usage in high-performance computing (HPC) servers. On multicore architectures, thread-level parallelism (TLP) exploitation, dynamic voltage and frequency scaling (DVFS), and uncore frequency scaling (UFS) are three popular methods applied to improve the trade-off between performance and energy consumption, represented by the energy-delay product (EDP). However, the complexity of selecting the optimal configuration (TLP degree, DVFS, and UFS) for each application poses a challenge to software developers and end-users due to the massive number of possible configurations. To tackle this challenge, we propose NeurOpar, an optimization strategy for parallel workloads driven by an artificial neural network (ANN). It uses representative hardware and software metrics to build and train an ANN model that predicts combinations of thread count and core/uncore frequency levels that provide optimal EDP results. Through experiments on four multicore processors using twenty-five applications, we demonstrate that NeurOPar predicts combinations that yield EDP values close to the best ones achieved by an exhaustive search and improve the overall EDP by 42% compared to the default execution of HPC applications. We also show that NeurOPar can enhance the execution of parallel applications without incurring the performance and energy penalties associated with online methods by comparing it with two state-of-the-art strategies.
Simulation Model for Brushless DC Motor Loads Controlling

Brushless DC (BLDC) electric motors are used worldwide for their efficiency, controllability, reduced power operability, high durability, and low noise. Replicating the real application set up in the simulation world, BLDC motors can be implemented in modern Spice programs in order to improve verification and testing processes. This paper presents an original behavioral modeling method to control three MOSFET half bridges used to drive a BLDC motor load based on feedback loop provided by the motor shaft angle. The PSpice motor load is implemented using real BLDC motor parameters, shaft angle and shaft speed being the reference motor parameters in order to monitor if a complete rotation is performed. The simulation results are compared with the real measurements and demonstrate the proof of the behavioral modeling concept on this specific automotive application.
Feasibility of Prediction of Student's Characteristics Using Texts of Essays Written During a Fully Online Course

The possibility of predicting anticipated scores representing student's characteristics from their essay texts using a machine learning technique was examined. Student's characteristics include critical thinking disposition ability, disaster-prevention conscientiousness, and personality. First, the potential for predicting essay assessment scores from essay texts using machine learning with each set of essay reports or essay comments was confirmed. However, prediction performance of one other set of essays and other scores of student's characteristics was insufficient. When the training procedure was re-designed using both sets of essays, performance for some scores improved. The limited performance was examined and further improvement procedure was discussed.
CSRR-based Low Power Microwave Heater for PCR Applications

In this study, a microwave (MW) heater design conceived around a Complementary Split Ring Resonator (CSRR) for Polymerase Chain Reaction (PCR) applications is presented. The placement of the sample in the gaps of the CSRR where the strong electric field is located and the novel method of exceptional impedance matching utilizing a phase shifter, result in efficient MW heating at 2.227 GHz. With a heating power of 2.7 W and 1 W, the sample with a volume of 4 $\mu \mathbf{1}$ was heated at rates of $21^{\circ}\mathrm{C}/\mathrm{s}$ and $7.6^{\circ}\mathrm{C}/\mathrm{s}$, respectively, demonstrating the rapid thermal cycling required for the PCR process.
Challenging the lambda-Half Element Pitch Limit by a Sparse Least-Squares Wrapping Inversion in k-Space

In ultrasound imaging, a transducer element pitch that is larger than half the minimum wavelength is prone to the occurrence of grating lobe artifacts. Most linear arrays fail to fulfill this limit by far, which strongly restricts steering capabilities. On the other hand, increasing the pitch even further has the potential to greatly reduce the required channel data throughput. We propose that the lambda-half limit can be circumvented by a dedicated compounding approach that leverages redundancies in k-space coverage for different transmit angles to correct for ambiguities in the receive angles. The performance of this approach was evaluated in two simulation studies. In a first "inverse crime" study, the contrast of an anechoic inclusion was increased by 11.6 dB along with a gCNR improvement from 0.94 to 0.99. For a public reference dataset (PICMUS), an artifact suppression of up to 18.1 dB was shown, while the resolution was preserved.
Combining Delay-Multiply-and-Sum and Covariance Matrix-based Weighting for Synthetic Aperture Ultrasound Imaging

The image quality of synthetic aperture (SA) ultra-sound imaging is often degraded due to low electrical signal-to-noise ratio and clutter and noise, resulting in low contrast and lateral resolution. The delay-multiply-and-sum (DMAS) beam-former has been previously studied in SA imaging to improve image quality in terms of resolution and contrast. However, the resolution and contrast performances obtained by DMAS is not well enough and the speckle variation is noticeable. Therefore, we hypothesize that an appropriate adaptive weighting approach for DMAS can help address this issue. This study combines DMAS with a covariance matrix-based adaptive weighting approach to further improve the imaging performance, and simultaneously enhancing the cyst detectability in SA ultrasound imaging. Results show that the proposed method effectively improves the resolution, contrast, and speckle smoothness of DMAS image in SA imaging.
Decoding Upper-Limb Movement Intention Through Adaptive Dynamic Movement Primitives: A Proof-of-Concept Study with a Shoulder-Elbow Exoskeleton

This work presents an intention decoding algorithm that can be used to control a 4 degrees-of-freedom shoulder-elbow exoskeleton in reaching tasks. The algorithm was designed to assist the movement of users with upper-limb impairments who can initiate the movement by themselves. It relies on the observation of the initial part of the user's movement through joint angle measures and aims to estimate in real-time the phase of the movement and predict the goal position of the hand in the reaching task. The algorithm is based on adaptive Dynamic Movement Primitives and Gaussian Mixture Models. The performance of the algorithm was verified in robot-assisted planar reaching movements performed by one healthy subject wearing the exoskeleton. Tests included movements of different amplitudes and orientations. Results showed that the algorithm could predict the hand's final position with an error lower than 5 cm after 0.25 s from the movement onset, and that the final position reached during the tests was on average less than 4 cm far from the target position. Finally, the effects of the assistance were observed in a reduction of the activation of the Biceps Brachii and of the time to execute the reaching tasks.
Data-Driven Determination of the Governing Equations of the Continuously Stirred Tank Reactor

This paper presents a data-driven approach for identifying the governing equations or ODEs of a continuously stirred tank reactor (CSTR) system. The paper employs the sparse identification of nonlinear dynamics (SINDy) algorithm, a popular and versatile method used for discovering nonlinear dynamical system models from data. The SINDy-PI (parallel, implicit) framework, a robust variation of the SINDy method, is used to find the implicit dynamics and rational nonlinearities of the CSTR for both Monod and Haldane types of specific growth rates. The simulation results demonstrate the accuracy of the method in inferring the ODEs of the CSTRs using limited and noisy data. The proposed method can be used to improve our understanding of complex systems and inform the design of control strategies.
Use of Periodic Pillar Array as Acoustic Coupler for AlN-Based Longitudinally-Coupled Resonator Filters

This paper discusses applicability of the periodic pillar array as the coupler for AlN-based longitudinally coupled resonator filters (LCRF). First, periodic pillar array composed of SiO2 and air is applied to the coupled layer of AlN-based LCRF, and its impact to the coupling strength is investigated. It is shown that the coupling strength increases with the duty ratio of SiO2 monotonically and changed parabolically with the thickness of the coupling layer. Besides, the critical coupling can be achieved for a wide range when the thickness of the coupler and duty ratio of SiO2 are set properly. Finally, 4.78% of -3 dB bandwidth of filter response can be obtained when the coupling strength is critically coupled.
A United Spatial-Angular Adaptive Scaling Wiener Postfilter based Beamformer for Improved Ultrafast Power Doppler Imaging

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio and poor image quality. This study proposed an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener postfilter to obtain high-quality microvessel images. The signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. We used in vivo experimental datasets to evaluate the imaging performance of the proposed method. Results show that the proposed method significantly improves the imaging quality of uPDI compared to delay-and-sum (DAS) and coherence factor (CF) beamformers. Overall, the proposed method has the potential to become a reliable microvascular imaging technique.
Cortical Bone Thickness Assessment from Multi-frequency Ultrasound RF Data using a Convolutional Architecture with Multi-head Attention

Cortical bone thickness is an important predictor of bone strength and fracture risk, and accurate classification is crucial for the diagnosis and treatment of osteoporosis. The thinning of the cortical layer, indicative of compromised bone microarchitecture due to imbalanced formation and loss, underscores its significance. Nonetheless, quantifying bone thickness is challenging due to the diverse skeletal sites and subject variations in bone structure and properties.A potential solution lies in multi-frequency ultrasound assessment of cortical bone, enabling comprehensive property characterization across varying wavelengths and penetration depths. This research strives to establish a robust methodology for evaluating cortical bone thickness by leveraging a convolutional model with an attention mechanism to analyse multi-frequency ultrasound data.