Miracle-dz/UniFAR-FTUs huggingface dataset:
7th International Workshop on ADVANCEs in ICT Infrastructures and Services

The Blockchain technology was initially adopted to implement various cryptocurrencies. Currently, Blockchain is foreseen as a general purpose technology with a huge potential in many areas. Blockchain-based applications have inherent characteristics like authenticity, immutability and consensus. Beyond that, records stored on Blockchain ledger can be accessed any time and from any location. Blockchain has a great potential for managing and maintaining educational records. This paper presents a Blockchain-based Educational Record Repository (BcER) that manages and distributes educational assets for academic and industry professionals. The BcER system allows educational records like e-diplomas and e-certificates to be securely and seamless transferred, shared and distributed by parties.
Combining climatic and genomic data improves range-wide tree height growth prediction in a forest tree

Predicting adaptive-trait variation across species ranges is essential to assess the potential of populations to survive under future environmental conditions. In forest trees, multi-site common gardens have long been the gold standard for separating the genetic and plastic components of trait variation and predicting population responses to new environments. However, relying on common gardens alone limits our ability to extrapolate predictions to populations or sites not included in these logistically expensive and time-consuming experiments. In this study, we aimed to determine whether models that integrate large-scale climatic and genomic data could capture the underlying drivers of tree adaptive-trait variation, and thus improve predictions at large geographical scales. Using a clonal common garden consisting of 34 provenances of maritime pine (523 genotypes and 12,841 trees) planted in five sites under contrasted environments, we compared twelve statistical models to: (i) separate the genetic and plastic components of height growth, a key adaptive trait in forest trees, (ii) identify the relative importance of factors underlying height-growth variation across individuals and populations, and (iii) improve height-growth prediction of unknown observations and provenances. We found that the height-growth plastic component exceeded more than twice the genetic component. The plastic component was likely due to multiple environmental factors, including annual climatic variables, while the genetic component was driven by the confounded effects of past demographic history and provenance adaptation to the climate-of-origin. Distinct gene pools were characterized by different total genetic variance, with broad-sense heritability ranging from 0.104 (95% CIs: 0.065-0.146) to 0.223 (95% CIs: 0.093-0.363), suggesting different potential response to selection along the geographical distribution of maritime pine. When predicting height-growth of new observations, models combining population demographic history, provenance climate-of-origin, and positive-effect height-associated alleles (PEAs; previously identified by GWAs) explained as much variance as models relying directly on the common garden design. Noteworthy, these models explained substantially more variance when predicting height-growth in new provenances, particularly in harsh environments. Predicting quantitative traits of ecological and/or economic importance across species ranges would therefore benefit from integrating ecological and genomic information.
Power Quality Improvement for PV fed NPC Converter for Grid-Tied Applications

This paper presents a single-phase neutral point clamped (NPC) multilevel converter (MLC) configuration for grid-connected photovoltaic (PV) applications. A single-stage control is implemented to feed PV array power to the grid at unity power factor. The direct and quadrature components-based controller is designed to have independent control on active and reactive powers injection. A low computational DC component injection based neutral point voltage (NPV) balance regulator ensures equal charging and discharging of DC-link capacitors. Moreover, the pulse width modulation technique with unipolar pattern is applied at lower switching frequency to adhere with minimum switching losses and power quality standards. The system performance in terms of parameters such as NPV balancing, converter voltage harmonics, the grid current harmonics, and unity power factor operation is analysed at variable irradiances. Simulation results are obtained using MATLAB/Simulink are validated using OPALRT test bench.
A Deep Learning Method for Reduction of Microbubble Accumulation Time in Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) enables unprecedented subwavelength spatial resolution by localizing the ultrasound contrast microbubbles (MBs). In conventional ULM methods, low-concentration MB solutions are used to ensure that the MBs are sufficiently sparse to enable localization of individual MB. This leads to a long data-acquisition time (i.e., microbubble-accumulation time) to accumulate enough MBs events using plane wave (PW) imaging. Considering the continuity of the microvessels and redundancy of MB tracks, in this study, we present a method that leverages deep neural network (DNN) to predict microvasculature using significantly reduced data-acquisition time and achieve quality comparable to that obtained using full data-acquisition time. Phantom experiments show that the proposed method significantly improves the density of localized MBs compared with the conventional method. Besides, in vivo experiments demonstrate that the proposed method is capable of reconstructing ULM images using reduced data-acquisition time with small difference, compared with the conventional method.
Design of Line Start Linear Permanent Magnet Synchronous Motor for Electromagnetic Catapult

The replacement of the conventionally used steam catapults with electromagnetic system in the launch application is proved to be more energy efficient and safe to handle. Electromagnetic launch system (EMLS) comprises many subsystems, of which, a linear electric motor (LEM) is a critical one. Linear electric motor provides additional thrust to launch for the shortest runway. Linear induction motor (LIM) is well preferred in EMLS since last two decades. Linear permanent magnet synchronous motor (LPMSM) has been selected to study due to many advantages they offer inspite of few disadvantages. Line start LPMSM is a induction start and synchronous run type motor. It is proved to be more efficient over induction motor in line start applications. Design and analysis of line start linear permanent magnet synchronous motor (LSLPMSM) is the main objectives of this work. The finite element analysis (FEA) of both LIM and LSLPMSM machines is carried out using ANSYS Electronic desktop (MAXWELL 2D). The comparison of the operational and equivalent circuit parameters of the LSLPMSM and LIM designs are also presented in this paper.
Analysis of Impedance Matching Technique for Novel Supercapacitor Assisted PV Systems

To extract the maximum power from a photovoltaic (PV) system, the maximum power point (MPP) of the PV array must be tracked continuously. A directly coupled load with the PV array does not track the MPP of the PV array because the load has a constant resistive value. Therefore, to track the MPP of the PV array, a technique called impedance matching is used. This is done by continuously matching the load impedance to the instantaneous impedance of the PV array. Switch mode DC-DC converters are widely used for this purpose which also helps to interface the DC output of the PV array with power distribution systems in order to deliver the power to the consumer end. However, the efficiency of these converters lies around 90% which degrade the end to end efficiency of the PV system. In this context, novel supercapacitor (SC) assisted PV systems have been introduced, which were able to enhance the end to end efficiency of PV systems. However, existing impedance matching technique is no longer valid for SC assisted PV systems. Therefore, still it is a challenge for these systems to track MPP of the PV array continuously while in operation. This paper presents a study on impedance matching technique for the novel SC assisted PV systems. Previously, it has been experimentally shown that a series connected SC bank between PV array and DC-DC buck converter of a PV system could deliver higher end to end efficiency than typical systems. This study validates the impedance matching technique for SC assisted PV systems by connecting a SC bank in series with buck, boost, and buck-boost converters. Simulation results indicate that this can be achieved by carefully designing the system with given parameters.
Improved Sensorless Direct Torque and Flux Control of IPMSM based on On-line Parameter Estimation

This paper presents an improved direct torque and flux control of a sensorless control system with Interior Permanent Magnet Synchronous Motor (IPMSM). The estimation of torque and flux is enhanced by the real-time update of machine parameters including stator resistances, d- and q- axis inductance, permanent magnet flux linkage. The estimation of rotor speed and position is based on the measurement of current slopes at one zero- and two active-voltage vectors during every PWM cycle. The d- and q- axis inductances are estimated at PWM frequency based on those current derivatives and the DC bus voltage. The stator resistance and permanent magnet flux linkage which vary slowly due to temperature and aging factors are estimated based on the use of recursive least square technique. The numerical simulation and experimental study of the proposed methods show the effectiveness of the proposed parameter identification method and the improvement of estimated torque and flux of the sensorless control system.
Smartmedia: Locally & Contextually-Adapted Streaming Media

Streaming media is gaining popularity, with numerous new services for video on demand and live broadcast. These services stream requested media content to user devices like smart TVs, personal computers and smartphones. Many of these devices are mobile devices, yet streaming media services do not adapt the delivered content to the context of the request, e.g., the location of the user, the time of the request or who are the people near the viewer. In this paper we illustrate how geofencing and geoblocking can be combined with adaptive streaming media to create a new technology of contextually-adapted streaming media. The new technology has the potential to create a new type of experience for streaming media by applying real-time modification of streamed content according to the viewer and the context, e.g., modify inappropriate content when the media is played in public places, change the length of a video played by a train passenger according to a train schedule, etc. The suggested technology can also be used for preventing password sharing in a non-intrusive way and for hyperlocal geoblocking, to facilitate copyright protection. We discuss the vision of modifying streaming media in real time based on the context, elaborate on some of the challenges in implementing this vision, and present novel applications of this new technology.
Adaptive Software-Configurable System Development to Receive, Transmit and Process Information

An adaptive software-configurable system to receive, transmit and process information that provides flexible and resistant to various interfering factors operation for multipurpose robotic complexes (RC) has been developed. Moreover, such RC are capable to work independently or as part of a group in various natural conditions (for example, on land in different weather, under water, etc.). This system includes the following components: open optical communication system; radio communication system, based on OFDM technology, resistant to concentrated power spectral density interferences and video processing system under the influence of interfering factors (rain, snow, fog, etc.). Adaptive change in the code rate of correction codes makes it possible to increase communication range over underwater wireless optical communication channel by more than 2 times. For the radio communication system based on OFDM technology, the placement of reserved tones in interference band allows combating spectrum-centered interference using classical methods, with a gain of up to 10% relative to full bandwidth. The proposed algorithm to process video images under the influence of interfering factors provides 3.1% … 14.8% lower normalized level of errors of the second kind, and the algorithm to increase contrast improves quality standards of Quality Edges (QE) up to 2 times compared to the approaches known.
An exact sinΘ formula for matrix perturbation analysis and its applications

In this paper, we establish an exact expression of the sin$\Theta$ angle for the singular subspace perturbation. This expression clearly characterizes the linear dependence of the sin$\Theta$ angle on the perturbation matrix. It serves as a tool that allows us to study a set of related problems in a more systematical way. In particular, we derive a useful perturbation bound for the Principal Component Analysis, which seems to be missing in the literature. We derive the perturbation bound for the singular value thresholding operator applied to full-rank matrices. We derive an improved $\ell_{2}\rightarrow \ell_{\infty}$ bound on singular vectors, which matches the best-known results for eigenvectors derived under more restricted conditions. We also derive a one-sided version of the classical sin$\Theta$ theorem.
Design of intrinsically disordered proteins that undergo phase transitions with lower critical solution temperatures

Many naturally occurring elastomers are intrinsically disordered proteins (IDPs) built up of repeating units and they can demonstrate two types of thermoresponsive phase behavior. Systems characterized by lower critical solution temperatures (LCST) undergo phase separation above the LCST whereas systems characterized by upper critical solution temperatures (UCST) undergo phase separation below the UCST. There is congruence between thermoresponsive coil-globule transitions and phase behavior. Specifically, the theta temperatures above or below which the IDPs transition from coils to globules serve as useful proxies for the LCST / UCST values. This implies that one can design sequences with desired values for the theta temperature with either increasing (UCST) or decreasing radii of gyration (LCST) above the theta temperature. Here, we show that the Monte Carlo simulations performed in the so-called intrinsic solvation (IS) limit version of the temperature-dependent ABSINTH implicit solvation model, yields a robust heuristic for discriminating between sequences with known LCST versus UCST phase behavior. Accordingly, we use this heuristic in a supervised approach, integrate it with a genetic algorithm, combine this with IS limit simulations, and show how novel sequences can be designed that have LCST phase behavior. These calculations are aided by direct estimates of temperature dependent free energies of solvation for model compounds that are derived using the polarizable AMOEBA forcefield. To demonstrate the validity of our designs, we calculate coil-globule transition profiles using the full ABSINTH model and combine these with the Gaussian Cluster Theory to show that the designed IDPs do show LCST phase behavior.
Large Dimension Parameterization with Convolutional Variational Autoencoder: An Application in the History Matching of Channelized Geological Facies Models

History matching is the problem of assimilating dynamic data in numerical models of oil and gas reservoirs. Among the methods available in the literature, the iterative ensemble smothers are often used in practice. However, these methods assume that all variables are Gaussian, which limits their application in a problem where the objective is to update the distribution of rock types (facies) in the model. In fact, updating models of geological facies using dynamic data is still an open issue in the oil industry. The problem relies on the development of a parametrical model able to preserve the geological realism of the models. In this context, parameterization techniques based on deep learning, such as convolutional variational autoencoders network (CVAE), have shown promising results in this area when combined with ensemble smothers. Nevertheless, these types of networks present difficulties of scalability for large-sized reservoir models, because as the input dimension increases, the number of network parameters increases exponentially. This work addresses this problem by introducing two new CVAE-based network architectures that can be used for modeling large-scale reservoir models. The first proposed network incorporates the “depthwise separable convolution” in its design, while the second introduces the “inception module”. Results show a considerable reduction of trainable parameters for the first network, while, for the second one, the number becomes invariant to the input dimension.
Virtual Transformer Operation of Solid State Transformer (SST)

This paper proposes a virtual transformer operation of a three-stage solid state transformer (SST) used in the distribution network. By tracking the AC voltage at medium voltage AC (MVAC) input side as the reference of low voltage AC (LVAC) output stage, voltage relationship between AC buses can be established, and the mutual voltage support can be formed through the SST. The employed control method can make the SST operate as an ideal AC transformer which has a constant transformation ratio. Circuit parameters design and control parameters calculation are given in this work. Simulation results based on this SST topology and control method prove that this virtual transformer operation based on a three-stage SST is feasible through the software Matlab/Simulink.
Modular Multilevel Converter and Cycloconverter Based Machine Drive Systems

The modular multilevel converter (MMC) becomes attractive in the medium- and high-power application with high modularity. In this paper, the MMC and cycloconverter based machine drive system is proposed. The six-phase MMC is used as the grid-side converter, which produces six-phase medium-frequency ac voltage for the machine side converter. The six-phase to three-phase cycloconverter is used as the machine-side converter, which converts the medium-frequency ac voltage to the low-frequency ac voltage to drive the machine. In addition, the corresponding control for the grid-side converter and the machine-side converter are also proposed. The proposed MMC and cycloconverter based machine drive system can effectively realize the control for the high-voltage, high-power and low-speed machine. What is more, the SM capacitance is significantly reduced owing to the medium-frequency ac output voltage of the MMC. The simulation studies of the proposed MMC and cycloconverter based machine drive system and control are conducted with a detailed PSCAD/EMTDC model, and the results show their effectiveness.
Intercity Simulation of Human Mobility at Rare Events via Reinforcement Learning

Agent-based simulations, combined with large scale mobility data, have been an effective method for understanding urban scale human dynamics. However, collecting such large scale human mobility datasets are especially difficult during rare events (e.g., natural disasters), reducing the performance of agent-based simulations. To tackle this problem, we develop an agent-based model that can simulate urban dynamics during rare events by learning from other cities using inverse reinforcement learning. More specifically, in our framework, agents imitate real human-beings' travel behavior from areas where rare events have occurred in the past (source area) and produce synthetic people movement in different cities where such rare events have never occurred (target area). Our framework contains three main stages: 1) recovering the reward function, where the people's travel patterns and preferences are learned from the source areas; 2) transferring the model of the source area to the target areas; 3) simulating the people movement based on learned model in the target area. We apply our approach in various cities for both normal and rare situations using real-world GPS data collected from more than 1 million people in Japan, and show higher simulation performance than previous models.
384 Channel Modular Ultrasonic CMUT Probe for Rapid Prototyping

This paper presents the design and fabrication of a novel 384 channel modular ultrasonic probe for row-column addressed capacitive micromachined ultrasound transducers. The nose-piece and front-electronics can be freely interchanged in situ, allowing different combinations of transducer arrays and front-end architectures to be characterized and compared, without fabricating an entire probe for each device under test. The probe is designed to facilitate rapid prototyping and full acoustic testing of $192+192$ RCA arrays at low cost, and within days after the arrays have been fabricated, but also supports conventional matrix arrays and linear arrays with up to 384 channels with only minor changes.
Optimal Power Allocation for Non-Linear EH Cooperative Network with Multiple Eavesdroppers

In this paper, the secure information transmission of an energy harvesting (EH) cooperative network is considered, in which multiple eavesdroppers can overhear the forwarded relay signal. To prevent multiple eavesdroppers from decoding confidential signals, the destination transmits the jamming signal while the source transmits the confidential signal to the relay. Simultaneously, the relay can harvest more energy from source signals and destination jamming by the power splitting (PS) protocol, which is depicted as a non-linear EH process. Considering the imperfect self-interference cancellation (SIC) at the destination, secrecy rate maximization optimization is formulated to optimize the transmission power of source and destination. However, the formulated optimization is non-convex. To solve this problem, an iterative algorithm is proposed based on the difference of convex functions (DC) programming, which can transform non-convex optimization problems into successive approximate convex problems. Simulation results show that the proposed algorithm has a quick convergence rate, and the proposed scheme leads to a higher achievable secrecy rate.
A Duty Cycle Based Finite-Set Model Predictive Direct Power Control for BLDC Motor Drives

This paper presents a duty cycle based finite-set model predictive direct power control (MPDPC) for Brushless DC (BLDC) motor drives. The conventional MPDPC achieves good dynamic and steady state performance by selecting an appropriate voltage vector. However, there is only one voltage vector in each control period, and hence the ideal performance cannot be achieved due to the limited number of voltage vectors. In this paper the concept of duty cycle is employed to MPDPC for BLDC motor drives. This novel method allocates a nonzero voltage vector in a fraction of one control period and a zero vector in the rest of the period. The duration of the nonzero voltage vector is determined by minimizing the errors in instantaneous complex power to optimize the reactive power flowing into the motor, and therefore the current ripples and torque ripples are further reduced compared to the conventional method. The effectiveness of the proposed method is validated by simulations.