Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Speed Nonlinear MPC with Long Prediction Horizon for Interleaved Switching AC/DC-Converters

The control of high-speed AC/DC-converters is still a very demanding task. This paper presents an online optimizing nonlinear model predictive control (MPC) strategy, which allows to reach long prediction horizons in the range of 100 times the sampling time. The MPC is complemented by estimation strategies for the grid phase voltages and the load current. To achieve a short calculation time of the MPC in the range of a few $\mu$s, the MPC is tailored to the efficient implementation on an FPGA. This is achieved by using the parallel computation capabilities of FPGAs and pipelining methods, and by avoiding time-and resource-demanding operations like trigonometric functions. Compared to other MPC methods discussed in the literature, the proposed MPC strategy allows for significantly longer prediction horizons. The performance and the robustness of the control strategy is evaluated by measurements on a hardware-in-the-loop test bench.
Comparison of Two and Three-Level DC-AC Converters for a 100 kW Battery Energy Storage System

This paper discusses a qualitative comparison between Two and Three-Level Voltage Source Converter (VSC) topologies for battery energy storage applications. Three-Level Neutral Point Clamped (NPC) and T-Type circuit topologies are benchmarked versus the state-of-art Two-Level VSC in terms of efficiency and power density considering a 100 kW system. Analytical equations for determining the power losses in the semiconductor modules are given, and the procedure for designing the output LCL filter and the DC-link capacitors is described. The analysis, based on off-the-shelf circuit components, shows that the Three-Level topologies perform better than the Two-Level one in both considered metrics, mainly due to their lower switching losses that allow operating at higher switching frequency without significantly degrading the system efficiency, and, at the same time, increasing the system power density. Additionally, it is found that the T-Type topology shows better performances than the NPC topology at full and high partial loads, being then more suitable for applications that require most of the operation at maximum power.
Encoding Rich Frequencies for Classification of Stroke Patients EEG Signals

The stroke, which is a sudden cut in the blood supply in the brain, has become a severe phenomenon. It has affected around 15 million people annually worldwide. Methods of stroke discovery and monitoring the patient’s recovery are a long process, ranging from the analysis of medical images to frequent reporting of the patients for progress assessment. In this paper, we aim to process stroke patient EEG signals by a deep learning approach, and classify a given EEG signal into stroke/non-stroke. In particular, our model consists of several sub-modules which convert and re-model widely used signal processing techniques such as the Fast Fourier Transform (FFT), Convolution in the Frequency Domain and the Inverse Fast Fourier Transform (IFFT) to learnable and differentiable functions that are completely learned and optimized in an end-to-end manner by neural networks. We demonstrate that our model outperforms several baselines by learning rich frequency features through our proposed model. The proposed model could potentially assist a medical doctor in analyzing stroke brain images with high accuracy rates. It can also be useful for rehabilitation centers to monitor the progress of stroke patients.
Malware Detection in Android Systems with Traditional Machine Learning Models: A Survey

Due to the increased number of mobile devices, they are integrated in every dimension of our daily life. To execute some sophisticated programs, a capable operating must be set up on them. Undoubtedly, Android is the most popular mobile operating system in the world. IT is extensively used both in smartphones and tablets with an open source manner which is distributed with Apache License. Therefore, many mobile application developers focused on these devices and implement their products. In recent years, the popularity of Android devices makes it a desirable target for malicious attackers. Especially sophisticated attackers focused on the implementation of Android malware which can acquire and/or utilize some personal and sensitive data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. In this work, we aimed to analyze the algorithms which are used in malware detection and making a comparative analysis of the literature. With this study, it is intended to produce a comprehensive survey resource for the researchers, which aim to work on malware detection.
The Chronic Toxic Effect of Water-Accommodated Fractions of 0# Diesel Oil on Splox Gene of Sea Urchin

0# diesel is common petroleum hydrocarbon pollutant of sea and marine organisms. One goal of this study was to assess the chronic toxic effect of water-accommodated fractions（WAFS）of 0# diesel oil on Splox gene of Strongylocentyotus intermedius by PCR-SSCP. The experiment was carried out with three concentrations (5mg/L, 20mg/L and 50mg/l). The results show that with the increase of exposure concentration and time, the mutation probability of the sea urchin increased. In the four primers, single base mutation took place in coding region of the 41 of Primer A-1, with the conversion of A-G, where amino acid changed from lysine into arginine.
Detecting Metamodel Evolutions in Repositories of Model-Driven Projects

Model-Driven Engineering (MDE) is a discipline that leverages abstraction and automation in software development. Projects are typically composed of inherently diﬀerent artifacts, including models, meta-models, model transformations, code generators, and concrete syntax deﬁnitions. Despite the increasing availability of reusable projects (e.g., through GitHub), their reuse possibilities depend on the availability of accurate, high-level metadata describing architectural information about the project at hand. In this paper, we enhance an existing approach for extracting relevant architectural information from model-driven projects to detect subsequent metamodel versions in evolution paths. In particular, we are interested in those refactorings that enhance the intrinsic quality of metamodels. The approach has been implemented by extending the existing MDEprofiler tool and has been validated on a dataset consisting of metamodels with diﬀerent size and covering distinct application domains.
Architectural and genotypic variation in the clonal shrub Taxus canadensis as determined from random amplified polymorphic DNA and amplified fragment length polymorphism

Canada yew (Taxus canadensis Marsh.) is a clonal species with two types of ramets. The most common type has bilateral symmetry and alternate branching. The radial symmetry and opposite branching of the rarer type are similar to the architecture expressed in a young plant grown from seed. Architectural analysis was conducted on four colonies of increasing age and size. A genetic analysis using random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) was conducted on 122 ramets, 93 sampled from nine colonies from a single population, 24 from eight other populations, and five from other Taxus species. Analysis of the architectural development sequence shows that yew follows the same architectural model as, for example, species of Abies or Picea. This model constitutes a level of organization intermediate between that of the ramet and of the genet. The molecular analyses confirm the greater variability of AFLPs over RAPDs and indicate that architecture is not directly related .
A tenacity learning algorithm for humanoid robot locomotion based on the human gait cycle

This paper shows a tenacity learning algorithm that prioritizes static over dynamic stability to decrease gait complexity for an articulated humanoid robot. Initially, we have an array of goal positions, which the algorithm must achieve during the gait cycle. A trial and error process leads the learning approach. Each robot motion attempt is available on an action list. Whenever the robot achieves a goal, the algorithm stores the sequence of movements in memory. If there is a failure, the action list provides the position before the fall - and the process starts from that point on. In order to test the algorithm, we developed a simulator using Matlab Simulink, together with the Simscape Multibody contact forces library. We present the simulation data through graphs that describe the behavior of the joints during the learning process of a gait cycle.
The Human Pathogen Paracoccidioides brasiliensis Has a Unique 1-Cys Peroxiredoxin That Localizes Both Intracellularly and at the Cell Surface

Paracoccidioides brasiliensis is a temperature-dependent dimorphic fungus that causes systemic paracoccidioidomycosis, a granulomatous disease. The massive production of reactive oxygen species (ROS) by the host's cellular immune response is an essential strategy to restrain the fungal growth. Among the ROS, the hydroperoxides are very toxic antimicrobial compounds and fungal peroxidases are part of the pathogen neutralizing antioxidant arsenal against the host's defense. Among them, the peroxiredoxins are highlighted, since some estimates suggest that they are capable of decomposing most of the hydroperoxides generated in the host's mitochondria and cytosol. We presently characterized a unique P. brasiliensis 1-Cys peroxiredoxin (PbPrx1). Our results reveal that it can decompose hydrogen peroxide and organic hydroperoxides very efficiently. We showed that dithiolic, but not monothiolic compounds or heterologous thioredoxin reductant systems, were able to retain the enzyme activity. Structural analysis revealed that PbPrx1 has an α/β structure that is similar to the 1-Cys secondary structures described to date and that the quaternary conformation is represented by a dimer, independently of the redox state. We investigated the PbPrx1 localization using confocal microscopy, fluorescence-activated cell sorter, and immunoblot, and the results suggested that it localizes both in the cytoplasm and at the cell wall of the yeast and mycelial forms of P. brasiliensis, as well as in the yeast mitochondria. Our present results point to a possible role of this unique P. brasiliensis 1-Cys Prx1 in the fungal antioxidant defense mechanisms.
Optimized Unequal APSK Constellation Mapping and Demapping for Non-equiprobable Transmission Systems

Amplitude phase shift keying(APSK) is an emerging modulation scheme due to its considerable shaping gain. In this paper, unequal APSK constellation mapping aiming at non-equiprobable transmission system is concerned. A simplified and optimized mapping and demapping method for non-equiprobable symbols’ transmission is proposed. Simulation results show that the proposed mapping and demapping method outperforms conventional constellations and mappings.
Research on Recognition Model with Random Forest and Entropy Weight for Chemical Gas Sensor Array

Electronic nose is one of the research hotspots in the field of engineering. In order to solve the chemical gas recognition problem of electronic nose (that is, sensor array), we designed and established a recognition model based on random forest, entropy weight and bootstrap aggregating. The model has been successfully applied to the UCI Gas Sensor Array Drift Dataset and achieved excellent effect and reliability, avoiding the adverse effects caused by drift problem and unbalanced data distribution to a certain extent. The design and implementation method of the recognition model has certain reference value to the research of related fields.
The Impact of DNS Insecurity on Time

We demonstrate the first practical off-path time shifting attacks against NTP as well as against Man-in-the-Middle (MitM) secure Chronos-enhanced NTP. Our attacks exploit the insecurity of DNS allowing us to redirect the NTP clients to attacker controlled servers. We perform large scale measurements of the attack surface in NTP clients and demonstrate the threats to NTP due to vulnerable DNS.
A Novel Sketch Recognition Model based on Convolutional Neural Networks

Deep neural networks have been widely used for visual recognition tasks based on real images as they have proven their efficiency. Unlike real images, sketches exhibit a high level of abstraction as they lack the rich features that the real images contain such as various colors, backgrounds, and environmental detail. Despite all of these shortages and being drawn with just a few strokes, they are still meaningful enough to encompass an appropriate level of meaning. The efficiency of deep neural networks on sketch recognition has been relatively less studied compared to the visual recognition of real images. To experiment with the efficiency of deep neural networks on sketch recognition, a novel sketch recognition model based on Convolutional Neural Networks is proposed in this study. The proposed model consisted of 21 layers and was tuned in an automated manner to find out the best-optimized model. In order to reveal the proposed model’s efficiency in terms of predicting the classes of the given sketches, the model was evaluated on a gold standard sketch dataset, namely, Quick, Draw! According to the experimental result, the proposed model’s accuracy was calculated as high as 89.53% which outperformed the related work on the same dataset. The key findings that were obtained during the conducted experiments were discussed to shed light on future studies.
Applying Convolutional LSTM Network to Predict El Niño Events: Transfer Learning from The Data of Dynamical Model and Observation

Neural network as a statistical method is widely used for weather forecasting. But for the prediction of El Niño grid data, the data record is short. In this paper, we use deep learning to handle spatiotemporal information and transfer learning to transfer knowledge from dynamical model (Zebiak–Cane model) data to the prediction of realistic El Niño. A ConvLSTM (Convolutional Long Short-Term Memory Network) architecture is constructed to predict the grid data of sea surface temperature and thermocline depth at lead times from 3 to 12 months. Cross-validation is used to evaluate the predictions. The entire data record from 1980 to 2018 is divided into 10 groups and used for training and validation. The experiment results show that transfer learning have a positive impact on the El Niño prediction, especially for the strong Eastern-Pacific type. Compared with the predictions of the Zebiak-Cane model, it can be inferred that the role of the model data in transfer learning is greater than the observation data.
A 1.67-GSps TI 10-Bit Ping-Pong SAR ADC With 51-dB SNDR in 16-nm FinFET

This letter introduces a novel SAR-based algorithm that speeds up the conversion by using two digital-to-analog converters that operate in a ping-pong fashion. It has been implemented in a <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> time-interleaved analog-to-digital converter in a 16-nm FinFET technology. Each channel uses the proposed ping-pong SAR (PP-SAR) algorithm in a coarse phase followed by a comparator-based asynchronous binary search (CABS) fine phase to realize 418 MHz/channel 10-bit conversion with 51-dB SNDR at Nyquist. The total power consumption is 7.9 mW corresponding to a Walden FoM of 16 fJ/conversion-step at Nyquist.
Online Laboratory Sessions for the Education of Electrical Machines and Drives

This paper presents a method to implement online laboratory sessions for education in Electrical Engineering on subjects related to electrical machines and drives. The assignments for the laboratory sessions consist of: the preparation homework, where students have to study relevant theories; the development of circuit simulations; and the participation on a practical session online. For the latter, the students have to implement remotely various models in a physical setup based on the results of their homework tasks. After taking the assignments, the students should be able to: set up circuit simulations of electrical machines and drives; to implement relevant control of motor drives in a physical setup; and finally, to interpret the differences between the simulation and experiment results. Detailed design of the physical setup, the assignments, the relevant Information and Communication Technology (ICT) facilities are presented and discussed in this paper. An example and the feedback of applying it in a master level course is presented. The experience gained so far shows that the proposed online laboratory sessions can be used as supplementary educational resource to existing online education platforms.
Computation and Analysis of Constrained Capacity of One-Bit Massive MIMO with CSIR

The one-bit massive multi-input multi-output (MIMO) with receiving channel state information is considered in this letter. This letter focuses on the constrained capacity, i.e., the mutual information with given distribution of the source, computation with given modulation. The complexity analysis shows that the complexity of the constrained capacity computation based on the definition grows exponentially with the number of antennas at the base station. We reduce part of the computation by deriving the definition formulas, and propose the Monte-Carlo method to calculate the remaining part. With the efforts, we reduce the complexity down to polynomial. The numerical results show that the proposed method provides the estimation of the constrained capacity with small error if the number of Monte-Carlo tests is large enough. Analysis based on the numerical results is shown, which provides the system designer a basis for choosing modulation type and signal-to-noise ration level. The result also indicates that the degradation is only 2dB due to the coarse quantization of one-bit system at the main signal-to-noise ration region.
An Effective Double Frequency Ripple Suppression Based on Sliding-Mode Control for Cascaded Multilevel Quasi-Z-Source Inverters

In this paper, a robust and efficient sliding mode control is proposed for a seven-level quasi-Z-source inverter. The proposed method simplifies the control of the entire system while suppressing the double frequency ripple (DFR) in inductor current and offering excellent features of the sliding mode control such as fast response and robustness. In this method, the values of modulation index and shoot through duty cycle are generated by the sliding surface functions. These functions are passed through separate boundary layers so that they become appropriate signals to be employed in the SPWM process. Simulation results confirm that the proposed method not only possesses robustness, fast response and easy implementation, but also reduces the DFR in inductor current. Simultaneous use of sliding mode control and multilevel qZSI has also enhanced the quality of the output voltage under linear and nonlinear loads.
Pneumothorax Segmentation with Effective Conditioned Post-Processing in Chest X-Ray

The automatic detection of abnormal elements in chest Xrays (CXR), such as pneumothorax, is important and challenging problem. Screening for unexpected findings or any surveys in the complicated conditions are the common scenarios for the radiologists in their clinical workflow, where the automated solutions are required. The pneumothorax can be caused by a blunt chest injury, damage from underlying lung disease or it may occur for no obvious reason at all [1]. This is one of the complex problems for the experts manual detection, which can be solved automatically and simplify the clinical workflow. Proposed method presents new segmentation pipeline for the CXR images with the multi step conditi oned post-processing. This approach leads to the significant improvement compare with any ”baseline” by the reduction of the totally missed and false positive detections of the pneumothorax collapse regions. Obtained results demonstrate very high accuracy and strong robustness due to very similar performance on the double-stage test dataset. Final Dice scores are 0.8821 and 0.8614 for ”stage 1” and ”stage 2” test datasets respectively, what is resulted in top 0.01% standing of the private leaderboard on the Kaggle competition platform. Code is available at https://github.com/n01z3/kaggle-pneumothoraxsegmentation.
Adaptive Motion Control of Modified E. Coli

Micro robotic systems are of growing interest for biomedical applications. The widely preferred means of locomotion for untethered microrobots is swimming via mimicry of single-celled organisms. Furthermore, such organisms are recently considered to be integrated into a micro-robotic system to achieve biocompatibility. The motion control of such bio-hybrid systems poses a considerable challenge due to their size and taxis-behavior. Here, an adaptive motion control scheme is investigated with a simulated coupled system of open kinematic chain and bacterium under in vitro conditions. The said control is achieved through magnetic coupling between the two via a single permanent magnet. The main focus of the study is the effect of the adaptive component and its ability to change the swimming direction of a single E. Coli minicell. Simulation results show that it is theoretically possible to achieve motion control via such an approach.
Mechanical Characterization of Polysilicon MEMS Devices: a Stochastic, Deep Learning-based Approach

We discuss a Deep Learning (DL) approach for the multi-scale characterization of polysilicon movable structures of MEMS (micro-electro-mechanical systems), based on data assimilation from two-dimensional stochastically representative images of polycrystalline films. A dataset of microstructures is collected and a convolutional neural network is trained, to provide the appropriate scattering in the value of the overall stiffness (in terms e.g. of Young’s modulus) of the grain aggregate. Since results are framed within a stochastic approach, the aim of the learning stage is to provide a fast method to be next used at the device level for statistical, Monte Carlo-like investigations of the relevant performance indices. Accuracy of the proposed approach is assessed for different values of the ratio between the dimension of the polycrystalline aggregate and the representative size of a single grain, i.e. for different number of grains gathered in the polycrystal, to next check if size effects are correctly captured.