Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Polarization Conversion Metasurface Based Circular Polarized Slot Antenna with Low Profile

A low-profile, wideband circular polarized (CP) slot antenna based on a novel polarization conversion metasurface (PCM) is illuminated in this paper. The CP antenna is composed of a feeding slot antenna and a novel PCM with two axisymmetric patches. PCM realizes the 90 degrees polarization conversion reflection for the incident wave from 5.8 GHz to 7.2 GHz. The measured results showed that the CP antenna achieves good performance with a -10 dB bandwidth of 2.5 GHz (4.5–7 GHz) and an AR bandwidth of 1 GHz (5.3–6.3 GHz). In addition, the proposed antenna has good circular polarization characteristics, simple structure and low profile, which make it a viable candidate for C-band (4–8 GHz) application.
High Efficient Hybrid Converter Using Center-tapped Clamp Circuit

In this paper, a hybrid converter combining a phase-shifted full-bridge converter and a resonant half-bridge converter with a new center-tapped clamp circuit is proposed to achieve high efficiency in electric vehicle battery charging systems. The proposed converter has the benefit of half-bridge integrated converters such as the extended zero-voltage-switching range. In addition, by using a new center-tapped clamp circuit, which consists of two diodes and two capacitors, the proposed converter can solve the drawbacks of conventional PSFB converters, such as the substantial circulating current and the severe voltage stress and the significant switching loss in the secondary full-bridge rectifier. With these advantages, the efficiency is considerably increased. In order to validate the feasibility of the proposed converter, a 3.3-kW prototype was tested.
Automated Driving Systems: Key Advantages, Limitations and Risks

Automated Driving System (ADS) is one of the main topics regarding technology in the automotive field for a better society. Various aspects have been dealt with over recent years, from ethics to sociological impacts, but despite this, common use of vehicles with autonomous driving still remains a future goal. In fact, the related technology is still a work in progress, and legislation still has to be better defined. This article reports some unusual considerations regarding ADS, and provides a vision for real progress in this area by highlighting some less analyzed driving risks and limitations to be considered while developing increasingly safe and reliable ADS. Furthermore, an analysis of current public data for conventional vehicles and existing highly automated ground vehicles is reported. Finally, prospects for a future scenario in this research area are provided.
Randomized MPC with Model Uncertainties Based on LSCR Approach

This paper deals with a probabilistic approach for online system identification in MPC models with parameter uncertainties. The problem is to construct robust control in probabilistic sense for all possible values from some predefined set. In that case it is a good way to shrink the set over time in order to get better sets. The proposed algorithm is based on the modified LSCR (Leave-out Sign-dominant Correlation Regions) and applied to state space model used in MPC. In the paper we address a convergence of the algorithm over time, which required significant modifications comparing to our previous work. Practical part demonstrates in more detail with Matlab and CVX how to obtain smaller control for a nonminimal-phase second order plant with two unknown parameters and investigates convergence of confidence regions over time.
Low Complexity Max-Min Uplink Power Control in Massive MIMO System with Experiment on Testbed

In this paper, we propose a simple max-min power control scheme for Multi-User MIMO (MU-MIMO) in massive MIMO systems. We model and formulate the issue as a max-min fairness power control problem, which needs to be considered in order to provide good experienced service for users in the system. By exploiting the special properties of the solution, we are able to propose a very simple power control algorithm with complexity $\mathcal{O}(M(N+M))$ where $M$ and $N$ are the number of users and base station antennas respectively. In addition, only the estimated channel response and maximum transmit power of each user are required as input information. We implement the proposed algorithm on a record-playback massive MIMO testbed to verify the performance in real environment. The result show that the received signal quality of the worst user is much more improved, and all users experience roughly the same level of quality of service. In addition, simulation is carried out to confirm the advantage of the proposed scheme in term of minimum spectral efficiency.
A Privacy-Preserving Signature Scheme for Network Coding

In this paper, a novel privacy-preserving signature scheme for network coding is proposed, which can mitigate both intra-generation and inter-generation pollution attacks efficiently, and leverage the inherent security property of random network coding to prevent eavesdropping attacks at the same time. Our scheme is constructed on a bi-linear map between two multiplicative cyclic groups with the same order, and its security relies on the hardness of the discrete logarithm problem. We proved that the construction of our scheme is correct, and also showed that our scheme is resistant against both pollution attacks and eavesdropping attacks by demonstrating that the probabilities of an adversary recovering the native messages and forging a valid signature for a bogus packet are both negligible functions of the security parameter. We evaluated the performance of our scheme in terms of communication overhead and computational complexity, and carried out extensive experiments to compare the running time of different procedures between our scheme and several previous schemes. The experimental results showed that our scheme is more efficient than the previous schemes simulated.
Deep Robust Reinforcement Learning for Practical Algorithmic Trading

In algorithmic trading, feature extraction and trading strategy design are two prominent challenges to acquire long-term profits. However, the previously proposed methods rely heavily on domain knowledge to extract handcrafted features and lack an effective way to dynamically adjust the trading strategy. With the recent breakthroughs of deep reinforcement learning (DRL), sequential real-world problems can be modeled and solved with a more human-like approach. In this paper, we propose a novel trading agent, based on deep reinforcement learning, to autonomously make trading decisions and gain profits in the dynamic financial markets. We extend the value-based deep Q-network (DQN) and the asynchronous advantage actor-critic (A3C) for better adapting to the trading market. Specifically, in order to automatically extract robust market representations and resolve the financial time series dependence, we utilize the stacked denoising autoencoders (SDAEs) and the long short-term memory (LSTM) as parts of the function approximator, respectively. Furthermore, we design several elaborate mechanisms to make the trading agent more practical to the real trading environment, such as position-controlled action and n-step reward. The experimental results show that our trading agent outperforms the baselines and achieves stable risk-adjusted returns in both the stock and the futures markets.
Design Challenges In Continuous Mode Power Amplifiers

This paper analyzes the appropriate design space for continuous mode power amplifiers that results into foster loads realizable with passive matching networks. An insight is presented to engineer the second harmonic loads to provide clockwise trajectory on Smith chart. This enables designers to choose the solutions which are feasible in terms of designing matching network. The scheme presented is validated with two waveform engineering based design examples using Continuous Class B/J and Continuous Class F modes of operation. A 15 W GaN HEMT transistors from Wolfspeed are used to design these power amplifiers to obtain high efficiency over a wide bandwidth.
Load Cell Based Ultrasonic Wattmeter For Ultrasonic Probe Calibration

In our country, many different brands and types of ultrasound devices are used very intensely in the health sector. However, these ultrasound devices do not undergo a legal / compulsory control or voluntary technical measurement. Ultrasonic probes, which are the most important part of ultrasound devices, must be calibrated according to the defined standards in order to verify that they work safely and at the specified performance. For this reason, load cell based Portable Ultrasonic Wattmeter (PUW) device for ultrasonic probe calibration was designed, produced and tested by our laboratory for the first time in our country. It has been found that the device meets the acceptance criteria specified in the standard IEC 61161:2013. However, this initial design was further developed in the light of the experiences gained. The new version design of the device has also been produced. The device works in the distilled water and probe tests are done by operating the probes in the water tank. In the first system produced, the power of the ultrasonic probes can be measured in the range of 15 W - 150 W, while in the next generation, the powers below 1 W can also be measured.
Input Magnitude Data Setting in Error-Reduction Algorithm for One-Dimensional Discrete Phase Retrieval Problem

In this paper we discuss how does the input magnitude data setting influence the behavior of error-reduction algorithm in the case of the one-dimensional discrete phase retrieval problem. We present experimental results related to the convergence or stagnation of the algorithm. We also discuss the issue of the zeros distribution of the solution, when the solution of the problem exists.
Non-Propagating Components of Surface Electromyogram Reflect Motor Unit Firing Rates

Interference surface electromyogram (EMG) recorded from linear electrode arrays aligned to muscle fibres can be separated into propagating and non-propagating contributions. The first reflects the propagation of action potentials along muscle fibres. The non-propagating components are here shown to be related to the overall firing pattern of active motor units (MU). Indeed, in simulations, the power spectral density (PSD) of non-propagating components shows a low frequency peak corresponding to the mean firing rate, even when such a contribution is not visible in the PSD of the EMG (either monopolar or single differential configuration, either rectified or not). Moreover, it has a high correlation with the PSD of the cumulative firings of the MUs in the detection volume of the recording system. Applications to experimental data confirm that the low frequency peak is more evident for the non-propagating components than for the raw signals and is related to the MU firing frequency. Potential future applications are expected in the study of the MU control in different conditions (e.g., training, fatigue or pathology, inducing changes, or modulation of firing rate) and in the investigation of common synaptic inputs to motor neurons.
A Universal Control Method to Realize Plug-and-Play Converters for Microgrids

In recent years, AC and DC microgrids have been extensively studied. By constructing microgrids, it is possible to promote installing renewable energy resources. Most of components (renewable energy sources, storage devices, load, and so on) are connected to the microgrids through power converters. Therefore, due to spread of microgrids, the importance and the use of power converters have been increasing. As a practical solution to build microgrids, our group has proposed a concept of “plug-and-play” converter. The concept means that the plug-and-play converter is flexibly applied to various components with a unified circuit topology in three-phase AC, single-phase AC, and DC microgrids. As one of the essential functions for development of the plug-and-play converter, this paper realizes universal grid interconnection control method. The proposed control method enables to transmit and receive desired active power in three-phase AC, single-phase AC, and DC microgrids, without changing the control system and circuit topology. Simulation studies verify the validity of the universal grid interconnection control method.
New Series-Resonant Solid-State DC Transformer Providing Three Self-Stabilized Isolated Medium-Voltage Input Ports

This work analyzes the properties of a new four-port, series-resonant, and bidirectional DC transformer topology in steady-state and transient conditions. The investigated topology evolves from the Integration of Three (3) Series-Resonant Converters (I3SRC), enables the realization of a converter with high efficiency (99.0%) and high power density (7.1 kW/dm3), due to the utilization of a three-phase High Frequency (HF) transformer and reduced capacitor currents in the secondary-side DC link, and features the use of a six-pack power module on the secondary side. The analysis is based on the results of detailed circuit simulations of an I3SRC, which takes the couplings between the phases, introduced by the three-phase transformer, into account and considers a DC transformer, i.e., DC-DC converter, with a total rated power of 15 kW, three primary-side DC voltages of 1.1 kV, and a secondary-side DC voltage of 700 V, which is a core part of a scaled demonstrator of a Solid-State Transformer (SST). The obtained findings clarify the self-stabilizing capabilities of the I3SRC for operation with Half-Cycle Discontinuous Conduction Mode (HC-DCM), even if the three isolated DC ports are subject to time-varying and substantially different power levels and different directions of energy transfer.
Security Challenges in the eHealth Domain: The VICINITY Approach

The eHealth constitutes the largest wave of change in the sector of healthcare. In this context, Internet of Things is of immense importance since connected data would facilitate treatment with more efficiency and comprehensive knowledge and would "act" as preventive medicine. Monitoring health data and making them ubiquitously accessible to predefined and authorized healthcare personnel are shared through various IoT platforms, which usually lack IoT-protocol-interoperability. In this paper, we present the impact of IoT systems in the eHealth services' evolution and we introduce the VICINITY ecosystem solution. Its high-level architecture is analyzed and several security considerations are offering "thought for food".
Mining Human Mobility from Social Media to support Urban Computing Applications

Analysis of people trajectories is key for implementing effective urban computing applications. Nowadays, social media represent one of the main sources of information concerning human dynamics within urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to predict human mobility by exploiting Twitter data. The prediction method is based on a hybrid approach combining frequent pattern mining, trajectory similarity and supervised classification. The trajectory pattern similarity allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. If none of the patterns satisfies the similarity threshold, a set of spatio-temporal features characterizing locations and movements among them are combined into a supervised learning approach based on M5 model trees. The experimental results obtained by using a real-world dataset show that the proposed method is effective in predicting the user's next places achieving a remarkable accuracy and prediction rate.
Compositional Abstractions of Interconnected Discrete-Time Switched Systems

In this paper, we introduce a compositional method for the construction of finite abstractions of interconnected discrete-time switched systems. Particularly, we use a notion of so-called alternating simulation function as a relation between each switched subsystem and its finite abstraction. Based on some small-gain type conditions, we use those alternating simulation functions to construct compositionally an overall alternating simulation function as a relation between an interconnection of finite abstractions and that of switched subsystems. This overall alternating simulation function allows one to quantify the mismatch between the output behavior of the interconnection of switched subsystems and that of their finite abstractions. Additionally, we provide an approach to construct finite abstractions together with their corresponding alternating simulation functions for discrete-time switched subsystems under standard assumptions ensuring incremental input-to-state stability of a switched subsystem. Finally, we apply our results to a model of road traffic by constructing compositionally a finite abstraction of the network containing 50 cells of 1000 meters each. We use the constructed finite abstractions as substitutes to design controllers compositionally keeping the density of traffic lower than 30 vehicles per cell.
Mitigating Biological Signalling Cross-talk with Feedback Control

Biological signalling networks provide decision making and control capabilities for cells as they interact with changing physiological and environmental conditions. Studies of these signalling networks have revealed that they are greatly inter-dependent, which can make isolated analysis of their behaviour challenging. An accurate understanding of the interdependence of these systems will thus be of great utility to synthetic biologists as they attempt to engineer control systems within living cells. Cross-talk, which arises when the response of one signalling pathway can directly influence that of another, should either be considered explicitly when designing new signalling networks or controlled to ensure its effects are minimised. In this paper we consider a simple one-kinase two-phosphatase system and investigate potential control schemes that can reduce the impact of cross-talk upon its behaviour, for example by acting to selectively inhibit a kinase enzyme's affinity for one of its target substrates. We demonstrate that these schemes can be used to reduce the impact of crosstalk, and that a closed-loop control architecture provides better performance over a larger range of parameter values when compared to an open-loop equivalent.
WInSiC4AP: Wide Band Gap Innovative SiC for Advanced Power

The use of SiC technology to face the automotive challenges in the coming years is among the objectives pursued in the ECSEL JU project WInSiC4AP. The project is supported with a synergy between ECSEL JU and ESI financing that will enable complementary activities with significant economic and social impact. The Project led by DTSMNS (Distretto Tecnologico Sicilia Micro e Nano Sistemi) started on June 2017 for a duration of 36 months on research, technology, package and applications. This paper will address the main activities in 2nd year of project for automotive with a focus on innovations in SiC technology and the power module packaging.
Study of coexistence between different services in novel 5G Frequency Bands

In this work we show the results of coexistence studies between incumbent systems and the forthcoming 5G networks. The analyses were conducted by means of a simple but rigorous prediction tool and based on the widely adopted Minimum Coupling Loss (MCL) methodology. We considered the three pioneer bands identified to offer 5G services in Europe by 2020 on a large scale are: the 700 MHz, the 3.5 GHz and the 26 GHz band, using for each band appropriate propagation models. Results show how coexistence is possible, both on a co-channel and an adjacent channel basis, provided that adequate separation is guaranteed between interfering services.
Balance-Compensated Trans-Directional Coupled-Line Based Planar Balun with Connecting Lines of Arbitrary Impedance

This paper presents a novel method for the balance compensation of trans-directional coupled line (TRD-CL) based planar balun with connecting lines. The proposed compensation technique together with the design equations allow for achieving an arbitrary impedance of the inserted connecting line. To demonstrate the proposed theory, comparisons are made with the uncompensated TRD-CL based balun. Finally, a 0.8-GHz TRD-CL based balun with a connecting line of 10° electrical length and 75-Ω characteristic impedance is designed and fabricated. The measured results approve the effectiveness of the proposed method.
A predictive learning approach to optimal load sharing in energy management systems

Given the total power demand, $P_{d}$, current practice of equal load sharing in the process industry is to distribute the load among power supply units and machines (e.g., diesel/aas/wind turbines) in proportion to the maximum power, i.e., $P_{i}=\frac{p_{\max}^{i}}{\sum_{j}P_{\max}^{j}}P_{d}$, where $P_{\max}^{i}$ denotes the maximum power of the ithunit. However, the efficiency of power supply units, vary in time and are highly individual, even in the case of units from same brand and model. Thus, by considering and utilizing these individual differences, it is possible to share the load in a more fuel/cost/energy optimal manner. To capture this potential, the work presented in this paper proposes an optimization- and learning-based approach for sharing the load among a set of heterogeneous power supply units. The main contributions of this paper include formulation of the overall energy management system algorithm, including power grid connection and Energy Storage System (ESS), as a Mixed Integer Non-Linear Program (MINLP), as well as algorithms for robust moving horizon estimation of efficiency curves and machine learning-based algorithms for classification of turbine state and prediction of future power demand needed for the predictive planning scheme. The soundness and performance of the proposed algorithms are verified using actual data from a power plant including gas turbines and power grid connection in combination with ESS.
Optimal Sizing of BESS for Hybrid Electric Ship Using Multi-Objective Particle Swarm Optimization

This paper presents an optimal sizing technique of Battery Energy Storage System (BESS) in Hybrid Electric Ship. The technique is based on both Multi-Objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting-based Genetics Algorithm II (NSGA II). Both total installed BESS size (kWh) and total fuel consumption are the objectives of the optimization. To meet the target lifespan of BESS, Depth-of-Discharge (DOD) of BESS is used as design variable together with usable energy of BESS. An example based on ferry case study has been studied. From the sizing technique, Pareto front is obtained. The ship designer or user can choose one of the results from the Pareto front based on their goal or requirement.
Robust Multi-stage NMPC under Structural Plant-model Mismatch Without Full-State Measurements

We address the problem of robust nonlinear model predictive control (NMPC) under plant-model mismatch in the absence of full-state measurements. We propose an approach that is based on the use of a model-error model (MEM) to handle the estimation errors and the structural plant-model mismatch in a Multi-stage NMPC framework. The MEM which consists of a linear model followed by a nonlinear operator with bounded gain captures the estimation error along with the unmodeled dynamics of the plant. Multi-stage NMPC explicitly considers the presence of feedback in the problem formulation, hence it is less conservative than other robust NMPC schemes. The advantages of the proposed scheme are demonstrated on a benchmark reactor problem.