Miracle-dz/UniFAR-FTUs huggingface dataset:
A 300 GHz Active Frequency Tripler in Transferred-Substrate InP DHBT Technology

This paper presents a monolithic integrated active frequency tripler implemented in an InP double heterojunction bipolar transistor (DHBT) transferred-substrate (TS) technology. The active tripler is based on a balanced topology with Marchand balun at the input and rat-race hybrid coupler at the output. Reflector networks are employed to enhanced the third harmonic response. The measured third harmonic output power is -2.4dBm at 267 GHz and -6.2 dBm at 303 GHz. The second harmonic leakage remains 10 dB lower than the third harmonic power over this bandwidth. The DC power consumption of the active tripler is only 37.6 mW at 303 GHz leading to a DC conversion efficiency of 0.64%.
Viability of using additive manufacturing for horn antennas fed with empty substrate integrated waveguide

Substrate integrated waveguides reduce the losses when compared to traditional planar transmission lines, while maintaining their low losses, low profile, and low cost manufacturing. The losses have been further reduced by partially or fully removing the dielectric, and many microwave devices have been successfully implemented with empty substrate integrated waveguides, including H-plane horn antennas. The directivity of the horn antenna can be increased using 3D pyramidal horns, and sacrificing the low profile. However, the low cost can be maintained by using additive manufacturing. In this work the viability of using additive manufacturing for two horn antennas fed with empty substrate integrated waveguide is studied.
Control of Takeoff and Landing of the Multicopter in a Moving Robotic Container Without External Navigation Systems

The problem of the movement of the quadcopter in long autonomous missions is discussed. The approaches are analyzed to the organization of its take-off, vehicle following, and landing into a robotic container, which combines the functions of transportation and recharging the battery. Based on the results of the study, structural schemes of multicopter and container functioning in the absence of external navigation systems are suggested. Experimental realization of the visual tracking algorithms confirms the applicability of the proposed approach.
Multi-Microgrid Energy Management System in Times of 5G

Microgrids promise an efficient integration of de-centralized energy resources (DER), mostly fueled by renewable energy sources (RES), and prosumers - pro-active consumers with own generation and storage capacities that can actively manage their load. The arising multi-microgrid concept inter-connects several microgrids in order to increase the operational stability and to raise additional economic benefits for the single microgrid by aggregating spare flexibility from the microgrids and providing it to the utility and energy markets as demand side management (DSM) services. Current research literature discusses both approaches extensively but so far microgrids have not spread widely because of extensive installation costs and operational difficulties. An open software platform based on available standards and control algorithms would decrease these barriers. This paper provides an initial requirement analysis for a multi-/microgrid energy management system (EMS) software platform implementation considering advancements in the telecommunication sector and especially the upgrade to the fifth generation (5G) of the wireless network. At the end of this paper, we present a concept for a scalable test bed for such platform implementations.
Observation Subsystem Design for an Unmanned Aerial Vehicle Under Undefined External Actions

As part of the synthesis of a tracking system for unmanned aerial vehicles (UAVs) under the action of external uncontrolled disturbances and incomplete measurements of the state vector, the structure of the observation subsystem, which includes two state observers, was developed. The first observer gives estimates of the velocities based on measurements of the UAV mass center coordinates. The second observer on the measurement of tracking errors gives estimates of mixed variables (functions of state variables, external influences and their derivatives) for which feedback is formed.
A Post-fabrication Tuning Method using Space Mapping and Surrogate Modeling Techniques

A fabricated microwave component generally requires a post-fabrication tuning process to compensate variations from fabrication process and improve the performance. In this paper, a post-fabrication tuning method based on space mapping and surrogate modeling technique is proposed for tuning fabricated microwave component. The tuning method is presented in detail. Surrogate modeling and parameter extraction process is discussed. Several tuning examples are given to validate the proposed method. By applying the tuning algorithm, we are able to significantly improve the performance to achieve the tuning specifications.
A Market Oriented, Reinforcement Learning Based Approach for Electric Vehicles Integration in Smart Micro Grids

In an independent self-sustained micro grid (MG) with limited energy resources, plugged-in electric vehicles (EV) must compete for available excess power supply or demand, modeled as a random variable. This paper proposes a distributed machine learning algorithm based on a Markov decision process (MDP) and non-cooperative game theory, that maximizes the EV’s profit under uncertainty of future MG supply/demand states, while satisfying specific battery constraints imposed by the EV owner. Performance evaluation of the proposed algorithm shows that even with no a priori knowledge of future MG supply/demand states, it achieves average profits of only 43% less than the global optimal profit. Results also show that using a cooperative version of the algorithm leads to a 12% increase in average profits.
High Speed Full Custom Parallel Multiplier Based on Radix-4 Booth

Multiplier is one of the most critical block which is extensively used in many digital systems. However, multiplier consumes large chip area and needs long processing time. Therefore, to improve system computational performance, high speed multiplier design is required. Radix-4 booth multiplier has faster processing time than conventional multiplier. This paper proposed a high speed multiplier based on Radix - 4 booth multiplier. The radix-4 booth multiplier is designed with fully combinational parallel architecture to achieve multiplication processing within one clock cycle. The proposed design is a full custom design using 130 nm CMOS technology. The DRC and LVS are used to verify the design. The proposed design layout has dimension of 207.5$\mu m \times 200.005 \mu m$ and an area of $41,501.0375 \mu m^{2}$. The worst case delay of this design is 378.74 ns and can be operated at 2.5MHz clock frequency.
Design LNA for RF Long Range Wide Area Monitoring and Control of Urban Farm

A Low Noise Amplifier RF communication system for urban farm monitoring and control systems has been designed. This LNA is used for wide area urban farm. The selected transistor is BFR840L3RHESD. This LNA works on the 2.4 GHz frequency. In this paper the initial design was carried out by looking for the bias of DC LNA parameters and simulating reinforcement, noise, and stability for first-level LNA. LNA designed has a gain of 15.319 dB and a NFM is 0.817.
A 2-38 GHz Linear GaAs pHEMT TIA for a Quasi-Coherent Optical Receiver

This paper presents the design and testing of a linear transimpedance amplifier (TIA) in a GaAs pHEMT technology. The TIA has been developed for integration within an optical receiver using a quasi-coherent technology. The maximum measured differential transimpedance is 63 dB $\Omega$, with a 3 dB operating frequency range from 2 to 38 GHz. The group delay variation is within ± 10 ps from 5 to 32 GHz, and the maximum input current for linear operation is 2 mA peak-to-peak at the 1 dB compression point.
Modeling of Short-Term Exchange Rates Dynamics

The new model of short-term exchange rate dynamics is constructed and the final factors dependence of the exchange rate is derived, based on the further development of the author`s concept of International Flows Equilibrium Exchange Rate modeling. The influence of crisis phenomena in the domestic Russian currency market on the model in the short term is investigated and quantified.
Initialized Model Reference Adaptive Control for Lower Limb Exoskeleton

In this paper, a Proportional-Integral-Derivative (PID) controller tuning scheme by Initialized Model Reference Adaptive Control (IMRAC) for a Lower Limb Exoskeleton (LLE) is presented. Mathematical expression of the LLE structure is determined using Lagrangian and Kirchoff’s equations. The transfer function of the structure based on the physical features of the links, and actuators is established under Range of Motion (RoM) condition. The PID controller of the LLE is tuned in a closed-loop control system using Ziegler-Nichols (Z-N) for initializing parameters of IMRAC. Adjustment mechanism is a gradient based method for real-time adaptation of tuned PID controller. A Lyapunov function has been applied to confirm the stability of IMRAC. The proposed IMRAC shows faster convergence in comparison with conventional non-initialized model reference adaptive control. It can be ascertained the proposed tuning scheme is applicable for real-time tuning of PID controller of LLE.
Data Recovery in Wireless Sensor Networks Based on Low Rank and Short-Term Stability

The most effective way to reduce the energy consumption of energy-limited wireless sensor networks is to reduce the amount of data collected. However, this will increase the difficulty of data recovery. At the same time, most of the data collection and recovery algorithms based on matrix completion are optimized by matrix decomposition. Therefore, the mathematical model and corresponding optimization algorithm will be more complicated and take a lot of running time. In order to deal with the above problems, we propose a data recovery method based on low rank and short-term stability in wireless sensor networks in this paper. The low-rank and short-term stability features of the sensor data are fused in the nuclear norm regularization minimization model, and the proposed method is used to optimize the model. The simulation results show that the mathematical model and data recovery method constructed in this paper outperform the state-of-the-art methods in terms of recovery performance and reconstruction accuracy.
DOENÇA AUTO-IMUNE DE TIREÓIDE E DISFUNÇÃO TIREOIDIANA EM PACIENTES COM ARTRITE REUMATÓIDE

Previous investigations have suggested an association between autoimmune thyroid disease (AITD), an organ-specific autoimmune disease, and rheumatoid arthritis (RA), a systemic autoimmune disorder. However, this association is controversial because of the great variability of the AITD prevalence in RA patients, the small number of studies and the varying opinions of researchers. The aim of this study is to determine whether there is relationship between AITD and RA and evaluate the correlation of thyroid disease with rheumatoid factor, antinuclear antibodies, and RA activity disease. This case-control study included 189 patients who were consecutively seen in the Rheumatology Clinics of HC-UFU/MG: 72 with RA (ARA criteria, 1987) and 117 with non-autoimmune rheumatic diseases. Blood samples were collected from all subjects for TSH, FT4, anti-TPO and anti-Tg measurement; RA patients were also tested for rheumatoid factor and antinuclear antibodies and the Disease Activity Score using 28 joints count was assessed to measure the disease activity. Thyroid dysfunction was found in 11 (14.86%) and thyroid autoantibodies in 15 (20.83%) RA patients, compared to 18 (15.20%) and 13 (11.11%) of the control group, respectively. There was no correlation between thyroid disease with rheumatoid factor, antinuclear antibodies, disease modifying drugs use, corticosteroids use or RA activity disease. In conclusion, no association could be demonstrated between thyroid disease and RA; this suggests that, at least in some populations, this association does not occur, or it is not strong enough – from the epidemiologic point of view – to justify the routine thyroid antibodies and thyrotropin tests in all RA patients.
Experimental Evaluation of a Beamforming-capable System using NI USRP Software Defined Radios

This paper presents the implementation, evaluation and validation of a radio system solution able to direct the main radiated lobe towards a target user (beamforming). The comprehensive hardware architecture integrates: (1) the transmitting sub-system, (2) the antenna array sub-system, (3) the synchronization sub-system, (4) the receiving sub-system, and (5) the data processing unit sub-system. Two beamforming algorithms are implemented: Minimum Variance Distorsionless Response and Phase Shift beamformer. The radiant system beamforming capacity is evaluated in terms of maximum pointing deviation of the main lobe relative to the direction where the target user is positioned, First Null Beam Width, and antenna gain.
A Novel Injection-Locked Frequency Tripler for V-band Applications

In this paper, a novel injection-locked frequency tripler (ILFT) implemented in the 90-nm CMOS process for V- band applications is presented. By adopting stacked CMOS cross-coupled pairs, a differential harmonic current injection circuit, and a resonator network technique, the locking range, the harmonic rejection ratio (HRR), and the power consumption of the proposed ILFT are improved. The locking range is from 61.2 to 64.2 GHz at an input power level of 0 dBm. The proposed ILFT has 30 dBc and 34 dBc to injected fundamental and second- order harmonic suppression, respectively. The core circuit consumes 7.48 mW dc power from a 1.1-V supply.
Modified PIMR-type ORC for Odd Harmonic Current Suppression in A Single-phase Grid-tied Inverter

A proportional integral multi-resonant type (PIMR-type) repetitive control (RC) scheme has both a good harmonics attenuation and a fast dynamics response. Nevertheless, the PIMR-type RC is sensitive to the reference frequency fluctuation which is common in distributed power generation systems (DPGS). Moreover, the reference and disturbance signals in single-phase grid-tied inverter are only odd harmonics period signals. In this paper, a modified PIMR-type odd RC (PIMR-type ORC) scheme for odd harmonic current suppression is investigated, which needs only a half data memory cells and has a wider frequency bandwidth at reference frequency and odd harmonics frequencies, and then has a faster dynamics response and a better robustness to frequency fluctuation than PIMR-type RC scheme. The stability analysis and parameters design are given in this paper. The results of simulation are presented to prove the feasibility and effectiveness.
Adaptive Neural Control for a Class of Uncertain Nonlinear Systems with Input Delay

This paper proposes a control scheme for a class of non-strict-feedback systems with input delay based on adaptive neural network technology. An appropriate auxiliary system is utilized to cope with the difficulties appeared in input delay. Through the utilization of backstepping, a controller based on adaptive neural network technique is proposed in this paper. The controller design enables the states in the system to be bounded. The main significance of this research is that an intelligent control scheme is extended to a class of nonlinear systems with non-strict-feedback form and input delay. Finally, an example is given to prove the effectiveness of the control method.
Music-Driven Dance Generation

In this paper, a novel model for synthesizing dance movements from music/audio sequence is proposed, which has variety of potential applications, e.g. virtual reality. For a given unheard song, in order to generate musically meaningful and natural dance movements, the following criteria should be met: 1) the rhythm between the dance action and music beat should be harmonious; 2) the generated dance movements should have notable and natural variations. Specifically, a sequence to sequence (Seq2Seq) learning architecture that leverages Long Short-Term Memory (LSTM) and Self-Attention mechanism (SA) is proposed for dance generation. The work in this article is interesting in the following aspects: 1) A cross-domain Seq2Seq learning framework is proposed for realistic dance generation; 2) A set of evaluation criterion is proposed for synthetization evaluation which do not have source for reference; 3) A dance dataset that including both music and corresponding dance motions collected, and very competitive results have been obtained against the-state-of-the-arts.
Multi-Threshold based Ground Detection for Point Cloud Scene

Point cloud is widely used in self-driving technology recently. Usually the first step of point cloud processing is segmentation of ground points and non-ground points. In this paper, a multi-threshold detector is proposed for point cloud scene captured by LiDAR mounted on an autonomous vehicle. The proposed algorithm uses variant thresholds which depend on the distance between two consecutive points. Furthermore, the algorithm also proposes additional rules for finding the start ground point of each scanning line and eliminating the backward slope. Simulation result shows the proposed algorithm works well in different testing environments, in terms of miss rate, accuracy and execution time. For one scene with more than 180,000 points, the segmentation can be done in 8 ms and with 99.5% accuracy rate by the proposed algorithm.
Stress Level Prediction Using Data From Wearables

Nowadays, stress is one of the main concerns when it comes to people's well-being and health. Knowing beforehand which events may cause stress, can be helpful in managing it. This paper aims to provide a solution for predicting stress levels caused by future events. We are proposing a system targeted at a representative test scenario for this problem, which uses calendar events and heart-rate variation data from wearables to offer predictions on stress levels for future events. This test scenario has a wide applicability as the majority of individuals in modern societies have desk jobs, and a significant part of their daily activities is recorded in their digital calendars. The system consists of a microservices data gathering infrastructure and a random forest algorithm for correlating heart-rate variations to calendar events. A dataset including data from 2 employees of a tech company over a period of one year, having attended a total of 1053 meetings, was used for evaluating the proposed system. The accuracy of the heart-rate sensors used in this study was validated against a professional electrocardiogram device. Our selected machine learning model for predicting heart rate variations for future events, trained on only 400 events, shows errors of less than 12 heart beats per minute 90% of the times. This represents a good performance considering the high heart rate variation when exposed to stress. Furthermore, we have performed our own validation experiment for the correlation of heart rate to stress, which revealed a strong positive correlation of 0.793. This offers us further confirmation that our system is capable of providing indicative estimations of stress levels for future events.
Gradient Models of Geological Medium to Safety of Large-Scale Fuel-Energy Systems

The zoning scheme for a large-scale fuel-energy system involving the natural gas wells and pipelines network can be made owing to the calculation of seismic gradient models coupled with modeling of a geographic cover of the being investigated land. This will give a possibility to outline places where the system functioning can be under risk because of the particular deep structure of the geological medium. On the other hand, such zoning can reveal the distinctive feature of subarea containing the gas deposit. The comparison of the results given by a novel algorithm and the basic technique of the gradient model computing is presented here.
Performances of Magneto-Electric Dipoles in an Antennas Array with a Reduced Beam Forming Network

In this paper, we propose an architecture of array based on parasitic element antennas with multiple excitations. The objective is to improve the directivity of these antenna arrays using Magneto-Electric Dipoles (MED). In this contribution, we consider a 49 (7 X 7) element array with only 8% of the elements directly fed. The others are fed by couplings and they are loaded on reactive values (ideally). The proposed radiating element is compared to a stacked patch design in the same reduced feed network configuration. A maximum directivity of 20 dBi is obtained, allowing several reconfigurable antenna scenarios based on such as design.
Spatial Approach and Mathematical Modeling of Dengue Disease Transmission by Seasonal using Statistical of the Data

This research studies the spatial approach and mathematical modeling of dengue disease transmission by seasonal using statistical of the data. The aims of the research are to study, analyze the relation and the effect of the seasonal with dengue epidemiology. The mathematical modeling for the epidemiology evolution of disease is constructed by considering the effect of seasonal and geographic information. The system of nonlinear differential equations are created and divide the human populations into 3 groups: the susceptible human, the infectious human and the recovered human who has the immune to that strain. For the infectious human group is subdivided into two groups, the infectious human in hot season and rainy season. The standard dynamical modeling method is applied to determine the behaviors of solutions to the model. The conditions require of the parameters for the disease free and endemic equilibrium states to be local asymptotically stable are obtained. Numerical simulations are seen to support the theoretical predictions in Songkla and Nakhonsithammarat Provinces.
A Robotic Peg-in-Hole Assembly Strategy Based on Variable Compliance Center

Many kinds of peg-in-hole assembly strategies for an industrial robot have been reported in recent years. Most of these strategies are realized by utilizing visual and force sensors to assist robots. However, complex control algorithms that are based on visual and force sensors will reduce the assembly efficiency of a robot. This issue is thoughtless in traditional assembly strategies but is critical to further improve the efficiency of assembly automation. In this work, a new assembly strategy that is based on a displacement sensor and a variable compliant center is proposed to improve robot performance in assembly tasks. First, an elastic displacement device for this assembly strategy is designed, and its performance is analyzed. The displacement signal generated by the displacement sensor is used to detect the contact state of the peg and hole and to guide the robot to adjust the posture. Second, an assembly strategy, including the advantages of passive compliance and active compliance, and a simple assembly control system are designed to improve the assembly efficiency. Last, the effectiveness of the proposed assembly method is experimentally verified using a robot with 6 degrees of freedom and a chamferless peg and hole with a small clearance (0.1 mm). The experimental results show that the assembly strategy can successfully complete the precision peg-in-hole assembly and assist the robot in accurate assembly in industrial applications.