Miracle-dz/UniFAR-FTUs huggingface dataset:
Estudio de las concentraciones de ozono superficial en la atmósfera de la Comunidad de Madrid usando muestreadores pasivos

The ozone is a secondary atmospheric pollutant which is generated for photochemical reactions of volatil organic compounds (VOC’s) and nitrogen oxides (NOx). In Spain the ozone is a big problem as a consequence of the solar radiation to reach high levels. Exposure over a period of time to elevated ozone concentrations can cause damage in the public health and alterations in the vegetation. The aim of this study is to carry out the development and validation of a measurement method to let asses the superficial ozone levels in the Comunidad de Madrid, by identifing the zones more significants, where to measure with UV photometric monitors (automatics methods) this pollutant and where the health and the vegetation can be affected. To such effect, passive samplers are used, which have glass fiber filters coated with a solution of sodium nitrite, potassium carbonate, glycerol and water. The nitrite ion in the presence of ozone is oxidized to nitrato ion, which it is extrated with ultrapure water and analyzed for ion chromatography, by seen proportional to the concentration existing in the sampling point. The results of validation from field tests indicate a excellent correlation between the passive and the automatic method. The higher superficial ozone concentrations are placed in rural zones, distanced of emission focus of primary pollutants (nitrogen oxides and volatil organic compounds.) principally in direction soutwest and northwest of the Comunidad of Madrid.
Nussbaum Gain Adaptive Control for Nonlinear Systems with State Constraints and Input Saturation

To deal with a class of uncertain nonlinear systems with time-varying full state constraints, input saturation and unknown control direction, a Nussbaum gain adaptive control scheme is provided in this paper. Time-varying asymmetric barrier Lyapunov functions (TVABLFs) are employed to prevent the transgression of state constraints. An auxiliary subsystem is utilized to analyze the effect of input saturation. Nussbaum gain technique is introduced to compensate the influence of unknown control direction and nonlinear terms arising from input saturation. It is shown that all the closed-loop signals are bounded in the semi-global sense, error signals converge to bounded compact sets around origin, and time-varying full state constraints and input saturation aren’t violated. The effectiveness of this method is indicated by a simulation example.
Effect of DBS Targeting Striatum on beta Oscillations in Parkinson’s Disease

The akinesia symptoms for Parkinson’s disease (PD) is characterized by the abnormal beta (13-30Hz) oscillations in the basal ganglia. Deep brain stimulation (DBS) can alleviate the movement disorders by targeting include thalamic ventralis intermedius nucleus, subthalamic nucleus, and globus pallidus. Here, we construct a computational model, an inhibitory loop between the striatum and globus pallidus externa (GPe). We present the firing pattern and spectral characteristics of striatal neurons and GPe neurons in simulated healthy and Parkinsonian states. Our model predicts that DBS can be applied to striatum neurons to inhibit beta oscillations. We show that the beta frequency activity of the GPe neurons is reduced when DBS is added to the medium spiny neuron (MSN) in the striatum. In particular, as the frequency of DBS becomes higher, the inhibition of beta oscillations in GPe neurons is better. Our model presents new predictions about the target of DBS for PD treatment.
A Novel Multi-Objective optimization Model for Integrated Photovoltaic/Hydroelectric Power Generation Operation

This paper proposes a multi-objective optimization model for integrated PV/hydroelectric power generation systems to minimize the fluctuation of the combined power output and at the same time maximize total income of the integrated systems. In addition, factors such as flood control, water supply, evaporation and precipitation are considered. The multi-objective optimization problem is solved by the non-dominated sorting genetic algorithm (NSGA-II) with improved sorting and diversity distribution method that significantly increases the efficiency and speed of the solution. Finally, the proposed model is applied to integrated Longyangxia PV/hydroelectric hybrid systems. Simulation results demonstrate the effectiveness of the proposed methods.
A Stochastic Hole Trapping-Detrapping Framework for NBTI, TDDS and RTN

A stochastic framework is presented to model hole trapping and detrapping into and out of individual defects that are present in the gate dielectric of a p-channel MOS transistor. The model calculates thermionic reactions between uncharged and charged states of a defect that are separated by an energy barrier, by using the Gillespie Stochastic Simulation Algorithm (GSSA). The model is validated using experimental data from small area devices under Negative Bias Temperature Instability (NBTI), Random Telegraph Noise (RTN) and Time Dependent Defect Spectroscopy (TDDS) studies.
Combination of Planes and Image Edges for a Visual Odometer for Weakly Textured Structured Environments

Camera trajectory estimation has generated a lot of interest during the last decades, especially for robotic positioning. As it is well-known, outdoors positioning mainly relies on GPS, whereas visual odometry is typically preferred indoors. Nevertheless, when the environment is weakly textured, visual odometers typically degrade in performance. Facing this situation, this paper develops and tests a novel visual odometer that combines image edges and planar information to estimate the trajectory of an RGB-D camera in environments that lack texture. We also present a plane matching method based on a graph matching technique. To conclude, a comparison involving the proposed odometer for two well-known datasets and other visual odometers and SLAM systems is reported. This comparison shows that our method outperforms the others for indoor poorly-textured environments, while similar performance is obtained for indoor highly-textured environments.
An Ultra-Compact 0.5~3.6-GHz CMOS VCO with High-Q Active Inductor

In this letter, an ultra-compact and wide-tuning-range voltage-controlled oscillator (VCO) with high-Q active inductor is designed, implemented, and verified in a standard 0.18-μm complementary metal-oxide-semiconductor (CMOS) process. The proposed active inductor topology with feedback resistors not only achieves high quality (Q) factor but also features wide inductance-tuning-range, which are impractical in conventional passive solutions. Moreover, due to the use of active inductors in the LC tank, the chip area of the circuit is only 0.018 mm2. With 1.8-V supply voltage and other tuning mechanisms, the VCO consumes 8.1~24.3-mW dc power. The VCO can be tuned from 0.5 GHz to 3.6 GHz, demonstrating a 151% frequency tuning range.
Oxygen Partial Pressure Estimation of the Cathode Channel for PEM Fuel Cell System

The oxygen partial pressure in the cathode channel is a crucial parameter for the output performance of proton exchange membrane fuel cells (PEMFC). However during the fuel cell operation, the oxygen partial pressure can not be measured by the transducer. In this paper, an observer is proposed to estimated the oxygen and nitrogen partial pressure in the cathode channel of PEMFC. Firstly, a model of the air supply system which include the oxygen and nitrogen dynamics is developed. In further analysis of the model, an intermediate variable is designed to transform the original nonlinear observer design problem into a linear observer design problem. The stability of the observer is analyzed through Lyapunov based method. The effectiveness of the proposed observer is proved based on the simulation results.
Human Activity Classification with Multi-frequency Spectrogram Fusion and Deep Learning

In this paper, we propose an approach that uses deep convolutional neural network (DCNN) based on multi-frequency spectrogram fusion to classify the human activity with respect to stepped frequency continuous wave (SFCW) radar. The main principle of the fused spectrogram is to adopt the guide image filter (GIF) to decompose all spectrograms into a noise-free base layer and a detail layer containing high-frequency detail information and noise. The weighted averaging (WA) method is used to fuse the body information contained in the base layer. The detail layer is denoised by the adaptive enhancement and denoising technology and then adopt direct superposition to keep the details in the fused detail layer. Finally, both of them are recombined to obtain the fused spectrogram. The fused spectrogram as an input to DCNN network achieves the classification. The algorithm based on spectrogram fusion reduces complexity of DCNN construction, and simultaneously improves classification accuracy.
Serial Elastic Actuators Torque Control Approach Based on Active Disturbance Rejection Control

Serial Elastic Actuator (SEA) system attracts much attention recent years in robotics research. The control methods for SEA is studied especially considering the unknown and time-varying dynamic load. Most methods are based on the predetermined nominal dynamics of the SEA system. However, imperfect dynamics and time varying reference torque make the torque tracking problem difficult. In this paper, the Active Disturbance Rejection Control (ADRC) method is proposed to solve the above problems. The dynamics of SEA is simplified by considering the dynamics of load as the unknown disturbance. An Extended State Observer (ESO) is designed to identify the model uncertainties. The control framework could observe and reject the disturbance effectively. Finally, the effectiveness of the ADRC based torque controller is verified and compared with traditional cascade PID controller by using MATLAB/ADAMS Co-simulation, especially considering the case that SEA is implemented in a vertical plane which makes the dynamics much more complicated.
Development of a Fault-Tolerant Permanent-Magnet Synchronous Motor

The failure of the motor or controller of a motor servo system could lead to terrible casualties and property loss in fields such as aviation and transportation. Current research mainly focuses on the algorithm for fault-tolerant control and the topological structure of hardware. Such approaches may reduce the system frequency through the use of a complex algorithm or increase the system cost through the use of special components. The present paper proposes and tests a multi-redundant, permanent-magnet synchronous motor. Through the parallel connection of multiple independent windings and a supporting control method that controls each redundancy separately, the system continues to run even when the winding or controller of one redundancy suffers an open-circuit or short-circuit fault. The system controller is easy to build and minimize without special components and does not require additional algorithms.
Digital Predistortion Concepts for Linearization of mmW Phased Array Transmitters

Linearization of millimeter-wave (mmW) phased arrays is one of the key enablers for improving the system performance in terms of power, efficiency and linearity. However, phased array transceiver topologies that have multiple parallel nonlinear components with a shared digital input challenge the standard digital predistortion techniques. In addition, different analogue beamforming techniques complicate the linearization even further due to the fact that the signal nonlinearity has to be observed or modelled over-the-air (OTA) together with the impacts of antennas and even the directive mmW radio channel. The best linearization strategy depends on the system level targets of linearity such as error vector magnitude and adjacent channel power ratio which have slightly different nature when observed in the radiated far-field. In this paper, we present our view and the status of the literature on the topic of phased array digital predistortion. We highlight that the nonlinear distortion have a beam shape which may be different from the linear part of the beam. We also review the antenna array figures of merit describing the nonlinearity. Finally, we show an experimental example of OTA linearization of a 28 GHz phased array transmitter.
Vertical Jump Control of Hydraulic Single Legged Robot (HSLR)

This paper presents a vertical jumping trajectory planning and landing buffer control methods that enable a hydraulic driven single legged robot to execute load-bearing take-off and stable landing motions. After a brief description of the robot, the kinematics and dynamics models of HSLR are established based on the actual structure of the robot. Then the paper describes the details of jumping trajectory planning method based on spring loaded inverted pendulum (SLIP) model, which can realize vertical jump control with a desired height. Moreover, landing buffer strategy based on single legged virtual model is presented to protect robots from the impact damage. Finally, the experimental results show that the proposed method for HSLR can achieve vertical jump to 0.2m as well as a stable landing buffer performance.
Defect Enhancement Generative Adversarial Network for Enlarging Data Set of Microcrack Defect

This paper presents a micro defect data set expansion method focuses on the microcrack defect of magnetic ring. Deep neural networks require a mass of training samples to be fully optimized. However, it is difficult to obtain a mass of defective samples in industrial field. In the case of insufficient samples, using GANs (Generative Adversarial Networks) for data expansion can effectively solve the problems of model over-fitting and low detection accuracy caused by insufficient training samples. However, it is difficult for conventional GANs to generate microcrack defective samples of high quality. This paper presents Defect Enhancement Generative Adversarial Network (DEGAN). This model can generate microcrack defects with obvious defect characteristics and high diversity. The experimental results show that the defective samples generated by DEGAN are very close to the real ones. The data set amplified by this model can significantly optimize deep neural network and achieve higher defect detection accuracy.
Impact of HV Battery Cables’ Emissions on the Signal Integrity of 2-Wire Ethernet Communication in Automotive Application

Beside the electrification of vehicle traction systems, the implementation of autonomous driving functions is the main future trend in the automotive sector. For the realization of autonomous driving a large number of sensors is required. Due to the large amount of data needed for autonomous driving, the data rates of the conventional communication systems are not sufficient and therefore, new communication systems such as 100Base-T1 are implemented in the vehicle architecture. On the other side, in electric and hybrid electric vehicles, the electrical traction system operates at voltages beyond 400 V in close proximity to the sensors and communication lines. To ensure the electromagnetic compatibility (EMC) of the overall vehicle, the traction system is entirely shielded and isolated form the other vehicle systems. However, the communication channel for the automotive Ethernet is realized using unshielded twisted pair (UTP) cable, which is prone to electromagnetic disturbances. As a result, the EMC coupling between these high voltage (HV) traction systems and the communication channels is an important aspect, which requires a comprehensive investigation. In this work a test setup consisting of an electric vehicle traction system is used in parallel to a 2-wire Ethernet communication via an UTP cable. The setup is used to investigate the impact of the disturbances from HV battery cables on the immunity and the signal integrity of the Ethernet communication.
Optimization of Protograph LDPC Codes based on High-Level Energy Models

This paper considers the optimization of the energy consumption of LDPC decoders. For a given protograph, two models are introduced to approximate the energy consumption of a quantized Min-Sum decoder. The first model takes into account the number of operations performed by the decoder, the second model considers the number of bits that are written into memory. An optimization problem is then formulated to minimize the decoder energy consumption with respect to the protograph and the number of iterations, while satisfying a given performance criterion. Finally, an optimization method based on Differential Evolution is proposed.
THz Generation in Laser Filament under Electric Field Conditions

We present experimental study results of THz range radiation generation in a laser plasma. Comparison of one and two-color optical pumping schemes is presented. Dependence of THz radiation energy from the electrical field application point to filament is shown. The maximum generation efficiency is observed when an electric field is applied to geometrical focus point (laser filament is formed before geometrical focus point). The mechanisms of this effect are discussed.
Research on the Application of Agricultural Big Data Processing with Hadoop and Spark

Numerous terminal equipment in the agricultural park collect environmental data that affects crop growth every day. Proper analysis of these massive amounts of data can acquire useful information on the status of crop growth. In this paper, two cloud computing frameworks, Apache Hadoop and Apache Spark are used to study agricultural big data analysis. This paper developed applications for real agricultural park big data analysis in both frameworks and implemented a yield prediction model based on multiple linear regression using Spark MLlib. The performance of the two frameworks in agricultural big data processing was studied and compared through various experiments. The experiments show that the comprehensive performance of Spark is higher than Hadoop, and the model can obtain better prediction results.
Bipartite Consensus for a Class of Double-Integrator Multi-Agent Systems with Antagonistic Interactions

In this paper, the bipartite consensus problem is addressed for a class of double-integrator multi-agent systems with antagonistic interactions. The cases with and without communication time delay are considered. In particular, if the communication time delay is not taken into account, the bipartite consensus of the multi-agent systems with directed signed graph can be achieved by the proposed distributed controller. If the communication time delay is considered, the bipartite consensus of the multi-agent systems with undirected signed graph can be achieved and the upper bound of the time delay is further derived. Finally, some numerical examples are provided to illustrate the correctness of the results.
A Mobile Phone Screen Cover Glass Defect Detection MODEL Based on Small Samples Learning

Mobile phone screen cover glass (MPSCG) defect detection is an important part to ensure the quality of mobile phone products. In view of the difficulty of obtaining a large number of defect samples on the industrial production line, this paper designs a MPSCG defect detection model suitable for small samples learning. Because of the high resolution of MPSCG original image, the pre-processing software is designed to automatically segment the original high-resolution image into a series of sub-images. Furthermore, the Deep Convolutional Generative Adversarial Networks (DCGAN) network is designed to automatically extract and fuse the defect features to augment and generate defect samples. Then, based on the augmented data set, we improve and train the detection model of Faster R-CNN. The detection model achieved a very better detection result, which solved the problem that the number of defect samples in the industry is small and the deep learning requires a large number of samples. The experimental results demonstrate the effectiveness and feasibility of DCGAN combined with Faster R-CNN for the defect detection of MPSCG.
Linear set-Membership state Estimation with l∞ bounded Disturbances

Given a pair of uncertain input and measurement disturbance signals $\eta(\Delta-1)$ and $\xi(\Delta)$, respectively, satisfying $|\mathrm{h}(\Delta -1)\Vert_{\infty}\leq 1, \Vert\xi(\Delta)\Vert_{\infty}\leq 1$, and an ellipsoid containing the uncertain system state $\mathrm{x}(\Delta-1)$, a sub-optimal approach to computing a set membership filter which constructs a minimal ellipsoid to contain $\mathrm{x}(\Delta)$ is derived.
DEMOS: a DEsign Method for demOcratic information System

The issue of democracy in society is at the heart of our current concerns. Organizations and their information systems are also concerned by this issue. Democracy in organization requires a debate about norms, values and language encapsulated in the information system. Participatory design approaches address this issue by proposing a democratic empowerment for users during design phase of projects. To go further, we propose a structured method to integrate democracy into information system. This method named DEMOS for DEsign Method for demOcratic information System is described and then illustrated by a real experiment provided by a “lifelong training” service at the University.
Effect of Confinement on Collective Dynamics of Nanoconfined Water in DMPC Multilamellar Vesicle

Confinement effect on collective reorientational dynamics of nanoscopically confined water in DMPC multilamellar vesicle (MLV) was investigated by using GHz dielectric relaxation spectroscopy. The collective dynamics of hydration and beyond the hydration layer was estimated when the interlamellar distance is reduced by increasing PEG concentrations. Beyond hydration layer is accelerated over bulk water and this means nanoconfinement induces longer-range perturbation on water dynamics than generally assumed perturbation range, i.e., hydration layer. As osmotic pressure is increased, both relaxation times begin to change and shows opposite trend. This result demonstrates that confinement induces coupling between hydration and beyond hydration region.
Research on Speech Emotion Recognition Technology based on Deep and Shallow Neural Network

Speech emotion recognition has become a hot topic in the field of human-computer interaction. In order to improve the accuracy of emotion recognition, this paper proposes a new speech emotion recognition technology based on the combination of deep and shallow neural networks. First, the speech signal is preprocessed, then the parallel training sample set is established, and the Deep Belief Network (DBN) is used to automatically extract and recognize the speech emotion features. Finally, the shallow neural network is used to obtain the final recognition results. In order to evaluate the quality of the new method, we compared three systems to identify five emotions, and the experimental results show that the proposed method can effectively improve the accuracy of emotion recognition.
Lidar-based Traversable Region Detection in Off-road Environment

Traversable region detection is a fundamental problem in the field of autonomous driving. This paper proposes a fast method to detect obstacles and obtain the traversable region in the off-road environment. Our method takes advantage of both radial features and transverse features based on the high definition of 3D Lidar points. First, we manage Lidar points by scanning lines and sectors in the polar system at the same time. Then the most obstacles can be quickly detected by using radial features in the polar system. For the false detection, transverse features are applied to verify the results. Finally, the constrained region within the nearest obstacle points in each sector defines the traversable region around the vehicle. Our method can detect positive obstacles, negative obstacles, and hanging obstacles in real-time. The experimental results show the robustness and accuracy of the proposed method in different kinds of off-road environments.