Miracle-dz/UniFAR-FTUs huggingface dataset:
Integral Super Twisting Sliding Mode Based Sensorless Predictive Torque Control of Induction Motor

In the direct torque control (DTC) of induction motor (IM) drive systems utilizing model predictive torque control (MPTC) in the inner loop and classical proportional integral (PI) control in the outer loop is prone to weaknesses, such as uncertainties, external disturbances, parameter variations, and nonlinear dynamics. A high-performance control system for speed and torque is required to guarantee a smooth and robust control architecture that can withstand such unpredictable effects. In this study, we propose an integral super twisting sliding mode control (ISTSMC) to address the shortcomings of the classical PI used in the outer loop of DTC drive systems. A sliding mode speed observer is also designed and utilized to overcome problems associated with mechanical sensors. The robustness of the proposed control strategy and fast dynamic speed response are compared favorably with a benchmark PI controller and conventional sliding mode control (SMC). The ability of the proposed system to regulate both speed and torque was evaluated through simulations, under various fault perturbations, parameter variations, and load disturbances, conducted in MATLAB/ Simulink. Various performance indices were used to demonstrate the superior performance of the proposed strategy compared to a benchmark PI system and conventional SMC-based MPTC approaches.
3D Convolutional Recurrent Global Neural Network for Speech Emotion Recognition

Nowadays emotion recognition has become the most interesting topic due its important role in Human Computer Interaction (HCI). Speech emotion recognition is a part of this topic which is gaining more popularity in the last years. To recognize emotion, many methods have been developed using machine learning. In this work, we use a deep neural network which takes as input personalized features. To test our proposed system we used several databases with different languages to train and to evaluate our model.
Switching Linear Parameter-Varying Controller Design With H∞ Performance Based on Youla Parameterization

This article utilizes the Youla parameterization method to design a switching linear parameter-varying (LPV) output-feedback controller. The switching controller consists of several LPV controllers designed in advance and independently with corresponding <inline-formula> <tex-math notation="LaTeX">$\text{H} \infty $ </tex-math></inline-formula> performance. Based on the Youla parameterization idea, the controller design procedure is decomposed into two steps. First, a central controller is designed to ensure global <inline-formula> <tex-math notation="LaTeX">$\text{H} \infty $ </tex-math></inline-formula> performance. Second, a switching controller is constructed to achieve the <inline-formula> <tex-math notation="LaTeX">$\text{H} \infty $ </tex-math></inline-formula> performance in each subset under arbitrary switching. This scheme is designed to develop state-space realizations of Youla parameters from the central controller. The realizations not only ensure that the closed-loop system satisfies the <inline-formula> <tex-math notation="LaTeX">$\text{H} \infty $ </tex-math></inline-formula> performance under arbitrary switching but also guarantees the corresponding <inline-formula> <tex-math notation="LaTeX">$\text{H} \infty $ </tex-math></inline-formula> performance for local subsystem at each fixed point. The simulation results show the effectiveness of the proposed method.
A multimodal Physiological-Based Stress Recognition: Deep Learning Models’ Evaluation in Gamers’ Monitoring Application

emotional states detection from physiological signals has many potential applications. In Human-Machine or Human-Human interaction systems, stress detection could provide users with improved services and can be a tool for monitoring and preventing potential stress-related pathologies. Traditional machine learning techniques for automatic stress recognition have been used in previous researches but they sometimes present specific limitations. The emergence of deep learning permits the reveal of underlying patterns in body response witch, otherwise would not be easily observed. In this paper we explore the application of Long Short-Term Memory (LSTM) and Deep Neural (DNN) Networks for real time stress monitoring in young gamers. We base our study on their body responses. For this, we use physiological signals such as the electrocardiography (ECG), the electrodermal activity (EDA), and the electromyography (EMG), measured by non-invasive wearable sensors. The result of the study provides an evaluation of both models’ capacity in predicting real time gamers’ emotional state built on the variation of their physiological parameters.
PUF Protected Variables: A Solution to RFID Security and Privacy Under Corruption With Temporary State Disclosure

RFID tag corruption is a powerful attack on RFID systems, especially when it reveals the tag’s temporary state. Under such an attack, no RFID scheme can achieve both security and privacy if the tags are not endowed with some hardware primitives, such as physically unclonable functions (PUFs), to prevent adversarial access to secret information. However, the use of such primitives does not constitute a guarantee for security and privacy because they do not substitute a good RFID system design. In this paper a general technique is proposed, to translate any (PUF-based) RFID scheme that is secure and private under corruption without temporary state disclosure into a PUF-based RFID scheme that is secure and private under corruption with temporary state disclosure. Our technique is optimal with respect to the tag overhead induced by PUFs. The technique is richly exemplified on both RFID and PUF-based RFID schemes. As a notable result, the first PUF-based RFID scheme is obtained, that is secure and forward private, but not destructive private, under corruption with temporary state disclosure. By using our technique, some flawed PUF-based RFID schemes that have been proposed so far in the literature can be fixed.
The Planning Model of Transmission Line Using Bidirectional Particle Swarm Optimization Based on Time Series

In the evaluation of construction cost of transmission line, due to the long construction time and large amount of engineering, the required construction materials are difficult to be purchased at the beginning of the construction. For such cases, based on the BP neural network prediction of the initial construction cost, and combined with the ARIMA model of time series, the floating construction cost of geographical information system raster matrix is evaluated. By improving the particle swarm optimization into a bidirectional search mechanism and introducing the corner construction cost comparison mechanism, the search amount and construction cost of algorithm are reduced to a greater extent, and the transmission line planning model under the time series construction process is finally obtained.
Associations of Non-Alcoholic Beverages with Major Depressive Disorder History and Depressive Symptoms Clusters in a Sample of Overweight Adults

Background: Meta-analysis of observational studies concluded that soft drinks may increase the risk of depression, while high consumption of coffee and tea may reduce the risk. Objectives were to explore the associations between the consumption of soft drinks, coffee or tea and: (1) a history of major depressive disorder (MDD) and (2) the severity of depressive symptoms clusters (mood, cognitive and somatic/vegetative symptoms). Methods: Cross-sectional and longitudinal analysis based on baseline and 12-month-follow-up data collected from four countries participating in the European MooDFOOD prevention trial. In total, 941 overweight adults with subsyndromal depressive symptoms aged 18 to 75 years were analyzed. History of MDD, depressive symptoms and beverages intake were assessed. Results: Sugar-sweetened soft drinks were positively related to MDD history rates whereas soft drinks with non-nutritive sweeteners were inversely related for the high vs. low categories of intake. Longitudinal analysis showed no significant associations between beverages and mood, cognitive and somatic/vegetative clusters. Conclusion: Our findings point toward a relationship between soft drinks and past MDD diagnoses depending on how they are sweetened while we found no association with coffee and tea. No significant effects were found between any studied beverages and the depressive symptoms clusters in a sample of overweight adults.
Modeling and Prediction of PV Module Performance Under Different Operating Conditions Based on Power-Law I–V Model

Single-diode model is commonly used for modeling the electrical properties of photovoltaic (PV) modules, in which the current–voltage (I–V) equation is implicit and not convenience in parameter identification and power prediction. In this article, a novel method based on the power-law model (PLM) is proposed to model and characterize the electrical properties of PV modules under various operating conditions. Due to inherent simplicity and explicit trait of PLM, I–V properties of PV module under different operating conditions are predicted without using any nonelementary functions or iterative process, which reduces the computational complexity and costs. The dependence of the parameters in PLM on solar irradiance and module temperature is derived and investigated in detail. Moreover, the dependence of shape parameters in PLM on operating conditions is discussed thoroughly for the first time. Using our method, the parameters in PLM under various operating condition are calculated and then the I–V curve is predicted accurately and effectively. The accuracy and reliability of the model are validated by massive I–V curves measured outdoor in large-scale conditions. The capability of the model is demonstrated for different types of PV modules. The proposed processes are simple and especially useful to calculate the actual performances of PV modules under operating conditions, making it scalable for direct online application.
Coherent Dual-Band Radar-Over-Fiber Network With VCSEL-Based Signal Distribution

Today, the advent of radar sensing in applications requiring ever higher precision, such as automotive and industrial monitoring, needs the development of systems with superior resolution, stability and accuracy on 2D/3D spaces. Multiple-input–multiple-output (MIMO) radar systems have been studied for more than twenty years, proving to achieve excellent resolutions by merging simultaneous observations from sparse multiple radars. In addition, long-term frequency/phase coherence among distributed signals to and from remote and widely distributed radar frontends, enables a centralized architecture where, in a common base station (BS) site, all the received signals can be coherently processed in synergy, allowing to further improve detection, localization, and imaging capabilities. Radio-over-fiber (RoF) solutions represent an enabling technology for guaranteeing coherence among all radar signals coherently generated in the BS and distributed/collected to/from the radar heads by means of optical fiber links. Despite the unprecedented frequency-agility provided by photonics-based radiofrequency (RF) up-/down-conversion in the optical domain, for applications requiring massive low-cost production and power efficiency, such as advanced driver-assistance systems, it might be convenient to transmit and receive conventionally generated RF signals, and to limit the use of photonics, as in RoF, to RF signal distribution through cheap high-capacity optical links, in order to avoid EM interference, minimize loss, signal distortion and link encumbrance, still keeping the coherence among distributed signals. In this work, we present a MIMO radar-over-fiber (RaoF) network, where a common BS transmits and receives RF signals through optical standard single-mode fiber (SSMF), to and from two remote radar transmitters (TXs) and four receivers (RXs), all remote with respect to the BS and distributed on a 3 m-long baseline, and employing cheap patch antennas. Electro-optic conversion is achieved through direct modulation (DM) of low-cost, power-effective, and high-speed vertical-cavity surface-emitting lasers (VCSELs) working in the 1.3 µm-wavelength regime. Such a wavelength ensures propagation over SSMF with negligible distortions up to the km-range. The system is tested in a down-scaled indoor scenario and an RF carrier of 8.5 GHz is used, due to the limited frequency response of the employed VCSELs. System performance is evaluated with 1.4 GHz-bandwidth signals, comparing non-coherent and coherent signal elaboration. In addition, dual-band operation in the same RF region is tested, confirming that VCSEL-based optical links are a viable way to guarantee phase coherence among the distributed signals and among multiple frequency bands. Coherent MIMO processing also confirm the potential to achieve superior range and cross-range resolution than non-coherent data fusion.
Pre-Overload-Graph-Based Vulnerable Correlation Identification Under Load Redistribution Attacks

The load redistribution (LR) attack, as a special type of false data injection attacks, aims to distort the results of the security-constrained economic dispatch (SCED) and drive the system to non-optimal or even unsecure operating conditions by injecting false data to the node power injection and branch power flow measurements. The LR attack represents a great danger to the safe and reliable operation of the current power grid where the physical infrastructure is increasingly intertwined with cyber components. In this paper, we propose a novel pre-overload vulnerability graph (POV-graph) approach to systematically assess, evaluate, and quantify the system vulnerability under an LR attack. The proposed approach is formulated based on the specific overloading mechanism of the LR attack as well as their cascading patterns. A set of case studies are performed on the IEEE 39-bus, 118-bus, and RTS-96 systems to validate the effectiveness of the proposed approach. Simulation results have shown, for the first time that, by revealing the vulnerability correlations among branches, the proposed approach allows the system operator to better utilize the defense resources and focus on strengthening a limited number of vulnerable branches to enhance the system’s overall security against LR attacks.
Indução artificial de lactação em bovinos: histórico e evolução

The early culling of dairy cows due to reproductive failure, management problems and other factors that lead the dairy cows to be dried-off without being pregnant, increase costs and decrease profitability of milk production. A possible alternative is the use of artificial lactation induction, a tool that mimics peripartum endocrine profiles with the aim of inducing milk synthesis from the mammary gland. Protocols of induction of lactation in cattle have been studied since the 1940s, and the first studies consisted of hormonal applications for nine months. In the 1970s, there was a significant evolution, when it was established that seven days of steroid hormones application were sufficient to induce lactation. Since then, several protocols using progesterone and estradiol have been tested. Currently, commercial protocols lasts for 21 days and use large volumes of hormones, requiring daily handling of animals. Although current protocols are efficient, enabling good response rate and milk production, so far the degree of discomfort to which animals are exposed is unknown. The purpose of this review is to provide background information on the evolution of protocols, their applications, drawbacks, possible solutions and prospects.
Predicting Quality and Popularity of a Movie From Plot Summary and Character Description Using Contextualized Word Embeddings

Narrative is an essential factor that makes games more enjoyable. However, predicting the story quality has been challenging for decades. In this paper, we propose to use contextual word embedding models such as BERT and ELMo, for predicting a story’s success in terms of quality and popularity by using the story text only. Since deep learning models generally require extensive data, we conducted experiments to test the efficacy of our proposed model by leveraging the movie plot summaries. We present the results of the evaluations and conclude with discussions.
An Embedded Level-Shifting Dual-Rail SRAM for High-Speed and Low-Power Cache

An embedded level-shifting (ELS) dual-rail SRAM is proposed to enhance the availability of dual-rail SRAMs. Although dual-rail SRAM is a powerful solution for satisfying the increasing demand for low-power applications, the enormous performance degradation at low supply voltages cannot meet the high-performance cache requirement in recent computing systems. The requirement of many level shifters is another drawback of the dual-rail SRAM because it degrades the energy-savings. The proposed ELS dual-rail SRAM achieves energy-savings by using a low supply voltage to precharge bitlines while minimizing the performance overhead by appropriately assigning a high-supply voltage to critical circuit blocks with effective level-shifting circuits. The sense amplifier embeds a level-shifting operation, thereby operating with a high supply voltage for a fast sensing operation. The proposed dynamic output buffer resolves the potential static current problem and improves the read delay. The number of level shifters is reduced using a proposed write driver, which conducts level-shifting and write-driving simultaneously. The proposed ELS dual-rail SRAM achieves low-power operation with 71.4% power consumption compared to single-rail SRAM with 72% performance overhead in circuit-level simulation, while the previous hybrid dual-rail SRAM shows 67.8% energy consumption with 270% performance overhead. In architecture-level simulation using Gem5 simulator with SPEC2006 benchmarks, the system with the ELS dual-rail SRAM caches shows, on average, 29% performance improvement compared to that of the system with the hybrid dual-rail SRAM caches.
Walking Balance Control for Humanoid Soccer Robot on Synthetic Grass

Humanoid Soccer Robot is one of the divisions in the Robocup Competition. Robocup is an organization that focuses on research and development of soccer robots. With the aim to build a robot soccer team that will be able to fight humans in 2050. In humanoid robots, the main problem in developing humanoid robots is how to make humanoid robots run stably on uneven surfaces. This paper describes how to maintain the stability of the humanoid football robot called EROS, by optimizing walking movements. Optimization is done by adjusting the movement of walking to the footing conditions. The default robot position will be adjusted based on the position of the robot's center of pressure (CoP), which is then combined with the Linear Inverted Pendulum Model (LIPM). To find out the condition of the pedestal base, the pressure sensor is mounted on the sole of the robot's feet. So it is known the position of the pressure center on the robot, which will be used as a parameter in planning the walking motion. This system has been implemented on humanoid robots with a height of 520 mm, as well as running on synthetic grass under different conditions at a constant speed. From the experiments that have been done it can be seen that by applying this method makes the robot more stable when it is on an uneven surface.
Cortex-M Simulator

This paper describes a project of simulator of Cortex-M microprocessors. This project builds on top of Unicorn Engine, which is used to simulate the ARM core. Benefit of this project is capability to load a production firmware and to replace microprocessor peripherals. The same firmware can be executed on a physical board and the same firmware can be simulated. This allows quick continuous integration and testing for development of embedded firmware.
An Attention-based Approach for Traffic Conditions Forecasting Considering Spatial-Temporal Features

Accurate and efficient traffic conditions forecasting is a significant challenge in Intelligent Transportation System (ITS) applications. The conditions can be characterized by traffic speed. To forecast the traffic conditions accurately, we propose a novel attention-based GCN-GRU hybrid model named AGG which can capture the spatial and temporal features of traffic speed simultaneously. In this model, the Graph Convolutional Network (GCN) captures the topological features for modeling spatial correlations. The Gated Recurrent Unit (GRU) captures the temporal features for modeling temporal correlations. The attention mechanism is used to assign weights to features according to the degree of importance of speed data, further improving model's forecasting precision. Then the experiments in real-world traffic speed data reveal that the AGG model can efficiently capture the spatial and temporal features of traffic speed and realize the precise forecasting of traffic conditions.
A Deep Neural Model for Pedestrians Detection with Danger Estimation

Personal mobility vehicles (PMV) with the autonomous driving system is required for supporting movement-limited persons. To realize their safe movement, researchers extract pedestrian features from images without considering pedestrian characteristics. However, before these extractions, it is also important to estimate which person should be paid attention and assigning priority order by the degree of danger for PMV’s collision avoidance. In this paper, we propose a deep neural model for pedestrian detection with danger estimation. Finally, we show some experimental results and discuss parameters that are important for pedestrian danger estimation for each experimental scenario.
Inhomogeneous Image Segmentation Using Hybrid Active Contours Model With Application to Breast Tumor Detection

The most fatal and frequent cancer amongst women is breast cancer. Mammography provides timely detection of lumps and masses in breast tissue, but effective diagnosis requires accurately identifying malignant tumor boundaries, which remains challenging, particularly for images with inhomogeneous regions. Therefore, we propose an active contour method based on a reformed combined local and global fitted function to address breast tumor segmentation. This combined function is strengthened by a proposed average energy driving function to capture obscure boundaries for regions of interest more precisely from inhomogeneous images. Including a p-Laplace term eliminates reinitialization requirements and suppresses false contours in the segmentation. Bias field signal, which causes image homogeneity corruption, is estimated by bias field initialization to ensure independence from the initial contour position. Local and global fitted models are incorporated by introducing bias fields within them. The proposed method was tested on the MIAS MiniMammographic Database, with quantitative analysis to calculate its accuracy, effectiveness, and efficiency. Experimentation confirmed the proposed method provided superior results compared with previous state-of-the-art methods.
Finite-Time Global Trajectory Tracking Control for Uncertain Wheeled Mobile Robots

In this article, a finite-time global trajectory tracking control is proposed for a class of uncertain wheeled mobile robot systems. It could ensure that the system output tracks the desired trajectory within finite-time. Combined with the dynamic model, the tracking error model is decomposed into an angular velocity error subsystem and a position error subsystem. The finite-time theory is applied to the design of finite-time control law and the stability analysis for the angular velocity error subsystem. Moreover, Backstepping technology is introduced into the position error subsystem. In addition, Fuzzy Logic Systems (FLSs) are investigated to approximate system’s unknown smooth function. Theoretical analysis shows that all signals are bounded, and the system has global progressive stability. The simulation verifies the effectiveness of the proposed method.