Miracle-dz/UniFAR-FTUs huggingface dataset:
Barrier Function-Based Asymptotic Tracking Control of Uncertain Nonlinear Systems With Multiple States Constraints

This technical note focus on the tracking control problem of uncertain nonlinear systems with multiple states constraints. Based on the Barrier Lyapunov function and backstepping technology, a new continuous smooth control solution can be ultimately synthesized to realize asymptotic tracking control in presence of multiple states constraints and modeling uncertainties. Firstly, the modeling uncertainties are divided into periodic and un-periodic components and Fourier expansion technology is employed to translate the periodic disturbance into the form which can be easily compensated. Then the Barrier Lyapunov function are flexibly utilized to design the virtual control law of every step and the final controller, which can guarantee the specified states within certain bounds regardless of the amplitude of system output. Meanwhile, a novel nonlinear control technology is introduced to each design step to realize the final asymptotic tracking control despite the matched and mismatched uncertainties. By analyzing the choice of the control parameters, the backstepping cross-term is skillfully dealt with and the stability of the whole system is proved rigorously. Finally, the simulation results on a three-order nonlinear hydraulic system demonstrate the satisfactory performance of the proposed control method.
Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model

The increase in the volume of user-generated content on Twitter has resulted in tweet sentiment analysis becoming an essential tool for the extraction of information about Twitter users’ emotional state. Consequently, there has been a rapid growth of tweet sentiment analysis in the area of natural language processing. Tweet sentiment analysis is increasingly applied in many areas, such as decision support systems and recommendation systems. Therefore, improving the accuracy of tweet sentiment analysis has become practical and an area of interest for many researchers. Many approaches have tried to improve the performance of tweet sentiment analysis methods by using the feature ensemble method. However, most of the previous methods attempted to model the syntactic information of words without considering the sentiment context of these words. Besides, the positioning of words and the impact of phrases containing fuzzy sentiment have not been mentioned in many studies. This study proposed a new approach based on a feature ensemble model related to tweets containing fuzzy sentiment by taking into account elements such as lexical, word-type, semantic, position, and sentiment polarity of words. The proposed method has been experimented on with real data, and the result proves effective in improving the performance of tweet sentiment analysis in terms of the $F_{1}$ score.
Detection of Astronauts’ Speech and Language Disorder Signs during Space Missions using Natural Language Processing Techniques

As technology evolves at an alert pace, the desire and need for exploration of the outer space becomes more and more prevalent. Human exploration and colonization of the Moon or Mars represents a strategic goal of international space agencies for the coming years. This implies a long-term exposure of astronauts in an extreme environment characterized by microgravity, isolation, confinement and other stressors which cause damage to both their bodies and minds. The longer the exposure to space environment, the greater the chance for an astronaut to face psychological problems. In, this regard, the herein paper is interdisciplinary orienting towards speech and language disorder analysis, especially on oral language disorders, for shaping a tool of behavioral psychology. Considering the well-known connection between speech and language disorders with interpersonal and intrapersonal psychological problems, this paper presents specific semantic assessments based on natural language processing techniques in order to gain understanding and shape a speech and language tool able to detect psychological impairments, especially for astronauts, but not merely.
Integrated Power and Device-to-Device (D2D) Communications Simulator for Future Power Systems

We present a simulator constructed in Mat-lab/Simulink used to simulate power systems and the underlying communication system, i.e., device-to-device (D2D) communication protocol stack of Long Term Evolution (LTE). The simulator is built using Matlab’s LTE System Toolbox, SimEvents, and Simscape Power Systems in addition to an in-house developed interface software to facilitate D2D communications in smart grid applications. To test our simulator, we demonstrate a simple fault location, isolation, and restoration (FLISR) application to show that the LTE message timing is consistent with the relay signaling in the power system. Moreover, it was demonstrated that communication failures occurred in the FLISR simulation when the signal to interference plus noise ratio (SINR) of received messages ranged from -2 to -6 dB, which is consistent with the D2D throughput conformance testing results available in Matlab LTE System Toolbox documentation.
Clustering of Spatial Data with DBSCAN: An Assessment of STARK

The ever-increasing diffusion rate of mobile devices, able to continuously gather sensing data, creates favorable conditions for the development of smart city infrastructures. In this field the analysis of spatial data plays a pivotal role, due to the relevance they assume in urban scenarios. To satisfy this need, the usage of large distributed computing infrastructures comes into play, supported by efficient frameworks, such as Apache Spark, one of the most relevant platforms to date. However, in order to better take advantage of data and computing resources, it is also necessary to have at disposal flexible and easy-to-use specialized instruments, granting domain specific capabilities for the analysis of spatial data. This paper focuses on a novel framework for processing of spatial data called STARK, giving an overview of its functionalities and presenting an in-depth assessment study of its performances when implementing spatial data clustering, namely DBSCAN. In particular, we focus on two implementations, called MR-DBSCAN and NG-DBSCAN. Of the latter we introduced an implementation in STARK, in order to enrich the framework and to test its capabilities.
Mobile Application and Smart City Orientation: The Moderating Role of Tech Savvy Population

Unceasing growth of population and urbanization, have intensified innovative ways to handle social and environmental impact. The smart city concept supports the idea of environmental sustainability and social sustainability as its main to address the challenges arise with issues such as: urbanization, population growth, greenhouse gas emissions, waste management, education and illiteracy through the deployment of innovative technologies. The growing interest in the smart city and the needs to solve those challenges lead to social innovation, creation of applications and technology development. The use of mobile application e.g. smart weather forecasting, community development, and smart transportation, which offers intelligent services influences population directly and lead to smart city orientation. Basically, smart city is mainly made for and by technology savvy people, whilst community is consisting of many varieties of citizens from silent generation to Z generation. In spite of a sizeable body of research describing smart city definitions, the architecture, and the study on how smart city’s performance should be assessed, the moderating role of tech savvy population in the relationship between mobile application usage and smart city orientation has rarely been examined. Therefore, this study highlights the role of tech savvy citizen in moderating the relationship between mobile applications usage towards the intention of smart city. The paper presents an overview of the relationship between mobile application usage and smart cities orientation, followed by the literature review and methodology of the research. Finally, we present the expected result and implication of the result.
3-DOF Gravity Compensation Mechanism for Robot Waists with the Variations of Center of Mass*

This paper presents a three-degrees-of-freedom (DOF) gravity compensation mechanism for the waists of cooperative robots whose centers of mass change according to the arm poses. It comprises 2-DOF and 1-DOF gravity compensation mechanisms in series, and is capable of natural and agile motions such as bobbing and weaving. The 2-DOF mechanism for the upper part of the waist acts as a universal joint. To compensate for the gravity of this 2-DOF motion, a simple and robust link mechanism with one compressive spring is proposed. Moreover, for the complete gravity compensation regardless of the arm poses, a three-dimensional (3D) parallel link structure that maintains the upper body parallel to the ground is introduced. The 1-DOF gravity compensation mechanism, which provides the motions of the hip and thigh, also has a parallelogram structure to maintain the waist parallel to the ground. The implemented 3-DOF waist mechanism can compensate for up to 23 kg and its own weight. It has an adjusting mechanism that can easily calibrate the amount of compensation in case the weight of the upper body changes. The large workspace enables the upper body to move in any direction in a 3D space without singularity. Therefore, the robot with this waist can touch the ground to pick up objects on the ground as well as reach an object at a height of 2145 mm.
Graph-Based Design of Hierarchical Reinforcement Learning Agents

There is an increasing interest in Reinforcement Learning to solve new and more challenging problems, as those emerging in robotics and unmanned autonomous vehicles. To face these complex systems, a hierarchical and multi-scale representation is crucial. This has brought the interest on Hierarchical Deep Reinforcement learning systems. Despite their successful application, Deep Reinforcement Learning systems suffer from a variety of drawbacks: they are data hungry, they lack of interpretability, and it is difficult to derive theoretical properties about their behavior. Classical Hierarchical Reinforcement Learning approaches, while not suffering from these drawbacks, are often suited for finite actions, and finite states, only. Furthermore, in most of the works, there is no systematic way to represent domain knowledge, which is often only embedded in the reward function.We present a novel Hierarchical Reinforcement Learning framework based on the hierarchical design approach typical of control theory. We developed our framework extending the block diagram representation of control systems to fit the needs of a Hierarchical Reinforcement Learning scenario, thus giving the possibility to integrate domain knowledge in an effective hierarchical architecture.
Robust High Accuracy Visual-Inertial-Laser SLAM System

In recent years, many excellent works on visual-inertial SLAM and laser-based SLAM have been proposed. Although inertial measurement unit (IMU) significantly improve the motion estimate performance by reducing the impact of illumination variation or texture-less region on visual tracking, tracking failures occur when in such an environment for a long time. Similarly, when in structure-less environments, laser module will fail since lack of sufficient geometric features. Besides, motion estimation by moving lidar has the problem of distortion since range measurements are received continuously. To solve these problems, we propose a robust and high-accuracy visual-inertial-laser SLAM system. The system starts with a visual-inertial tightly-coupled method for motion estimation, followed by scan matching to further optimize the estimation and register point cloud on the map. Furthermore, we enable modules to be adjusted automatically and flexibly. That is, when one of these modules fails, the remaining modules will undertake the motion-tracking task. For further improving the accuracy, loop closure and proximity detection are implemented to eliminate drift accumulation. When loop or proximity is detected, we perform six degree-of-freedom (6-DOF) pose graph optimization to achieve the global consistency. The performance of our system is verified on public dataset, and the experimental results show that the proposed method achieves superior accuracy against other state-of-the-art algorithms.
Double Refinement Network for Efficient Monocular Depth Estimation

Monocular depth estimation is the task of obtaining a measure of distance for each pixel using a single image. It is an important problem in computer vision and is usually solved using neural networks. Though recent works in this area have shown significant improvement in accuracy, the state-of-the-art methods tend to require massive amounts of memory and time to process an image. The main purpose of this work is to improve the performance of the latest solutions with no decrease in accuracy. To this end, we introduce the Double Refinement Network architecture. The proposed method achieves state-of-the-art results on the standard benchmark RGB-D dataset NYU Depth v2, while its frames per second rate is significantly higher (up to 18 times speedup per image at batch size 1) and the RAM usage is lower.
Learning by Demonstration and Robust Control of Dexterous In-Hand Robotic Manipulation Skills

Dexterous robotic manipulation of unknown objects can open the way to novel tasks and applications of robots in semi-structured and unstructured settings, from advanced industrial manufacturing to exploration of harsh environments. However, it is challenging for at least three reasons: the desired motion of the object might be too complex to be described analytically, precise models of the manipulated objects are not available, the controller should simultaneously ensure both a robust grasp and an effective in-hand motion. To solve these issues we propose to learn in-hand robotic manipulation tasks from human demonstrations, using Dynamical Movement Primitives (DMPs), and to reproduce them with a robust compliant controller based on the Virtual Springs Framework (VSF), that employs real-time feedback of the contact forces measured on the robot fingertips. With this solution, the generalization capabilities of DMPs can be transferred successfully to the dexterous in-hand manipulation problem: we demonstrate this by presenting real-world experiments of in-hand translation and rotation of unknown objects.
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition

Skeleton-based human action recognition is becoming popular due to its computational efficiency and robustness. Since not all skeleton joints are informative for action recognition, attention mechanisms are adopted to extract informative joints and suppress the influence of irrelevant ones. However, existing attention frameworks usually ignore helpful scenario context information. In this paper, we propose a cross-attention module that consists of a self-attention branch and a cross-attention branch for skeleton-based action recognition. It helps to extract joints that are not only more informative but also highly correlated to the corresponding scenario context information. Moreover, the cross-attention module maintains input variables’ size and can be flexibly incorporated into many existing frameworks without breaking their behaviors. To facilitate end-to-end training, we further develop a scenario context information extraction branch to extract context information from raw RGB video directly. We conduct comprehensive experiments on the NTU RGB+D and the Kinetics databases, and experimental results demonstrate the correctness and effectiveness of the proposed model.
Negative Pressure Wound Therapy (NPWT) – Performance and Limitations

NPWT is an effective therapeutic method for the successful treatment of both chronic and acute wounds; it has proven positive results in stimulating wound healing. We included 23 patients in our study, in charged in the Plastic, Reconstructive and Burn Surgery Clinic from the "Sf. Spiridon" Emergency County Hospital Iasi, during a 4-year period (2015-2019). We used portable NPWT devices, with standard manufacturer-produced consumables kits. NPWT is a highly used treatment for a variety of wound types. NPWT has been cited by various authors to facilitate wound healing and also to minimize the total cost of the treatment, as well as to reduce the wound size and the bacterial population.
Deep Neural Network Approach in Electrical Impedance Tomography-based Real-time Soft Tactile Sensor

Recently, a whole-body tactile sensing have emerged in robotics for safe human-robot interaction. A key issue in the whole-body tactile sensing is ensuring large-area manufacturability and high durability. To fulfill these requirements, a reconstruction method called electrical impedance tomography (EIT) was adopted in large-area tactile sensing. This method maps voltage measurements to conductivity distribution using only a few number of measurement electrodes. A common approach for the mapping is using a linearized model derived from the Maxwell’s equation. This linearized model shows fast computation time and moderate robustness against measurement noise but reconstruction accuracy is limited. In this paper, we propose a novel nonlinear EIT algorithm through Deep Neural Network (DNN) approach to improve the reconstruction accuracy of EIT-based tactile sensors. The neural network architecture with rectified linear unit (ReLU) function ensured extremely low computational time (0.002 seconds) and nonlinear network structure which provides superior measurement accuracy. The DNN model was trained with dataset synthesized in simulation environment. To achieve the robustness against measurement noise, the training proceeded with additive Gaussian noise that estimated through actual measurement noise. For real sensor application, the trained DNN model was transferred to a conductive fabric-based soft tactile sensor. For validation, the reconstruction error and noise robustness were mainly compared using conventional linearized model and proposed approach in simulation environment. As a demonstration, the tactile sensor equipped with the trained DNN model is presented for a contact force estimation.
Sliding Mode based Speed Observer Design for Speed control of five Phase Induction Motor

This paper present technique for speed estimation for five phase induction motor. This work incorporates a sliding mode observer design for sensorless speed control of five-phase induction motor. A Vector control employing space vector pulse width modulation-based Sliding Mode Observer (SMO) is designed and evaluated, considering its high accuracy in estimating speed. The proposed current control model based on SMO provides better speed estimation under parameter variations, fault perturbations and load variations. Simulations with different operational conditions including load and resistance variation are conducted to test the sensorless control performance. The stability analysis of the proposed observer is presented through Lyapunov method.
A Comparative Analysis on the use of Autoencoders for Robot Security Anomaly Detection

While robots are more and more deployed among people in public spaces, the impact of cyber-security attacks is significantly increasing. Most of consumer and professional robotic systems are affected by multiple vulnerabilities and the research in this field is just started. This paper addresses the problem of automatic detection of anomalous behaviors possibly coming from cyber-security attacks. The proposed solution is based on extracting system logs from a set of internal variables of a robotic system, on transforming such data into images, and on training different Autoencoder architectures to classify robot behaviors to detect anomalies. Experimental results in two different scenarios (autonomous boats and social robots) show effectiveness and general applicability of the proposed method.
Design of Robot Leg with Variable Reduction Ratio Crossed Four-bar Linkage Mechanism

Generally, large knee joint torque is required when a leg is flexed. However, the large torque motor will increase the robot size and total weight. Also, a large reduction ratio gear box to realize the large torque will decrease output speed and lower backdrivability of the joint. It makes the robot difficult to perform agile and flexible motion like animals. In this paper, we propose a robot leg with a knee joint mechanism consisting of a variable reduction ratio crossed four-bar linkage mechanism (VRRCFLM) based on cruciate ligament of an animal. The VRRCFLM is to increase the reduction ratio for large knee flexion postures without greatly reducing the total backdrivability. In this paper, we developed a robot leg with a knee joint mechanism consisting of the VRRCFLM. In order to design the link parameters of the leg mechanism, optimization design aimed at maximizing the jumping height of the robot was performed. The robot model with the designed mechanism was evaluated through the dynamics simulations. Thanks to the VRRCFLM, the required torque of the knee motor at the large flexion postures was decreased. Moreover, the vertical jumping height was improved by 24.6 % comparing with a model without the mechanism. In experiments using the prototype, the required static torque was decreased as in simulation, and the jumping height was more than one leg length.
Using electronic sensors to support gender neutral STEM activities in primary school

The Science, Technology, Engineering, and Mathematics (STEM) gender divide is still a nowadays multidimensional problem. During the last decades, research has been studying the factors that can contribute to close the gender gap in what concerns participation, achievement and progression in STEM education. The empirical study presented in this paper assessed, with a gender perspective, the results of the use of electronic sensors to explore and solve environmental health problems by children of four primary school classes. The developed experiment was designed to promote gender inclusion. The results evidenced that children acquired knowledge linked to the use of the sensors, and were able to produce suggestions to solve the explored problems. Nonetheless, gender differences were not promoted by the experiment didactic activities, evidencing affordances of such activities to narrow gender gaps.
ScleraSegNet: An Attention Assisted U-Net Model for Accurate Sclera Segmentation

This paper proposes a novel sclera segmentation approach based on an attention assisted U-Net model, named ScleraSegNet. Several off-the-shelf or improved attention modules are incorporated into the central bottleneck part or skip connection part of the original U-Net, helping the new model implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using an external ROI localization model of cascade frameworks. The proposed approach is evaluated on several public, challenging eye datasets and experimental results show that introduced attention modules consistently improve the segmentation performance over the original U-Net across different datasets, and the best performing ScleraSegNet (CBAM) model achieves state-of-the-art segmentation performance. By utilizing the high stage’s semantic information to guide the selection of features from the low stage in channel-wise and spatial-wise, the further improved ScleraSegNet (SSBC) model ranked first in the Sclera Segmentation Benchmarking Competition 2019 (SSBC 2019), part of ICB 2019, with a Precision value of 92.88% and a Recall value of 90.34% on the MASD.v1 dataset, and a Precision value of 83.19% and a Recall value of 80.01% on the MSD dataset, respectively.