Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Fusion for Tour-Guide Robot Localization

Doris, the social robot girl, is under development to be employed in museums and trade fairs as a tour guide. External sensorial information must be inputted so that Doris moves around each new location by using landmark identification points that can improve the real localization of the robot in combination with an extended Kalman filter. Doris is equipped with a semantic map that contains several information points such as the building structure, sites that the robot must pass, features (obstacles) of the built environment, and landmark locations. Three additional sensors were installed on Doris: a laser range finder LMS-200, an omnidirectional Mobotix C25 camera, and an RFID system Speedway Revolution 220 by Impinj. The use of these sensors implies the use of different types of landmarks: 35-cm-high circular landmarks, placed on the ground and covered with a reflective laser-detectable material; markers similar to QR codes placed at 250 cm above the ground level that the omnidirectional camera can identify; and RFID detectable dogbone antennas. One contribution is to prove a simple methodology of localization by using sensor fusion with a semantic map, without mapping the whole environment by creating a point cloud map and without using the SLAM technique. Additionally, another contribution for the research is to define a good methodology for a precise sensors calibration. The initial results showed that each sensor functions efficiently, when using only the laser and the camera, due to the low accuracy of the RFID system alone. The final results show the behavior of the robot localization in the presence of people and different objects when both sensors are working at the same time. Occlusions may affect the reflective landmarks or visual markers. Therefore, the sensor fusion is implemented to achieve better robustness in the location estimation.
Fractional-Order PI Controller for DFIG-Based Marine Tidal Current Applications

This paper deals with the current and speed control of a Doubly-Fed Induction Generator (DFIG)-based marine tidal current applications. DFIG has been already considered for similar applications, particularly wind turbine systems using mainly Integer-Order PI (IOPI) controller. However, such kinds of controllers do not adequately handle DFIG parameter variations under the harsh marine environment and decrease the system performances. As a result, to increase the generated power and the efficiency of the system, Fractional-Order PI (FOPI) controller, which is developed from the conventional IOPI controller and fractional calculus theory, is proposed in this paper. This FOPI controller has the outstanding advantages in terms of stability, robustness and dynamic performance. The proposed control strategy is inserted into a global simulation tool that accounts for the resource and the marine turbine models. The simulation comparisons between IOPI and FOPI controllers are presented and analyzed.
A Distributed Controller Approach Using P2P Protocol for Software Defined Networks

The main advantage of software defined network (SDN) is a separation of data and control planes to facilitate network management functions. This separation nevertheless poses a scalability and availability problem for controllers in the control plane. Controllers become a single point of failure in SDN. This paper presents an approach of a hybrid controller network for improving scalability and availability in both local and global domains. This approach extends controller architecture to providing direct communication among controllers in the global domain. It applies a peer-to-peer (P2P) protocol for controllers to facilitate group formation and data synchronization. We have extended the prototyping implementation of the Gnutella protocol to evaluate the performance of the hybrid controller network and to simulate controller failure and switch migration. The experimental results show that controllers perform large data update efficiently with low extra workload as number of controllers increase significantly.
Real Time Drone Object Tracking Using Histograms of Oriented Gradients and Particle Filters

Unmanned Aerial Systems have various applications in the areas of military and surveillance. The use of computer vision as a sensory input provides an increased visual capacity which can be used by several robotic platforms to perform tasks. Its use can be found in the control and navigation of aerial vehicles, or for other forms of remote sensing. In this work, we present the development of a framework which is capable of developing an exact tracking of a person or an object and follow it autonomously. The tracking and following is performed without any help of GPS, only using the front monocular camera of the drone and the computation work is done by a laptop.
A Novel Simplified Space Vector Modulation Algorithm for Multilevel Converters

Space Vector Modulation (SVM) is widely used in traditional three-phase converters, but it suffers from issues of heavy computation cost and intense memory usage when extended to multilevel converters. This paper proposed a novel simplified SVM algorithm based on a new coordinate system, referred to as H-D coordinates, and the mapping table method. The calculation based on H-D coordinates is independent of voltage level. Its computation cost is the least compared with other orthogonal and non-orthogonal coordinate systems. The mapping table method enables SVM algorithm to achieve a constant space complexity for storage and it can be further simplified when combined with H-D coordinates. For the problem of selecting switching state, the criteria of minimum common-mode voltage is adopted. Thus obtaining the switching state is quite simple. Based on the statements above, the proposed algorithm is suitable for large level-number converters like modular multilevel converters (MMCs). Simulations in various operating conditions are given.
Framework for Haptic Teleoperation of a Remote Robotic Arm Device

Considering the increasing use of sophisticated machinery in remote and hazardous environments, the field of teleoperation aim to improve the human capabilities for the remote manipulation of equipment by providing a human operator with the necessary information to endow the interaction with the remote location as transparent as possible. In this context, a haptic interface allows an operator to perceive the remote environment via tactile feedback or other bodily sensations. Furthermore, it is bi-directional, been capable of both reading and writing input from and to the human user. In this work, we propose a framework for the remote operation of a servo motor robotic arm using haptic feedback. The objective is to provide the sense or presence through a tactile interface using torque feedback from the device motors and a 3D visualization of the arm in real time. As a result, we observed how the load carried by the manipulator during the path impacted accuracy and precision. Also, we perceived the influence of the torque variation of the motors causing users fatigue and mitigated this problem by applying a complementary filter in the force feedback.
Proportions of Standing Figures in the North-West Palace of Aššurnaṣirpal II at Nimrud

My purpose in this paper is to examine the proportions of standing figures in Neo-Assyrian art using methods similar to those that have proved effective on the art of ancient Egypt. In Egypt artists developed a canon of proportions for the human figure during the Old Kingdom, and by the 12th dynasty they had introduced a squared grid system as an aid to obtaining these proportions. Standing figures were drawn on a grid consisting of 18 squares between the hairline and the baseline, and key points of the body lay on or near particular lines. The junction of the neck and shoulders related to horizontal 16, the breast to 14, the elbow of the hanging arm to 12, the small of the back to 11, the lower border of the buttocks to 9 and the top of the knee to 6. The width across the shoulders was six squares and between the armpits four squares, so that the upper arm was a square wide. Standing figures continued to be drawn on an 18-square grid at least into the 19th dynasty and probably throughout the New Kingdom and Third Intermediate Period. By the 25th dynasty the grid system had altered, and the 18-square system was replaced by one in which the squares were five-sixths of their size in the early system, so that there were now 21 squares between the base and top of the eye. Before this, however, from the mid-18th dynasty onwards, bodily proportions began to change, and this process can be charted by the changing relationship between some of the key points of the body and the lines of the grid. The levels of the small of the back, the lower border of the buttocks and the top of the knee all rise above their original positions, while the width across the shoulders was often reduced by roughly half a square and the width of the upper arm became less than a square wide.
An Error Corrector for Dynamically Accuracy-Configurable Approximate Adder

To reduce power consumption, approximate computing is an efficient approach for error-tolerant applications such as image processing. Approximate arithmetic adders can be used for the approximate computing, and can trade off accuracy for power. CMA, a dynamically accuracy-configurable approximate adder, had been proposed. CMA can sharply reduce power consumption compared with other accuracy-configurable approximate adders, while allowing it to change accuracy-setting at run-time. In this paper, we propose a tiny error corrector for CMA that needs to only two gates for each digit. By consuming 1.4% extra power, the proposed value corrector can improve accuracy for CMA by up to 29.3% on average.
Unknown input observer design for faults estimation using linear parameter varying model. Application to wind turbine systems

This paper proposes a sensor and actuator estimation algorithm based on linear parameter varying (LPV) model. Considering sensor noise and disturbance, an unknown input observer (UIO) is developed. In this scheme, by building an augmented system with a filter, sensor fault and noise of the original system become into a part of actuator fault and disturbance. According to this augmented system, an UIO and fault estimation method have been designed. Then, by solving the linear matrix equalities (LMEs) and the linear matrix inequalities (LMIs), the parameters of the UIO are obtained. In addition, we analyze the convergence of the observer. In order to verify the proposed method, a wind turbine system with torque actuator fault and pitch angle sensor fault has been tested. From simulation results, it presents an efficient performance on both state and fault estimation.
Near-optimal assignment of complex tasks for Green Wireless Sensor Networks

In this paper, we intend to tackle the important problem of reaching near-optimal solutions for the execution of complex tasks in Wireless Sensor Networks in polynomial time. We try to minimize the overall energy required for the accomplishment of complex tasks in WSNs while maintaining acceptable performance levels. Firstly, we provide a modeling framework for tasks, networks, and task assignment process. Then, we formulate the assignment of tasks as an optimization problem and we propose three approximation algorithms that provide near-optimal solutions. Our simulation results validate the proposed approaches and compare their performance against several cost functions.
Centralized Control for an Heterogeneous Line Formation Using Virtual Structure Approach

This work proposes a control structure to guide a formation of two mobile robots. Such a formation includes an unmanned ground vehicle (a differential drive wheeled mobile robot) and an unmanned aerial vehicle (a quadrotor), thus characterizing a heterogeneous formation. The strategy adopted corresponds to consider the whole formation as a single virtual structure, which corresponds to a line formation in the 3D space. The formation is characterized and the proposed control law is presented. Some results of simulated experiments considering the proposed control system and the line formation are also presented and discussed, which validate the proposed control system.
A ROSPlan-Based Multi-Robot Navigation System

Multi-robot systems is a growing research field in robotics and artificial intelligence. Applying multiple robots on single robot systems, generally increase the execution robustness while minimizing its time. Besides this, if the execution requires joint or cooperative actions there isn't better option. This paper proposes a multi-robot version of RosPlan, which that is a framework for embedding a generic task planner in a ROS system. The main contributions include a new method to dispatch the actions; a system to ensure that each robot execute only its actions and a new ROS Package for multi-robot navigation.
Full-State Feedback Based Active Damping Control Design for LCL-type Grid-Connected Converter under Weak Grid

LCL-filters are widely used in high-power grid-connected inverter system and its inherent resonance should be damped. The change of the equivalent grid impedance under weak grid, the delay of sampling and the lag of the control in the digital control will make regular damping scheme not applicable. This paper presents a method using full-state feedback for LCL-filter grid-connected converters under the weak grid. And the general methodology for poles assignment is addressed with design examples. Three restrictions of poles placement and restrictive inequality are offered. Simulation and experimental results are given for validation.
Deep Learning for Human Visual Attention Recognition Using Transfer Learning

In this paper, we propose a method for recognizing human visual attention by utilizing the image data. In our method, human visual attention is recognized in three steps: data processing, feature learning, and transfer learning - based classification. We rigorously experimented on the PRIMA dataset from GRAVIR Laboratory, INRIA Rhone-Alpes. The experiment results show that human visual attention can be distinguished by more than 95% accuracy. These achievements have shown the feasibility of real-world human visual attention application such as smart classroom, healthcare, and criminal psychology investigation.
A Robust and Efficient Algorithm for Multi-body Continuous Collision Detection

Multi-body collision detection is a key and impor-tant technology in societies of computer graphics, system simu-lation, virtual reality, etc, and has been widely used in various applications. To deal with the collision problems in large scale multi-body simulations robustly and efficiently, we in this paper proposed a robust and efficient algorithm of continuous multi-body collision detection based on the kinetic "Sweep and Prune" (SaP) technique and the event-driven mechanism. Our algorithm first culls redundant detection calculations among very large numbers of moving bodies, and then automatically generates events to predict these collisions, probably taken place in coming time, of the object pairs. All these events are been pushed into a priority queue, which is used to drive our algorithm to run. By introducing a new hybrid bounding box hierarchy in the event processing process, our algorithm can detect positions where the object pairs collide. We discovered the event blocking problem potentially occurred during event processing, and further proposed several methods to alarm or relieve the system from the event blocking state. Experimental results show that our algorithm has good stability and strong robustness, and it can improve the speed and accuracy of the multi-body collision detection effectively.
A High-Efficiency LLC-C Resonant Converter for Wide-Gain-Range Application

In this paper, an LLC-C resonant converter is proposed. Utilizing the topology-morphing technology, the converter is able to integrate two circuits into one novel topology. It has the advantages the two circuits have, while averting their drawbacks. At rated, the converter operates as an LLC circuit, and high rating efficiency is easy to obtain; when the input voltage is high, the LCCL circuit is used instead, providing wide gain range with relatively high efficiency. Experiments are conducted to verify the feasibility: when the input voltage varies from $110V$ to $400V$, the output voltage is constant at $200V$ (gain range from 1.8 to 0.5). The efficiency is always beyond 90%, and the highest point reaches 97.5%. The good nature of wide gain range and high efficiency makes the proposed converter suitable for the applications, where voltage range and efficiency are required strictly.
Vehicle-to-Grid On-Board Charger using SiC MOSFET and High Frequency Coaxial Transformer

This paper proposes a multifunctional electric vehicle (EV) on-board charger which can provide three ancillary functions of static-compensator (STATCOM), voltage regulation and current harmonics reduction (APF operation) while operating in G2V/V2G mode. The proposed EV charger exhibits a lower power loss due to using SiC based MOSFETS and a high-efficient high frequency coaxial transformer (HFCT), and lower battery stress because of employing an interleaved two-leg buck-boost dc/dc converter (ITBBC). Different operational modes of the EV charger and its improved performance are validated through both simulation and experimental results.
Dynamic Routing Using Inter Capsule Routing Protocol between Capsules

A capsule is a combination of multiple neurons, which designed to analyze specific feature representations in an image, a capsule network resembles with CNN multiple layer network model, in which convolutional and ReLu function is followed by max pooling which help in reducing the input values for processing. However, we observed that for each capsule in layer l to layer (l +1), we need to calculate a prediction vector, whose magnitude defines the detected feature in an image and the orientation define the pose of the object in an image. Now calculating the prediction vector for each capsule in the lower layer is prolonged. This is because, in capsule network, we are not securing the gained knowledge for future use. In this paper, we are introducing a protocol named as ICRP (Inter Capsule Routing Protocol), which uses FDM (Feature Detection Matrix) to store the learned information at every iteration in the capsule network. This reduces the processing time for feature detection in an image gradually, by searching the similar feature in FDM before processing the network.
A Coupled Inductors Based High Gain Non-Isolated Three-Port DC-DC Converter

A novel high gain non-isolated three-port DC-DC converter (TPC) based on coupled inductors is proposed in this paper, which interfaces with one photovoltaic (PV) port, one bidirectional battery port and one load port. Coupled inductor technique is applied to obtain high voltage gain. The use of diode-capacitor technique also enables the converter to obtain an additional double voltage gain while reducing the voltage stress of the switches. The steady state of the converter is divided into three operation modes, and the output voltage analytical expressions under each mode is derived in this paper. The control strategy for the topology is also proposed to realize constant output voltage, battery protection, and maximum power point tracking (MPPT) at the same time. Simulation and experimental results verify the performance of the proposed converter and the feasibility of the control strategy.
Sentiment Classification of Russian Texts Using Automatically Generated Thesaurus

This paper is devoted to an approach for sentiment classification of Russian texts applying an automatic thesaurus of the subject area. This approach consists of a standard machine learning classifier and a procedure embedded into it, that uses thesaurus relationships for better sentiment analysis. The thesaurus is generated fully automatically and does not require expert’s involvement into classification process. Experiments conducted with the approach and four Russian-language text corpora, show effectiveness of thesaurus application to sentiment classification.
A Model for Metricising Privacy and Legal Compliance

In order for a dataset to be legally compliant - in some sense - with privacy laws such as the General Data Protection Regulation (GDPR) various steps must be taken to ensure the removal of data that might compromise or reveal personal data. This can be achieved through a process of removal of information content or semantics; which if done incorrectly can render that dataset in violation of such laws. Machine learning presents a technology based around the analysis of dependencies and correlations of a dataset. This can be used to measure information content within the bounds of the dependencies estimators used. Utilising this we can measure the effects of anonymisation upon a dataset and the efficacy of said anonymisation functions. If we additionally characterise what anonymisation means in terms of information loss and construct classification functions we have a framework in which the decision over whether an anonymisation is sufficient can be made. This can then be extended to an automation scenario where it becomes potentially possible that texts such as as the GDPR can be rendered as said classification functions.
A Classification Scheme Based on Directed Acyclic Graphs for Acoustic Farm Monitoring

Intelligent farming as part of the green revolution is advancing the world of agriculture in such a way that farms become evolving, with the scope being the optimization of animal production in an eco-friendly way. In this direction, we propose exploiting the acoustic modality for farm monitoring. Such information could be used in a stand-alone or complimentary mode to monitor constantly animal population and behavior. To this end, we designed a scheme classifying the vocalizations produced by farm animals. More precisely, we propose a directed acyclic graph, where each node carries out a binary classification task using hidden Markov models. The topological ordering follows a criterion derived from the Kullback-Leibler divergence. During the experimental phase, we employed a publicly available dataset including vocalizations of seven animals typically encountered in farms, where we report promising recognition rates outperforming state of the art classifiers.
Tracking Vehicles System Based on License Plate Recognition

The license plate recognition for tracking stolen vehicles, the system will read the car license plate and compare with the database. Then, the system will send the warming immediately if such car is in the blacklist. The research and development team has developed the license plate Recognition by developing from microsoft visual studio 2015, VB.NET language and EmguCV command. We use the image processing and k-Nearest Neighbor (k-NN) algorithm to recognize numbers on the license plate. The experiment results, shows that the system is able to read the license plate in the middle of the image, at 1- meter distance with 100% accuracy from the samples of 30 images. Moreover, it is able to recognize the license plate from the tilted position at 80 degrees’ angle and 100 degrees’ angle for the distance more than 1 meter with 50% accuracy from the samples of 30 images.