Miracle-dz/UniFAR-FTUs huggingface dataset:
Altruistic Coordination Strategy for On-Ramp Merging on Highway of a Formation of Cooperative Automated Vehicles

It is getting increasingly recognized that, to get full advantage from Automated Vehicles (AVs), a number of situations involving multiple AVs will compulsory require the coordination of their relative activities and movements. Under the Multi-Vehicle Systems (MVS) paradigm, instead of considering individually each AV, it is proposed to create with several AVs a group that evolves under a certain coordination strategy. In this paper, it is proposed to utilize Cooperative Automated Vehicles (CAVs) synchronization ability to tackle one challenging scenario: on-ramp merging on highway. The main contribution of this paper is an overall collaborative approach, called Altruistic Formation Reconfiguration Strategy (AFRS), based on a multi-criteria optimization, to guarantee the safety and the energetic efficiency of CAVs, performing on-ramp merging on highway. Under the AFRS, it is proposed the extension of the Constrained Optimal Reconfiguration Matrix (CORM) [1] in order to overcome the CORM limitations, while guarantying both the CAV’s non-collision requirement and the smooth collaborative navigation of the fleet. Several simulations are performed to evaluate the safety and reliability of the proposed approach.
Dynamic Multi-Scale Dual Kalman Filtering for State of Charge and Capacity Estimation on Lithium Titanium Oxide Cells for Traction Applications

This paper addresses the estimation of battery state variables (state of charge, overpotentials and remaining capacity) of a battery cell based on lithium titanium oxide anode chemistry using measurements of cell current and voltage. The algorithms developed use an equivalent circuit model that mimics the electrochemical processes in the cell while being simple and computationally efficient. The observer for estimating the state of charge, namely an adaptive extended Kalman Filter, is combined with a dual estimator that runs in parallel on a slower and asynchronous time scale and estimates the cell capacity. The novelty of this approach consists in a formulation that computationally simplifies the algorithm while maintaining good estimation accuracy as the cell ages. The results obtained are robust and reliable, and are validated using operative data from batteries deployed in rolling stock fleets.
A robotized environmental sensor array for gravitational wave observatory sites

A movable array of environmental sensors is intended for the feed-forward cancellation of the Newtonian noise generated by atmospheric density fluctuations and seismic displacements at the Virgo gravitational wave detector site, with the prospect of being used at the sites of future 3rd generation detectors. Each robot unit is equipped with a seismic sensor - optionally also a microphone and a magnetometer - for low-noise and low-frequency observations. The robots need to move autonomously in the experimental areas indoors, avoiding obstacles to reach the assigned positions where they start the data acquisition process. Then, the data will be transferred wirelessly to the control software that elaborates them and imparts to all robots the command to move to new optimal positions. Essential requirements of the system are accuracy in positioning and timing. The project is described highlighting the technical choices and their implementation.
Robust Trajectory Tracking Control for Quadrotor UAV with External Disturbances via Fully Actuated System Approach

This paper proposes a robust trajectory tracking control strategy based on the high-order fully actuated system approach and disturbance observer. In spite of the strong coupling characteristic and high nonlinearity of the quadrotor unmanned aerial vehicle (QUAV) system, the linear time-invariant system with desired eigenstructures can be obtained by designing a greatly simple robust controller via the direct parametric method. Meanwhile, a disturbance observer (DO) is designed to estimate the external disturbances, and the output of the DO can be adapted for the controller design to compensate for the affect of external disturbances on control performance. Eventually, the numerical simulation results of the QUAV trajectory tracking demonstrate the effectiveness of the developed robust control scheme.
Miniaturized SIW-based Cavity-Backed Antenna

This paper comprises a single-layered Substrate Integrated Waveguide (SIW) based cavity-backed antenna used for military and WiMAX applications. The SIW is a technology that can efficiently integrate waveguide into a substrate itself. The cavity-backed antenna is constructed on a single PCB having metallic vias array shorted from top to ground conducting planes. The proposed antenna is miniaturized, planar, robust, and efficient. The performance of the antenna is investigated in terms of the S11-parameter, efficiency, and gain. The antenna miniaturization is achieved by 22.8% in both of the frequency bands. The bandwidth is achieved around 900 MHz at 4.4 GHz and 700 MHz at 5.6 GHz. The directivity of the antenna is 5.49 dBi in the lower frequency band and 4.21 dBi in the upper-frequency band while efficiency is better than 67% in both frequency bands.
Depalletisation humanoid torso: Real-time cardboard package detection based on deep learning and pose estimation algorithm

This paper presents a system for efficiently depalletising textureless and versatile cardboard boxes, even when they are tightly packed together, by utilizing deep learning and point clouds. The system uses an industrial humanoid torso equipped with a 6 DoF dual-arm and an RGB-D camera allowing for fast, accurate, and adaptable object handling without the need for additional sensors or setup. Operating in a warehouse environment, the robot is responsible for loading/unloading pallets of cardboard packages and placing them onto conveyor belts for further processing. The system uses ROS interface for communication, control, and MoveIt for dual-arm path planning and achieves impressive F1 scores of 0.81 and 0.90 for single-face and multi-face boxes, respectively, as demonstrated through real-time testing. These results provide valuable insights into the system’s capabilities and potential future improvements.
Ultra-Wide Band Semi-elliptical antenna for MIMO Applications

Ultra-Wide Band (UWB) systems can support high data rate transmission because of which they are in demand in several applications and have brought challenges in antenna design. When compared to common narrow-band antennas UWB antennas are cheap, have low power consumption, good effectively, and achieve broad bandwidth. In this paper, a four-element semi-elliptical UWB MIMO antenna is designed for wireless applications. The MIMO antenna is printed on a low-cost FR-4 Epoxy substrate of size 52 × 48 × 1.6 mm3 and is fed by an asymmetric coplanar strip (ACS) lin. The proposed antenna radiates across 3 GHz to 12 GHz frequency range. It has good reflection coefficient characteristics and it generates an acceptable omnidirectional radiation pattern, with a peak gain of 3.7 dBi at 8 GHz. Good performance in diversity gain, and Envelope Correlation Coefficient (ECC), which is suitable for MIMO applications is achieved.
Reduction of Stator Flux Ripple and Current Harmonic Distortion using Constant Switching Flux Controller-based DTC of Five-Phase Induction Motor

A five-phase induction motor's classical hysteresis-based DTC exhibits excellent dynamics and suffers from higher torque and flux ripple, higher current %THD, and variable switching frequencies that vary with speed. In this article, a triangular-based constant switching flux (CSF) controller is introduced in place of the standard hysteresis-based flux controller without altering the hysteresis-based torque controller for implementing the lookup table-based direct torque control of a five-phase induction motor. The proposed DTC scheme improves the average flux ripple and current harmonic distortion for all operating speeds without altering the superior speed and torque dynamic characteristics as that of the classical DTC scheme. The proposed carrier-based CSF controller smoothens the flux pattern and in turn, improves the current profile with suitable duty variations. The proposed DTC forces flux status change at regular intervals which eliminates the drooping of stator flux under very low speeds and light load cases. The hardware and simulation experimental results are analyzed to know the effectiveness of the proposed constant switching flux controller-hysteresis torque controller-based DTC (CSFHTC-DTC) control scheme over the classical hysteresis flux and hysteresis torque controller-based DTC (HFT-DTC) control scheme with the same lookup table.
GPS-free Stabilization Control for Autonomous Vehicle without Parking Space Marking: Simulation and Experiment

This paper focuses on the stabilization control problem for autonomous vehicles in GPS-denied environments, specifically addressing the challenge of automatic parking without access to parking space marking information. The geometric planning approach is proposed to find alternative reference points for vehicles in the absence of parking space markings. Subsequently, the proposed geometric planning combined with relative pose measurement, is utilized to develop automatic parking control laws for scenarios without parking space marking information. These control laws ensure safe navigation and enable automatic parking in both obstacle-free and obstacle-blocked scenarios. Eventually, the effectiveness and performance of the proposed controllers are validated through numerical simulations and experiments.
Measuring the distance and orientation of a planar surface using nonstructured lighting

This paper proposes a method of 3-dimensional measurement of a planar surface by using two fixed light sources and a TV camera. A set of two images is recorded by the camera switching on each light alternately. The peak of the luminance distribution in each image is detected, and the orientation and distance of the planar surface are calculated. The position of the peak of a luminance distribution can be estimated accurately by using an image processing. The light sources can be conventional apparatus with no particular structure. The method is simple and suitable for a vision system on an indoor mobile robot.
Human-Like Decision-Making of Autonomous Vehicles in Dynamic Traffic Scenarios

With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like autonomous driving is expected due to the impact of the differences between autonomous vehicles and human drivers on safety. Although human-like decision-making has become a research hotspot, a unified theory has not yet been formed, and there are significant differences in the implementation and performance of existing methods. This paper provides a comprehensive overview of human-like decision-making for autonomous vehicles. The following issues are discussed: 1) The intelligence level of most autonomous driving decision-making algorithms; 2) The driving datasets and simulation platforms for testing and verifying human-like decision-making; 3) The evaluation metrics of human-likeness; personalized driving; the application of decision- making in real traffic scenarios; and 4) The potential research direction of human-like driving. These research results are significant for creating interpretable human-like driving models and applying them in dynamic traffic scenarios. In the future, the combination of intuitive logical reasoning and hierarchical structure will be an important topic for further research. It is expected to meet the needs of human-like driving.
An Efficient Cross Array Beamforming for Underwater Acoustical 3D imaging

Real-time 3D acoustic imaging underwater is becoming increasingly important, as 3D geometry information of underwater objects is required for autonomous underwater vehicles to avoid obstacles. A major problem for real-time imaging is the time required to process the data associated with large planar arrays and the hardware cost of uniform 2D receiving arrays. The solutions to the problem are to reduce the number of elements in the receiving array and find fast beamforming techniques for 3D imaging. In this work, we propose an efficient cross-array beamforming using spatial cross correlation of azimuth and elevation beams for underwater real-time 3D imaging. The simulation result demonstrated that the proposed method could reconstruct a 3d image of an underwater target with an inferior but acceptable resolution. The proposed method is twenty-two times faster than the conventional delay and sum beamforming for 120 × 120 beams.
ConceptTM: Concept Topic Model for Engineering Design

Design research in the intelligent age is often inseparable from information and knowledge, and work on automated or semi-automated design emerges under this background. With this upsurge, we propose ConceptTM, supporting the conceptual design of physical architectures. Different from common knowledge organization forms such as semantic networks and knowledge graphs, ConceptTM is a probabilistic graphical model, which can capture the systematic correlation among design concepts rather than just the fragmented pairwise or triplet relationship. The architecture of ConceptTM is designed by imitating the thinking mode of human designers, taking relevant technical fields as a prior, and functional requirements as a likelihood, to carry out Bayesian inference to obtain a posterior of the physical architecture. It also refers to the topic model to obtain the clustering characteristics of design concepts. We built a technology-related training corpus with massive invention patents for ConceptTM and obtained a variety of instances under different hyperparameters. We evaluated these instances and selected the most appropriate one for the final case studies. The case studies show that ConceptTM can effectively support design automation and provide inspiration for human designers.
Simultaneous optimization of class configuration and feature space for object recognition

A new algorithm for object classification based on an extension of the Fisher's discriminant analysis is presented. Object recognition algorithms using the standard Fisher's algorithm, such as the Fisherface, train the classifier using sample-class pairs, where, for the classes, object categories determined in the application systems are used directly. In contrast, the new algorithm automatically produces subclasses, within each predetermined category that are actually used for classification, via unsupervised learning. In order to perform this, we combine the Fisher's discriminant analysis with the Akaike information criterion, optimizing the class configuration, i.e. sample-subclass correspondences, and the feature extraction function simultaneously, thereby improving the potential of linear separability. By applying this new method to face recognition, we show how it outperforms the traditional Fisher-based method.
Stable sliding mode control of dynamical systems subject to unknown disturbance

Sliding mode control strategies are best known for ensuring a high degree of system robustness with respect to disturbance with negligible computational overhead. However, such strategies typically require information about lower and upper bounds of disturbance to ensure a stable motion of the system. Such bounds cannot be feasibly obtained in many practical applications. Motivated by this problem, we propose a new approach to sliding mode control of perturbed systems, which involves on-line estimation of unknown disturbance. Indeed, evolution of the disturbance is modeled as a random value with parameters determined from a number of past samples. Then, an appropriate disturbance estimator is defined in order to minimize the future effect of this disturbance on sliding motion of the system. It is demonstrated that the proposed approach guarantees a stable and robust response of the system even when no prior information about disturbance is available.
Realistic Cooperative Perception for Connected and Automated Vehicles: A Simulation Review

This paper investigates realistic perception in vehicular simulations, demonstrating and comparing two different methods used mainly by the community. To render the photorealistic environment and generate RGB camera images, we use the open source simulator CARLA-SUMO co-simulation designed for automated driving research that allows us to generate realistic perception data. We show that the detection accuracy differs depending on vehicle layout, proportion of connected vehicles, weather conditions, and distance between the sensing vehicle and the target vehicle, which can also be realistically modelled in vehicular simulation. We show that a realistic perception is essential in vehicular simulations and can not be neglected or oversimplified to ensure a reliable Cooperative Perception Solution (CPS). Our experimental results show that a vision-based optical sensor is able to detect more vehicles than a gridbased projection sensor in cooperative perception scenarios.
Compliance Flatness Based Control of a Collaborative Upper-Limb Exoskeleton Robot

This paper focuses on two challenges when developing the Smart Robotic Exoskeleton (SREx), a new exoskeleton robot that collaborates with individuals with Upper Limb dys-functions. The first challenge is estimating the wearer’s desired intended motion (DIM) for the robot accurately. The second challenge is implementing a robust control strategy for the SREx, which entails transforming variables to achieve the triangular flat canonical normal form (TCNF) for nonlinear dynamic systems with the flatness geometric property. Damped Least Square (DLS) is used in the proposed geometrical transformation-based control strategy to link the estimated DIM to the flatness-compliant control, allowing the SREx to track the wearer’s motion easily. Experiments and comparative studies are presented to show the effectiveness of the proposed control scheme.
Analysis of Feed Network Efficiency in Time-Modulated Linear and Circular Array

In this paper, the feed network analysis and directivity have been addressed using time-modulated linear and circular antenna arrays. 16-Element linear and circular antennas are considered for calculating feed network efficiency and directivity using CRPSO (Craziness-based Particle Swarm Optimization). An optimal combination of switch-ON time sequences provides an array pattern with feed network efficiency and directivity using this technique. Simulations are done in MATLAB. The results are shown in the numerical result section.
A Quantum-inspired Genetic Algorithm for Data Clustering

The conventional K-Means clustering algorithm must know the number of clusters in advance and the clustering result is sensitive to the selection of the initial cluster centroids. The sensitivity may make the algorithm converge to the local optima. This paper proposes an improved K-Means clustering algorithm based on Quantum-inspired Genetic Algorithm (KMQGA). In KMQGA, Q-bit based representation is employed for exploration and exploitation in discrete 0-1 hyperspace by using rotation operation of quantum gate as well as three genetic algorithm operations (Selection, Crossover and Mutation) of Q-bit. Without knowing the exact number of clusters beforehand, the KMQGA can get the optimal number of clusters as well as providing the optimal cluster centroids after several iterations of the four operations (Selection, Crossover, Mutation, and Rotation). The simulated datasets and the real datasets are used to validate KMQGA and to compare KMQGA with an improved K-Means clustering algorithm based on the famous Variable string length Genetic Algorithm (KMVGA) respectively. The experimental results show that KMQGA is promising and the effectiveness and the search quality of KMQGA is better than those of KMVGA.
Texture based Scene Categorization using Artificial Neural Networks and Support Vector Machines: A Comparative Study

Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. This paper uses gray level cooccurrence matrix method to extract features from the scenes and trying to recognize the scene categories called ‘MIT-street’ and ‘MIThighways’. Artificial neural networks and support vector machines classifiers are used for the classification. The comparative results are proving efficiency of support vector machines towards scene categorization problems. The sample images are taken from the real world dataset.
Online State of Health Estimation of Lithium-Ion Battery for Electric Vehicle

Nowadays, lithium-ion batteries (LiBs) are present in many applications and are increasingly becoming of interest in the electric vehicle (EV) sector. State of charge and state of health estimations are of fundamental importance to predict and quantify the remaining EV range and battery degradation level. The latter is usually related to the capacity fade or internal resistance increase. In the present work, the focus was on the capacity fade estimation to evaluate the actual battery capacity considering the battery aging. To do this, it is possible to use model-based methods or data-driven methods. Even if, the former can be used online, they can require high performance of the battery management system and high computational effort. The latter, conversely, are simpler to be implemented, but they require collecting a lot of data offline. In most cases, they need the knowledge of the whole open circuit voltage (OCV) curve resulting not suitable for EV applications. In this work, the possibility to estimate the actual battery capacity, for EV applications, starting from the knowledge of just two experimental OCV points was proposed and analyzed. Different aging tests were performed on a LiB to validate the proposed method for different aging levels.
NRC Publications Archive Archives des publications du CNRC

We provide an in-depth analysis of the integration of an Arabic-to-English translit-eration system into a general-purpose phrase-based statistical machine translation system. We study the integration from different aspects and evaluate the improvement that can be attributed to the integration using the BLEU metric. Our experiments show that a transliteration module can help significantly in the situation where the test data is rich with previously unseen named entities. We obtain 70% and 53% of the theoretical maximum improvement we could achieve, as measured by an oracle on development and test sets respectively for OOV words (out of vocabulary source words not appearing in the phrase table).
Decentralized tracking control for high-speed train: A fully actuated system approach

To implement a smoother automatic train operation (ATO) for high-speed train (HST), we investigates the classical tracking problem for the cascaded mass-point longitudinal model of HST with train schedule, which is subject to the constraints of speed and power. The most challenge is the existence of nonlinear coupler forces between front and rear cars. In this paper, we introduce the fully actuated system approaches to remove the coupler forces from the train dynamic model, and then the second-order full-actuation system is established for each car. Therefore the decentralized controller of the individual car can be easy to been designed. In addition, the sufficient conditions for the existence of the decentralized controller are given by a set of linear matrix inequalities (LMIs). Some simulation results show the effectiveness of the fully actuated system approaches, and then the control scheme ensures the consensus of the relative displacement and velocity of coupler.