Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Visual SLAM Integrated with IMU for Unmanned Scenarios

The common SLAM algorithm is based on the visual spatial relationship theory established with the static environment as the basic assumption. The static assumption of the scene is harsh for the unmanned scenarios with moving vehicles. This paper aims to introduce a dynamic visual SLAM method integrating inertial measurement unit for unmanned scenarios. Our method is not limited to the static assumption of the scene, but tracks the dynamic objects in the scene according to the visual information. At the same time, in order to deal with the problems of rapid vehicle movement and insufficient light, the IMU sensor is used to integrate the vehicle angular velocity and acceleration provided by IMU into the unified estimation algorithm of dynamic objects and static structure, which is more robust and effective than the existing visual-only dynamic SLAM. This paper has realized the fusion of IMU preintegration and pose estimation with visual-only dynamic SLAM, and verified it on the KITTI Tracking Dataset with good visual information. The results are basically better than visual-only dynamic SLAM, while our method is much more robust under bad visual conditions.
Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

The local path planning, as one of the navigation stages, plays a significant role in the energy consumption of Self-Guided Vehicles (SGV). Since SGV must operate for several hours on a single battery charge to transport loads, its energy consumption is a critical issue. Therefore, this article puts forward an approach for boosting the energy efficiency of the local path planning stage using load position. Unlike other similar works which solely use robots’ kinematic and kinetic constraints to develop energy-efficient local path planners, this article considers the effect of load position on SGV’s dynamic. In this regard, first, the kinetic model of the differential drive SGV is developed to consider the change of SGV’s Center of Mass (CoM) affected by load properties. Second, machine learning methods are used to create two learning models for online estimation of the position of CoM (PoCoM) and prediction of required energy of sample trajectories. Hence, the generated SGV’s kinetic model is used to train the learning models. Finally, estimated parameters are employed to add a new constraint to extend the cost function of the local path planner. The outcomes of the study show that the proposed planner generates smoother and shorter paths to pass obstacles and corridors than a general one. Thus, SGV’s energy consumption decreases by considering the load effect.
Quantum Ratchet in Disordered Quantum Walk

Symmetrically evolving discrete quantum walk results in dynamic localization with zero mean displacement when the standard evolution operations are replaced by a temporal disorder evolution operation. In this work we show that the quantum ratchet action, that is, a directed transport in standard or disordered discrete‐time quantum walk can be realized by introducing a pawl like effect realized by using a fixed coin operation at marked positions that is, different from the ones used for evolution at other positions. We also show that the combination of standard and disordered evolution operations can be optimized to get the mean displacement of order ∝ t (number of walk steps). This model of quantum ratchet in quantum walk is defined using only a set of entangling unitary operators resulting in the coherent quantum transport.
Detection and Mitigation of Functional Insufficiencies in Autonomous Vehicles: The Safety Shell

Autonomous vehicles (AVs) promise to reduce greenhouse gas emissions, increase comfort and throughput of transportation, while simultaneously significantly reducing traffic deaths. To allow autonomous vehicles to satisfy the high safety levels for unsupervised participation in realistic traffic, the faults and functional insufficiencies of AV systems need to be mitigated during operation. Unfortunately, current state-of-the-art functional insufficiency detection and mitigation methods do not provide large enough safety improvements, without impeding the availability of safe autonomous functionality. To fill this gap, we propose the Safety Shell, an implementable multi-channel architecture and arbitration method. The ability to increase the number of parallel AV function channels allows for a path to safe AV systems, while the novel arbitration method ensures availability for comfortable journey continuation. The flexibility and benefits of the Safety Shell are shown using use case studies.
Robust Control of Linear Dynamical Systems

Background and motivation: what is robust control? historical perspective models and modelling model reduction. Introduction to robust control: elements of robust control theory design objectives and specifications shaping the loop gain Signals and systems: signals and their norms computation of H2 norm computation of H, H norm and associated algebraic relationship all-pass systems, the adjoint operator inverse systems. Matrix fraction description: factorization of polynomial matrices testing for coprimeness rational functions - matrix fraction description the Smith-McMillan form, factorization over a ring of stable matrices coprime fractional representation in state space. Parametrization of stabilizing controllers: well posedness and internal stability the Youla parametrization approach co-prime factorization in state space revisited strong stabilization Sensitivity minimization and robust stabilization: sensitivity minimization 1-block, 2-block, and 4-block problems sensitivity trade-offs for multivariable plants design limitations due to R.H. plane zeros plant uncertainty and robustness robust stabilizing controllers. Balanced realization and Hankel norm approximation: balanced realization best approximation in Hilbert space the Hankel operator the Hankel norm approximation problem Glover's method. H2 and H optimization: LQG methodology H optimization techniques the Nevanlinna-Pick interpolation problem operator-theoretic methods. Generalised plan-controller configuration: the standard configuration stabilizability criteria parametrization of stabilizing controllers for G22 reduction to the standard problem from standard form to model matching. Controller synthesis in state space: LQG control-full information problem the Kalman filter parametrization of output feedback controllers. (Part contents).
Two-Branch Attention Adversarial Domain Adaptation Network for Hyperspectral Image Classification

Recent studies have shown that deep domain adaptation (DA) techniques have good performance on cross-domain hyperspectral image (HSI) classification problems. However, most existing deep HSI DA approaches directly use deep networks to extract features from the data, which ignores the detailed information of HSI in spectral and spatial dimensions. To effectively exploit the spectral–spatial joint information for DA of HSIs, we propose a two-branch attention adversarial DA (TAADA) network in this article. In the TAADA network, a two-branch feature extraction (TBFE) subnetwork is first designed as a generator to extract the attention-based spectral–spatial features. Then, a discriminator based on two classifiers with the multilayer FC-BN-ReLU-Dropout structure is constructed. Based on adversarial learning between the generator and the discriminator, the ability of discriminative feature extraction and cross-domain classification is improved simultaneously. Finally, the TAADA network can adjust the distribution between the source and target domains and extract domain-invariant features. Experimental results on three cross-scene HSI classification tasks show that our proposed TAADA outperforms some existing DA methods.
Radiography Images with Transfer Learning on Embedded System

A serious public health concern is Novel Coronavirus Disease (COVID-19), which spread quickly over the globe at the end of 2019. This coronavirus is still able to propagate rapidly even after two years. Chest X-rays are crucial for diagnosing infected individuals in the worldwide battle against this illness. Therefore, various COVID-19 quick classification technologies can provide excellent classification accuracy to help medical professionals make the best choices. Here, we propose a trustworthy, compact network that, with the aid of encouraging classification results, can correctly identify COVID-19 from chest X-rays. The experimental findings demonstrated that, in a low-power embedded system, the modified architecture of the proposed model produced excellent performance metrics for four classes. The suggested classification architecture had an overall accuracy speed of 97.67% and an f1-score of 97.64%. This classification model is better than the other classification models used to classify patients with COVID-19 infection.
Runtime Assurance for Autonomous Driving with Neural Reachability

The development of safe autonomous vehicles remains a challenge. Modern autonomous driving controllers are able to operate safely in most operating conditions, however, rare conditions may cause undesired behaviors. To alleviate this issue, a complex controller may be paired with a simpler, yet verified, controller in a simplex architecture. To switch between the two, we take inspiration from formal methods, namely reachability analysis, to develop an online method called neural reachability that estimates the reachable sets of the system. The method predicts the possible states of the system under uncertain initial states and disturbances in control. The approach utilizes deep neural networks for conservative approximation of reachable sets in bounded time. If an intersection of the reachable set with an unsafe set is detected, a simple verified controller is utilized. We show how a deep neural network is trained using existing reachability analysis algorithms and demonstrate how neural reachability may be utilized in a simplex architecture. Also, we provide probabilistic guarantees based on statistical model checking approaches. Finally, the approach is evaluated as part of a resilient safety architecture for autonomous vehicles in a simulated environment with several maneuvers. Our evaluation demonstrates that reachability analysis can be done within a fraction of a second and outperforms traditional nonlinear reachability tools by two orders of magnitude.
CPG-based Motion Planning of Hybrid Underwater Hexapod Robot for Wall Climbing and Transition

Most of the existing underwater legged robots are capable of moving on small-angled slopes, but few of them can climb the large-angled slope or transition from one plane to another, such as transition from horizontal plane to vertical plane. In this paper, we propose a motion planning method of a hybrid underwater hexapod robot (HUHR) driven by six C-shape legs and eight thrusters. By analyzing the relationship between rotation and displacement of the hip joint, we establish a single-leg kinematic model. By analyzing the force at the touchpoint, we propose a locomotion mechanism to ensure no slip of the C-shape leg. Based on the central pattern generator (CPG) and tripod gait, we design an aperiodic mapping between the oscillator outputs and the desired rotation angles of hip joints. Overall, a gait planning and control method for our robot is proposed to realize continuous legged locomotion from one plane to another, including directional climbing and transition between them. Furthermore, the effectiveness of the proposed method has been verified on HUHR.
Digital Twins for Medieval Monuments: Requirements from Art Historians and Technical Challenges for Analysis and Restoration

Research on Visual Computing and Cultural Heritage naturally requires the involvement of multidisciplinary teams comprising art historians and technical experts. Despite the vast literature on digital 3D models in support of Cultural Heritage, the field is so rich and diverse that specific projects often imply distinct, unique requirements which often challenge the computational technologies and suggest new research opportunities. In this paper we discuss the requirements and technical challenges within the EHEM project, an ongoing research program for the analysis, documentation, interpretation, restoration, and communication of medieval artistic heritage. We describe the three monuments that serve as test cases for the project, all of them with a rich history in terms of architecture and paintings. We discuss the art historians' view of how digital models can support their research, the expertise and technological solutions adopted so far, as well as the technical challenges in multiple areas spanning geometry and appearance acquisition, color analysis and restoration as well as the representation of the temporal transformation of the architecture and the paintings.
Investigating the Effect of Cosmic Opacity on Standard Candles

Standard candles can probe the evolution of dark energy over a large redshift range. But the cosmic opacity can degrade the quality of standard candles. In this paper, we use the latest observations, including Type Ia supernovae (SNe Ia) from the “joint light-curve analysis” sample and Hubble parameters, to probe the opacity of the universe. A joint fitting of the SNe Ia light-curve parameters, cosmological parameters, and opacity is used in order to avoid the cosmological dependence of SNe Ia luminosity distances. The latest gamma-ray bursts are used in order to explore the cosmic opacity at high redshifts. The cosmic reionization process is considered at high redshifts. We find that the sample supports an almost transparent universe for flat ΛCDM and XCDM models. Meanwhile, free electrons deplete photons from standard candles through (inverse) Compton scattering, which is known as an important component of opacity. This Compton dimming may play an important role in future supernova surveys. From analysis, we find that about a few per cent of the cosmic opacity is caused by Compton dimming in the two models, which can be corrected.
Radio Altimeter Interference Modelling, Evaluation and Coding Mitigation Strategies for an OFDM-based WAIC system

The paper analyzes the impact of radio altimeter (LA) echoes on the signal collected by a Wireless Avionics Intra-Communication (WAIC) system installed on an aircraft. It characterizes the LA echoes with respect to power, occupied bandwidth and time occurrence for an aircraft flying at different altitudes and over different terrain typologies. The information is used for understanding the impact of LA signals on the performance of an outdoor WAIC system. Channel coding techniques for forward error correction are evaluated as interference mitigation strategies.
A classical-mechanical analogue of Hawking black hole radiation

One of the most fascinating phenomena in general relativity is the black hole, and the associated Hawking radiation, which predicts that escape from the hole can be possible for relativistic particles under certain conditions. This phenomenon has been recently highlighted by a quantum condensed matter analogue, using a microscopic lattice model of a Weyl semimetal with tilted nodes. Here, we are motivated to imitate black hole Hawking radiation in a purely classical-mechanical system. We propose a coupled double chain model featuring frequency dispersion that coincides with the tilted Weyl semimetal bandstructure near the vertices. The resulting directional inter-site couplings can be realized by an active mechanical metamaterial with an underlying programmable feedback network.
G ENERATIVE MODELS FOR PARTICLE SHOWER SIMULATION IN FUNDAMENTAL PHYSICS

Simulations in fundamental physics connect underlying first-principle theories and empirical descriptions of detectors with experimentally observed data. They are indispensable, e.g., in particle physics, where they underpin statistical inference tasks and experiment design. However, these Monte-Carlo-based simulations are computationally costly. Growing data volumes produced by upcoming experiments exacerbate this problem and need to be matched by a commensurate growth in simulated statistics for precision measurements. Generative machine learning models offer a potential way to leverage the effectiveness of classical simulation via amplification. This contribution presents state-of-the-art performance for simulating the interaction of two different types of elementary particles with different characteristics — photons and pions — with high granularity detectors of 27k and 8k read-out channels, respectively.
Obtaining Nanoparticles by Underwater Laser Ablation – A Brief Review

This research examines various methods of producing nanoparticles. The comparison and preferences of various laser parameters based on their classification and characteristics are discussed. The main objective of this study is to reveal the dominant laser parameters for the laser ablation process to generate nanoparticles in aqueous media. The method of pulsed laser ablation in liquid to produce nanoparticles is explained in detail. The paper is divided into several main parts, starting with a brief description of various methods of nanoparticle generation and followed by how basic laser parameters and factors affect the laser ablation process. The review also discusses some results on laser ablation of nanoparticles obtained from different materials such as silver, zinc, palladium, and titanium as a function of changing the laser processing parameters of the materials. The study for understanding the process, in brief, is important as nanoparticles could be generated to be used in medicine and pharmaceuticals.
SOSUAS: Stability Optimized Swarming for Unmanned Aerial Systems

Unmanned aerial systems (UASs) have unprecedented potential in various fields, from surveillance to packet delivery. The swarms formed by multiple UAS increase the number of potential UAS applications as they can be utilized for complicated tasks. Therefore, various swarming methodologies are proposed to coordinate the UAS positions in swarms. Virtual forces and molecular geometry based approaches have been instrumental in UAS swarming applications. These approaches apply forces between UAS nodes similar to electron pairs for near-ideal volume coverage. However, the ratio of these forces and the stability of the node positions in the network have been vexing challenges. In this paper, we propose an approach to optimize the virtual forces for a stable 3D swarming behavior in a realistic communications environment. The results of extensive simulations show that the approach can identify the relationship among the attractive and repulsive virtual forces for given positional properties of a UAS swarm.
Spatial-Temporal Graph-Based Transformer Model for Traffic Flow Forecasting

Realtime traffic flow forecasting is a core issue of intelligent traffic control and management. To make accurate traffic flow forecasting, it is necessary to take into account both the temporal and spatial characteristics of traffic flow in the flow forecasting methods. In this paper, we build a spatial temporal graph-based transformer model (STGT) for traffic flow forecasting. In the proposed model, we introduce the distance correction matrix, step correction matrix and attention mechanism to improve the traditional graph convolutional networks (GCN). The improved GCN is used to extract the spatial information. We further introduce the Transformer model to exploit the temporal information, so as to improve the accuracy of forecasting results. The proposed STG T model is tested using the real-world dataset from the Caltrans Performance Measurement System (PeMS). In the comparison experiment, the accuracy of STGT on MAE and RMSE reached 22.08 and 33.44 respectively, which is better than all selected baseline models.
Human Vs. Autonomous Agents: Drone racing and Obstacle Avoidance

With the increase of autonomy in aircraft systems over the past decade, the question of whether autonomous agents provide better performance when compared to human operators is of utmost importance. Current research using machine learning for obstacle avoidance in small Unmanned Aircraft Systems has gained extensive attention in recent years, but who is faster, the human or the machine? This study leverages programmable coding languages using Unmanned Aircraft Systems (UAS) to navigate a fabricated obstacle course environment. By applying various methods of intelligent agent training, human pilots were compared to the performance of the automated system. The Artificial Intelligence (AI) training ensured that the automated flight path and speed for the Unmanned Aircraft System were optimized for the course environment. We collected a dataset from a sample size of 12 participants, including zero knowledge of flight training, to experienced pilots. The findings of this work aimed to show the performance comparison, including Key System Attributes and Key Performance Parameters, by considering drone speed, pilot skill level, and course crashes between man and machine to determine which is a safer and faster solution.
Research on Named Entity Recognition Method Based on Deep Learning in Electric Power Public Opinion Field

Finding entity information quickly from the electric power public opinion field is difficult. The traditional language model can’t deal with the semantic information between contexts well, and it can’t get a good recognition result for polysemy and entity inclusion. To address this problem, we propose a RoBERTa-BilSTM-CRF model to extract relevant entities in the text. Through RoBERTa pre-training model, features are extracted by combining Chinese characters’ five strokes sequence and radical. We use various types of feature information for comprehensive feature extraction to solve the problem of incomplete and inaccurate information feature extraction caused by single-mode feature extraction. By adding the Attention mechanism to the model, long texts can learn more appropriate representation vectors during training. We adopt a position index manner to annotate sequence, which can reduce inclusion among annotated entities. The experimental results show that the accuracy of entity extraction is significantly improved, and the inclusion problem between entities is effectively solved.
Multi-Agent Cooperative Camera-Based Evidential Occupancy Grid Generation

About a decade ago the idea of cooperation has been introduced to self-driving with the aim to enhance safety in dangerous places such as intersections. Infrastructure-based cooperative systems emerged very recently bringing a new point of view of the scene and more computation power. In this paper, we want to go beyond the framework presented in the vehicle-to-infrastructure (V2I) cooperation by including the vehicle's point of view in the perception of the environment. To keep the cost low, we decided to use only two-dimensional bounding boxes, thus depriving ourselves of depth information that contrasts with state-of-the-art methods. With this in-the-scene point-of-view, we propose a new framework to generate a cooperative evidential occupancy grid based on the Dempster-Shafer Theory and which employs a Monte Carlo framework to incorporate position noise in our algorithm. We also provide a new cooperative dataset generator based on the CARLA simulator. Finally, we provide an extended review of our new cooperative occupancy grid map generation method which improves the state-of-the-art techniques.