Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Improvement in 3D Object Landmark Inference for Semantic Mapping

Recent works on semantic Simultaneous Localization and Mapping (SLAM) utilizing object landmarks have shown superiority in terms of robustness and accuracy in tracking and localization. 3D object landmarks represented by a cubic or quadric surface are inferred from 2D object bounding boxes which are typically captured from multiple views by an object detector. Nevertheless, bounding box noises and small camera baseline may lead to an inaccurate 3D object landmark inference. Inspired by the dual quadric enveloping property, in this work, we introduce the horizontal support assumption to constrain rotation w.r. t. roll and pitch for a quadric representation. As the result, we reduce the number of quadric parameters and narrow down the solution space, and ultimately produce a relatively accurate inference. Extensive experimental evaluations under both simulated and real scenarios are conducted in this paper. Quantitative results demonstrate that our approach outperforms the state-of-the-art.
Deep Ensemble Approach for COVID-19 Fake News Detection from Social Media

Social media networks such as Facebook and Twitter are overwhelmed with COVID-19-related posts during the outbreak. People have also posted several fake news among the massive COVID-19-related social media posts. Fake news has the potential to create public fear, weaken government credibility, and pose a serious threat to social order. This paper provides a deep ensemble-based method for detecting COVID-19 fake news. An ensemble classifier is made up of three different classifiers: Support Vector Machine, Dense Neural Network, and Convolutional Neural Network. The extensive experiments with the proposed ensemble model and eight different conventional machine learning classifiers are carried out using the character and word n-gram TF-IDF features. The results of the experiments show that character n-gram features outperform word n-gram features. The proposed deep ensemble classifier performed better, with a weighted F1-score of 0.97 in contrast to numerous conventional machine learning classifiers and deep learning classifiers.
Modal Dynamic Modelling and Experimental Validation of a Curved Extensible Continuum Manipulator

This study presents a novel dynamic modelling method for analyzing the modal properties of a curved extensible continuum manipulator (ECM). Considering the variable bending angle and the length, the kinematic and static models are firstly established for descripting the geometric posture deformation and deflection generated by gravity. Then, the modal dynamic model is developed along the centerline direction of the ECM in the polar coordinates, by which the modal properties, include modal frequency and mode shape of curved continuum manipulator with different posture parameters are analyzed. The modal response is further analyzed based on the generalized coordinate method. Finally, the modal properties of ECM with variable posture are experimentally tested and compared with the theoretical value using the in-house developed ECM physical prototype. In the experiments, the bending, length varying and circular tracking motion are performed for the comprehensive validation of the proposed dynamics modelling method.
An efficient approach to closed-loop shape control of deformable objects using finite element models

Robots are nowadays faced with the challenge of handling deformable objects in industrial operations. In particular, the problem of shape control, which aims at giving a specific deformation state to an object, has gained interest recently in the research community. Among the proposed solutions, approaches based on finite elements proved accurate and reliable but also complex and computationally-intensiveIn order to mitigate these drawbacks, we propose a scheme for shape control that does not require to run a real-time simulation or to solve an implicit optimization problem for computing the control outputs. It is based on a partition of the nodal coordinates that allows deriving a control law directly from tangent stiffness matrices. This formulation is also coupled with the introduction of reduced finite element models. Simulation and experimental results in the context of linear deformable object manipulation demonstrate the interest of the proposed approach.
Figure-of-Merits Mismatch in Liquid Crystals mmWave Phase Shifters

This work conducts computational analysis into a liquid crystal (LC) based enclosed coplanar waveguide (ECPW) phase shifter, comparing among 16 types of LC respectively at 60 GHz. Results reveal a mismatch between the patterns of the device’s figure-of-merit (FoM, the ratio of maximum differential phase shift to maximum insertion loss) and those of the LC materials’ intrinsic FoM (the ratio of tunability to dissipation factor). Noteworthy, phase shifters with GT7-29001 and MDA-03-2838 obtain the highest FoM for devices, outperforming those filled with GT5-28004 and TUD-566 claimed with the highest FoM for materials.
Sparse Checkerboard Corner Detection from Global Perspective

Detecting corners from an image is an essential step for camera calibration in geometric computer vision and image processing applications. In this paper, a novel framework is proposed to detect sparse checkerboard corners with a global context from an image. The proposed framework addresses two major problems that the previous neural network-based corner detection networks have had: locality and non-sparsity. Our framework encodes the global context from an image and uses the context to determine the per-patch existence of the checkerboard. It enables the network to distinguish between the checkerboard pattern and pattern-like noise in the image background while preserving pixel-level detection details. Also, the patch-wise sparse regularization is introduced using counting distribution to obtain clear-cut predictions while maintaining the true positive rate. The experimental results demonstrate that parsing the global context helps the proposed network to decrease false positive detection significantly. Also, the proposed counting regularization improves to detect true positives while decreasing false negatives concurrently. It enables the proposed network to precisely detect sparse checkerboard corners, leading to significant improvements over the state-of-the-art methods.
Backstepping and Sliding Mode Control for AUVs Aided with Bioinspired Neurodynamics

Research on tracking control has been on-going for many years. The accuracy and the practicality of the tracking control method has always been one of the most important aspects when designing the control strategy. Autonomous Underwater Vehicles are becoming increasingly important in the applications of ocean surveillance and military, etc. Therefore, this paper aims to develop a control method for autonomous underwater vehicles based on bioinspired neural dynamics. The proposed method practically solves the speed jump and chattering issues that are respectively in conventional backstepping and sliding mode controls with the aid of the bioinspired neural dynamics. In addition, the proposed control method also takes the dynamic uncertainties for the autonomous underwater vehicle into the consideration. The combined tracking method has relatively good overall performance for autonomous underwater vehicle against model uncertainties and disturbances.
Control of a Transfemoral Prosthesis on Sloped Terrain using Continuous and Nonlinear Impedance Parameters

The design of impedance controllers for sloped walking with a transfemoral prosthesis is a complex control problem that generally results in numerous tuning parameters. This study proposes an easy-to-tune sloped walking control scheme. While the ankle is controlled using impedance control, the knee is controlled using a hybrid strategy of impedance control and trajectory tracking. This study derived continuous, nonlinear impedance functions for the ankle and knee joints using optimization. Principal component analysis of the impedance functions revealed trends that can be used to design impedance controllers for any given slope angle. Said trends were further used to establish a tuning regime which was subsequently tested on a transfemoral prosthesis in an emulator study. The generated gait kinematics and kinetics were found to follow the trends of healthy sloped walking data.
PZT Ferroelectric Synapse Device with Multi-level of Conductance State for Neuromorphic Applications

To fundamentally solve the bottleneck of Von Neumann’s computing architecture, a neuromorphic thin-film transistor (NTFT) employing Pb(Zr, Ti)O3 (PZT) was investigated. The indium gallium zinc oxide (IGZO) channel back gate TFT structure was chosen to solve the diffusion of atoms that form a channel layer during the annealing process for crystallization of PZT. A post-deposition process with IGZO after annealing PZT and using an oxide-based material as a channel structure can minimize the diffusion phenomenon of junction materials and oxygen together, which leads to a high and reliable performance of the NTFT. The basic operations of synapses short-term memory (STM) and long-term memory (LTM) were also analyzed to confirm the application of a neuromorphic device. The high dielectric constant and polarization properties of Pb(Zr, Ti)O3 (PZT) allow the power consumption of spike signals used in spike dependent plasticity change to be reduced to 10 pJ. Moreover, a wide dynamic range of Gmax / Gmin ≅ 1000 was obtained, and the channel conductance was maintained over 40000 seconds. The optimized pulse achieved multi-level states (>32), which made the learning process efficient. This study verified that the PZT-TFT structure has a high potential and merits for neuromorphic devices.
Nvidia Data Center Processing Unit (DPU) Architecture

NVIDIA DPU Enables the Data Center as the New Unit of Computing The CPU can no longer do it all Must offload & isolate server infrastructure tasks to a DPU Effective DPU must offer hardware acceleration and security isolation To enable such effective DPU, need to develop broad software eco-system to utilize hardware acceleration across variety of disciplines (e.g. HPC, AI/ML, Storage, Networking, Security) - DOCA NVIDIA DPU & DOCA is a computing platform with rich stack optimized ideal for AI, bare metal cloud, cloud supercomputing, storage, gaming, 5G wireless, and more NVIDIA is committed to line rate performance every generation.
SAR Target Classification Based on Integration of ASC Parts Model and Deep Learning Algorithm

Automatic target recognition of synthetic aperture radar (SAR) images has been a vital issue in recent studies. The recognition methods can be divided into two main types: traditional machine learning methods and deep-learning-based methods. For most traditional machine learning methods, target features are extracted based on electromagnetic scattering characteristics which are interpretable and stable. However, the extraction process of effective recognition features is often complex and the computational efficiency is low. Compared with the traditional methods, the deep learning methods can directly learn the high-dimensional features of the target to obtain higher target recognition accuracy. However, these algorithms have poor generalization performance and are difficult to explain. In order to comprehensively consider the advantages of the two kinds of methods, this article proposes a novel method for SAR target classification based on integration parts model and deep learning algorithm. First, part convolution and modified bidirectional convolutional-recurrent network are used to extract local feature of target through parts model which is calculated based on attribute scattering centers. Then, modified all-convolutional networks are used to extract the global feature of the target. The final classification result is achieved through decision fusion of local and global features. Experimental results on the moving and stationary target acquisition and recognition show the superiority of the proposed method, especially under complex conditions. Besides, a brief analysis of target key parts with part occlusion method is given, which is helpful to the interpretability of the deep learning network.
An Autonomous Robotic Flexible Endoscope System with a DNA-inspired Continuum Mechanism

In this paper, we proposed an autonomous robotic flexible endoscope system for the laparoscopic bariatric surgery (LBS). This system comprises a UR5 robot and a flexible endoscope equipped with a novel continuum joint, named reinforced double helix continuum mechanism. Compared with the simple helix structure, the compressional and torsional stiffness of the proposed joint are improved significantly. To automate the robotic flexible endoscope, image-based visual servoing technique is employed. A deep learning algorithm named TernausNet-16 is improved and incorporated into the control framework to detect surgical instruments inside the camera view. The experimental studies verified the effectiveness and feasibility of the robotic flexible endoscope system for the visual serviong control scheme assisted by deep learning methods.
Road Traffic Sign Detection and Recognition using Adaptive Color Segmentation and Deep Learning

Traffic sign detection and recognition (TSDR) is one of the main area of research in autonomous vehicles and Advanced Driving Assistance System (ADAS). In this paper a method is proposed to detect and classify the prohibitory subset of German Traffic Sign data set. The traffic sign detection module utilizes adaptive color segmentation based on mean saturation value of local neighborhood, and Circular Hough Transform (CHT) to locate the traffic signs in the input images. Adaptive color thresholding shows improvement in segmenting traffic signs where images have uneven lighting or very high or low contrast levels, compared to global thresholding. Furthermore, number of false alarms are minimized by utilizing an additional validation stage. For the recognition phase of the algorithm, multiple deep Convolutional Neural Networks (CNN) with different structures are developed from scratch to compare their performance and identify the network with highest accuracy.
Model Predictive Control for Dynamic Quadrotor Bearing Formations

Formation control of multi-agent systems deals with groups of robots forming specific spatial geometries. Combined with the advancements of unmanned aerial vehicles (UAVs) in the past decade, formation control may potentially be applied to tasks such as search-and-rescue, surveillance, even collaborative manipulation. A key challenge is the decentralization of formation control, where each agent behaves independently using onboard sensors and computation, improving the scaleability and robustness of the system.This paper proposes a decentralized controller based on model predictive control (MPC), for the control of formations of quadrotor UAVs defined by inter-agent bearings. The use of MPC allows the controller to account for attitude kinematics, improving upon the results of existing bearing formation control methods based on rigidity and visual servoing approaches, which typically only consider the quadrotor as a single or double integrator. Furthermore the near-optimality of MPC permits a more optimal use of the quadrotors dynamic capabilities for faster maneuvering. Extensive simulations are performed to demonstrate the improved transient formation convergence and fast maneuvering permitted by this controller. Experiments show that it is indeed a real-time feasible solution for bearing formation control.
Fast Replanning Multi-Heuristic A *

In this paper, we proposed a novel path replanning algorithm on arbitrary graphs. To avoid computationally heavy preprocessing and to reduce required memory to store the expanded vertices of the previous search, we defined the feature vertices, which are extracted from the previous path by a simple algorithm to compare the costs between adjacent vertices along the path once. Proper additional heuristic functions are designed for these feature vertices to work as local attractors guiding the search toward the previous path’s neighbors. To avoid unnecessary expansions and speed up the search, these additional heuristic functions are properly managed to stop intriguing or guiding search toward the feature vertices. The proposed algorithm of Fast Replanning Multi-Heuristic A* is a variation of Shared Multi-Heuristic A* while removing or deactivating the additional heuristic functions during the search. Fast Replanning Multi-Heuristic A* guarantees the bounded suboptimality while efficiently exploring the graph toward the goal vertex. The performance of the proposed algorithm was compared with weighted A* and D* lite by simulating numerous path replanning problems in maze-like maps.
Comparison of Selected Support Vector Machine Approaches for Stochastic Power Electronic Circuit Simulation with Parasitics

This paper provides a comparison between op-timization methods used for tuning the hyperparameters of Support Vector Machine model in a stochastic circuit simulation for conducted interference. The methodology is used to create a surrogate model of the frequency and amplitude of the dominant mode of the interference, which is a result of presence of parasitics in the considered switching circuit. Optimization algorithms are compared by obtaining the computational time and by computing a posteriori error of their predictions. The best optimization algorithm in the example provided here is found to be the quasi-Newton Broyden–Fletcher–Goldfarb–Shanno algorithm.
A Novel Gait Phase Detection Algorithm for Foot Drop Correction through Optimal Hybrid FES-Orthosis Assistance

As a life-threatening disease, stroke can lead to long-term problems affecting the patients’ daily living ability. A common problem facing post-stroke patients is foot drop. An emerging modality of interest for correcting the foot drop is to combine both actuated ankle-foot orthosis (AAFO) and functional electrical stimulation (FES). Such hybrid assistive system not only ensure effective assistance but also can avoid fast muscular fatigue due to excessive muscular stimulation. Due to the significant changes in the ankle joint’s kinematics and kinetics with gait cycles, optimization control strategies for hybrid AAFO and FES systems are highly demanded. However, it is challenging to develop accurate gait phase detection algorithms to guide the control of AAFO and FES while ensuring robustness with respect to the diversity and variability of patients’ gaits. In this paper, we present a novel swing sub-phase detection algorithm based on a moving average convergence divergence (MACD) indicator. The proposed detection algorithm uses only information collected from the affected leg by means of two inertia measurement units (IMU) and the AAFO. Moreover, a gait-phase based control strategy is developed to optimize the assistive effect of a hybrid AAFO and FES system. Experimental results with five healthy show the potential of the proposed approaches in ensuring both satisfactory ankle joint trajectory tracking and effective reduction in stimulation intensity, compared to the use of conventional FES assistance.
Personal Protective Equipment Detection with Live Camera

With the recent outbreak and rapid transmission of COVID-19, medical personal protective equipment (PPE) detection has seen significant importance in the domain of computer vision and deep learning. The need for the public to wear face masks in public is ever increasing. Research has shown that proper usage of face masks and PPE can significantly reduce transmission of COVID-19. In this paper, a computer vision with a deep-learning approach is proposed to develop a medical PPE detection algorithm with real-time video feed capability. This paper aims to use the YOLO object detection algorithm to perform one-stage object detection and classification to identify the three different states of face mask usage and detect the presence of medical PPE. At present, there is no publicly available PPE dataset for object detection. Thus, this paper aims to establish a medical PPE dataset for future applications and development. The YOLO model achieved 84.5% accuracy on our established PPE dataset comprising seven classes in more than 1300 images, the largest dataset for evaluating medical PPE detection in the wild.
A Self-Training Approach-Based Traversability Analysis for Mobile Robots in Urban Environments

This paper presents a method for LiDAR sensor-based traversability analysis for autonomous mobile robots in urban environments. Although urban environments are structured environments, a typical terrain comprises hazardous regions for mobile robots. Therefore, a reliable method for detecting traversable regions is required to prevent robots from getting stuck in the middle of the road. Conventional approaches require considerable efforts to obtain a model for traversability analysis for a specific robot or environment. In particular, learning-based methods require explicit training data. This paper introduces a method for traversability mapping based on a self-training algorithm to eliminate the hand labeling process. A neural network was applied to the underlying classifier of the self-training algorithm. With our approach, the model can be learned with even weakly labeled data obtained from robot-specific parameters and the robot’s footprint. In practical experiments, the self-trained model performed better performance than the existing supervised learning method. Moreover, as the fraction of unlabeled data increased, the performance also increased. Therefore, the demonstrations in the urban environments indicate the effectiveness of the proposed method for traversability mapping.
Building Intelligent Moving Average-based Stock Trading System using Metaheuristic Algorithms

Many studies have been proposed to prove that technical analysis can help investors make trading decisions. The moving average (MA) is a widely used technical indicator that plays an important role in this field since it directly reflects stock fluctuations. However, most studies ignore the parameter settings of the MA, which leads to underestimation of the potential of the MA. Therefore, this paper is the first attempt to remove all restrictions and extend the limits of the MA to take advantage of the MA’s capability well. It also uses different kinds of MA, such as the weighted moving average (WMA) and the exponential moving average (EMA), to compose trading strategies. Our system proposes the global best-guided quantum-inspired tabu search algorithm (GQTS), which is better at searching than traditional algorithms, to optimize trading strategies based on the MA. Furthermore, an innovative 2-phase sliding window is invented to consider more investment situations in changeable stock markets. In summary, this paper intended to investigate the ability ofMAand proposed dynamic and intelligent trading strategies based on MA, GQTS, and 2-phase sliding window to assist investors to make trading decisions. The experiments show that the proposed system flexibly discovers better trading points. Our system outperforms traditional methods and beats the buy-and-hold strategy to yield significant profits both in developed and emerging stock markets.