Miracle-dz/UniFAR-FTUs huggingface dataset:
Collision Avoidance with Proximity Servoing for Redundant Serial Robot Manipulators

Collision avoidance is a key technology towards safe human-robot interaction, especially on-line and fastreacting motions are required. Skins with proximity sensors mounted on the robot’s outer shell provide an interesting approach to occlusion-free and low-latency perception. However, collision avoidance algorithms which make extensive use of these properties for fast-reacting motions have not yet been fully investigated. We present an improved collision avoidance algorithm for proximity sensing skins by formulating a quadratic optimization problem with inequality constraints to compute instantaneous optimal joint velocities. Compared to common repulsive force methods, our algorithm confines the approach velocity to obstacles and keeps motions pointing away from obstacles unrestricted. Since with repulsive motions the robot only moves in one direction, opposite to obstacles, our approach has better exploitation of the redundancy space to maintain the task motion and gets stuck less likely in local minima. Furthermore, our method incorporates an active behaviour for avoiding obstacles and evaluates all potentially colliding obstacles for the whole arm, rather than just the single nearest obstacle. We demonstrate the effectiveness of our method with simulations and on real robot manipulators in comparison with commonly used repulsive force methods and our prior proposed approach.
Matrix Factorization Enriched with Item Features

This paper1 presents a novel matrix factorization (MF) recommendation model, FeatureMF, which extends item latent vectors with item representation learned from metadata. By taking into account item features, the model addresses the cold-start item problem and data-sparsity problem of collaborative filtering (CF). Extensive experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better prediction accuracy than some of the popular state-of-the-art MF-based recommendation models.
Evaluation of Activation Mechanism of Ankle Muscles During Upright Stance Push-recovery

Studying the neuromuscular control mechanism of the ankle joint is very important for revealing upright balance dynamic properties. This paper aims at the evaluating the activation mechanism of ankle joint muscles in the case where the human upright stance is disturbed. To this end, this paper first combines the stretch reflex control with force feedback control to establish muscle reflex control model. Then, the established model is implemented to predict muscle activation levels of multiple subjects when they are disturbed. The disturbance is designed by releasing sandbags from the different height to hit on the back of subjects. The real-time simulator is used to collect the surface electromyography (sEMG) signals of the tibialis anterior (TA), gastrocnemius (GAS) and soleus (SOL) muscles as well as the ankle joint kinematics and dynamic data synchronously. Also, the parameters of the muscle reflex model are obtained and optimized by parameter identification based on particle swarm optimization algorithm (PSO). The experimental data are used as an input of the model to predict the muscle activation levels. Finally, the validity of the muscle reflex control model is verified by experimental tests and data analysis.
TACTO-Selector: Enhanced Hierarchical Fusion of PBVS with Reactive Skin Control for Physical Human-Robot Interaction

In a physical Human-Robot Interaction for industrial scenarios is paramount to guarantee the safety of the user while keeping the robot’s performance. Hierarchical task approaches are not sufficient since they tend to sacrifice the low priority tasks in order to guarantee the consistency of the main task. To handle this problem, we enhance the standard hierarchical fusion by introducing a novel interactive task-reconfiguring approach (TACTO-Selector) that uses the information of the tactile interaction to adapt the dimension of the tasks, therefore guaranteeing the execution of the safety task while performing the other task as good as possible. In this work, we hierarchically combine a 6 DOF Position-Based Visual Servoing (PBVS) task with a reactive skin control. This approach was evaluated on a 6 DOF industrial robot showing an improvement of 36.37% on average in tracking error reduction compared with a standard approach.
Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks

This paper presents a method to incorporate ergonomics into the optimization of action sequences for bi-manual human-robot cooperation tasks with continuous physical interaction. Our first contribution is a novel computational model of the human that allows prediction of an ergonomics assessment corresponding to each step in a task. The model is learned from human motion capture data in order to predict the human pose as realistically as possible. The second contribution is a combination of this prediction model with an informed graph search algorithm, which allows computation of human-robot cooperative plans with improved ergonomics according to the incorporated method for ergonomic assessment. The concepts have been evaluated in simulation and in a small user study in which the subjects manipulate a large object with a 32 DoF bimanual mobile robot as partner. For all subjects, the ergonomic-enhanced planner shows their reduced ergonomic cost compared to a baseline planner.
A Heterogeneous Implementation of the Sobel Edge Detection Filter Using OpenCL

Today, edge detection is a cornerstone technique as edges are essential in many applications, such as image processing and biometric imaging. One popular algorithm for edge detection is the Sobel. Many researchers have focused on accelerating the Sobel filtering, but to the best of our knowledge we are the first to propose a $5 \times 5$ convolution kernel implementation using OpenCL. In this work, we implement the Sobel filter, one of the most effective and popular edge detection algorithms in image processing, in the OpenCL programming language. From the implementation of the Sobel algorithm we compare the performance of the CPU and GPU through OpenCL, in typical images ranging from $64 \times 64$ to $4096 \times 4096$ pixels. The Sobel operator uses a pair of $3 \times 3$ horizontal and vertical convolution kernels for edge detection functions. We apply $3 \times 3$ and $5 \times 5$ convolution kernels using OpenCL and compare them. The results have shown that for all image sizes, the GPU speed up ranges from 11,18 to 15,46 times with $3 \times 3$ convolution kernels, while speed up is from 10,05 to 13,46 times for the $5 \times 5$ convolution kernels. Finally, the results of our implementation are compared to other existing implementations and found to achieve better performance.
A Comparative Study on Various Intrusion Detection Techniques Using Machine Learning and Neural Network

Security is of great concern these days as a large amount of data is being transmitted every hour in each domain. This paper provides an explained survey on the prevention and detection of intrusion using a neural network for the security of data. Various types of Intrusion Detection (IDS) and Intrusion Prevention System (IPS) have been studied and compared in this study. Moreover, Comparative study is made on different techniques like Network-Based, Host-Based, Anomaly Based, Signature-Based and Application-Based Intrusion Detection Techniques using Artificial Neural Network. The task of the IDS is to monitor the system for all sorts of malicious errors beforehand. The above techniques are compared based on the parameters like selecting the right feature, checking LAND attack, traffic filtering, blocking and many more. It can be concluded that based on the feature selection method Anomaly Detection is by far the best method for Intrusion Detection. This paper explains various models and techniques for an intrusion detection system. The paper highlights how can neural network and machine learning be used to detect Intruders using the IDS technique to prevent it from all malicious errors that can occur in the system.
Cross-context Visual Imitation Learning from Demonstrations

Imitation learning enables robots to learn a task by simply watching the demonstration of the task. Current imitation learning methods usually require the learner and demonstrator to occur in the same context. This limits their scalability to practical applications. In this paper, we propose a more general imitation learning method which allows the learner and the demonstrator to come from different contexts, such as different viewpoints, backgrounds, and object positions and appearances. Specifically, we design a robotic system consisting of three models: context translation model, depth prediction model and multi-modal inverse dynamics model. First, the context translation model translates the demonstration to the context of learner from a different context. Then combining the color observation and depth observation as inputs, the inverse model maps the multi-modal observations into actions to reproduce the demonstration, where the depth observation is provided by a depth prediction model. By performing the block stacking tasks both in simulation and real world, we prove the cross-context learning advantage of the proposed robotic system over other systems.
Deep Learning Methods for Land Cover and Land Use Classification in Remote Sensing: A Review

Land Cover and Land Use classification is a problem in remote sensing imagery data. The volume of remote sensing data in the earth observation, which need analysis and interpretations, is extremely increasing in this ‘Big Data’ era. To overcome this critical challenge, Deep learning methods have been identified as a recent powerful modelling technique to extract hidden information from big remote sensing imagery for LCLU classification. Classification of RS data in earth domain is vital for land administration and environmental protection in planning and decision making. The applications of deep learning methods for LCLU classification using remote sensing imagery data become increasing in recent. Deep learning methods such as CNN, GAN and RNN are able to be used to classify remote sensing image data. We are planning to use deep CNNs models to analyse and compare their results using various parameters and three different scale remote sensing data sets: UC Merced, NWPU-RESISC45 and EuroSAT: Sentile 2.
Broadband Circularly Polarized Antenna With Non-Planar Reflector

In this article, a compact broadband circularly polarized (CP) antenna with non-planar reflector is presented. In this antenna, two fan-shaped cross-dipoles as the primary radiator and a stepped ground as a reflector are proposed to generate a broadband CP radiation. Besides, four L-shaped patches are introduced to effectively extend the bandwidth (BW) and extract a gain improvement together with the stepped ground. The measured -10 dB impedance BW is 132.08 % (1.36-6.65 GHz) and 3 dB axial ratio (AR) BW is 128.6% (1.39-6.4 GHz), closely with the simulated AR BW 136.73% (1.18-6.28 GHz), showing the proposed antenna features a wider bandwidth, comparing with other broadband CP antennas using similar structures. Due to the compact structure, the overall size of the proposed antenna is only <inline-formula> <tex-math notation="LaTeX">$0.27\lambda _{\mathrm {l}}\times 0.27\lambda _{\mathrm {l}}\times 0.12\lambda _{\mathrm {l}}$ </tex-math></inline-formula> (where <inline-formula> <tex-math notation="LaTeX">$\lambda _{\mathrm {l}}$ </tex-math></inline-formula> is the free-space wavelength at 1.36 GHz). The proposed antenna has a wide application scope, such as the wireless network and satellite telecommunications.
Ordinal Classification: Working Definition and Detection of Ordinal Structures

Ordinal classification (OC) is an important niche of supervised pattern recognition, in which the classes constitute an ordinal structure. In general, the ordinal structure can be identified, either according to the natural occurrence of the current task (e.g. healthy - mild condition - moderate condition - severe condition), or by extracting expert knowledge. However, we assume that many multi-class classification tasks might have a hidden ordinal structure, which, once identified, can facilitate and hence leverage the classification process. Therefore, we propose a working definition for OC tasks, which is based on the decision boundaries of standard binary Support Vector Machines. Moreover, resulting from our proposed definition, we introduce a simple algorithm for the detection of ordinal structures. Our proposed definition is easy to interpret and reflects an intuitive understanding of ordinal structures. Another main advantage is that our proposed definition is easy to apply. Therefore, there is no more dependence on expert knowledge for the identification of (non-intuitive) ordinal class structures. In the current study, we include ten benchmark data sets from the field of OC to experimentally evaluate and hence to confirm the validity of our proposed definition. Additionally, we analyse our proposed definition based on a small set of traditionally non-ordinal multi-class classification tasks. Furthermore, we provide an analysis of the computational cost of our proposed detection algorithm, and discuss the limitations of our proposed working definition.
Safe high impedance control of a series-elastic actuator with a disturbance observer

In many series-elastic actuator applications, the ability to safely render a wide range of impedance is important. Advanced torque control techniques such as the disturbance observer (DOB) can improve torque tracking performance, but their impact on safe impedance range is not established. Here, safety is defined with load port passivity, and passivity conditions are developed for two variants of DOB torque control. These conditions are used to determine the maximum safe stiffness and Z-region of the DOB controllers, which are analyzed and compared with the no DOB case. A feedforward controller is proposed which increases the maximum safe stiffness of the DOB approaches. The results are experimentally validated by manual excitation and in a high-stiffness environment.
Trigger-Based Pothole Detection Using Smartphone and OBD-II

Maintenance of road infrastructure has gathered a lot of attention in recent days. Bad road conditions often cause damages to vehicles and passengers that cannot be neglected. Some of the major factors that cause damage are potholes, speed-breakers, uneven patches etc. The presence of such abnormalities also affects ride quality and safety of passengers. The paper describes a low-cost method that employs a smartphone and an OBD-II module to detect and locate potholes on roads. It utilizes vision data, sensor data and OBD data to create and validate triggers caused by potholes. The image-triggered and data-triggered methods proposed recognize potholes on roads and create a trigger through image/video processing and data processing respectively. The results obtained from each method is cross validated by the other method that help in evaluating ride quality based on road conditions.
Fabrication and Mechanical Analysis of Bioinspired Gliding-optimized Wing Prototypes for Micro Aerial Vehicles

Gliding is the most efficient flight mode that is explicitly appreciated by natural fliers. This is achieved by high-performance structures developed over millions of years of evolution. One such prehistoric insect, locust (Schistocerca gregaria) is a perfect example of a natural glider capable of endured transatlantic flights, which could potentially inspire numerous solutions to the problems in aerospace engineering. However, biomimicry of such aerodynamic properties is hindered by the limitations of conventional as well as modern fabrication technologies in terms of precision and availability, respectively. Therefore, we explore and propose novel combinations of economical manufacturing methods to develop various locust-inspired tandem wing prototypes (i.e. fore and hindwings), for further wind tunnel based aerodynamic studies. Additionally, we determine the flexural stiffness and maximum deformation rate of our prototypes and compare it to their counterparts in nature and literature, recommending the most suitable artificial bioinspired wing for gliding micro aerial vehicle applications.
State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

The state of charge (SOC) is a crucial parameter of a battery management system for Li-ion batteries. The SOC indicates the amount of charge left in the battery of electric vehicles—akin to the fuel gauge in combustion vehicles. An accurate SOC knowledge contributes largely to the longevity, performance, and reliability of the battery. However, the SOC of Li-ion batteries cannot be easily measured by any apparatus. Furthermore, the SOC can also be influenced by numerous incalculable factors such as battery chemistry, ambient environment, aging factor, etc. In this article, we propose an SOC estimation model for a Li-ion battery using an improved deep neural network (DNN) approach for electric vehicle applications. We found that a DNN with a sufficient number of hidden layers is capable of predicting the SOC of the unseen drive cycles during training. We developed a series of DNN models with a varying number of hidden layers, and its training algorithm was to investigate their respective performance when evaluated on different drive cycles. We observe that the increasing number of hidden layers in the DNN (up to four hidden layers) decreases the error rate and improves SOC estimation. An additional increase in the number of hidden layers beyond that increases the error rate. In this study, we show that a four-hidden-layer DNN trained on Dynamic Stress Test drive cycle is capable of predicting SOC values unexpectedly well of other unseen drive cycles such as Federal Urban Driving Schedule, Beijing Dynamic Stress Test, and Supplemental Federal Test Procedure, respectively.
Two Dimension Frequency Scanning Antenna

In this paper, a high-frequency frequency scanning antenna based on metasurface is designed. The antenna proposed here consists of three parts: an 1 × 16 unequal divider, 16 BJ900 waveguides and 160 metasurface. The proposed antenna is fed by a standard waveguide BJ900 with periodic metasurface covered on it. The working band is 76–81 GHz. To realize two-dimension beam scanning, an unequal divider is designed. After the final optimization, we can get from the simulation results that the working angle is 30° in both two dimension and the radiation efficiency can reach -0.1 dB. The total realized gain of the antenna can reach 20 dB.
Sample-and-computation-efficient Probabilistic Model Predictive Control with Random Features

Gaussian processes (GPs) based Reinforcement Learning (RL) methods with Model Predictive Control (MPC) have demonstrated their excellent sample efficiency. However, since the computational cost of GPs largely depends on the training sample size, learning an accurate dynamics using GPs result in low control frequency in MPC. To alleviate this trade-off and achieve a sample-and-computation-efficient nature, we propose a novel model-based RL method with MPC. Our approach employs a linear Gaussian model with randomized features using the Fastfood as an approximated GP dynamics. Then, we derive an analytic moment-matching scheme in state prediction with the model and uncertain inputs. As a result, the computational cost of the MPC in our RL method does not depend on the training sample size and can improve the control frequency over previous methods. Through experiments with simulated and real robot control tasks, the sample efficiency, as well as the computation efficiency of our model-based RL method, are demonstrated.
Decision level fusion schemes for a Multimodal Biometric System using local and global wavelet features

Local and global features of biometric data play a vital role to improve the performance of a multimodal biometric system. In this paper, we propose three decision level fusion schemes - Local Decision Fusion (LDF), Global Decision Fusion (GDF) and Local-Global Decision Fusion (LGDF)- by exploiting local and global information. The proposed methods extract such information by utilizing low and high frequency wavelet sub-bands. Subsequently, the sub-bands are classified separately using nearest neighbor classifier, and the resulting classes are fused using weighted majority voting. The proposed LDF and GDF methods show an improvement in the average recognition rates to a maximum extent of 9.4%, 11%, 10.6% and 11.5% in comparison to unimodal LDF, GDF and two low frequency sub-band based methods respectively. Further, the proposed LGDF method is superior to feature-score hybrid fusion by a highest average recognition rate of 6.75%.
3D Point Cloud Classification for Autonomous Driving via Dense-Residual Fusion Network

Compared with the state-of-the-art architectures, using the 3D point cloud as the input of the 2D convolutional neural network without preprocessing will restrict the feature expression of the network. To address this issue, we propose a high-precision classification network using bearing angle (BA) images, depth images, and RGB images. Due to the development of unmanned vehicles, determining how to recognize objects from the information collected by sensors is important. Our approach takes data from LiDAR and a camera and projects a 3D point cloud into 2D BA images and depth images. The RGB image captured by the camera is used to select the region of interest (ROI) corresponding to the point cloud. However, only adding input information is not enough to improve the classification ability of general convolutional neural networks. In our approach, we use a Dense-Residual Fusion Network (DRF-Net), which consists of Dense-Residual Blocks (DRBs). The Dense-Residual Fusion Network can achieve 97.92% accuracy with three input formats on a KITTI raw dataset.
Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

Service-oriented computing (SOC) promises a world of cooperating services loosely connected, constructing agile Web applications in heterogeneous environments conveniently. Web application interface (API) as an emerging technique attracts more and more enterprises and organizations to publish their deep computing functionalities and big data on the Internet, Web API has become the backbone to promote the development of SOC, thus forming the prosperous Web API economy. However, the number of available Web APIs on the Internet is massive and growing constantly, which causes the Web API overload problem. Quality of service (QoS) as an indicator is able to well differentiate the quality of Web APIs and has been widely applied for high quality Web API selection. Since testing QoS for massive Web APIs is resource-consuming, and the QoS performance depends on contextual information such as network and location, hence accurate QoS prediction has become very crucial for personalized Web API recommendation and high quality Web application construction. To address the above issue, this paper presents a context aware deep factorization machine model (CADFM for short) for accurate Web API QoS prediction. Specifically, we first carry out detailed data analysis using real-world QoS dataset and discover a positive relationship between QoS and contextual information, which motivates us to incorporate beneficial contexts for enhancing QoS prediction accuracy. Then, we treat QoS prediction as a regression problem and propose a context aware CADFM framework that integrates the contextual information via embedding technique. Particularly, we adopt MF and MLP for high-order and nonlinear interaction modeling, so as to learn the complex interaction between users and Web APIs accurately. Finally, the experimental results on real-world QoS dataset demonstrate that CADFM outperforms the classic and the state-of-the-art baselines, thereby generating the most accurate QoS predictions and increasing the revenue of Web APIs recommendation.