Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Planar Inkjet-Printed Antennas on a Low-Cost Origami Flapping Robot

An investigation on antenna solutions for expendable origami paper flapping robots is presented. An origami flapping robotic bird that can be produced using standard A4 paper is employed. Antennas resonating at the two commonly used frequency bands, 2.4 GHz and 5.2 GHz, are designed for the limited space available on the folded origami structure. Two developments of the same dual-band monopole antenna are discussed. The first antenna is located on the robot’s spine and the second on its tail. A space diversity configuration is also studied. The antennas are printed directly onto a photo paper substrate and then folded into an origami robotic crane’s body structure. An ordinary desktop inkjet printer fitted with silver nanoparticle conductive ink cartridges has been employed. CST Microwave Studio™ has been used to design the antennas. A good agreement between the measured and simulated $S_{11}$ results is achieved with a reasonable −10dB impedance bandwidth realized in the three cases studied. The radiation patterns are omnidirectional in the XZ plane which is desirable for the specific application. The diversity configuration has a mutual coupling of <−23dB and a gain of 1.4 dB and 2.8 dB at 2.4 GHz and 5.2 GHz respectively. The aim is to provide a new vision for antennas embedded into expandable flying robots based on traditional origami structures.
Observability Analysis of Flight State Estimation for UAVs and Experimental Validation

UAVs require reliable, cost-efficient onboard flight state estimation that achieves high accuracy and robustness to perturbation. We analyze a multi-sensor extended Kalman filter (EKF) based on the work by Leutenegger. The EKF uses measurements from a MEMS-based inertial system, static and dynamic pressure sensors as well as GPS. As opposed to other implementations we do not use a magnetic sensor because the weak magnetic field of the earth is subject to disturbances. Observability of the state is a necessary condition for the EKF to work. In this paper, we demonstrate that the system state is observable – which is in contrast to statements in the literature – if the random nature of the air mass is taken into account. Therefore, we carry out an in-depth observability analysis based on a singular value decomposition (SVD). The numerical SVD delivers a wealth of information regarding the observable (sub)spaces. We validated the theoretical findings based on sensor data recorded in test flights on a glider. Most importantly, we demonstrate that the EKF works. It is capable of absorbing large perturbations in the wind state variable converging to the undisturbed estimates.
Voice Command Decoding for Position Control of Jaco Robot Arm using a Type-2 Fuzzy Classifier

The present study is an endeavor to develop a notable technique to decode the voice commands of disabled subjects to control the movement of a Jaco robot arm that will serve the basic needs of such individuals. Ten healthy/normal subjects participated in the present experimental study who uttered five basic English language words, ‘hello’, ‘up’, ‘down’, ‘left’ and ‘right’, each of which is associated with the movement of the robotic arm in a specific direction. Each uttered word has been recorded utilizing a single channel microphone. The acquired signal has been first filtered to remove the effects of unwanted noise components and then transmitted to an Independent Component Analysis (ICA) module to abolish the remaining effects of unwanted segments. After the pre-processing phase, the procured signal has been transferred to a feature extraction module to abstract the most important features pertaining to the current task. Power Spectral Density (PSD) is used as the feature extraction method. The abstracted feature space is reduced by Differential Evolutionary (DE) algorithm to select the optimal features. Finally, the selected set of features is transferred to a novel type-2 fuzzy classifier module to classify five distinct class labels. Performance analysis undertaken confirms the superlative behavior of the proposed classifier with respect to other standard ones. Moreover, statistical evaluation also assures the superior performance of the proposed classifier model. Thus, the proposed methodology can act as an efficient technique to cater the daily requirements of individuals suffering from Arm Amputation, Monoplegia, Hypokalemic Periodic Paralysis and other forms of muscle oriented disorders. Moreover, this technique can also be utilized in various industrial scenarios where a robot can be accessed via voice commands to perform some critical task where human admittance is quite dangerous.
2D to 3D Line-Based Registration with Unknown Associations via Mixed-Integer Programming

Determining the rigid-body transformation be-tween 2D image data and 3D point cloud data has applications for mobile robotics including sensor calibration and localizing into a prior map. Common approaches to 2D-3D registration use least-squares solvers assuming known associations often provided by heuristic front-ends, or iterative nearest-neighbor. We present a linear line-based 2D-3D registration algorithm formulated as a mixed-integer program to simultaneously solve for the correct transformation and data association. Our formulation is explicitly formulated to handle outliers, by modeling associations as integer variables. Additionally, we can constrain the registration to SE(2) to improve runtime and accuracy. We evaluate this search over multiple real-world data sets demonstrating adaptability to scene variation.
Vehicle Localization Based on Visual Lane Marking and Topological Map Matching

Accurate and reliable localization is crucial to autonomous vehicle navigation and driver assistance systems. This paper presents a novel approach for online vehicle localization in a digital map. Two distinct map matching algorithms are proposed: i) Iterative Closest Point (ICP) based lane level map matching is performed with visual lane tracker and grid map ii) decision-rule based approach is used to perform topological map matching. Results of both the map matching algorithms are fused together with GPS and dead reckoning using Extended Kalman Filter to estimate vehicle’s pose relative to the map. The proposed approach has been validated on real life conditions on an equipped vehicle. Detailed analysis of the experimental results show improved localization using the two aforementioned map matching algorithms.
Run-time and Collective Adaptation of Gameful Systems

The term gamification has been introduced in the early 2000s [13] and has as central idea the usage of game elements in non-entertainment application domains to foster motivation [8], [15], [6]. There is a considerable amount of literature concerning gamification concepts [5], [16], related taxonomies [18], [17], and literature reviews [10].
SR-HGAT: Symmetric Relations Based Heterogeneous Graph Attention Network

Graph neural network, as a deep learning based graph representation technology, can capture the structural information encapsulated in graphs well and generate more effective feature embedding. We have recently witnessed an emerging research interests on it. However, existing models are primarily focused on handling homogeneous graphs. When designing graph neural networks for heterogeneous graphs, heterogeneity and rich semantic information bring great challenges. In this paper, we extend graph neural network to heterogeneous graph scenes, and propose a novel high-order Symmetric Relation based Heterogeneous Graph Attention Network, denoted as SR-HGAT, which takes into account the features of nodes and high-order relations simultaneously, and exploits the two-layer attention mechanism based aggregator to efficiently capture essential semantics in an end-to-end manner. The proposed SR-HGAT first identifies the latent semantics underneath the observed explicit symmetric relations guided by different meta-paths and meta-graphs in a heterogeneous graph. The nested propagation mechanism for aggregating semantic and structural features that different links contain is then designed to calculate the interaction strength of each symmetric relation. As the core of the proposed model, to comprehensively capture both the structural and semantic feature information, a two-layer attention mechanism is applied to learn the importance of different neighborhood information as well as the weights of different symmetric relations. These latent semantics are then automatically fused to obtain unified embeddings for specific mining tasks. Extensive experimental results offer insights into the efficacy of the proposed model and have demonstrated that it significantly outperforms state-of-the-art baselines across three benchmark datasets on various downstream tasks.
MSCNN-AM: A Multi-Scale Convolutional Neural Network With Attention Mechanisms for Retinal Vessel Segmentation

Automatic retinal vessel segmentation has drawn significant attention in early diagnosis and treatment of many diseases, such as diabetes, retinal diseases, and coronary heart disease. However, due to vessels exhibit variations in morphology and low contrast, it is still challenging to obtain accurate segmentation results. In this paper, aiming at upgrading the accuracy and sensitivity of existing vessel segmentation methods, we propose a Multi-Scale Convolutional Neural Network with Attention Mechanisms (MSCNN-AM). For extraction of blood vessels at different scales, we introduce atrous separable convolutions with varying dilation rates, which could capture global and multi-scale vessel information better. Meanwhile, in order to reduce false-positive predictions for tiny vessel pixels, we also adopt attention mechanisms so that the proposed MSCNN-AM can pay more attention to retinal vessel pixels instead of background pixels. Because the green channel shows better vessel contrast and less noise than other channels in the RGB image, our proposed MSCNN-AM is trained and tested with green channel images only, excluding extra pre-processing and post-processing steps. The proposed method is evaluated on three public datasets, including DRIVE, STARE, and CHASE_DB1. In addition, we adopt six objective metrics to verify the performance of the MSCNN-AM, including sensitivity (Se), specificity (Sp), accuracy (Acc), F1-score, an area under a receiver operating characteristic curve (AUC-ROC), and an area under precision/recall curve (AUC-PR). Experimental results indicate that our proposed method outperforms most of the existing methods with a sensitivity of 0.8342/0.8412/0.8132 and an accuracy of 0.9555/0.9658/0.9644 on DRIVE, STARE, and CHASE_DB1 separately.
Deep Feature Aggregation Network for Hyperspectral Remote Sensing Image Classification

Hyperspectral remote sensing images (HSIs) are rich in spectral–spatial information. The deep learning models can help to automatically extract and discover this rich information from HSIs for classifying HSIs. However, the sampling of the models and the design of the hyperparameters depend on the number of samples and the size of each sample's input space. In the case of limited samples, the description dimension of features is also limited and overfitting to other remote sensing image datasets is evident. This study proposes a novel deep feature aggregation network for HSI classification based on a 3-D convolutional neural network from the perspective of feature aggregation patterns. By introducing the residual learning and dense connectivity strategies, we established a deep feature residual network (DFRN) and a deep feature dense network (DFDN) to exploit the low-, middle-, and high-level features in HSIs. For the Indian Pines and Kennedy Space Center datasets, the DFRN model was determined to be more accurate. On the Pavia University dataset, both the DFDN and DFRN have basically the same accuracy, but the DFDN has faster convergence speed and more stable performance on the validation set than the DFRN. Therefore, when faced with different HSI data, the corresponding aggregation method can be chosen more flexibly according to the requirements on number of training samples and the convergence speed. This is beneficial in the HSI classification.
Low-Profile Beam Steerable Patch Array With SIW Feeding Network

This paper presents a novel beam steerable array antenna that encompasses fully planar, low profile and lightweight characteristics for satellite communication (SatCom) applications. The proposed array contains a wideband linear source generator (LSG), an array of circular patches, and an inductive surface. The LSG is designed based on the substrate-integrated-waveguide (SIW) technology, whereas the patches are regarded as coupling circular scatterers, placed on the inductive surface for converting surface wave into the radiating wave. Each scatterer acts as a location-dependent phaser that can tailor the farfield behaviour to leverage beam steering, and is achieved by changing its distribution mechanically. A prototype of the proposed design has been fabricated and measured to evaluate the antenna performance. The measured results are in good agreement with the simulated results. The array antenna operates well with <inline-formula> <tex-math notation="LaTeX">$S_{11} < -10$ </tex-math></inline-formula> dB in the frequency band of 10.75-12.5 GHz and has stable radiation performance with beam steering capability of nearly ±60° in the elevation plane. The total height of the proposed array is about <inline-formula> <tex-math notation="LaTeX">$5.1~mm$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$0.19\lambda _{\circ }$ </tex-math></inline-formula>). It is envisaged that the proposed antenna array will empower small moving platforms due to low cost, low profile and suitability for mass production.
Spatial Attention Based Real-Time Object Detection Network for Internet of Things Devices

Target detection algorithms for Internet of things (IoT) devices often require both high real-time performance and low computational complexity. Real-time object detection network: You Only Look Once Version 3 (YOLOv3) makes full use of multi-scale features to detect objects by using feature pyramid network structure, and achieves good performance on the premise of guaranteeing fast detection speed. The feature pyramid network of YOLOv3 includes bottom-up feature extraction, top-down sampling and lateral connection of low-level detail features and high-level semantic features. But not all features are useful for object detection. In this article, a novel object detection network Spatial Attention based YOLOv3 (SA-YOLOv3) is proposed. The proposed method adds spatial attention network to the top-down sampling process. The spatial attention network calculates the feature weight matrix based on the up-sampling feature map. SA-YOLOv3 uses the feature weight matrix to filter low-level features and retain more valuable features. Finally, the selected low-level feature map and high-level feature map are concatenated together and feature maps with both spatial information and rich semantic information are obtained. The experimental results on PASCAL VOC2012 datasets and RSOD datasets show that SA-YOLOv3 outperforms YOLOv3.
Panoramic Image Mosaics Assisted by LiDAR in Vehicle System

Panoramic sensing system has attracted great attention in intelligent vehicle because of the wide range of perception areas. In order to make the spatial direction of each pixel more precise in panoramic image, we propose a panoramic image mosaics assisted by LiDAR. Firstly, a fusion system consisting of LiDAR and panoramic camera is built, and the related calibration equipment is elaborately designed for the system considering the sparse point cloud from 16-line LiDAR. Secondly, the center point of calibration plate is utilized to construct the geometry constraint for the extrinsic calibration of dual fisheye cameras, and the rotation matrix of calibration parameters makes the alignment of dual fisheye images. Thirdly, in order to offset calibration deviation and the distance between optical centers, the cos(x) smoothing function is introduced to eliminate the splicing marks in final panoramic image. Experimental results show that the proposed method can effectively be used in panoramic image mosaics, and the spatial direction of each pixel in the panoramic image is accurate.
Assistive Force of a Belt-type Hip Assist Suit for Lifting the Swing Leg during Walking

This paper proposes a relatively simple function of assistive force for a belt-type hip assist suit developed by the authors’ group. The function, which is inspired by the muscle force of the rectus femoris, contains only two parameters, the magnitude and a phase shift factor. Thus, it can reduce the amount of calculation in generating the desired assistive force during walking. Tests were performed on three healthy subjects to confirm its effect and to investigate its influence on the motions of hip, knee and ankle joints. It was demonstrated that the effect of the assist depended greatly on the phase shift factor, i.e., the location of the peak of the assistive force in a swing period. A large effect was observed when the peak of the assistive force came at mid-swing phase. The results of the tests showed that the proposed force function could help to increase walk ratio (the ratio of step length to the number of steps per minute) by an average value of 11.2% at a force magnitude of 35 N, which could produce an assistive torque of the same order as the magnitude of the muscle force of the rectus femoris around the hip joint.
Game theoretic decision making based on real sensor data for autonomous vehicles’ maneuvers in high traffic

This paper presents an approach for implementing game theoretic decision making in combination with realistic sensory data input so as to allow an autonomous vehicle to perform maneuvers, such as lane change or merge in high traffic scenarios. The main novelty of this work, is the use of realistic sensory data input to obtain the observations as input of an iterative multi-player game in a realistic simulator. The game model allows to anticipate reactions of additional vehicles to the movements of the ego-vehicle without using any specific coordination or vehicle-to-vehicle communication. Moreover, direct information from the simulator, such as position or speed of the vehicles is also avoided.The solution of the game is based on cognitive hierarchy reasoning and it uses Monte Carlo reinforcement learning in order to obtain a near-optimal policy towards a specific goal. Moreover, the game proposed is capable of solving different situations using a single policy. The system has been successfully tested and compared with previous techniques using a realistic hybrid simulator, where the ego-vehicle and its sensors are simulated on a 3D simulator and the additional vehicles’ behavior is obtained from a traffic simulator.
Evaluation of Human-Robot Object Co-manipulation Under Robot Impedance Control

The human-robot collaboration is a promising and challenging field of robotics research. One of the main collaboration tasks is the object co-manipulation where the human and robot are in a continuous physical interaction and forces exerted must be handled. This involves some issues known in robotics as physical Human-Robot Interaction (pHRI), where human safety and interaction comfort are required. Moreover, a definition of interaction quality metrics would be relevant.In the current work, the assessment of Human-Robot object co-manipulation task was explored through the proposed metrics of interaction quality, based on human forces throughout the movement. This analysis is based on co-manipulation of objects with different dynamical properties (weight and inertia), with and without including these properties knowledge in the robot control law. Here, the human is a leader of task and the robot the follower without any information of the human trajectory and movement profile. For the robot control law, a well-known impedance control was applied on a 7-dof Kuka LBR iiwa 14 R820 robot.Results show that the consideration of object dynamical properties in the robot control law is crucial for a good and more comfortable interaction. Besides, human efforts are more significant with a higher no-considered weight, whereas it remains stable when these weights were considered.
Cluster-Specific Latent Factor Estimation in High-Dimensional Financial Time Series

Unsupervised learning methods have been increasingly used for detecting latent factors in high-dimensional time series, with many applications, especially in financial risk modelling. Most latent factor models assume that the factors are pervasive and affect all of the time series. However, some factors may affect only certain assets in financial markets, due to their clustering within countries, asset classes, or sector classifications. In this paper we consider high-dimensional financial time series with pervasive and cluster-specific latent factors, and propose a clustering and latent factor estimation method. We also develop a model selection algorithm, based on the spectral properties of asset correlation matrices and asset graphs. A simulation study with known data generating processes demonstrates that the proposed method outperforms other clustering methods and provides estimates with a high degree of accuracy. Moreover, the model selection procedure is also shown to provide stable and accurate estimates for the number of clusters and latent factors. We apply the proposed methods to datasets of asset returns from global financial markets using a backtesting approach. The results demonstrate that the clustering approach and estimated latent factors yield relevant information, improve risk modelling and reduce volatility in optimal minimum variance portfolios.
NB-PLC and RF Characterization for AMI Smart Grid Application In Urban Environment

The paper evaluates the performance and characterization of Narrow Band Power Line Communication (NB-PLC) and Wireless technologies for the frequency ranges: 9–500 kHz and 863–870 MHz respectively in urban environment. Simulation results show that in low density urban condition, wireless may present a good candidate for AMI data transmission with a difference of more than 5dB in high SNR values compared to power line technology. As the network gradually condenses, PLC becomes more efficient and pioneer to support AMI task with an acceptable performance up to ≈10−4. In contrast, the performance of wireless system decreases as the network density increases.
Predicting Stock Price Using Sentimental Analysis Through Twitter Data

Artificial intelligence and Machine learning methods in combination with data mining are used in multiple scenarios to solve many problems. These Machine learning methods and techniques have already proved to be effective, highly accurate and it saves a lot of time. In recent days, people have started investing in stocks and shares as it is a profitable option in order to increase one's income. If there are proper planning and good guidance there are chances of doubling the annual revenue from the returns we get from the stock market. But even in the present day's people think stock investments still remain a risky theory. Investment experts have very high income along with the ignorance of the general public with respect to the financial problems, some issues like these behave as barriers for many people to invest in stocks. The anxiety of losing the invested money also behaves as a barrier to the people. These facts are the motivation factors for applying the capacity of machine learning to do the prediction on the movements of stocks. The sentimental analysis is used on the tweets which are obtained by using the Twitter API. Such forecasts are of great use for stock investors so that they can take calculative decisions and invest in stocks that are profitable.