Miracle-dz/UniFAR-FTUs huggingface dataset:
Millimeter-Wave Three-Dimensional Substrate-Integrated OMT-Fed Horn Antenna Using Vertical and Planar Groove Gap Waveguides

A three-dimensional (3-D) substrate integration method by using both the planar dimensions and the vertical dimensions in multilayered substrates for millimeter-wave devices and circuits design is presented in this article. The vertical substrate-integrated groove gap waveguide (SIGGW) is studied first, whose multilayered geometry without metal contact between stacked substrates and wide operating band supporting the single quasi-TE10 mode with stable propagation features enables it to be a promising vertical waveguide structure with ease of realization. By simultaneously utilizing the vertical and planar SIGGWs, a fully 3-D substrate-integrated orthomode transducer (OMT) consisting of E-plane power dividers, U-shaped connections, and a turnstile junction that are difficult to fulfill in previous works is successfully implemented in the Ka-band. Its compact geometry and significantly performance improvement demonstrate the merits of the proposed method. After that, the OMT is used for feeding a dual-polarized pyramidal horn antenna also with a 3-D substrate-integrated configuration. The overall multilayered antenna module with experimentally confirmed wide bandwidths, high isolations, and stable radiation patterns can still be fabricated conveniently by employing the standard single-layered printed circuit board (PCB) facilities. Taking the advantages of the flexible design freedom and satisfying operating characteristics, the presented design method and devices are valuable to advanced millimeter-wave applications.
A Review of Image-Based Pavement Crack Detection Algorithms

Regular road surface inspection and maintenance on highways, bridges, tunnels and other transportation infrastructure are of great significance. As one of the common pavement diseases, cracks can pose a potential threat to the safety of bridges, tunnels, airports, etc., and may cause severe damage to pavements if they are not properly repaired in time. Therefore, crack detection and segmentation as well as repairing are essential to maintain the overall condition of different pavements. This paper aims to discuss the development of image-based automatic pavement crack detection algorithms as well the remaining challenges. Firstly, traditional methods that are based on spatial-domain, frequency-domain, etc. are described. Then the methods using deep learning techniques are introduced. Datasets and metrics for crack detection are introduced next. Finally, some remaining problems and the future research directions are presented.
Mechanically Reconfigurable Slot Array Using Accordion-Like Microactuators

A novel mechanically reconfigurable 8×11 slot array using accordion-like microactuators as an alternative to conventional electronically steered arrays is reported. The array scans 0° to –30° in elevation by simultaneously varying the inter-element spacing between sliding slot elements. To maintain low slidelobe levels, the radiating slots are designed to have nonuniform width. The array is fed by a single slotted waveguide from one side. The actuator provides continuous motion with a precision of 30 μm (λ0/625) and is controlled via a printed circuit board (PCB) circuit. The accordion-like structure provides a uniform yet tunable spacing between each slot column, eliminating the need for costly and complex electronic scanning. This high precision approach reduces the system footprint, ensures high power handling capability, and ensures ease of integration in future applications. The structure, operating at 16 GHz, is fabricated using a laser-etching process and in-house additive manufacturing. Measurement results are congruent with simulations.
Reinforcement Learning Based Fast Worm Detection for Smart Grids

Smart grids suffer from worm attacks that inject worms to the vulnerable smart meters, make the infected meters propagate the infection to other vulnerable meters, and even damage the smart grids. In this paper, we present a deep reinforcement learning based worm detection scheme for smart grids without relying on the worm propagation model and network topology. This scheme measures the spectrum feature of the traffic in smart grids to detect the worms and applies two dueling neutral networks (NNs) to select the detection threshold based on the current state including the size of the traffic logs, the detection error rate in previous time slots and the estimated number of the infected meters. We apply an estimated NN and a target NN to select the detection threshold and calculate the expected long-term discount utility, respectively, to reduce the overestimation errors, and use the dueling architecture consisting of two fully connected layers to reduce the computational complexity. Simulation results show that the proposed scheme effectively reduces the detection error rate and worm detection latency.
Distributed L2 −L∞ consensus of multiagent systems with nonconvex constraints

This paper discusses the L<inf>2</inf> −L<inf>∞</inf> consensus problem for multiagent systems with non-convex constrains. Both L<inf>2</inf> −L<inf>∞</inf> nonconvex constraints and external disturbance are taken into consideration in the same time, under the expected performance, a distributed algorithm based on the introduction of constraint operators is proposed. By using Lyapunov function and constraint operators, some sufficient conditions are given to ensure that each agent reach the L<inf>2</inf> −L<inf>∞</inf> consensus can be achieved.
Variable Admittance Control with Robust Adaptive Velocity Control for Dynamic Physical Interaction between Robot, Human and Environment

In the field of collaborative robot whose end-effector is complaint in response to human force, the human operator is able to execute tasks based on their judgement. The advantages include human power augmentation and utilization of human's judgement. A force/torque sensor is usually mounted on the end -effector to sense the human force, which is sent to an interaction controller to yield velocity command for the robot to follow. However, when human guides the end-effector to contact an extra dynamic environment that moves in multiple directions, it poses challenges on controller design. The interaction controller needs to provide the stable interaction for human operator to contact such a dynamic contact force (due to the moving environment) while to provide compliant cooperation for the human to follow the motion of the environment. To address these issues, we propose variable admittance control based on robust adaptive velocity control. The human and environmental forces are considered in the variable admittance controller to obtain the suitable interaction between robot, human, and environment. Additionally, robust adaptive control is employed to enhance the tracking performance. Experiments are conducted and the results show the proposed controller enables the human operator to stably contact a dynamic environment and compliantly track the motion of it.
Backscatter Communications

This paper addresses backscatter communications as a new paradigm for Internet of Things (IoT) sensor wireless solutions. The paper clearly presents the history background of the technology and then makes a summary of the more recent developments form several groups around the world, with emphasis in US and Europe groups. The analysis done in this paper will address circuit and physical layer approaches for such a solution and its real implementations, having in mind IoT sensors for long range high speed communications.
Trajectory Tracking Control for Mecanum-wheel Cambered Mobile Robots Based on Online Adaptive Critic Optimal Controller

Mecanum-wheel cambered mobile robot (MWCMR) working in the Gas Insulated Switchgear (GIS) cavity is truly a complex system with highly non-linear. In order to control MWCMR for better trajectory tracking, a discrete intelligent trajectory tracking algorithm is presented in this paper. When realizing this algorithm, the kinematic model and dynamic model of MWCMR are also proposed, where the dynamic model is described by the second order Lagrange’s equation. This discrete intelligent trajectory tracking algorithm is a single network online adaptive critic optimal trajectory tracking control method, which is based on the idea of the critic-actuator structure and optimal control theory. The structure of the critic used for approximating the cost function adopts Neural Network (NN). Moreover, in order to ensure the stability of the system, a supervisor is also designed to make the difference of Lyapunov function negative definite. Then simulation is performed to test the trajectory tracking control algorithm for this GIS cavity robot, and the results are compared with the traditional neural network trajectory tracking control method and PD method. Finally, the effectiveness and advancement of this method are analyzed.
Multiple Reception Field Network with Attention Module on Bone Fracture Detection Task

Object detection in computer aided diagnosis (CAD) systems can greatly improve the efficiency and reduce the workload of clinicians. In this paper, we propose a novel multiple reception field network with attention module for fracture detection CAD system. The main improvement includes three aspects. First, a double pathway backbone network with respective dilated convolution is designed to gain more fractural information based on different reception fields. Second, an attention module is designed which engaged in the backbone network to build channel-wise relevance. Third, we propose a refined Feature Pyramid Network along with attention mechanism to generate more detailed feature maps. We evaluate the proposed network on a bone fracture dataset of 3842 X-rays and results show that the proposed network outperformed other state-of-the-art object detection networks with average precision (AP) of 88.7% and have strong potential application in practical environments.
Asymptotic Nonlinear Sampled-Data Control for Dual Rigid Bodies Relative Pose Coordination

This study focuses on the nonlinear sampled-data relative position tracking and attitude synchronization control problem between feature points of two rigid bodies in a three-dimensional space. The Euler-approximation model with respect to tracking error for the relative pose tracking system is firstly obtained. Then, a nonlinear sampled-data relative pose tracking controller is designed so that the relative position tracking error and attitude synchronization error of Euler-approximation system asymptotically converge to zero. In particular, the skew-symmetric property of the dynamical system is used to simplify the controller design. Finally, the effectiveness of proposed control method is verified by numerical simulations.
Exact tracking control of hypersonic flight vehicles with full state constraints

This paper investigates the exact tracking control problem of hypersonic flight vehicles (HFVs) with consideration of full state constraints. The constrained states are transformed into new states which are free of constraints by using nonlinear state transformations, and the constrained control problem is addressed as a simpler bounded control problem. Besides, by introducing Nussbaum-type functions in the control design, the exact racking performance is guaranteed even when the unknown system dynamics and the full state constraints are both considered. Only two adaptive parameters are required to be updated online, which significantly reduces the computational burden. The stability of the closed-loop system is proved via Lyapunov theory, and the effectiveness of the proposed control algorithm is demonstrated by simulation results.
Improving Room-Level Location for Indoor Trajectory Tracking with Low IPS Accuracy

With the development of indoor positioning methods, such as Wi-Fi positioning, geomagnetic sensor positioning, Ultra-Wideband positioning, and pedestrian dead reckoning, the area of location-based services (LBS) is expanding from outdoor to indoor spaces. LBS refers to the geographic location information of moving objects to provide the desired services. Most Wi-Fi-based indoor positioning methods provide two-dimensional (2D) or three-dimensional (3D) coordinates in 1–5 m of accuracy on average approximately. However, many applications of indoor LBS are targeted to specific spaces such as rooms, corridors, stairs, etc. Thus, they require determining a service space from a coordinate in indoor spaces. In this paper, we propose a map matching method to assign an indoor position to a unit space a subdivision of an indoor space, called USMM (Unit Space Map Matching). Map matching is a commonly used localization improvement method that utilizes spatial constraints. We consider the topological information between unit spaces and moving objects’ probabilistic properties, compared to existing room-level mappings based on sensor signals, especially received signal strength-based fingerprinting. The proposed method has the advantage of calculating the probability even if there is only one input trajectory. Last, we analyze the accuracy and performance of the proposed USMM methods by extensive experiments in real and synthetic environments. The experimental results show that our methods bring a significant improvement when the accuracy level of indoor positioning is low. In experiments, the room-level location accuracy improves by almost 30% and 23% with real and synthetic data, respectively. We conclude that USMM methods are helpful to correct valid room-level locations from given positioning locations.
Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images

Buildings can be introduced as a fundamental element for forming a city. Therefore, up-to-date building maps have become vital for many applications, including urban mapping and urban expansion analysis. With the development of deep learning, segmenting building footprints from high-resolution remote sensing imagery has become a subject of intense study. Here, a modified version of the U-Net architecture with a combination of pre- and post-processing techniques was developed to extract building footprints from high-resolution aerial imagery and unmanned aerial vehicle (UAV) imagery. Data pre-processing with the logarithmic correction image enhancing algorithm showed the most significant improvement in the building detection accuracy for aerial images; meanwhile, the CLAHE algorithm improved the most concerning UAV images. This study developed a post-processing technique using polygonizing and polygon smoothing called the Douglas–Peucker algorithm, which made the building output directly ready to use for different applications. The attribute information, land use data, and population count data were applied using two open datasets. In addition, the building area and perimeter of each building were calculated as geometric attributes.
Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks

We propose a tool-use model that enables a robot to act toward a provided goal. It is important to consider features of the four factors; tools, objects actions, and effects at the same time because they are related to each other and one factor can influence the others. The tool-use model is constructed with deep neural networks (DNNs) using multimodal sensorimotor data; image, force, and joint angle information. To allow the robot to learn tool-use, we collect training data by controlling the robot to perform various object operations using several tools with multiple actions that leads different effects. Then the tool-use model is thereby trained and learns sensorimotor coordination and acquires relationships among tools, objects, actions and effects in its latent space. We can give the robot a task goal by providing an image showing the target placement and orientation of the object. Using the goal image with the tool-use model, the robot detects the features of tools and objects, and determines how to act to reproduce the target effects automatically. Then the robot generates actions adjusting to the real time situations even though the tools and objects are unknown and more complicated than trained ones.
Joint DL/UL Decouple User Association in Microwave and mmWave Enabled Beyond 5G Heterogeneous Networks

Beyond fifth-generation (5G) heterogeneous networks (HetNets) are facing the challenge of accommodating enormous mobile users and data traffic with scarce spectrum resources in the microWave (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Wave) band. In this work, we consider both challenges in HetNets comprising large size, high power base station (LHB) and relay operating in <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Wave band and small size, low power base station (SLB) and device-to-device (D2D) operating in millimeter wave (mmWave) band. We formulate user association optimization problems to pitch user association schemes based on downlink uplink decoupled (DU-DPL) access against traditional downlink uplink coupled (DU-CPL) access in HetNets to gauge the performance of these access schemes in terms of accommodating users and spectrum efficiency in <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Wave and mmWave bands. Formulated problems are non-deterministic polynomial-time hard (NP-hard) and solved using <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-optimal algorithm. Simulation results show the edge of DU-DPL access over DU-CPL access in terms of users association and spectrum efficiency.
CreditBC: Incorporating Blockchain to Credit Management for Cross-domain Service

Cross-domain service, which integrates the services from various types of businesses in multiple administrative domains, has been proposed and instantiated along with the development of modern service industry. By converging the various services from different administrative domains, the efficiency and quality of services are promoted. As the important element of services, the user’s credit information is vital to evaluate the user’s credibility in the fields of e-commerce, financial services, etc. However, due to the heterogeneity and complexity of the cross-domain service, the issue of credit management is more complicated compared with the traditional service. This paper firstly explores the definition and representative features of credit management in the cross-domain service. Furthermore, we propose a blockchain-based credit management model with a hierarchical management committee structure, and the complete workflow including credit collection, credit sharing and credit convergence will also be presented. Finally, future challenges in credit management of the cross-domain service are illustrated and discussed.
Performance Evaluation of Multiple Missiles Based on Information Entropy and IAHP

In this paper, we investigate the performance evaluation of multiple missiles. To this end, information entropy and IAHP are introduced to develop a combined algorithm to compare the performance of different parameter configurations, and we analyze some typical factors affecting a cooperative guidance law with impact angle constraint. The proposed algorithm is fully comprehensive and capable of combining subjective experience with objective data, and the performance of the introduced guidance law against maneuvering target is rigorously analyzed. Simulation results verifies the feasibility of the proposed method.
A New Method for Stance Detection Based on Feature Selection Techniques and Ensembles of Classifiers

Stance detection is one of the promising areas of computational linguistics, the task of which is to automatically recognize the author’s viewpoint on the target object. In our study, to detect the stance, we propose the Ensemble-based Stance Detection method (ESD). First, we calculate the optimal number of features that are most relevant to the given domain based on the function approximating the dependence of F1-score on the number of features. Then we form a relevant feature set using the homogeneous ensemble of feature selection methods. At last, we build the optimal composition of classifiers using the cross-validation procedure. Furthermore, we study the impact of various feature types on the performance in the stance detection task. The proposed ESD method is evaluated on the SemEval-2016 text corpus of tweets and the UKP Sentential Argument Mining corpus, and it outperforms the state-of-the-art systems.
Lane Detection and Tracking under rainy weather challenges

Lane marking detection is challenging under adverse weather, especially in rain. Rain affects the visibility by changing the amount of light reflected from the road and splashing of rain periodically blocks vision through the car windshield. Colored (yellow, blue etc.) and eroded lane markings have lower contrast between lane and road surface. Rain affects the visibility of these colored and eroded lanes severely. In this paper, a robust lane detection and tracking method is proposed to detect lane under heavy rainfall condition. Here, an automatic adjustable threshold (AAT) is proposed, which improves the performance of canny edge detector to detect colored, eroded and blurred lane markings. Probabilistic Hough Transform (PHT) is used to detect candidate lane lines. A lane verification technique slope and angle based geometric constraints (SAGC) is used to detect and verify lane markings. Finally, a lane tracking technique by angle range validation (ARV) is introduced, which can keep track of the lane position when either left or right or both lane markings are partially or fully invisible due to erosion or occlusion or when lanes are not even painted on the road. Experimental results show that the average detection rate is 94.12%, and the average detection time is 30.24ms per frame which outperformed state-of-the-art method.
Time Series Forecasting in a CVD Reactor for Polysilicon Production

Usually high-grade polysilicon production is obtained by the Chemical Vapor Deposition (CVD) process in the Siemens reactor. Predicting key parameters in the reaction can help increase production, which is a time series regression problem. We propose to use neural networks to predict the key variables of the CVD reactor. The performance measure for the model is the mean standard error (MSE). The experimental results show that the MSE of the target parameter prediction is 0.00612. Compared with Artificial Neural Networks(ANN), Prophet, and other algorithms, this algorithm achieved high accuracy and has been used in actual production. We believe the proposed approach is of great interest for the operation of the polysilicon CVD process.
Adaptive Output Feedback Control of Nonlinear System with Input Constraints Using HOSM Observer

An adaptive output feedback control scheme based on the high-order sliding mode (HOSM) observer is proposed in this paper for uncertain nonlinear systems that are subject to input actuator constraints, including the amplitude and rate saturation. The HOSM observer is used to estimate the unmeasured states of the transformed canonical nonlinear system. An auxiliary dynamics driven by input error caused by the actuator saturation is constructed to form an augmented tracking error, which is used in the adaptive law to guarantee the stability of the closed-loop system under actuator saturations. Model uncertainties, such as unknown parameters and disturbance, including the unknown control input gain, are considered in the system design. Stability analysis and simulation examples on delta wing aircraft show the effectiveness of the proposed control methods.