Miracle-dz/UniFAR-FTUs huggingface dataset:
The Research of Random Sample Consensus Matching Algorithm in PCA-SIFT Stereo Matching Method

The stereo matching is the most important part of stereo vision. SIFT algorithm is obviously superior to other character descriptions in the feature point scale, rotation and invariant illumination, so it is extended in this paper. Because of traditional Scale-invariant feature transform (SIFT) method’s high dimensions, it is very complex and need much time to calculate. In order to optimize the algorithm, Principal Component Analysis of Traditional Scale-invariant feature transform (PCA-SIFT)+Random Sample Consensus (RANSAC) matching algorithm is proposed. PCA-SIFT uses PCA which can reduce dimension to SIFT in image matching. Although PCA-SIFT can effectively solve the problem of time-consuming, the accuracy of matching is not high. Using PCA-SIFT extract feature points in image and matching them. Firstly, we build Gaussian pyramid, detect extreme value points, and select key points with strong robustness. Using gradient direction distribution features of neighborhood pixels of the key point calculate the direction of key point can be obtained by calculating gradient direction distribution features of neighborhood pixels of the key point. Then using PCA algorithm generates 32d feature descriptors. According to the ratio of nearest neighbor algorithm for image matching, it gets matching points and sort in the size of the ratio of nearest neighbor. RANSAC algorithm is used to carry on the transformation matrix parameters fitting, and the resulting matrix model is used to remove the error matching points. To verify the effectiveness of algorithm, SIFTǃPCA-SIFTǃPCA-SIFT + RANSAC are compared by using many images and data. The experimental results show that the PCA-SIFT + RANSAC algorithm is more stable, more accurate and more rapid.
Height Estimation of Ultrasonic Array Based on Integrated Navigation for UAVs

Aiming at the problem that the single ultrasonic sensor is not concentrated in energy and it is easy to cause false alarm and low resolution with larger beam, an ultrasonic array sensor based on small beam angle is designed to measure the true height between UAVs and the ground. Firstly, the advantage of the wide measurement angle of the ultrasonic sensor is verified by the simulation of the vertical take-off of UAVs accompanying the posture movement; Secondly, the higher resolution of the ultrasonic array sensor is verified by the simulation of UAVs flying flatly into obstacles; Finally, considering the hysteresis of the measurement data of the ultrasonic array sensor, the integrated navigation information is used to fuse by the Federated filter, and the simulation experiments are carried out under various terrain conditions. The simulation results show that the fusion results of the vertical takeoff phase of UAVs and horizontal flight into sinusoidal terrain are better than those of using ultrasonic array sensors alone.
Identification Method of Cement Clinker Composition Change Based on Locality Preserving Projections and K-medoids

The accurate identification of the change of cement clinker composition has great significance for improving the quality of cement clinker. In this paper, a method for identifying changes in cement clinker composition based on locality preserving projections and K-medoids algorithm was proposed. Firstly, the main features used to identify changes in the composition of cement clinker were determined according to the cement process. Then, the locality preserving projections algorithm was used to reduce high-dimensional features to low-dimensional ones. The K-medoids algorithm was used to cluster low-dimensional features to achieve the identification of changes in cement clinker composition. Finally, taking a cement company as an example, the method proposed in this paper has been verified. The verified results showed that the identification method of cement clinker composition change based on locality preserving projections and K-medoids was feasible. This paper provides a feasible method for the identification of cement clinker composition change.
Controlling Smart Home Activities Using IoT

The fast development in the field of communications and semiconductors has led to increasing the Internet of things (IoT) applications used in all areas of life (such as industry, agriculture, transportation, health, and smart cities). IoT devices like (sensors, actuators, embedded devices, and RFID) may suffer from inadequate processing, memory, and energy therefore. Designing and implementing smart home is a very complicated work because of the diversity of sensor, actuator technologies, and connected services that may be tangled in smart home. The aim of this paper monitoring and controlling smart home activities using internet of things to support client anywhere an ability to monitor data of temperature, humidity and ultrasonic sensors constantly of home furthermore client can control many types of actuator such air condition, room light, refrigerator and water pump by changing on/off relays through exchange of data among the things through server. Smart monitoring for home application based IoT was decreased the person labor to control home devices and made living easier and simpler throughout raspberry Pi3 as microcontroller for controlling various types of actuator and monitoring different types of sensor data in addition all activities of visitors whom interred to this system were registered.
Finite-time adaptive fault-tolerant control for strict-feedback nonlinear systems

In this paper, we design an adaptive fault-tolerant control for a class of strict-feedback nonlinear system to address tracking control problem in the finite-time. Fuzzy logic systems are applied to deal with the unknown nonlinear faults, and the traditional adaptive control and backstepping technique are combined to design controller. It is indicated that the presented controller ensures that the tracking error converges to a small area around the origin and all signals within the closed-loop system remain bounded. The simulation results are supplied to prove the availability of the presented controller.
Event-based improved FAST corner feature detection algorithm

In the era of Industry 4.0, the smart driving and unmanned driving have become hot technologies. According to the principle of biological optic nerve, a new sensor--event camera is designed. The camera has the Characteristics of high dynamics, low data redundancy and low delay. And it’s suitable for real-time target detection and tracking. In this paper, an improved event-based FAST corner feature detection algorithm is pro-posed. With the improvement of the FAST corner detection algorithm, an adaptive FAST corner detection algorithm is proposed to improve the anti-noise performance and robustness of the algorithm. And in the case of high speed rotation, the target can be well tracked. Finally, experiments based on simple and complex, translation and rotation scenes verify that the improved event-based FAST corner feature detection algorithm has better robustness and real-time performance.
A Generic Framework for Learning Explicit and Implicit User-Item Couplings in Recommendation

The nature of recommendation is Non-IID, which has potential in improving recommendation quality and addressing issues such as sparsity and cold start. However, existing many state-of-the-art methods assume users and items are independent and same distributed while ignoring complex coupling relationships within and between users and items, resulting in limited performance improvement. To solve this issue, this paper proposes a novel neural user-item coupling learning model, short for CoupledCF, based on non-IID learning for collaborative filtering. CoupledCF joint learns explicit coupling with CNN and implicit coupling with deepCF within/between users and items accompanying user/item side information for recommendation tasks. User/item side information contains of attribute-based and feature-based. For different user/item side information, we use different embedding methods to learn embedding representation. We conduct comparative experiments on (1) two datasets from MovieLens1M and Tafeng with attribute-based user/item information for Top-K recommendation. (2) two datasets from MovieLens1M and BookCrossing with attribute-based user/item information for rating prediction. (3) two datasets from Amazon Movies and TV (AMT) and Yelp for feature-based user/item information for Top-K item recommendation and rating prediction tasks. Empirical results on five available real-world large datasets prove our proposed CoupledCF model is able to obtain better recommendation accuracy compared with several mainstream approaches for recommendation: BMF, neural matrix factorization, Google’s Wide&Deep network, DeepFM, convMF, and $A^{3}NCF$ model.
Capacity-Driven Scaling Schedules Derivation for Coordinated Elasticity of Containers and Virtual Machines

With the growing complexity of microservice applications and proliferation of containers, scaling of cloud applications became challenging. Containers enabled the adaptation of the application capacity to the changing workload on the finer level of granularity than it was possible only with virtual machines. The common way to automate the adaptation of a cloud application is via autoscaling. Autoscaling is provided both on the level of virtual machines and containers. Its accuracy on dynamic workloads suffers significantly from the reactive nature of the available autoscaling solutions. The aim of the paper is to explore potential improvements of autoscaling by designing and evaluating several predictive-based autoscaling policies. These policies are naive (used as a baseline), best resource pair, only-Delta-load, always-resize, resize when beneficial. The scaling policies were implemented in Scaling Policy Derivation Tool (SPDT). SPDT takes the long-term forecast of the workload and the capacity model of microservices as input to produce the sequence of scaling actions scheduled for the execution in future with the aims to meet the service level objectives and minimize the costs. Policies implemented in SPDT were evaluated for three microservice applications and several workload patterns. The tests demonstrate that the combination of horizontal and vertical scaling enables more flexibility and reduces costs. Schedule derivation according to some policies might be compute-intensive, therefore careful consideration of the optimization objective (e.g. cost minimization or timeliness of the scaling policy) is required from the user of SPDT.
Lidar-IMU and Wheel Odometer Based Autonomous Vehicle Localization System

Localization is a critical issue in autonomous navigation and path planning. The traditional localization method for automatic driving is to use the GPS. But in many cases, such as near the high buildings, under the viaduct, in the basement or the tunnel, GPS signal will disappeared or be weakened, resulting in localization failure. This paper proposed a precise localization method based on Lidar, IMU and wheel odometer combined with point cloud data map matching. In this paper, information in the form of depth point cloud collected by Lidar is matched with the pre-known point cloud map data, and a Point-to-Plane Iterative Closest Point algorithm (PP-ICP) is introduced. In order to avoid the mismatch and localization failure, the start of the autonomous vehicle is set to the initial coordinates of the point cloud map data. Then, the predicted state equation which consists of the throttle control and steering wheel control of the autonomous vehicle is established. The first observation equation consists of the acceleration and angular velocity measured by the IMU. The second observation equation consists of the position and attitude measured by the Lidar matching point cloud, along with speed and distance measured by the wheel odometer. Finally, the extended Kalman filter is used to fuse the information data of the three sensors and thus update and correct the localization. Experiments using multi-sensor fusion were carried out in underground garages and the experimental results showed that the robustness and positioning accuracy can meet the engineering requirements.
Hyperplane design for sliding mode control of sampled systems based on the discrete-time Riccati equation

For sliding mode control, the specification of switching hyperplane is very important because the steady state system performance depends on it. The fundamental requirement on choosing the hyperplane matrix is previously based on certain properties of some matrices in terms of invertibility and stability, which is not intuitive. In solving this problem, the Riccati equation and the stabilizability assumption have been utilized in some studies. However, the inter-relations among these factors have not been clearly explained. This paper is devoted to clarifying this issue.
Historical Multi-Station SCADA Data Compression of Distribution Management System Based on Tensor Tucker Decomposition

In order to deal with the problem of massive historical multi-station SCADA data storage in distribution management system, this paper proposes a data compression method for power distribution system based on tensor Tucker decomposition. Firstly, to maintain the high-dimensional spatial structure of multi-station SCADA data of distribution management system in the presentation stage, we establish a third-order tensor representation model of multi-station SCADA data. Then, a historical SCADA data compression method for distribution management system based on tensor tucker decomposition is proposed. This method can compress the data while maintaining the spatial intrinsic structure of multi-station SCADA data. Finally, the effectiveness of the method is verified using real data. And, the results of comparison with singular value decomposition method and principal component analysis method show that the proposed method is superior to the traditional method.
A New Path Tracking Algorithm for Four-Wheel Differential Steering Vehicle

Four-wheel differential steering vehicles are increasingly used in our lives, but there are few studies on the path tracking of four-wheel differential vehicles. According to the circular motion model of the differential steering vehicle, the pure-pursuit algorithm which was generally used in the front wheel steering vehicle is applied in this paper. In the process of experimental research, we used the integrated navigation system to record the vehicle’s real-time positioning and tracking points, found that the error in the positioning process had an impact on the tracking effect. In order to reduce the impact of GPS record point data fluctuation on the tracking effect, a method to optimize the target path by minimizing lateral deviation and course deviation is proposed in this paper, which makes the target path easier to be tracked. Based on the established vehicle model and the pure tracking algorithm and the tracking curve optimization algorithm, we designed a new path tracking algorithm suitable for four-wheel differential steering vehicle, and conducted the linear and curve tracking experiments. Experimental results show proposed algorithm have extremely great tracking performance, which verifies the feasibility and effectiveness of the algorithm.
Fault Detection of Flight Vehicle Electric Servo System by Extended Kalman Filter

An extended kalman filter based fault detection method for the servo system of flight vehicle is proposed in this paper. The servo system considered in this paper is a electric servo system together with a nozzle. By considering the electric servo system and the nozzle as a whole, the nonlinear model of them is built and the possible faults are analyzed. Then an extended kalman filter is introduced to detect the faults of the whole system. An experiment on the a simulation equipment with same size and weight as a real one is given to show the effectiveness of the proposed fault detection methods.
Radar and Communication Coexistence: An Overview: A Review of Recent Methods

Increased amounts of bandwidth are required to guarantee both high-quality/high-rate wireless services (4G and 5G) and reliable sensing capabilities, such as for automotive radar, air traffic control, earth geophysical monitoring, and security applications. Therefore, coexistence between radar and communication systems using overlapping bandwidths has come to be a primary investigation field in recent years. Various signal processing techniques, such as interference mitigation, precoding or spatial separation, and waveform design, allow both radar and communications to share the spectrum.
Study on Electric Actuator Control System

In this paper, electric actuator controls the operation of the motor directly or indirectly to control the movement of the target load, so as to follow the position of the control target. The operation of the motor is controlled by the computer, the signal is transformed into the mechanical displacement. This paper establishes the mathematic model of BLDCM, introduces the control strategy, compares the traditional PID and fuzzy PID and introduces fuzzification of the Fuzzy algorithm, the establishment of the fuzzy rule and the de-fuzzification. In this paper, PWM pulse width is used to drive motor. Finally, the control performance and dynamic response performance are tested through experiments. The experimental results show that the control system of the electric actuator can work stably to meet the control requirements and has good dynamic performance.
Real-time implementation of the Purkinje network on digital neuromorphic system

The Purkinje cells are vital nucleus responsible for the neural information processing in cerebellar network, which is important for human motor control. Digital neuromorphic implementation is a promising field, which uses spiking neural network to realize brain-inspired computation. In this study, a Purkinje network is implemented on a digital neuromorphic system LaCSNN. A set of efficient implementation methods are provided, and the firing dynamics can be regenerated in real time. The application of biologically inspired control is also discussed. The presented work is an important work towards the implementation of large-scale human brain network, and can be applied in various fields of real-time control area.
Fractional-Order Memristor response to periodic current functions

The Memristor is a new passive element representing relationship between electric charge q and flux ϕ. The concept of memristors in the generalized fractional order domain has gained much relevance due to their unique behavior which cannot be obtained in conventional elements. The generalized state equations of the memristor is discussed in the fractional-order sense and the effects of the added fractional-order parameter on the memristor characteristics and output behavior are studied. Numerical analyses of the model are presented for case 0<α<1 with sinusoidal, symmetric sawtooth and square-wave excitation currents. The instantaneous power and area under the v-i curves at values of fractional order is observed and it is found that the shape and the area enclosed by the hysteresis loop depend on the fractional-order and excitation frequency; the area is less dependent on frequency for the square wave current excitation.
Dynamics Modeling and Analysis for Unmanned Quadrotor Transportation Systems with Double-Pendulum Swing Effects

Due to the double-pendulum phenomenon in practical applications of the quadrotor transportation systems, most existing control methods based on simplified single-pendulums are no longer applicable. To achieve simultaneous quadrotor positioning control and swing elimination for the hook and the payload, an accurate system model is of great significance. Specifically, the dynamic equation of the plant is obtained by Lagrange’s modeling method. The main characteristics of the system is then analyzed based on the derived model. Numerical simulations for the two control schemes are presented to reveal the characteristics of the unmanned quadrotor transportation systems.
Multi-feature Extraction for Bearing Fault Diagnosis Using Binary-tree Mahalanobis-Taguchi System

Rolling bearings are widely used in rotary machinery systems, their condition directly determines the normal operation of machinery, and effective recognition of bearing fault can greatly reduce maintenance time and cost. This paper proposes an integrated model which combines Binary-tree and Mahalanobis-Taguchi system (BT-MTS) to detect faults of rolling bearing. The BT-MTS consists of three stages. Firstly, the time domain, frequency domain, and time- frequency domain features are extracted from the vibration signals by feature extraction technology, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, the Fisher ratio method is utilized at each node of the binary tree to design the architecture of the BT-MTS modeling. Ultimately, the trained BT-MTS is used to predict the fault types. The experimental results demonstrate that the proposed method is effective in recognizing the different categories of rolling bearings faults.
On the Safe Road Toward Autonomous Driving: Phase noise monitoring in radar sensors for functional safety compliance

The first approaches to improve vehicle safety were so-called passive safety systems, which did not directly interfere with the driving process but protected the occupants during a crash. In contrast, the first assistance system was the antilock braking system (ABS) successfully introduced in the early 1970s. This active system was developed to avoid an accident by automatically intervening in the braking behavior of the car. At about the same time, the first automotive radar prototype was presented. Since the invention of this very unwieldy radar system, organizations all around the world spent significant efforts in pushing the development of automotive radar systems forward. Today, radar sensors together with ultrasound sensors, lidar, and cameras form the backbone of advanced driver assistant systems (ADASs) as well as autonomous driving (AD), which is in the prototype stage. In particular, because of their robustness against adverse lighting and weather conditions, radar sensors are considered a key technology for modern vehicle safety and comfort systems. Along with the trend toward higher automation, more cars will be equipped with radar sensors in the near future. Because ADASs directly influence the vehicle dynamics, new regulating functional safety (FuSa) requirements, such as the ISO 26262 standard, were introduced. These requirements are mandatory to protect the road users.
A Distributed Algorithm for Economic Dispatch with Packet Drops and Time Delays on Digraph

This paper proposes a distributed algorithm to solve the economic dispatch problem (EDP) over fixed directed communication networks, which is based on ratio consensus and running sum method. Under the assumption that the network is strongly connected, the distributed algorithm can handle both packet drops and bounded time delays. The analyses of packet drops and time delays are based on the augmented digraph idea, which models the packet drops and time delays as "virtual buffers" and "virtual" nodes respectively. Finally, some numerical examples are used to verify the proposed algorithm.
Vehicle Type Recognition using Capsule Network

Vehicle type recognition plays an increasingly important role in the problem of dealing with traffic safety. In order to address this problem, a novel vehicle type recognition model based on capsule network is proposed, which dynamic routing algorithm of the capsule network is redesigned to save training time and to accelerate convergence rate. To compare with the original model, we conduct experiments on the dataset including five different vehicle types from different angles and lighting conditions. It is proved that the proposed model costs only half time of original model while maintaining a high recognition rate, and the reconstruction from the output of capsule network keeps important details of vehicle types.