Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliable Detection of Dynamic Anomalies with Application to Extracting Faint Space Object Streaks from Digital Frames

We consider a problem of reliable detection of dynamic anomalies in observed data. The problem is of importance for many applications, e.g., for signal and image processing where dynamic anomalies (corresponding to changes in distributions of observed processes) take place when signals appear and disappear at unknown points in time or space. We assume that the duration of the change may be finite and unknown and focus on Bayesian and maximin optimality criteria of maximizing the probability of detection in a prespecified time interval under constraints imposed on the rate of false positives. Using optimal stopping theory we find optimal Bayesian and maximin detection procedures. We then compare operating characteristics of the optimal procedures with the popular Finite Moving Average (FMA) rule, using Monte Carlo simulations, which show that typically the FMA procedure has almost the same performance as the optimal ones. The FMA rule is applied to the extraction of faint streaks of satellites from digital frames captured with telescopes.
A Comparison of Data Mining Approaches on Predicting the Repayment Behavior in P2P Lending

Default loan detection for P2P lending market is of significant importance with the present-day expansion of the market share of P2P loans. Besides general data mining methods for default loan detection, novel data mining techniques, such as XGBoost, AdaBoost and Random Forest approaches have gained increasing attention and have achieved better performances on the distinction between default loans and normal loans. Due to the development of data mining, most of these new techniques have attained more accurate results than the previous data mining techniques. However, very few studies discuss the performance of difference data mining approaches in a thorough manner, from data cleaning to the final results, for predicting the loan statuses. Thus, in this paper, we provide a thorough data analysis process for predicting the loan statuses by investigating the performance of the above mentioned approaches.
Development of the Detecting System of the Landmark Tags to Increase the Navigation Accuracy of an Unmanned Vehicle in a Known Location

This paper proposes the landmark detection system for use in unmanned vehicle based on NVIDIA Jetson embedded device. The paper describes the synthetic dataset generation and training YOLOv4 neural network using data augmentation and transfer learning techniques to detect the landmark in incomplete data conditions. The real-time landmark detection module was implemented on the NVIDIA Jetson Xavier. The final mean average precision was resulted as 83.8%.
ORB-SLAM2S: A Fast ORB-SLAM2 System with Sparse Optical Flow Tracking

This paper presents ORB-SLAM2S, a fast and complete simultaneous localization and mapping (SLAM) system based on ORB-SLAM2 for monocular, stereo, and RGB-D cameras. The system works, ensuring accuracy simultaneously, in real-time on standard central processing units (CPU) at a faster speed in small and large indoor and outdoor environments. The system includes a lightweight front-end which is a sparse optical flow method for non-keyframes to avoid the extraction of keypoints and descriptors that allows for high-speed real-time performance. For keyframes, a feature-based method is used to ensure the accurate trajectory estimation almost the same as ORB-SLAM2. The evaluation of famous public sequences shows that our method achieves almost the same state-of-the-art accuracy as ORB-SLAM2 and faster speed performance which is 3~5 times that of ORB-SLAM2, being in most cases the faster SLAM solution. As proved by experiments, the system provides a fast and lightweight visual SLAM while ensuring accuracy for low-cost mobile devices.
Prediction of Air Pollutants Using Supervised Machine Learning

Air pollution is a severe problem in areas where population density is high such as metropolitan cities. Various types of emissions caused by people's actions, such as transportation, power, and fuel use, are affecting air quality. The machine learning in its components has become the key focus areas of all companies from the early stage of startups to major platform vendors. Machine learning is a field in which an artificial intelligence device collects sensor data and learns to act. One of those factors, the proposed research work has chosen to learn and forecast the air quality index with the opportunity to adapt to the machine learning algorithms. Air Quality Index is a device used for determining the air quality and the causes of air pollution. Air pollution is widely regarded as a concern in India. The highest possible values that are considered fit for public medical services are relatively higher than the daily air quality quantified attributes. The situation is particularly bad in larger cities like Delhi, where the AQI has reached an all-time high of 999. A few of the air pollution control processes are carried out by the federal government and state governments. Many researchers implement some of the algorithms we use, but none of them compares their performance across all six algorithms in a single study under similar conditions and using the same data. This paper portrays different strategies followed in forecasting the Air Quality Index using supervised machine learning procedures. The supervised machine learning technique (SMLT) was used to analyze a dataset and capture multiple pieces of information, including variable recognition, uni-variate analysis, bi-variate and multivariate analysis, missing value treatments, and data analysis. Moreover, the Estimation of the air quality index can be accomplished based on pollutants causing effect viz., PM10, PM2.5, SO2, CO, and NO2. These are the components used in supervised machine learning procedureres to compare the air quality of an environment. The main goal is to create a machine learning model and investigate the air quality index by predicting the best results from various machine learning algorithms based on their precision. We used logistic regression, decision tree, support vector machine, random forest tree, Nave Bayes theorem, and K-nearest neighbor as six basic machine learning algorithms.
A-share Stock Reactions to the Approval of COVID-19 Vaccine Clinical Trial: An Event Study Model of Listed Pharmaceutical Firms’ Returns

There is a growing body of literature that conducts empirical analysis based on COVID-19 pandemic. This paper is to investigate the influences of approval of clinical trial of COVID-2019 vaccine developed by China National Biotec Group on pharmaceutical enterprises’ return in the A-share market. Daily stock returns of 218 pharmaceutical firms were selected to conduct the study with the market model of event study. The announcement of the world’s first inactivated COVID-2019 vaccine clinical trial is found to have a positive impact on the stock market where the maximum abnormal return as well as cumulative abnormal return both reaches the top on the 2 days after the event day. It indicates that shareholders reacted to the event rapidly and actively. The findings confirm that during the pandemic, people are sensitive to the news about R&D of vaccine, mapping to the strong actions to the stock exchanges. This serves governments and investors as a proof to respond to the economic market more effectively.
Automatically Diagnosing HIP Conditions from X-Rays using Landmark Detection

When patients present with symptoms of hip pain a clinician might diagnose a condition called femoroacetabular impingement (FAI), where the ball and socket of the hip joint rub together during movement. To diagnose FAI a doctor inspects an x-ray, and records the angles between certain key points in the image. If the angles are ‘too big’ then FAI is diagnosed. We anticipate that these key points can be located in an x-ray using deep learning and thus the angles measured and FAI diagnosed automatically. In this paper we deploy a stacked hourglass network to automatically locate key-points in hip x-rays, which we then use to automatically diagnose FAI in a patient. On a test set of 112 hips our algorithm diagnoses cam impingement, one of two types of FAI, correctly 90% of the time. To our knowledge this is the first time any kind of FAI has been automatically diagnosed.
A Dual-Band Circularly Polarized Elliptical Patch Reflectarray Antenna for High-Power Microwave Applications

The demand for multi-band high-power antennas, which have been rarely reported, becomes urgent in high-power microwave field. To meet the demand, a C/X dual-band high-power circularly polarized patch reflectarray antenna is proposed in this paper. Two main technologies are adopted to make the proposed antenna realize the high-power feature. Firstly, the elliptical patch with no abrupt structure is proposed to be the dual-band reflecting element to mitigate the electric field concentration. Secondly, the elliptical patches are proposed to be embedded into substrate to avoid the triple junction. The proposed dual-band reflecting element is designed by arranging two embedded elliptical patches with different sizes in a square lattice grid. The larger elliptical patch operates in C-band and the smaller one works in X-band. The elliptical patches are proposed to be rotated to realize continuous $360^{\mathrm {\circ }}$ phase response in both bands. By optimizing the thickness of substrate, the element spacing, and the size of the patches, the dual-band element shows good reflection amplitude and phase response and low mutual coupling performance. An antenna prototype with the aperture size of 345 mm $\times345$ mm is simulated and measured. The measured radiation results of the designed reflectarray antenna are in good agreement with simulations in both C-band and X-band. The measured gain of the antenna at 6.2 GHz is 24.6 dBi, and at 9.3 GHz is 27.8 dBi. Full-wave simulations show that the power capacity of the antenna at 6.2 GHz is about 10.2 MW, and at 9.3 GHz is about 3.9 MW in air condition. The low mutual coupling performance of the proposed antenna is verified both in element level and array level. All of the results show that the proposed antenna has the merits of high-power capacity, dual-band, and low mutual coupling.
Enzymatic Activity and Enchytraeids Abundance in Agricultural Mountain Soils / Aktywność enzymatyczna i liczebność wazonkowców w glebach górskich użytkowanych rolniczo

Abstract The number of soil mesofauna and enzymatic activity of soils are good indicators of changes in soil influenced by cultivation. The aim of this study was to compare density of enchytraeids and the activity of dehydrogenases (ADh), urease (AU), and invertase (AI) in the soils of grassland and arable land. Relationships that exist between those biological parameters and the basic soil properties (the content of total organic carbon (TOC) and nitrogen (TN), pH, texture, and total porosity) were defined. In the research, soil material from humus horizon of 12 soils which were located in the Mały Beskid and Silesian Foothills (S Poland) was used. The main density of enchytraeids in grassland soils (12 982 ind⋅m-2) was twice higher than in arable land soils (6099 ind⋅m-2), and the differences were statistically significant. Grassland soils were characterised by higher enzymatic activity than arable land soils. However, only ADh, which were almost three times higher in grassland than in arable soils (2024 and 742 μmol TPFkg-1h-1, respectively), showed significant differences. In grassland soils more favourable edaphic conditions for the development of soil organisms occurred in comparison with arable land.
3D Detection and 6D Pose Estimation of Texture-Less Objects for Robot Grasping

Due to illumination variation under different lighting conditions, texture-less objects have posed significant challenges to visual object localization algorithms for robot grasping. We propose a method to determine the 6D pose of both textured and texture-less objects from a single RGB-D image with a Kinect. First, we apply hierarchical clustering strategy to pre-process the point cloud of a scene. Then, we achieve the 3D object detection by comparing the diameter between clustering point cloud and object model. Last, the rough pose of object is estimated through Hough voting and the estimation result is refined by ICP (Iterative Closest Point). Experimental results show that the accumulation error between the model and the corresponding point in the scene is less than 6mm and the attitude error is less than 1$. 5^{\mathrm{o}}$. The average detection accuracy rate of the proposed method reaches 97%, which can satisfy the grasping requirements of the manipulator. We also demonstrate that our approach has good performance in dynamic lighting conditions.
Urban Search and Rescue with Anti-pheromone Robot Swarm architecture

In case of a natural disaster, the use of human-robot cooperation is a great option, for this reason the research area Robotic Urban Search and Rescue (USAR) is very promising. This paper proposes a new distributed architecture based on a swarm of robots that allows to solve this problem in an efficient way. For this, it is proposed the fusion of 3 different technologies: hector SLAM, anti-pheromone exploration and computer vision. The use of Nvidia's jetbot robot with a LIDAR sensor is proposed and we use ROS as development platform. The architecture has been tested in two different scenarios in the NetLogo simulation environment. This solution seems promising, bringing new advantages with respect to the state of the art, due to the fact that the time to find a potential victim is shorter with the increasing number of robots in the swarm.
Interactive Trading Model Among Multiple Market Participants Based on Hierarchical Game Frame in Distribution Network

With the opening of the electricity market, considering the complexity of interactive trading is the focus of current research. In view of the problems caused by multi-agent participation in market exchanges, this paper builds a multi-agent interactive transaction model of distribution network based on layered game. First of all, the introduction of load accumulation risk agency agents to diversify users to enter the same transaction in the market, and considering the demand response of the agent's flexible load, the trapezoid utility function is introduced to build an evolutionary game model between load accumulation dealers. Secondly, considering the full choice of load gold merchants, introducing multiple electricity sales companies, constructing non-cooperative game models among electricity sales companies, and proving the existence of equilibrium solutions for the above two game models. Finally, the simulation results prove the validity of the model.
Deep Learning For Light Field Microscopy Using Physics-Based Models

Light Field Microscopy (LFM) is an imaging technique that captures 3D spatial information in a single 2D image. LFM is attractive because of its relatively simple implementation and fast acquisition rate. However, classic 3D reconstruction typically suffers from high computational cost, low lateral resolution, and reconstruction artifacts. In this work, we propose a new physics-based learning approach to improve the performance of the reconstruction under realistic conditions, these being lack of training data, background noise, and high data dimensionality. First, we propose a novel description of the system using a linear convolutional neural network. This description is complemented by a method that compacts the number of views of the acquired light field. Then, this model is used to solve the inverse problem under two scenarios. If labelled data is available, we train an end-to-end network that uses the Learned Iterative Shrinkage and Thresholding Algorithm (LISTA). If no labelled data is available, we propose an unsupervised technique that uses only unlabelled data to train LISTA by making use of Wasserstein Generative Adversarial Networks (WGANs). We experimentally show that our approach performs better than classic strategies in terms of artifact reduction and image quality.
Cooling, Heating and Electrical Load Forecasting Method for Integrated Energy System based on SVR Model

In order to further reduce environmental pressure and promote the integration of renewable generation, integrated energy system (IES) has become a promising way of energy consumption. The economic dispatch and optimal operation of the IES rely on accurate load forecasting. In this paper, a Support Vector Regression (SVR) based multiple load forecasting method for cooling loads, heating loads and electrical loads of integrated energy system is established. First, through Pearson correlation analysis, the correlation between cooling loads, heating loads and electrical loads are investigated. Then, a load forecasting model based on SVR is designed, and Particle Swarm optimization (PSO) is adopted to optimize model parameter setting. Electrical loads, heating loads, cooling loads, day type, and weather data are used as inputs in the prediction model. A case study on a realistic IES of a park in Yunnan Province is implemented to verify the proposed method. Comparing results of the proposed method with that of traditional models show that, the proposed model can effectively consider the coupling of power load, cooling load and heating load, and has better prediction accuracy.
Implementation of communication aid using zigbee technology

In the real time application, it is important in order to process the status information that is received every minute. A modern technology used in this system LPC 2148 based ARM7 processor is used due to its high speed, more memory and data efficiency. In globalwireless communication voice frequency is measured automation systemwhich is designed using ARM7 processor, voice recognition board and RF module. In this the robot is operated by human commands, which are sensed by a speech recognizer, processed by the LPC2148 microcontroller, and sensing loads are organized according to the programmer's instructions to the microcontroller. These orders are sent from the transmitter section to the receiver section via ZigBee wireless techniques. The order from our speech is transmitted to the receiver section via ZigBee from the speech recognizer kit and the controller in this device. The ZigBee on the receiver side receives the order from the transmitter and ZigBee, and then passes it on to the controller on the receiver side, where the commands are used to control the loads. When comparing indoor and outdoor use of ZigBee communication, it is important to remember that the data rate of ZigBee communication is much higher.
Improved Graphical User Interface for Crawler Robot Servosila Engineer

Robots are widely used in many areas of modern life, and a teleoperated control plays a significant role in critical mission applications. For such applications Graphical User Interface (GUI) is one of the most popular interfaces for robot navigation and manipulation manual control. In this article, we present an improved GUI for a Russian crawler robot Servosila Engineer, which provides an operator with images from four cameras of the robot, a 3D view of a current configuration and an ability to control every joint of the robot. In addition to minor adjustments and extensions of a previous version of the GUI, a special attention is paid for modelling and control of the robot gripper.
Texture-Generic Deep Shape-From-Template

Shape-from-Template (SfT) solves the registration and 3D reconstruction of a deformable 3D object, represented by the template, from a single image. Recently, methods based on deep learning have been able to solve SfT for the wide-baseline case in real-time, clearly surpassing classical methods. However, the main limitation of current methods is the need for fine tuning of the neural models to a specific geometry and appearance represented by the template texture map. We propose the first texture-generic deep learning SfT method which adapts to new texture maps at run-time, without the need for texture specific fine tuning. We achieve this by dividing the problem into a segmentation step and a registration and reconstruction step, both solved with deep learning. We include the template texture map as one of the neural inputs in both steps, training our models to adapt to different ones. We show that our method obtains comparable or better results to previous deep learning models, which are texture specific. It works in challenging imaging conditions, including complex deformations, occlusions, motion blur and poor textures. Our implementation runs in real-time, with a low-cost GPU and CPU.
An ESD and Interference-Robust Protection Circuit for Cascode Low-Noise Amplifier in CMOS-SOI Technology

In this paper, we propose a double diode network including an additional stacked diode, which provides robustness against Electrostatic discharge (ESD) and high-frequency interference signals. The proposed stacked diode in the conventional double diode network protects the integrated circuit from instantaneous voltage events by providing an additional current path to the double diode network. Also, it can structurally minimize the parasitic capacitance generated in the diode. The general operating principle and limitation of the conventional double diode network for ESD events and high-frequency interference signals were analyzed and simulated. For experimental verification, a cascode LNA with inductive source degeneration was designed in a 130nm CMOS Silicon-on-insulator (SOI) process for the time-division long-term evolution (TD-LTE) application at the coexistence band. The LNA with the proposed double diode network provides a noise figure of 1.08 dB and a small-signal gain of 18.7 dB at 2.65 GHz (Band 41). And it was measured to be able to protect the internal circuit at 2000 V HBM event, and the RF performances were not affected even with a high-frequency interference signal close to 30 dBm.
Power Quality Disturbances Classification Using Sparse Autoencoder (SAE) Based on Deep Neural Network

Power quality is main concern for the electrical energy consumptions and electrical equipment. Hence, the power quality disturbances needed to monitor, improve and control. However, most of the research are focusing to the accuracy of the classification analysis. In this paper, an approach to classify the power quality disturbances is presented using the deep neural network algorithm. A raw data containing various types of the power quality disturbances, like swell, interruption, harmonics, and normal signal is evaluated. This several types of power quality disturbance will be extracted using the Sparse Autoencoder (SAE). The various values of weight decay parameter, $\lambda$ and sparsity parameter, $\rho$ are applied to determine which features give optimal values. Optimal features learned from the SAE are then used to train a neural network classifier for identifying power quality disturbances.