Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Dosing Method for Water Treatment Based on Data Mining Combined with Image Recognition and Self-Learning

To deal with the low efficiency in the traditional flocculation link of steel plant wastewater treatment, an intelligent dosing system combined with image recognition, LightGBM algorithm, and self-learning ability is proposed to automatically monitor water quality and recommend optimal dosage analyzing both image and water quality parameters. Images acquired from the industrial camera and water parameters from underwater detectors are used as input for the dosing models which are trained by the LightGBM algorithm, then the recommended dosage could be given by the model and sent to an automatic dosing system controlled by a programmable logic controller (PLC), which forms a closed- loop for intelligent dosing. The resulting parameters are also iteratively recorded for self-learning. Based on two high-density pools with one adopting the proposed intelligent dosing system and the other completely manually-controlled, the experiment shows that the proposed intelligent dosing system completely realizes unmanned control and improves the average water qualification rate up to 97.52%, increasing it by 85.81% compared with traditional artificial dosing, and dosing costs decrease by approximately 41%.
Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

Space situational awareness (SSA) system requires recognition of space objects that are varied in sizes, shapes, and types. The space images are challenging because of several factors such as illumination and noise and thus make the recognition task complex. Image fusion is an important area in image processing for various applications including RGB-D sensor fusion, remote sensing, medical diagnostics, and infrared and visible image fusion. Recently, various image fusion algorithms have been developed and they showed a superior performance to explore more information that are not available in single images. In this paper, we compared various methods of RGB and Depth image fusion for space object classification task. The experiments were carried out, and the performance was evaluated using 13 fusion performance metrics. It was found that the guided filter context enhancement (GFCE) outperformed other image fusion methods in terms of average gradient (8.2593), spatial frequency (28.4114), and entropy (6.9486). additionally, due to its ability to balance between good performance and inference speed (11.41 second), GFCE was selected for RGB and Depth image fusion stage before feature extraction and classification stage. The outcome of fusion method is fused images that were used to train a deep ensemble of CoAtNets to classify space objects into ten categories. The deep ensemble learning methods including bagging, boosting, and stacking were trained and evaluated for classification purposes. It was found that combination of fusion and stacking was able to improve classification accuracy largely compared to the baseline methods by producing an average accuracy of 89 % and average F1 score of 89 %.
An Automated Program Repair Method Using Atomic Replacement for Java Programs

Automated program repair is one important research area in software engineering which includes methods for automatically fixing programs. This paper proposes a method for fixing bugs related to variable misuses, wrong methods and operators in Java programs. Program repairing includes two stages: fault localization and fix generation. In the fault localization stage, suspicious bug locations were identified with the support of a well-known library named GZoltar. After determining the bug location, the fix generation stage is executed. The proposed method focuses on the fix generation stage. The key idea of this method is to generate patches using atomic replacement in an abstract syntax tree. An implemented tool, named AtomRepair supporting the proposed method and experimental results are presented and discussed. The tool was evaluated on Defects4J benchmark and compared with CapGen and FixMiner tools. The results show that the tool can generate correct patches to fix 25/28 bugs with an accuracy of 89.3%, higher than that of CapGen with 84% and FixMiner with 81.3%.
Attention-based U-Net extensions for Complex Noises of Smart Campus Road Segmentation

The previous work investigated SDSB (Self-Driving Sweeping Bot) visual intelligence in terms of road segmentation for constraint sweeping area and sweeping efficiency. To do so, this paper explores the U-Net learning approach with three extensions on Pred U-Net, Pred resU-Net and Pred AttresU-Net, and validated multiple weather conditions of road images, including, fully cloudy, full sunshine, partially sunshine shadowing by giant trees or facilities etc. In addition, the road surrounding with complex objects, such as, cracks, roads traffic signs, white arrows and lines, manhole cover and zebra crossings etc. are also considered and analyzed. The experimental result is shown that the Pred AttresU-Net for road segmentation given the superior segmented efficiency compared with Pred U-Net and Pred resU-Net.
Variance Components and Related Methods for Mapping Quantitative Trait Loci

Variance components methods have been used in various aspects of genetic analysis for many decades. This article discusses their application to data on quantitative traits for the estimation of polygenic and environmental variances as well as, for the detection of quantitative trait loci using methods of linkage and association. Multivariate approaches are discussed, along with regression-based related methods. Additionally, examples of the application of these methods from the literature are presented.
Outdoor Image Segmentation based on Light Polarization Signatures

Semantic image segmentation is an important step in scene understanding and is used in many applications, like computer vision, remote sensing, precision agriculture, autonomous driving etc. We propose a new handcrafted image segmentation algorithm based on light polarization pixel signatures, by acquiring images with a circular polarization filter rotated by 360° (in steps of 10°). We use the spectral angle mapper (SAM) to compare the polarization signatures of neighbor pixels and then, with the SAM values we construct three standard deviation image planes in a multi-resolution approach. The K-means clustering is used for the final segmentation, being applied on a 7-plane feature image. The results are compared with the classic K-means clustering and with the SegNet neural network using several quality metrics.
Statistical Core Theorems

Abstract Based on the recently introduced concept of a statistical cluster point, a definition is given for the statistical core of a complex number sequence which is analogous to the Knopp core. The theorems proved here are statistical analogues of theorems about the Knopp core concerning its containment or invariance under a matrix transformation.
Renewable Energy Resources Connected Utility Microgrid for the Tulalip Tribes

Microgrids increase resilience, sovereignty, and reduce carbon emissions by utilizing renewable energy resources. This paper explores the feasibility of installing a new microgrid at the Tulalip Tribes’ Administration Building. The building will act as a "resiliency zone" for the Tribes, for use as a shelter or command post during natural disasters. This microgrid will supply sufficient power generation and storage for the building while meeting the needs of the client for emergency event coverage and environmental impact. Solar photovoltaic (PV) panels and a Battery Energy Storage System (BESS) are proposed as alternate resources, along with a pre-existing diesel generator for emergency power generation to build a sustainable microgrid. However, PV and BESS placement and sizing must be assessed properly before delivering it as an effective solution. This use case is not typical, as system constraints and operating conditions are unique from a Tribal community perspective. An extensive study has been conducted using Hybrid Optimization of Multiple Energy Resources (HOMER) software to find a feasible case where all requirements were reached for the Tribal community and associated utility. All field equivalent datasets are being used to constitute the model in HOMER, and utility outage information is excised to perform as a backup during normal and extreme scenarios.
Combined Engineered Cells For Multiband, Wide Band, And Miniaturized Antennas

This work considers an antenna's electromagnetic (EM) response toward two combined cells outlining the electromagnetic band gap (EBG) structures. It has been demonstrated that EBG structures either act as a high impedance surface (HIS) or as an artificial magnetic conductor (AMC) that usually assist in achieving different functions. In this paper, the standard mushroom-like cells are combined with either EBG-CS or EBG-CSML cells (which are deeply explored previously). They are arranged in three periodic manners (Chessboard-like, Intervallic-like, and Mirror-like). The aim is to study the EM features of these EBG structures when integrated with antennas. The motivation behind this work is to bring out the eventual ability of any of those periodic configurations under test, either to obtain multiband antennas with competitive performances or to reach wide bandwidth resonating systems. The result shows a very interesting performance of the Intervallic structure when integrated with our printed dipole antenna (PDA), which manifests in wide bandwidth equal to 0.6 GHz at 3.2 GHz (the realized fractional bandwidth FBW is equal to 20%) and an additional band around 5 GHz. The presented EBG antenna is designed for the purpose of making a new competitive compact and miniaturized multiband antenna, as the central frequency was shifted from 4.6 GHz (PDA in free space) to 3.2 GHz (PDA with the Intervallic architecture). The result is validated by simulating the 73 Ohm half-wave dipole antenna (HWDA) with the proposed configuration, the result is agreeable. This system could be resized to work around mobile's 5G low and mid bands (0.7 GHz-2.6 GHz) to make attractive and competitive tiny antennas.
Theory of magnetic connectivity in the solar corona

[1] Although the analysis of observational data indicates that quasi-separatrix layers (QSLs) of magnetic configurations have to play an important role in solar flares, the corresponding theory is only at an initial stage so far. In particular, there is still a need of a proper definition of QSLs based on a comprehensive mathematical description of magnetic connectivity. Such a definition is given here by analyzing the mapping produced by the field lines which connect photospheric areas of positive and negative magnetic polarities. It is shown that magnetic configurations may have regions, where cross sections of magnetic flux tubes are strongly squashed by this mapping. These are the geometrical features that can be identified as the QSLs. The theory is applied to quadrupole configuration to demonstrate that it may contain two QSLs combined in a special structure called hyperbolic flux tube (HFT). Both theoretical and observational arguments indicate that the HFT is a preferred site for magnetic reconnection processes in solar flares.
A Memory Efficient Deep Reinforcement Learning Approach For Snake Game Autonomous Agents

To perform well, Deep Reinforcement Learning (DRL) methods require significant memory resources and computational time. Also, sometimes these systems need additional environment information to achieve a good reward. However, it is more important for many applications and devices to reduce memory usage and computational times than to achieve the maximum reward. This paper presents a modified DRL method that performs reasonably well with compressed imagery data without requiring additional environment information and also uses less memory and time. We have designed a lightweight Convolutional Neural Network (CNN) with a variant of the Q-network that efficiently takes preprocessed image data as input and uses less memory. Furthermore, we use a simple reward mechanism and small experience replay memory so as to provide only the minimum necessary information. Our modified DRL method enables our autonomous agent to play Snake, a classical control game. The results show our model can achieve similar performance as other DRL methods.
Blood Volume Pulse Signal Extraction based on Spatio-Temporal Low-Rank Approximation for Heart Rate Estimation

We propose a novel blood volume pulse (BVP) signal extraction method for heart rate estimation that incorporates the self-similarity properties of BVP in the spatial and temporal domains. The main novelty of the proposed method is the incorporation of the temporal self-similarity of BVP via low-rank approximation in the time-delay coordinate system for BVP signal extraction. To make a low-rank approximation of BVP in the time domain, we introduce knowledge of linear time-invariant systems, i.e., the autoregressive (AR) model lies in the low-rank subspace in the time-delay coordinate system. In the medical field, it is widely known that BVP has quasi-periodic temporal characteristics owing to the cardiac pulse and exhibits self-similarity properties in the temporal domain. Hence, we model the temporal behavior of BVP as an AR process, allowing for a low-rank approximation of BVP in the time-delay coordinate system. Low-rank approximation of BVP in the time and spatial domains enables reliable BVP signal extraction, resulting in accurate heart rate estimation. The experiments demonstrate the effectiveness of the proposed method.
Two regularization methods for an axisymmetric inverse heat conduction problem

Abstract We consider an axisymmetric inverse heat conduction problem of determining the surface temperature from a fixed location inside a cylinder. The problem is ill-posed: the solution (if it exists) does not depend continuously on the data. A modified Tikhonov regularization method and a Fourier regularization method are applied to formulate regularized solutions, which are stably convergent to the exact ones with order optimal error estimates.
Recent Variations of Sea Ice and Air Temperature in High Latitudes

Feedbacks resulting from the retreat of sea ice and snow contribute to the polar amplification of the greenhouse warming projected by global climate models. A gridded sea-ice database, for which the record length is now approaching four decades for the Arctic and two decades for the Antarctic, is summarized here. The sea-ice fluctuations derived from the dataset are characterized by 1) temporal scales of several seasons to several years and 2) spatial scales of 30°–180° of longitude. The ice data are examined in conjunction with air temperature data for evidence of recent climate change in the polar regions. The arctic sea-ice variations over the past several decades are compatible with the corresponding air temperatures, which show a distinct warming that is strongest over northern land areas during the winter and spring. The temperature trends over the subarctic seas are smaller and even negative in the southern Greenland region. Statistically significant decreases of the summer extent of arctic ice are.
Computer Vision based Tools and Technologies for Navigational Assistance for the Visually Impaired

One of the most pressing challenges concerning visual impairment is navigation in complex real-life environments. Blind people often use guide dogs which are expensive, canes, or other people to find their way around. Over the years, various tools have been developed to make the process of way-finding easier for the visually impaired, based on numerous technologies, including computer vision, SLAM algorithms and embedded systems. In the following paper, a number of the most effective tools and their corresponding mechanisms are discussed. These have been divided into software and hardware-oriented tools to provide the reader with better clarity. Furthermore, an analysis of the effectiveness of these alternative methods has been done, along with possible solutions to any shortcomings posed by these tools. The review concludes with a discussion about the different aspects to keep in mind while building navigational tools to assist the visually impaired.
Cooperative Positioning with the Aid of Reconfigurable Intelligent Surfaces and Zero Access Points

Due to their capability in creating a controllable wireless environment, extending coverage and improving localization accuracy, reconfigurable intelligent surfaces (RISs) are expected to be a main component of future 6G networks. In this paper, we present a novel cooperative positioning (CP) use-case of the RIS in mmWave frequencies. We show that two mobile stations (MSs) are able to estimate their positions through device-to-device (D2D) communications, and processing the signals reflected from the RIS. We start by building the system model based on the uniform linear array (ULA) architecture of the RIS elements. Then, we derive the Fisher information matrix (FIM) and the Cramér-Rao lower bound (CRLB) for calculating the MSs positioning error. After that, we optimize the RIS configuration to minimize the CRLB. Finally, simulation results compare the localization performance of random phases at the RIS with the optimal configuration.
Development of practice support system for violin bowing

Recently, there has been an increase in the demand for playing musical instruments at home, primarily because of the restrictions enforced due to the COVID-19 pandemic. We propose a system that can automatically estimate the angle between the bow and the string of a violin during playing from the playing sound and provide feedback to the player to realize a violin bowing practice support system. In this study, the automatic estimation of the angle performed using harmonics structure of the violin tone. The angle between the bow and the string were estimated using the ratio values between the harmonics level in the low frequency band and the level in the total frequency band. It was confirmed that there was a significant difference in the ratio between the sound played at a right angle and the sound played at an acute angle of approximately 20-30 degrees from the right angle. We are developing a practice support system using this method. The system judges the bowing angle to be good or bad and provides feedback to the player using LEDs. In full paper, we will describe the bowing skill identification method on the system in detail and report the evaluation results of the system.
On Circumstellar Envelope Formation

The origin of magnetic fields observed in circumstellar envelopes and proto-planetary nebulae is investigated theoretically. The build-up region of these fields is likely to be found deep in the progenitor (assumed to be an asymptotic giant branch red giant). With toroidal fields ~106 G produced by a dynamo mechanism in the degenerate core (roughly assimilated to a white dwarf of mean radius ~109 cm), we find values ~50 G in the convective envelope (mean radius ~1013 cm) and 1 mG in the circumstellar envelope (mean radius ~1016-1017 cm). A model for the ejection of massive winds is presented. We point out that the two phenomena (magnetic activity into the degenerate core and ejection of gas by the star) could be strongly interrelated.
A Compact Integrated Converter for Plug-In Electric Vehicles

Plug-in electric vehicles have gained interest in recent years, to make pollution free environment in future. A key attribute of plug-in electric vehicles is their ability to be recharged from electrical outlets. In a conventional system, onboard converters are used for charging purposes in plug in mode of operation (Static mode) has been mounted in the vehicle. However, for propulsion and regenerative braking mode of operation (Running modes), a bidirectional DC-DC converter is used, and it is integrated with the vehicle's power train components. In order to overcome the conventional methods, a compact integrated converter for plug-in electric vehicles has been proposed in this work. The compact integrated converter which operates in three modes namely plug-in charging mode, propulsion mode, regenerative braking mode, has been placed along with the power train components of the vehicle. The Proposed work focused on simulating the integrated buck-boost converter on plug-in charging mode with two-loop control strategy using MATLAB software. The battery charging of 8 kW/h using a single-phase supply on the proposed configuration was observed in the simulation.
Transmembrane Carbon Nanotubes for Intracellular Stimulation

Carbon nanotubes (CNTs) are hollow cylindrical carbon materials with unique physical, mechanical, and electrical properties. Furthermore, CNTs cut to about 10 nm can spontaneously insert into cell membranes and artificial lipid membranes. Consequently, CNTs are attracting attention for their potential use as protective membranes for sensors, drug delivery systems, nano-dipolar electrodes, and other applications when combined with lipid membranes. CNTs with hydrophobic surfaces can interact with the hydrophobic membrane core to induce membrane deformation. Although changes in membrane morphology are one cause of cytotoxicity, the effects of cut CNTs on membrane morphology are not well understood. In this study, we exposed CNTs to artificial cell membrane vesicles (giant unilamellar vesicles), which are used as a cell model, and evaluated the changes in membrane morphology for each CNT concentration by fluorescence microscopy.
Channel-distribution Hybrid Deep Learning for sEMG-based Gesture Recognition

In recent years, CNNs (Convolutional Neural Networks) with their powerful feature representation and feature learning capabilities, have played an important role in gesture recognition tasks based on sparse multichannel surface EMG signals. As each muscle group in the upper limb plays a different role in a particular hand movement, we propose a hybrid CNN model that considers the spatial distribution of muscle groups in the myoelectric channel to improve the accuracy of hand gesture recognition. The model takes the spectrogram of CWT (Continuous Wavelet Transform) as input, based on the spatial distribution of channels, decomposes all channels into multiple input streams, lets the CNN learn the features of each stream separately, and gradually fuses (slowly fusion) the features learned by each stream, and then performs gesture classification. Finally, the results of several of these stream-division methods are fused for decision making to obtain classification accuracies. The proposed model was validated and tested on the Nina Pro DB4 dataset, and the average accuracy was improved compared to both traditional machine learning methods and multi-stream CNN models that do not take into account the spatial distribution of channels.
Development of a large-aspect-ratio rectangular turbulent free jet

This paper reports the results of detailed mean flow and turbulence measurements made with hot-wire anemometry in the flowfield of a turbulent free jet of air issuing from a sharp-edged rectangular slot of aspect ratio 20 into still air surroundings. All three components of the mean velocity vector, the three Reynolds normal stresses and the two Reynolds primary shear stresses were measured. The mean streamwise vorticity and the turbulence kinetic energy have been calculated from the relevant measured data. It was found that the cross section of the jet, in the near flowfield, is dominated by counter-rotating streamwise vortices that facilitate rapid nearfield mixing. Rapid mixing is also evidenced by a short potential core length and high values of the turbulence kinetic energy and the Reynolds primary shear stresses compared with those found in an axisymmetric jet at the corresponding streamwise locations. In addition, mean streamwise velocity off-center peaks, which may be the result of the self-induction of the counter-rotating streamwise vortices, were found in the near flowfield, and the jet was found to spread monotonically in the central plane of the slot minor axis and to contract initially in the central plane of the slot major axis.