Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiphysics Modeling and Parameter Analysis of Industrial Power-to-Hydrogen Electrolyzers

This paper proposes the multiphysics model of the industrial power-to-hydrogen electrolyzers. Based on the multi-energy flow in the industrial power-to-hydrogen electrolysis plant, the coupling among the multiphysics in the electrolyzer, including the electrochemical reaction, the flow field, the mass transfer and the temperature field, are unveiled. The governing equations and coupling mechanisms of the physical fields are introduced in detail, which are further illustrated according to the simulation results in COMSOL Multiphysics. Furthermore, several critical parameters, including the cell voltage, temperature and the concentration of the electrolyte, are analyzed based on the proposed model. Through the analysis on the current density- voltage characteristics, it can be concluded that the operating of the industrial electrolyzer usually faces the trade-off between the hydrogen production rate and the efficiency. The proposed model provides multiphysics explanations on the industrial statistics of the electrolyzer.
Image Recovery Through Scattering Media via GAN Reconstruction and SNES Optimization

Optical image recovery through scattering media is a significant yet challenging problem. Iterative wavefront shaping is one of the powerful tools to re-distribute the diffusive light and compensate for the diffuser by controlling the incident wavefront. However, in the scenario that only a feedback signal on the camera can be obtained, this technology would fail due to the lack of target images. In this paper, we propose a new scheme for recovering images through scattering media in an absence of target images. In particular, we employ an improved Generative Adversarial Network (GAN) for computational reconstruction and separable natural evolution strategy (SNES) for wavefront shaping optimization. Both simulation and experimental results suggest that the proposed scheme will open up new opportunities in the applications of biomedical imaging, optical encryption, holographic display, etc.
MOAB: Multi-Modal Outer Arithmetic Block for Fusion of Histopathological Images and Genetic Data for Brain Tumor Grading

Brain tumors are an abnormal growth of cells in the brain. They can be classified into distinct grades based on their growth. Often grading is performed based on a histological image and is one of the most significant predictors of a patient’s prognosis; the higher the grade, the more aggressive the tumor. Correct diagnosis of the tumor’s grade remains challenging. Though histopathological grading has been shown to be prognostic, results are subject to interobserver variability, even among experienced pathologists. Recently, the World Health Organization reported that advances in molecular genetics have led to improvements in tumor classification. This paper seeks to integrate histological images and genetic data for improved computer-aided diagnosis. We propose a novel Multi-modal Outer Arithmetic Block (MOAB) based on arithmetic operations to combine latent representations of the different modalities for predicting the tumor grade (Grade II, III and IV). Extensive experiments evaluate the effectiveness of our approach. By applying MOAB to The Cancer Genome Atlas (TCGA) glioma dataset, we show that it can improve separation between similar classes (Grade II and III) and outperform prior state-of-the-art grade classification techniques.
Design of 28/38 GHz Antenna Array With Improved Gain and Bandwidth

In this paper, started with the design and simulation of a single element patch antenna which operates at 28 and 38 GHz bands for 5G applications. Slots have been introduced within the radiating element to attain dual band operation. To improve the gain and bandwidth of the array, the defective ground plane technique was used. Simulation were performed for important parameters such as return loss, bandwidth and gain, the obtained results satisfactory. For the purpose to get a high gain with improved bandwidth, parasitic element technique was used for the 1x4 antenna array. Simulation results, showed that the gain and bandwidth of the array antenna were highly improved.Comparison of simulated results were performed between the enhanced antenna array with other works available in the open literatures.
Three Dimensional Emotion State Classification based on EEG via Empirical Mode Decomposition

Electroencephalography (EEG) is useful for mapping emotions directly from the brain, but its heterogeneous signals make it challenging to extract features accurately. Prior works for emotion classification uses EEG data without removing data heterogeneity leading to misclassification or inaccurate classification. This paper proposes an EMD-based methodology for EEG data that segments signals into multiple IMFs to remove heterogeneity and extract significant features. The proposed approach uses a Feed-Forward Neural Network (FFNN) to classify emotions via the VAD model and shows a 5-6% increment in accuracy, precision, and recall scores for emotion classification. Experimental results demonstrate good evaluation performance scores for classifying emotional states on two publicly accessible emotional datasets, AMIGOS and DREAMER.
Conformal Bootstrap in Two Dimensions

In this dissertation, we study bootstrap constraints on conformal field theories in two dimensions. The first half concerns two-dimensional (4, 4) superconformal field theories of central charge c = 6, corresponding to nonlinear sigma models on K3 surfaces. The superconformal bootstrap is made possible through a surprising relation between the BPS N = 4 superconformal blocks with c = 6 and bosonic Virasoro conformal blocks with c = 28, and an exact moduli dependence of a certain integrated BPS four-point function. Nontrivial bounds on the non-BPS spectrum in the K3 CFT are obtained as functions of the CFT moduli, that interpolate between the free orbifold points and singular CFT points. We observe directly the signature of a continuous spectrum above a gap at the singular moduli, and find numerically an upper bound on this gap that is saturated by the A1 N = 4 cigar CFT. The second half concerns the semiclassical limit of two-dimensional CFTs, motivated by holography. In this limit, the conformal block decomposition of the fourpoint function is dominated a particular weight, and the crossing equation simplifies drastically. We find that if a certain “weakness” condition is satisfied, then the OPE coefficients follow a universal formula given by the semiclassical limit of the fusion kernel. This is matched with a bulk action evaluated on a geometry with three conical defects, analytically continued in the deficit angles beyond the range for which a metric with positive signature exists. The analytically continued geometry has a codimension-one coordinate singularity surrounding the heaviest conical defect. This singularity becomes a horizon after Wick-rotating to Lorentzian signature, suggesting a connection between universality and the existence of a horizon.
Heart Failure Detection Using Deep Neural Network

An estimated million people die from heart disease each year, which makes it the most common cause of death in the world. Heart failure can be predicted early, which can increase mortality rates. In this context, numerous classification techniques are described in the literature. As a result, it has turned into a difficult issue in the healthcare domain. In this paper, a deep learning-based classifier is proposed for heart failure detection. Additionally, pre-processing steps like normalization and feature encoding strategies are introduced in a novel way to enhance the performance of the suggested classifier. To eliminate the struggle of hyper-parameters selection, grid-search is used. To show the robustness of the proposed classifier, a standard heart disease dataset is utilized, and results are obtained under various training-validation-testing ratios. The findings show that the performance of the proposed classifier is better than the methods currently in use for classifying heart disease. The suggested classifier might be employed as a tool to help experts make early predictions about heart disease.
Robust Visual-Inertial Odometry for Ground Robots in Dynamic Environments

This paper introduces a novel approach to improve robustness of visual-inertial odometry for ground mobile robots in dynamic environments. The proposed approach extends a standard Visual-Inertial Navigation System with weighted least squares state estimation problem using alternating factor graph optimization. Additionally, we establish a reliability model for visual landmarks based on semantic prior knowledge of the scene. The robustness of our approach is validated on real-world experiments and evaluation results show stable and accurate state estimation in high-dynamic environments.
A Shared Control Approach for Autonomous Vehicles via Driver Behaviors Learning

Vehicles today are in a phase between human control and full autonomy. A reliable and stable approach that shares the decisions of both human and autonomous controllers is needed. This paper introduces a shared control approach to offer control signal from autonomous controller to complement commands of drivers to reduce driving risks and improve driving performance. Knowing future actions of drivers is essential in final decision making. To predict the behavior of driver, Gaussian process regression is adopted to model the driver with the data gathered in the driving process. Utilizing the drivers model and vehicles model, autonomous signal is provided by Model Predictive Controller (MPC) and combined with commands of drivers in certain proportion constituting the final outputs. With the shared control algorithm, both tracking and heading errors are significantly eliminated.
Researching on Signal Transmission Performance of LoRa Technology in Urban Environment

The wireless sensor network plays a key role in the Internet of Things (IoT), and the performance characteristic that low-power long-distance transmission has become the focus of academic and industrial research. Long Range (LoRa) technology adopts an unlicensed frequency band, and it can provide a longer communication distance for Low-Power Wide-Area Network (LPWAN) when compared with similar technologies. This paper studies the communication quality of LoRa technology in the complex urban environment with 433MHz band. In this experiment, the LoRa transceivers based on SX1278 were used. Firstly, we studied the communication quality of LoRa transceivers in complex environment. Then, we tested the power consumption of LoRa transceivers and obtained the result that there is a certain linear relationship between the power consumption of node and transmitting power. The higher the transmitting power of the transmitting node is, the higher the power it consumes, and the better the communication quality of the signal and the lower the packet loss rate. The comprehensive experimental results show that the LoRa communication link can be completely established in 433MHz band on the condition that the range between 2 LoRa transceivers in urban environment is within 1200m at least and low-cost rubber rod antennas are used.
Spatially Dispersive Metasurfaces—Part II: IE-GSTC-SD Field Solver With Extended GSTCs

An integral equation (IE)-based field solver to compute the scattered fields from spatially dispersive metasurfaces is proposed and numerically confirmed using various examples involving physical unit cells. The work is a continuation of Part-1 by Rahmeier <italic>et al.</italic>, which proposed the basic methodology of representing spatially dispersive metasurface structure in the spatial frequency domain, <inline-formula> <tex-math notation="LaTeX">$\mathbf {k}_{||}$ </tex-math></inline-formula>. By representing the angular dependence of the surface susceptibilities in <inline-formula> <tex-math notation="LaTeX">$\mathbf {k}_{||}$ </tex-math></inline-formula> as a ratio of two polynomials, the standard generalized sheet transition conditions (GSTCs) have been extended to include the spatial derivatives of both the difference and average fields around the metasurface. These extended boundary conditions are successfully integrated here into a standard IE-GSTC solver, which leads to the new IE-GSTC-SD simulation framework. The proposed IE-GSTC-SD platform is applied to various uniform metasurfaces, including a practical short conducting wire unit cell, as a representative practical example, for various cases of finite-sized flat and curvilinear surfaces. In all cases, computed field distributions are successfully validated, either against the semianalytical Fourier decomposition method or the brute-force full-wave simulation of volumetric metasurfaces in the commercial Ansys FEM-HFSS simulator.
High Precision ORB-SLAM Dense Reconstruction Based on Depth Visual Odometer in Dynamic Environments

Most SLAM systems are based on the assumption of a static environment, while most of the real scenes with dynamic will cause mismatching in the process of camera pose estimation and affect localization accuracy and system robustness. To improve the effect and accuracy of SLAM dense reconstruction in dynamic environments and to increase the real-time performance of advanced tasks such as navigation and human-computer interaction, this paper proposes a real-time SLAM dense reconstruction method based on object detection in dynamic environments. Firstly, we use YOLOv5 for frame-by-frame detection and semantic marking of images to accurately identify the image feature points under dynamic semantic masks. Further, this paper proposed a keyframe filtering algorithm based on dynamic semantic marking to ensure the accuracy of dense map reconstruction by eliminating frames containing dynamic objects, which solves the interference of point cloud redundancy and moving objects in dynamic environments. Finally, this paper takes the scene depth information as an important reference for characterizing the geometric structure of the scene. By introducing the depth factor into the traditional feature extraction algorithm, we explored a new joint-depth-information based feature extraction and feature descriptor calculation method, and proposed a high precision visual SLAM based on deep joint visual odometer. Experiments on the TUM RGBD public dataset show that the trajectory accuracy of constructing dense point cloud maps under partial dynamic scene sequences is improved by 16.1% over DS-SLAM and 82.3% over ORB-SLAM2.
MSFNet: A Multi-Level Semantic Feature Fusion Network for Instance Segmentation of X-ray Security Images

In order to eliminate security risks and reduce the probability of terrorist incidents, X-ray security scanners have been installed in places such as airports, customs and railway stations to check whether luggage packages contain prohibited items. At present, the security inspection is manually checked by professional security personnel. In the face of a large number of objects to be detected, the efficiency of manual detection is low, and the detection results are susceptible to human experience and psychological interference. The design of automatic identification and detection of prohibited items in X-ray security inspection scenes can effectively improve the efficiency of security inspection and reduce the difficulty of potential risks and human operation. With the development of deep learning in recent years, computer vision technology can realize fast and reliable automatic detection of prohibited items. However, due to the penetrating imaging nature of X-ray, the X-ray security images contain a lot of occlusion and overlap, which increases the difficulty of identifying prohibited items. Based on the SCNet [1] algorithm, this paper proposes an instance segmentation algorithm multi-level semantic feature fusion network(MSFNet). MSFNet enhances the upsampling ability of the model to recover detailed information, and effectively fuses low-order shallow detail information and high-order abstract semantic information. Firstly, a dynamic upsampling feature fusion module(DUFM) was proposed to capture a longer range of context information and reduce the interference of occlusion noise. Secondly, a multi-stage global semantic extraction module(MGSEM) is proposed to extract multi-scale global semantic information from semantic features of different granularities. Experiments on the Pidray dataset [2] show that MSFNet has further improvement in accuracy compared with common instance segmentation algorithms, and the accuracy index in detection and segmentation tasks reaches 72.3%and 62.4% mAP respectively.
Augmented Reality-based Indoor Navigation using Unity Engine

The evolution of technology with the introduction of mobile devices has provided users with day-to-day advancements in existing technologies. Augmented Reality (AR) is combined with an Artificial Intelligence navigation agent to trackthe environment that is provided specifically. Unity 3D engine provides an inbuilt AR framework easily accessible and the navigation mesh agent tracks penetrating paths with segregated walkable areas and areas that cannot be accessed. The current machine learning systems use inertial sensors and high-end cameras for computer vision-based motion tracking. Whereas, this system does not require external hardware as the environment model is constructed with provided measurements in Unity Engine and virtual trackers, which can be functioned in any AR-enabled device. The proposed model aims to provide the framework with real-time virtual visualization for a better user experience.
Hybrid Ship Motion Control Algorithm that Eliminates Control Loop Saturation

The paper presents the results of testing a hybrid algorithm of the ship's motion control system, which provides a return to a given trajectory-attractor for any lateral deviation of the ship from a given trajectory. A model of the operation of the control loop was built, consisting of a control unit and a model of the ship's dynamics. The results of computational experiments demonstrate the stability of particular linear and discrete models in a hybrid control system (CS).
Learning directed acyclic graph models based on sparsest permutations

We consider the problem of learning a Bayesian network or directed acyclic graph model from observational data. A number of constraint‐based, score‐based and hybrid algorithms have been developed for this purpose. Statistical consistency guarantees of these algorithms rely on the faithfulness assumption, which has been shown to be restrictive especially for graphs with cycles in the skeleton. We here propose the sparsest permutation (SP) algorithm, showing that learning Bayesian networks is possible under strictly weaker assumptions than faithfulness. This comes at a computational price, thereby indicating a statistical‐computational trade‐off for causal inference algorithms. In the Gaussian noiseless setting, we prove that the SP algorithm boils down to finding the permutation of the variables with the sparsest Cholesky decomposition of the inverse covariance matrix, which is equivalent to ℓ0‐penalized maximum likelihood estimation. We end with a simulation study showing that in line with the proven stronger consistency guarantees, and the SP algorithm compares favourably to standard causal inference algorithms in terms of accuracy for a given sample size. Copyright © 2018 John Wiley & Sons, Ltd.
Joint Beamforming Algorithm for Multi-Stream MIMO Systems Assisted by Multiple Reconfigurable Intelligent Surfaces

In recent years there has been a growing interest in reconfigurable intelligent surfaces (RISs) as enablers for the realization of smart radio propagation environments which can provide performance improvements with low energy consumption in future wireless networks. However, to reap the potential gains of RIS it is crucial to jointly design both the transmit precoder and the phases of the RIS elements. Within this context, in this paper we study the use of multiple RIS panels in a parallel or multi-hop configuration with the aim of assisting a multi-stream multiple-input multiple-output (MIMO) communication. To solve the nonconvex joint optimization problem of the precoder and RIS elements targeted at maximizing the achievable rate, we propose a novel iterative algorithm based on the monotone accelerated proximal gradient (mAPG) method which includes an extrapolation step for improving the convergence speed and monitoring variables for ensuring sufficient descent of the algorithm. Based on the sufficient descent property we then present a detailed convergence analysis of the algorithm which includes expressions for the step size. Simulation results in different scenarios show that the use of multiple RIS panels combined with the proposed algorithm can be an effective solution for improving the achievable rates.
A Single-Input Dual-Output DC/DC Buck Converter for Rotorcraft Unmanned Aerial Vehicle

Application of small rotorcraft unmanned aerial vehicle (UAV) is expanded greatly with the improvement of its performance, which will lead to the increase of power units. To satisfy the different voltage requirements of these power units, a single-input and multiple-output converter is required during the development of small rotorcraft UAV. Here, we developed a single-input dual-output converter that can output two different voltages. The topology and working principle of the converter were analyzed. We then designed the basic structure of the converter. Furthermore, operation of the single-input dual-output converter was simulated using Simulink in Matlab. The voltage output of the converter was detected by oscilloscope. Our results indicate that this converter can satisfy the requirement of different voltage outputs of small UAVs.
Market Analysis and Forecasting using Trending Data Analytical Tool

Marketing refers to the process of promoting, selling, and distributing products to potential consumers. It involves identify the preferences and needs of target consumers, developing a service or product that satisfies those needs, determining the most effective way to reach the target market, and creating and implementing a marketing plan that includes strategies for product pricing, promotion, and distribution. Marketing can take many forms, including social media marketing, internet search engine optimization, marketing via email, and conventional marketing and many others. The ultimate aim of marketing is to increase drive sales, brand awareness, and generate leads. Perfect marketing requires a deep understanding of the target market, their needs and desires, and the competitive landscape. Marketing plays a significant role in our society. The opportunity to increase one’s income is a big advantage of investing in the stock market. Analysis and Forecasting of the marketing sector are among the most challenging tasks. That’s also due to a number of factors, such as volatility in the market and a wide range of other independent as well as dependent variables that affect the stock’s market value. So, Stock market experts will find it difficult to perfectly forecast the market collapse or growth using all these factors. There are many marketing analytical tools available previously. This research work was done by the R data analytics tool for forecasting the best result.