Miracle-dz/UniFAR-FTUs huggingface dataset:
A Knowledge Graph-Based Framework for Integrated Network-Centric Warfare Strategies for Cyber-Physical-Social World

The availability of multichannel data in the cyber, physical and social world has challenged Artificial Intelligence (AI) based methods to develop novel tools for integrated modern warfare strategies. Networks are widely being used to represent enriched data in combination with AI tools for deeper insights. The formation of static and dynamic networks from audio, video, GPS, text and social data has shaped network-centric warfare strategies in an integrated setting. The present paper highlights a set of challenges related to inference methods in the cyber, physical and social world. A novel framework is presented in this paper that allows the representation of multidimensional data with a single network-driven viewpoint and representing multidimensional networks in the form of knowledge graphs. The knowledge graphs are presented as an effective tool that has the ability to be utilized in surveillance, strategy development and real-time information sharing for cyber-physical-social worlds. The presented components in the strategic framework are implemented and validated under certain constraints for better understanding.
Anomaly-based detection Technique using Deep Learning for Internet of Things: A Survey

Cyber-attacks are increasing dramatically and becoming an inevitable threat which is challenging to eliminate. These attacks include broad range of internal intrusions and external intrusions as well as new (zero-day) form of attacks which make the conventional security techniques obsolete. The growth in the volume of emerging cyber threats plays a critical factor that impedes the advances in technologies such as the Internet of things (IoT). IoT has significantly evolved in recent years to improve several aspects of every Industry. However, the vast acceleration toward adopting IoT technology has exacerbated the size of attack surface and magnetized plenty of cyber-attacks. Deep learning (DL) characteristics has motivated investigations to explore the capabilities of DL in perceiving the security of IoT architecture. In this paper, a review is presented on DL approaches used for IoT anomaly-based attacks detection and their effectiveness in conquering the security challenges in IoT environment. In addition, a comparative study is presented to highlight the performance indicators and architecture of each DL technique. Several DL models are used to detect malicious attacks in different IoT areas. Implementing DL methods with relevant vast datasets can significantly resist different security and privacy concerns.
Force Observer-Based Admittance Control Design for Robot Manipulators

This paper proposed a novel admittance controller for robot manipulators suffering from nonlinear dynamics and force disturbances without contact force sensors. A nonlinear observer is designed to simultaneously estimate both contact force and unmeasurable joint velocities. These estimated values are fed back to the model-based admittance control framework which includes the outer admittance control loop and inner position control loop. Simulation with a two-degree-of-freedom (2-DOF) manipulator is conducted to verify the effectiveness of the proposed algorithm.
Statistical Analysis of Water Resource in a Hydrographic Basin with Complex Hydropower Development

The energy produced in hydropower plants is dependent on both the operation management and a factor that can be less controlled by humans, namely the variation of rivers flows. The aim of the paper is to analyze the evolution of chronological recorded flows over a period of 57 years, from 1951 to 2007 and to observe the trend of these time series data. During the spring-summer period, river flows are considerable, so they can generally ensure energy generation. The analysis done in this paper demonstrates that in the series of monthly flows averaged over 6 months (September-February) there is an increasing trend of river flows, so in winter months.
Decoder Implementation of Spatially Coupled LDPC Codes

In order to meet the requirements of high reliability and high flexibility of wireless communication, the Space-coupled Low-Density Parity-Check (SC-LDPC) codes are deeply studied. At present, the main research direction of SC-LDPC codes is to reduce the complexity of the algorithm and reduce the occupation of decoding resources. In terms of hardware implementation, there is relatively little research. Therefore, in view of the above problems, considering hardware resource occupation and coding and decoding performance, the FPGA design and implementation of the SC-LDPC code codec are carried out, and the functional correctness test is carried out on the Xilinx xc7k325tffg900-2 chip.
UNF-SLAM: Unsupervised Feature Extraction Network for Visual-Laser Fusion SLAM

In this paper, we propose a new visual-laser fusion SLAM. Firstly, the dense depth map is obtained from the sparse laser information by depth completion. Then, a network that can extract fused features based on visual and laser information is proposed. After the depth completion which can complete depth and generate pixel-level confidence maps from lidar, RGB frames and completion confidence maps are fed into the network to create feature points that utilize more reliable information from lidar and are better for tracking. The Unsupervised Network for Fusion SLAM (UNF-SLAM) provides a new method of using a learning network for tight coupling of multi-source information. Experiments are conducted on the KITTI dataset and real scenarios. The results show that the proposed SLAM can achieve high accuracy and great robustness. Source code is available at https://github.com/salt0107fish/UNF_SLAM-master.
Real-time ROS Implementation of Conventional Feature-based and Deep-learning-based Monocular Visual Odometry for UAV

Localization or state estimation is one of the most important tasks for UAVs based on different kinds of sensors such as GPS, IMU, Lidar or cameras. However, localization based on only a monocular camera or visual odometry is one of the most challenging research topics. Conventional methods are proposed based on the detection of key features in each image and matching them on consecutive images to estimate the camera motions. Deep-learning methods have also been studied to solve the problem. Although the current learning-based visual odometry methods score high results on public datasets, there is a lack of real-time implementation of the methods in common robot operating systems such as ROS to integrate them into a navigation system. In this paper, we introduce a ROS implementation of state-of-the-art conventional feature-based method, ORB-SLAM3, together with a deep-learning-based method, SC-SfMLearner for real-time UAV localization. A photo-realistic simulator, Flightmare, is used to test the implementation together with another navigation task such as control. The implementation can evaluate both algorithms in real-time operation to compare their performances. Based on evaluation results from the simulated environments, the limitation or failure cases of the algorithms could be found, then, the best parameters of the algorithms can be adjusted to improve the algorithms to avoid failures in practical experiments.
Dynamic Performance Assessment of Microgrids under Consensus-based Distributed Control

Recent years have witnessed increasing interests in distributed control for microgrids. Its ease of implementation has been widely studied, but its impact on microgrid performance, especially when communication delays are considered, has not been explicitly studied. To investigate this issue, this paper proposes an analytical framework to evaluate the dynamic performance of such microgrid systems. The proposed analytical framework uses frequency domain representations to model microgrid components and communication delays and evaluates them according to the generalized Nyquist theorem. Various design specifications and their corresponding impacts are studied and generalized into design rules to improve system performance. To validate the proposed framework, case studies are conducted with promising results observed.
SentiMage: A Sentiment-Image-based COVID-19 Health Misinformation Detection using Machine Learning

The rapid dissemination of misinformation (generally known as fake news) has become worrisome, especially during the on-going COVID-19 pandemic both globally, and locally. In fact, the proliferation of health-related misinformation intensified on social media, which many experts believe is contributing to the threats of the pandemic. Sentiment has been shown to improve detection mechanisms in various social media related studies, however this aspect is under-researched in the context of health misinformation. Further, metadata such as location or image that constitute part of real and fake news were not fully explored as well. This study develops a health misinformation detection model using machine learning algorithms, and further assesses the impact of sentiment and image on the model performance. Local data gathered from a fact-checking portal were pre-processed, translated, and used to train the detection model. Evaluation results show Support Vector Machine to yield the best performance with 99.4% for F-measure and accuracy of 99.1%, followed closely by Random Forest when sentiment was included, however, the presence of image was not found to significantly improve health misinformation detection.
Underwater Simultaneous Localization and Mapping Based on 2D-SLAM Framework

The location problem of underwater robot is always the key of its underwater operation. At present, most underwater robots use inertial navigation and dead reckoning to locate. The positioning error can be corrected by means of GPS or acoustic underwater beacon, but these operations are not suitable for a large area of unfamiliar waters. The Simultaneous Localization and Mapping (SLAM) was extensively used in terrestrial robots. If applying it to underwater ones, the underwater robots can build a map with its own environmental through sensing sensors and correct positioning errors autonomously. In this paper, the mechanically scanned imaging sonar (MSIS) data from the submarine cave were collected for simultaneous localization and mapping, by using the existing mature laser radar 2D-SLAM frame. The main works are that we use pre-processing operations including beam segmentation, noise removal and threshold filtering to make sonar scan lase-like. After these operations, the sonar data can be transformed into a 2D laser radar packaging format and use in the frameworks. By using odometer information and IMU data in the data set, the motion distortion of the sonar point cloud is removed. Then we improve the Gmapping frames to make it more suitable to this underwater data set. Finally, the sonar-based dataset is successfully applied to the classical 2D laser SLAM frames Gmapping and Cartographer.
Two Nodes for Positioning System

Localization is an essential matter in monitoring and controlling a moving target. The localization could be distributed indoors and outdoors for three or more dimensions based on the required applications. For two-dimensional positioning, mathe-matically, at least three anchor nodes with known positions to locate a target are needed. The first two anchor nodes are utilized to compute the mobile position but with its ambiguous position. Then, the third node is used to eliminate the ambiguous position. This work presents an algorithm to eliminate the ambiguous position without using the third node by creating a criterion to select the correct position. The developed approach could be implemented in indoor or outdoor environments with specific constraints to eliminate the ambiguous position. With these constraints, the target position should be obtained in a perfect situation. The proposed approach is proven mathematically and evaluated using simulation experiments by MatLab and actual experiments using Ultra Wide Band (UWB) technology in the line-of-sight and non-line-of-sight conditions. The average accuracy of this approach in simulation and actual experiments is about 0.2721 $m$ 2 and 0.7630 $m$ 2 of mean square error (MSE), respectively,
Measurement of Dissemination Characteristics of LTE-V2X and ITS-G5 for Safety-Critical Vehicle-to-Vehicle Communication

Vehicle-to-vehicle communication enables the exchange of information between road users with the aim of improving safety and efficiency in the traffic environment. The highly dynamic environment with fast moving participants presents major challenges for the transmission technologies that are used. Furthermore, safety-critical applications in particular, place high demands on the reliability of the systems. Currently, there are two competing technologies for Vehicle-to-Vehicle communication: ITS-G5 and LTE-V2X. These two technologies have the potential to be used together due to their specific characteristics. As a result, they can complement each other constructively. In preparation for a comprehensive study of such a hybrid utilization, measurements were carried out to determine the characteristics of each technology in real traffic environment. The results of these measurements with both systems show, on the one hand, the characteristic properties of the technologies. On the other hand, they provide the information needed for the subsequent development of situation- and environment-dependent transmission models, that can be used in simulation environments to investigate complex traffic scenarios. This investigation should provide a direct comparison between different transmission technologies for the V2X communication. The measurement is focused on the successful transmission of information. The goal is to characterize the technologies by parameters that can be directly compared with the requirements of V2X use cases. The measurement results should be later used to create dissemination models for a simulation environment to investigate the communication for several use cases in a larger scale, especially in hybrid V2X networks.
SNIFS: a wideband integral field spectrograph with microlens arrays

SNIFS is an integral field spectrograph devoted to the observation of supernovae. This instrument is today in the manufacturing phase and should be able to observe supernovae at the end of this year (2003) on the 2.2m telescope of University Hawaii. The concept of SNIFS is to split the 6” x 6” field of view into 225 samples of 0.4” x 0.4” through a microlens array. Then the spectral decomposition of each sample is imaged on a 2k x 4k CCD. In order to cover all the large spectral range with a high resolution, the spectrograph is composed of two modules, one for the blue wavelengths (320 nm to 560nm)with a resolution around 1000 at 430 nm and one for the red wavelengths (520 nm to 1 µm) with a resolution around 1300 at 760 nm. First we will present the optical design and detail the function of each optical component. Then the mechanical design will be shown with some maps of the structure. Finally the first pictures taken during the alignments will be displayed.
An Optimized Key-Value Raft Algorithm for Satisfying Linearizable Consistency

Nowadays, the distributed systems have been widely applied in a variety of fields. However, this raises more concerns on reliability. Consensus algorithm is an important measure to ensure reliability of distributed systems, but its strong consistency may reduce the performance, resulting in cluster failure or even downtime. To this end, we propose an accelerated log backtracking optimization Raft algorithm, called ALB-Raft. It can improve the performance of traditional raft algorithm by enabling the backward tracker to update quickly. In particular, to achieve strong consistency, we construct a fault-tolerant distributed key-value (KV) service which conforms to the linearizable semantics. The experimental results illustrate that, when compared to the traditional raft algorithm, the proposed ALB-Raft consensus algorithm can resolve 20% of hundreds of log entry conflicts. Moreover, the ALB-Raft algorithm can also avoid the linear increase in the number of messages with the aggravation of log conflicts to ensure strong consistency.
Effect of absorption of solar radiation by water of different optical types on convection and heat transfer just under the air–water interface: the case of zero wind speed

A horizontally infinite water layer cooled from above and absorbing solar radiation is considered. Optical water types are classified according to Jerlov (Marine Optics, Scientific, 1976). The convection and the heat transfer are simulated numerically based on the linear equation of state and the time-dependent two-dimensional heat-conduction and Navier–Stokes equations. To take into account the effect of solar radiation, the conventional heat-conduction law for water with a free surface is corrected by introducing a particular function of heat and solar-radiation fluxes. Analytical expressions for this function are fitted.
Axisymmetric bubble collapse in a quiescent liquid pool. I. Theory and numerical simulations

In this paper we analyze the final instants of axisymmetric bubble pinch-off in a low viscosity liquid. We find that both the time evolution of the bubble dimensionless minimum radius, R0(t), and of the dimensionless local axial curvature at the minimum radius, 2r1(t), are governed by a pair of two-dimensional Rayleigh-like equations in which surface tension, viscosity, and gas pressure terms need to be retained for consistency. The integration of the above-mentioned system of equations is shown to be in remarkable agreement with numerical simulations and experiments. An analytical criterion, which determines the necessary conditions for the formation of the previously reported tiny satellite bubbles, is also derived. Additionally, an estimation of the maximum velocity reached by the high speed Worthington jets ejected after bubble pinch-off, in the case axisymmetry is preserved down to the formation of the satellite bubble, is also provided.
Constrained Control for a Class of TS Fuzzy Systems

This article investigates handling control constraints in the controller design phase for a class of Takagi–Sugeno (TS) fuzzy systems. Line integral Lyapunov function (LILF) is used to reduce the conservativeness compared to common quadratic Lyapunov function (CQLF) and fuzzy Lyapunov function (FLF). Two cases, namely unsaturating and saturating control, are studied. First, an inclusion condition is set to guarantee the behavior in a region where control constraints violation does not occur. For the second case, the saturation may take effect and is allowed. Thanks to the saturation function, convex writing to outrun the nonlinearity inconvenience. For both cases, stabilizing design procedures are proposed such that closed-loop stability and its linked performances are not affected. Furthermore, the proposed conditions enlarge the domain of stability as possible. Hence, comparisons are made with other approaches to show the effectiveness of our approach.
Fast Sparse Magnetic Resonance Image Reconstruction

In statistical modeling, sparsity can be exploited to yield a numerically stable solution for ill-conditioned inverse problems and to increase interpretability of predictive models. Moreover, the data acquisition process and reconstruction accuracy in magnetic resonance imaging can be improved significantly using sparse modeling. In this paper, we solve, for the first time, the nonconvex ℓ0-penalized regression problem with a total variation constraint. We develop a novel and efficient algorithm to accelerate magnetic resonance imaging that yields a more accurate reconstructed image than that obtained by solving the convex relaxation problem using the ℓ1 norm. We propose, for the first time, the Bayesian information criterion for model selection in neuroimaging problems. Using standard reconstruction quality metrics, we demonstrate the superiority of the proposed algorithm compared to the state-of-the-art in magnetic resonance reconstruction.
Performance Evaluation of Double Gate PNPN TFET and Extended-Source Double Gate PNPN TFET as Label-Free Biosensor

Performances of a dielectrically-modulated dual- metal hetero structure PNPN double gate TFET biosensor (DG- PNPN) and an extended-source dual-metal hetero structure PNPN double gate TFET biosensor (ES-DG-PNPN) were explored for identifying charged and uncharged (neutral) biomolecules. With the help of the dielectric modulation method, charged and uncharged biological molecules are immobilized in the nanogap cavities of the TFET biosensors separately for the label-free identification of biomolecules. Different sensitivity measures, such as Ins- V GS sensitivity, SS sensitivity, Ion sensitivity, Ion/Ioff sensitivity, etc., were used to evaluate the sensing capabilities of the devices. The analysis of the sensitivity study was executed using 2D TCAD (Technology Computer-Aided Design) simulations. The impact of cavity length variation reveals that increasing cavity length reduces the performance of both biosensors. The simulation result shows that the DG- PNPN TFE T biosensor provides better sensitivity than the ES-DG PNPN TFET biosensor in all circumstances.
Effect of Lateral Pressure on the Strength of Friction Stir Welding (FSW)

Friction Stir welding has been done on low melting and high thermal conductivity metals. Welding is a fabrication process that connects materials, typically metals or thermoplastics, by inducing fusion, which is distinct from lower temperature metal-joining techniques such as brazing and soldering. Extensive research is underway to mature the friction stir welding process. Researchers have performed experiment to find the effect of pin tool geometry on the strength of the welded joints and effects of variables i-e forces, feed rates and rpm. This research comprises the study of lateral pressure effect on friction stir welding and to find the best optimized lateral pressure. whereas, best parameters of welding were chosen. The purpose of this research is just to highlight the effect of different factor parameters on the friction stir welding. Stir welding was performed at 1000 rpm of spindle speed, 8 mm/min of traverse speed, 72, 62, 52 MPa lateral pressures generated by applying torque and 110x50mm plate made up of AA6061-T6 with 5mm thickness. In this work the effect of lateral pressure has been used to influence tensile strength and hardness attributed to friction stir welded AA6061-T6 butt joints. The H 13 tool steel included distinct pin geometry; simple cylindrical (SC) shape was used for welding. Maximum tensile strength and hardness of 79 MPa and 66 HV was achieved at tool rotation 1000 rpm, tool feed 8 mm/min, and pressure 72 MPa. Hardness and tensile strength improved on increasing applied pressure during friction stir welding.