Miracle-dz/UniFAR-FTUs huggingface dataset:
Stereoscopic imaging: filling disoccluded areas in depth image-based rendering

Depth image based rendering (DIBR) is a method for converting 2D material to stereoscopic 3D. With DIBR, information contained in a gray-level (luminance intensity) depth map is used to shift pixels in the 2D image to generate a new image as if it were captured from a new viewpoint. The larger the shift (binocular parallax), the larger is the perceived depth of the generated stereoscopic pair. However, a major problem with DIBR is that the shifted pixels now occupy new positions and leave areas that they originally occupied "empty. " These disoccluded regions have to be filled properly, otherwise they can degrade image quality. In this study we investigated different methods for filling these disoccluded regions: (a) Filling regions with a constant color, (b) filling regions with horizontal linear interpolation of values on the hole border, (c) solving the Laplace equation on the hole boundary and propagate the values inside the region, (d) horizontal extrapolation with depth information taken into account, (e) variational inpainting with depth information taken into account, and (f) preprocessing of the depth map to prevent disoccluded regions from appearing. The methods differed in the time required for computing and filling, and the appearance of the filled-in regions. We assessed the subjective image quality outcome for several stereoscopic test images in which the left-eye view was the source and the right-eye view was a rendered view, in line with suggestions in the literature for the asymmetrical coding of stereoscopic images.
Modelling and Study of the Downlink Physical Layer in 5G NR Mobile Network

Realistic evaluation of 5G NR performance require standard-compatible simulation models. This paper discusses the MATLAB 5G NR-based downlink physical layer simulation model. The basic architecture of the simulation model and the block architecture of the 5G NR transmitter and receiver models are described. The presented model is used in the performance analysis by estimating BER (Bit Error rate), BLER (Block Error Rate) and throughput under different conditions in the transmission channel. The results are presented and analysed when conducting the simulation with and without enabling the mechanisms of HARQ (Hybrid Automatic Repeat reQuest) and at different values of CQI (Channel Quality Indicator).
Age-of-Information in Networks with Periodic Workflow

We derive a description for the Age-of-Information distribution in a system whose activities are strictly limited to periodic instances on a global time grid. In particular, one agent periodically generates updates while the other agent periodically uses the most recently received of those updates. Likewise, transmission of those updates over a network can only occur periodically. All periods may differ. We use a network model which, among others, accounts for stochastic transmission failure, making the results applicable to instances with wireless communication. A suitable approximation for the expected Age-of-Information and an upper bound for its largest occurring value are developed. Additionally, we present numerical results on the approximation error based on extensive evaluations over a tight parameter space.
An Unmanned Network Intrusion Detection Model Based on Deep Reinforcement Learning

Network assaults pose significant security concerns to network services. Hence it is imperative to use new technical ways to enhance the performance of intrusion detection systems. Several reinforcement learning algorithms for network intrusion systems (e.g., Markov and other methods) have been introduced in recent years to satisfy the unmanned and intelligent requirements of IDS. This paper presents a deep Q-learning-based network intrusion detection model that provides continuous automatic learning capability for network environments based on reinforcement learning and incorporates a deep feed-forward neural network approach that can detect various types of network attacks. Experiments were done using CSE-CIC-IDS2018 as a dataset to test the model's performance, which provides a complete set of real network traffic. Experimental results demonstrate that the proposed model successfully detects network threats, outperforms conventional machine learning techniques, and has promising possibilities for unmanned IDS in complex network settings.
Justice League: Time-series Game Player Pattern Detection to Discover Rank-Skill Mismatch

When rank and skill do not coincide in competitive games, this might be a sign of issues such as boosting, smurfing, and trolling occur. The fair gaming culture of online gaming is disrupted and offended by cheating like boosting, smurfing, and trolling. The player's play style must be used to determine the rank that appears in account information. In this study, we classified League of Legends' low and high tiers using a sequence-based CNN-LSTM model. Using input perturbation, the model can explain its own importance for certain features. The experimental progress: First, we selected features that show a difference between tiers by an extracted score estimating a cumulative sum graph. Second, we construct the dataset format variously with variable or fixed sequence length, compare performance, and analyze the pros and cons. Finally, we consider the possibility of early detection by measuring performance over game elapsed time. Along with the experiment, a rank classification performance of the model achieved AUC 0.9036 and found that we can distinguish from the 24 minutes after the start of the game. In addition, We derived that ccReduction and MinionsKilied were the information that had the most influence on skills among various features.
Transient air-fuel ratio control of direct injection gasoline engine based on LADRC algorithm

As an important part of engine electronic control system, air-fuel ratio control is very important to engine emission control and engine performance. In order to realize the relevant research on the air-fuel ratio control algorithm of the direct injection gasoline engine in the cylinder, a prediction model of the in-cylinder intake air volume of the direct injection gasoline engine is constructed based on theoretical analysis and data fitting. Calibration was carried out; then an air-fuel ratio feedback control system based on the intake model and LADRC algorithm was constructed, and the control results were compared and analyzed using the simulation data. The results show that the constructed intake model can accurately calculate the intake air volume in the cylinder, so as to complete the calculation of the feedforward basic fuel injection volume; compared with the traditional PID and fuzzy PID control, the adopted LADRC feedback control algorithm has better performance. Small steady-state error and overshoot have certain advantages in comparison.
Software-Defined Networking Security Threats, Solutions, and Real-World Applications: A Survey

Software Defined Networking (SDN) is the most advanced technique of the recent era. It provides reliability, flexibility and logical centralization in terms of network control. SDN has the capacity to adapt according to the operating environment. It has a centralized and cost effective architecture, which is capable of providing good network visibility. This not only increases resource utilization, but also boosts performance. The pervasive existence and increase in smart programmable devices has given birth to SDN security, energy, efficiency and network visualization. SDN has successfully countered security threats and attacks. A survey has been included that defines different strategies implemented during the process. We discuss the main ongoing research efforts, challenges, and research trends in this specific field in order to forecast the future evolution of this new paradigm. To summarise, readers can delve deeper and gain a better understanding of the SDN structure, architecture, security threats, and countermeasures.
Development of Torque-Speed Control Algorithms For Power-Split Hybrid Electric Vehicle

In the last few years, there has been speedily developing and popularizing of hybrid electric vehicle (HEV) in the market. There are varieties of HEV types, and the degree to which they function as electric vehicle varies as well. HEVs make use of efficiency-improving technology to improve fuel consumption and emissions. Especially, the power-split HEV has combined the advantages of series and parallel hybrids. The control algorithms of the HEV have been developed diversely. These algorithms depended on the configuration of HEV and main components such as internal combustion engine, motors/generators, battery, and power-split device. Moreover, the control strategy is “the heart” of HEV, and it is very complex. Therefore, to improve performances of HEV, the control strategy must be developed and perfected. This study focused on developing control strategies of power flow in power-split HEV system. Based on the analysis of efficiency in a representative driving cycle, two control strategies, rule-based and fuzzy torque control strategies, were developed to optimize the power management of the power-split HEV. The first proposed control strategy was designed with “if-then” rules to control on or off internal combustion engine (ICE) and traction motor. The second one, the fuzzy-based controller, was developed with fuzzy logic to control ICE and motor. The simulation results of the proposed HEV control strategies were compared the fuel economy with real HEV. The power-split HEV model had been built with Matlab/Simulink package, and the simulation results showed significant improvements in fuel consumption.
An overview of Suomi NPP VIIRS calibration maneuvers

The first Visible Infrared Imager Radiometer Suite (VIIRS) instrument was successfully launched on-board the Suomi National Polar-orbiting Partnership (SNPP) spacecraft on October 28, 2011. Suomi NPP VIIRS observations are made in 22 spectral bands, from the visible (VIS) to the long-wave infrared (LWIR), and are used to produce 22 Environmental Data Records (EDRs) with a broad range of scientific applications. The quality of these VIIRS EDRs strongly depends on the quality of its calibrated and geo-located Sensor Date Records (SDRs). Built with a strong heritage to the NASA’s EOS MODerate resolution Imaging Spectroradiometer (MODIS) instrument, the VIIRS is calibrated on-orbit using a similar set of on-board calibrators (OBC), including a solar diffuser (SD) and solar diffuser stability monitor (SDSM) system for the reflective solar bands (RSB) and a blackbody (BB) for the thermal emissive bands (TEB). Onorbit maneuvers of the SNPP spacecraft provide additional calibration and characterization data from the VIIRS instrument which cannot be obtained pre-launch and are required to produce the highest quality SDRs. These include multiorbit yaw maneuvers for the characterization of SD and SDSM screen transmission, quasi-monthly roll maneuvers to acquire lunar observations to track sensor degradation in the visible through shortwave infrared, and a driven pitch-over maneuver to acquire multiple scans of deep space to determine TEB response versus scan angle (RVS). This paper provides an overview of these three SNPP calibration maneuvers. Discussions are focused on their potential calibration and science benefits, pre-launch planning activities, and on-orbit scheduling and implementation strategies. Results from calibration maneuvers performed during the Intensive Calibration and Validation (ICV) period for the VIIRS sensor are illustrated. Also presented in this paper are lessons learned regarding the implementation of calibration spacecraft maneuvers on follow-on missions.
A quadratic correlation algorithm with variable sets of lags for frequency estimation

The range resolution is a critical element for accurately detecting an object in the frequency modulated continuous wave (FMCW) radar system. However, the efficacy of existing correlation-based range estimate algorithms suffers dramatically under the low signal-to-noise ratio (SNR). A quadratic correlation algorithm with variable sets of lags is proposed to improve the estimation accuracy in order to overcome this challenge. The time translation parameter is varied in the proposed method to reduce noise influence, and a method for selecting the lag sequence sets is proposed to reduce computing complexity. Firstly, a variable time translation parameter is utilized to extract two signal segments from the received signal. Then, the autocorrelation function of the first signal segment along with the cross-correlation function of the two signal segments are obtained, and the quadratic correlation of the above two correlation functions is calculated for a lag sequence set with an initial lag step. Moreover, the expression for the frequency estimation as well as the range estimation is derived, and the mean and standard deviation of range estimation can be statistically obtained from the range estimates. Finally, if the standard deviation of the range estimation is above the prefixed standard deviation threshold, the lag step is modified and the above range estimation process is repeated; otherwise, the range estimation is obtained. The simulation results show that the proposed quadratic correlation algorithm with variable sets of lags outperforms existing approaches in terms of range accuracy.
An Experimental Study of Acoustically Induced Cavitation in Liquids

An apparatus has been assembled for the study of sonically induced cavitation in the frequency range of 20 to 40 kilocycles. Radially symmetric standing waves are produced in a spherical resonator driven externally by a magnetostriction transducer. The acoustic pressure within the sphere is measured by a small barium‐titanate transducer positioned at secondary maxima of the standing pressure wave. Equipment is provided for measuring the gas content of the liquid being studied.Measurements of cavitation thresholds as functions of air content, hydrostatic pressure, temperature, and surface tension have been made using different liquids. The highest thresholds measured were 200 bars for water and 140 bars for benzine (petroleum ether). The air content of the liquid appears to be the dominant factor limiting the maximal negative pressure which the liquid can withstand as long as the air content is greater than five percent of saturation. At lower percentages of air saturation the threshold becomes essentially .
Efficient Simulation of Complex Capillary Effects in Advanced Manufacturing Processes using the Finite Volume Method

The accurate representation of surface tension driven flows in multi phase systems is considered a challenging problem to resolve numerically. Although there have been extensive works in the past that have presented approaches to resolve these so called Marangoni flows at the phase boundaries, the question of how to efficiently resolve the interface in a universal and conservative manner remains largely open in comparison. Such problems are of high practical relevance in many manufacturing processes, especially in the microfluidic regime where capillary effects dominate the local force equilibria. In this work, we present a freely available numerical solver based on the Finite Volume Method that is able to resolve arbitrarily complex, incompressible multi phase systems with the mentioned physics at phase boundaries. An efficient solution with respect to the number of degrees of freedom can be obtained by either using high order WENO stencils or by employing adaptive cell refinement. We demonstrate the capabilities of the solver by investigating a model benchmark case as well as a single track laser melting process that is highly relevant within laser additive manufacturing.
Implementation of Sliding Mode Control Method for Switched-Inductor Quasi-Z-Source Inverter

DC-AC inverters have been widely used in different applications. According to the advantages of a quasi-Z-source inverter (QZSI) such as lower component rating and continuous input current, it is preferred to implement in generally various fields like renewable energy, energy storage and grid-connected systems. Also, new topology forms have been developed and presented to increase efficiency. In this paper, instead of the qZSIs, switched-inductor quasi-Z source inverter (SL-qZSI) is proposed. The two-level cascaded SL-qZSI has been investigated in order to expand the application areas and increase the efficiency and robustness of the system. The control of the system is structured on sliding mode control (SMC), which responds more dynamically to uncertainties as compared to the conventional ones. The simulation results are verified by MATLAB/Simulink.
Motor bearing failures detection by using vibration data

The use of methodologies for condition monitoring of rotating machines has been growing to reduce unplanned downtime and to increase the reliability of the industrial processes. The companies must select a correct maintenance strategy to follow the evolution of rotating machines. Condition monitoring is the collection of data related to the health status of the machine and it has been widely studied so far. Different methodologies have been developed to identify specific behaviors in the condition of induction motors. This paper proposes a methodology for bearing failure detection by using vibrations data, based on the frequency spectrum applied to induction motors. This methodology allows the use of vibration data obtained from motor bearings to establish their condition and therefore determine the type of damage to the bearing. The effectiveness of the proposed methodology is validated using a data set obtained from NASA (National Aeronautics and Space Administration). The results showed that this type of approach is very useful for analyzing bearings and in this way creating maintenance routes based on the condition of the electric machines.
Stackelberg and MAB Models for Decision-Making Process

This paper considers the Stackelberg and the Multi-Armed Bandit (MAB) models in the context of Cognitive Radio (CR) in order to enhance the spectrum learning of the secondary users (SU s), In such context, a SU tries to maximize selfishly its utility function which generally depends on the throughput, transmit power level, and the interference from other users. Recently, MAB model is widely suggested in the literature to learn the availability of channels for a single SU. While, in this work, beside the MAB, we focus on the Stackelberg model to consider more developed scenarios, such as: Learn channels' availability, enhance the Quality of Service, adjust transmit power level and decrease the interference among SUs. We also focus on the priority access, where a leader reacts first and then the followers make their decisions and actions by taking into consideration the action of the leader. We show that the Stackelberg model can represent an optimal solution in a real radio environment to help a SU make a good decision.
Static Classification of IoT Malware using Grayscale Image Representation and Lightweight Convolutional Neural Networks

The widespread of the Internet of Things (IoT) has made it a good target for adversaries to carry out sophisticated cyberattacks. A critical point has been reached in IoT security in 2016 through a major distributed denial of service attack that disturbed the operation of large Internet service providers and online services, carried out by a new IoT malware named Mirai. The release of its source code online led to the emergence of new malware types and the reemergence of old ones such as Gafgyt who has been around since 2014. In this context, quickly identifying the family of malware enables security professionals to apply suitable countermeasures. However, IoT malware classification is still in its infancy as most classification approaches require some domain expertise. In this work, we propose a malware classification approach of grayscale image representation of malware binaries using a new machine learning model in the form of a lightweight convolutional neural network. Using a dataset of more than 89,000 malware samples, we were able to predict correctly 9 malware classes with 95% accuracy. Our approach outperforms other evaluated CNN-based classifiers by more than 4%.
Deep Network for Smart Precision Agriculture through Segmentation and classification of Crops

Agriculture meets two of humanity's most fundamental needs, including food and fiber. Advancements in different farming techniques have paced up the agricultural field in the past century. However, the growing population and rising food demand require advancements in technology to fulfill the demand for food and agricultural products, meeting future food demands at a lower cost. Precision agriculture is gaining popularity due to its efficient output through high-tech machinery and robots. In this paper, our primary focus is on Smart Precision Agriculture (SPA) for segmentation and classification of crops and weeds through data augmentation technique such as training of network through image patches. Initially, we divided the images into patches and normalized them. Then, these patches were fed to Convolutional Neural Networks (CNN) to generate the pixel-wise segmentation of crops and weeds. Furthermore, we extracted region of interest (ROI) vegetation blobs by using the segmentation results. Finally, these ROIs are classified in crops and weeds using another network. We used the Carrot/Weed image and Sugar Beets datasets to perform experiments. The proposed system showed superiority in the experimental results.
Multi-Objects Robotic Grasping Optimization Employing a 2D camera

Artificial intelligence algorithms can be exploited to enhance identification, localization, and grasping performance in robotics applications, employing low-cost vision systems (such as 2D cameras). The aim of this paper is, indeed, to optimize the camera pose to improve object detection tasks considering a mul- tiple objects scenario. Therefore, transfer learning capabilities are required to minimize the experimental effort in subsequent grasps. Bayesian optimization (BO) with transfer learning (TL) capabilities has been proposed to address the mentioned scenario. A grasping task of multiple parts has been considered, being executed by an ABB Yumi single-arm manipulator IRB 14050 with a 2D Cognex AE3 In-Sight camera mounted at its end- effector. The proposed BO+ TL methodology has been compared with BO (without TL). The achieved results show that BO+TL is more efficient than BO exploiting the already available data.
Seed Quality Prediction using Computer Vision and Convolutional Neural Networks

Knowing the quality of the seeds is the most important thing for a farmer. The quality of the seed is critical for obtaining a good yield. Farmers typically purchase seeds from companies. They will ensure that the seeds are of high quality. However, because they use manual force to collect good quality seeds, there is a chance that they will replace good quality seeds with damaged ones. The precaution to be taken is to predict the seed quality. Our goal is to provide a solution that eliminates the need for manual seed quality checks in commercial farming. Without human intervention, the detection of pure and damaged seeds requires the use of computer vision and deep learning techniques. The proposed model employs OpenCV to detect every seed grain in the seed lot, as well as a convolutional neural network to predict the quality of the detected seed grain. The model's output is a prediction of the seed lot's purity percentage. This paper suggests a solution that reduces the amount of manual labor and time required to filter out damaged seeds.