Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rapid Optimization Method for Visual Indirect SLAM Using a Subset of Feature Points

In this paper, we propose a novel optimization method for indirect Simultaneous Localization And Mapping(SLAM). This method avoids error-intended map points using a random subset of extracted feature points. As a result, this method making the calculation time short at the cost of some accuracy. The published code of ORB-SLAM2 has longer calculation time compared to other systems such as Semi-direct odometry(SVO). This is due to feature extraction time of an indirect method, which makes the indirect methods robust. However, considering CPU performance on edge devices like MAVs, a lightweight system is welcome. ORB-SLAM2 is widely known as a robust and rapid visual Simultaneous-Localization-And-Mapping (vSLAM) method than before. It extracts FAST feature points from a given image and optimizes using all of these points for camera-posing estimation. However, some points disturb accurate estimation causing large reprojection error after optimization. Although these points are eliminated as outliers after optimization, they may be an obstacle for accurate pose estimation. To make this system more robust and more rapid, We suggest an improved method to estimate camera-posing, where we use a subset of extracted points for estimation, avoiding mixing large-reprojection-error points in bundle adjustment. The experimental results on the SLAM benchmark dataset KITTI odometry demonstrated that the proposed method outperformed the original implementation of ORB-SLAM2 from the viewpoint of calculation time.
Iron Loss and Temperature Field Analysis of Four-Phase 8/6 Structure Switched Reluctance Motor for Electric Vehicles

This paper studies about the influence of different polarity distribution on the iron loss and temperature field on a four-phase 8/6 structure switched reluctance machine for electric vehicles. Firstly, a switched reluctance iron loss mathematical calculation model is proposed by fitting the core silicon sheet iron loss data of the prototype. Then, a 2D finite element model is constructed to analyze the magnetic flux density waveforms of iron core. Magnetic flux densities of the iron core under two different magnetic poles distributions are also compared. The iron loss of the different magnetic pole distribution is calculated respectively. Finally, the 2D finite element thermal model of the prototype is established and the simulation results are analyzed. Then, the experimental test platform is built. The experimental results show that the temperature rise is in good agreement with the simulated temperature rise.
ELMO: An Efficient Logistic Regression-Based Multi-Omic Integrated Analysis Method for Breast Cancer Intrinsic Subtypes

Breast cancer is one of the most frequently occurring female cancer types and represents a major cause of death among women worldwide. Breast cancer is heterogeneous in both molecular characteristics and clinical outcomes for its different molecular subtypes. High-throughput technologies facilitated the fast accumulations of the multiple Omic data for cancer patients. These data sources posed a computational challenge for the efficient integrated multi-Omic analysis. The existing studies usually investigated the differential representation or machine learning problems using a single type of Omic data. This study hypothesized that different Omic types contributed complementary information to each other, and their integrated analysis may improve the single-Omic models. An efficient logistic regression-based multi-Omic integrated analysis method (ELMO) was proposed to integrate the RNA-seq and DNA methylation data to detect the breast cancer intrinsic subtypes. ELMO achieved the highest accuracy with a smaller number of features compared with the existing filter and wrapper feature selection methods in this study. The experimental data supported our hypothesis that multi-Omic models outperformed the single-Omic ones.
MOOCs and NOOCs in the Training of Future Geography and History Teachers: A Comparative Cross-Sectional Study Based on the TPACK Model

MOOCs (<italic>Massive Open Online Courses</italic>) and NOOCs (<italic>Nano Open Online Courses)</italic>, which are part of e-learning, are being incorporated into the online teaching strategies of higher education institutions around the world. Research on the outcomes of their implementation has shown both their educational potential and their limitations. However, little is known about the instructional effectiveness of these courses and their potential contribution to the acquisition of specific competencies linked to the TPACK (<italic>Technological Pedagogical Content Knowledge</italic>) model for the training of future Geography and History teachers. To this end, this study examines perceptions regarding the instructional effectiveness of two MOOCs and two NOOCs on geographical and historical education, through an analysis of the performance in TPACK competency dimensions of future teachers (<inline-formula> <tex-math notation="LaTeX">$n = 1993$ </tex-math></inline-formula>). Based on a cross-sectional descriptive approach, central tendency statistics analyses (mean and standard deviation) and inferential analyses (Mann-Whitney <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> test and Wilcoxon rank-sum) were used. Participants reported optimal levels of satisfaction across all competency dimensions of the model for their teacher training. Inferential statistical analyses further revealed the existence of statistically significant differences depending on participants’ university and gender. Both the instrument applied and the results obtained are of use in making educational decisions regarding the design and implementation strategies of MOOCs and NOOCs specifically aimed at the professional development of future Geography and History teachers.
A Sentiment Polarity Categorization Technique for Online Product Reviews

Sentiment analysis is also known as opinion mining which shows the people’s opinions and emotions about certain products or services. The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral. Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations. In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos. A comprehensive data set of five hundred thousand online reviews is used in our research. There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive). We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization. Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent.
Tackling Build Failures in Continuous Integration

In popular continuous integration(CI) practice, coding is followed by building, integration and system testing, pre-release inspection, and deploying artifacts. This can reduce integration risk and speed up the development process. But large number of CI build failures may interrupt the normal software development process. So, the failures need to be analyzed and fixed quickly. Although various automated program repair techniques have great potential to resolve software failures, the existing techniques mostly focus on repairing source code. So, those techniques cannot directly help resolve software build failures. Apart from that, a special challenge to fix build failures in CI environment is that the failures are often involved with both source code and build scripts. This paper outlines promising preliminary work towards automatic build repair in CI environment that involves both source code and build script. As the first step, we conducted an empirical study on software build failures and build fix patterns. Based on the findings of the empirical study, we developed an approach that can automatically fix build errors involving build scripts. We plan to extend this repair approach considering both source code and build script. Moreover, we plan to quantify our automatic fixes by user study and comparison between fixes generated by our approach and actual fixes.
Hardware and Software Complex for the System of Human Gait Parameters Registration

The publication is focused on description of the results of developing a hardware and software complex for the system of human gait parameters registration by means of accelerometers. In the presented work, the modern techniques for recording the human gait parameters and subsequent analysis are considered. At the moment, gait analysis with the help of wearable sensors is an inexpensive, convenient, and efficient way to provide information on human movement for a variety of health-related issues. For example, as a clinical tool used in rehabilitation and diagnosis of the patient's health condition. Registration of such parameters and subsequent analysis require implementation of hardware component of the system: selection of components, system assembly. Creation of software for processing the indications (accelerations, including angle acceleration and Euler angles) from the measuring devices, such as gyroscope-accelerometer (hereinafter referred to as sensors) for recording the human gait parameters. The publication also deals with the initial interpretations of readings and creation of a 3D gait model. The proposed complex can be used both as a part of the gait analysis system and individually. This work is based on the use of accelerometry as a tool for step analysis with development of common parameters.
Experimental and Analytical Analysis of Macro-Scale Molecular Communications Within Closed Boundaries

Molecular communication (MC) is an emerging field where the transmission of information occurs using particles (i.e., molecules, pheromones) instead of electromagnetic (EM) waves. This change in propagation medium opens up new possibilities for MC in areas where EM is inefficient or impossible such as underwater and underground communications. This study reports transmission experiments conducted to analyze the propagation behaviour in a closed boundary. It is shown that the behaviour can be explained by using the advection-diffusion equation (ADE) where the diffusion parameter of the equation plays a pivotal role in the process of the propagation. The signal properties of the transmission are analyzed and modelled theoretically and it is shown that the communication exhibits complex behaviour for signal amplitude, signal energy and signal-to-noise ratio with respect to transmission distance.
Compact and Wide Stopband Substrate Integrated Waveguide Bandpass Filter Using Mixed Quarter- and One-Eighth Modes Cavities

This letter presents the design of mixed quarter- and one-eighth modes substrate integrated waveguide (SIW) bandpass filter (BPF). The transmission zeros (TZs) of the proposed SIW BPF can provide wide stopband response and high selectivity characteristics by using different mode SIW cavities. For validation, two- and three-stage SIW BPFs with Chebyshev response were designed at a center frequency (<inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula>) of 8 GHz. The measured results are consistent with the simulations. For two-stage BPF, the insertion loss smaller than 0.9 dB is measured within the passband of 0.65 GHz (7.75–8.4 GHz). The return loss higher than 19.7 dB is measured within the same passband. The spurious is produced at around 18 GHz (<inline-formula> <tex-math notation="LaTeX">$ < 2.25f_{0}$ </tex-math></inline-formula>). The stopbands are attenuated more than 17.31 dB from the dc to 5.68 GHz (<inline-formula> <tex-math notation="LaTeX">$0.71f_{0}$ </tex-math></inline-formula>) and from 9.28 GHz (<inline-formula> <tex-math notation="LaTeX">$1.16f_{0}$ </tex-math></inline-formula>) to 16.67 GHz (<inline-formula> <tex-math notation="LaTeX">$2.08f_{0}$ </tex-math></inline-formula>). The TZs are produced at 10 GHz and around 18 GHz due to a small cross-coupling between source/load and the interaction of the higher resonant modes of cavities, respectively. For three-stage BPF, the <inline-formula> <tex-math notation="LaTeX">$\vert S_{21}\vert $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert $ </tex-math></inline-formula> smaller than −1.3 dB and −18 dB are measured within the passband of 7.57–8.45 GHz fractional bandwidth (FBW = 11%), respectively. The TZs are produced at 1.102, 1.9, and <inline-formula> <tex-math notation="LaTeX">$2. 39f_{0}$ </tex-math></inline-formula> and provide higher selectivity and attenuation compared to two-stage BPF.
DOS-Robust Dynamic Speed Tracking Controller for an Integrated Motor-Gearbox Powertrain System of a Connected Car

This paper deals with the speed tracking control for a connected vehicle with an integrated motor transmission (IMT) powertrain system subject to a denial of service (DOS) attack. For a pure electric vehicle (PEV) with an IMT powertrain system, ensuring speed synchronization is a basic and important control requirement. Connected cars have considerable advantages. However, the in-vehicle controller area network (CAN) connected the external networks will bring network attack (e.g., DOS attack), which may greatly reduce the performance of the control system. To address these problems, a DOS-induced delay model and a dynamic output-feedback robust controller satisfying energy-to-peak performance is designed in this paper. The impact of a DOS attack on the control system is described by CAN message delays which are modeled by polytopic inclusions using Taylor series expansion. Then, a linear parameter varying (LPV) dynamic closed-loop control augmentation model in discrete-time frame considering time-varying delays is established. Furthermore, a dynamic output-feedback robust controller satisfying energy-to-peak performance is designed and the gains is calculated offline by solving a set of linear matrix inequalities (LMIs). Finally, the dynamic controller gains are used for the online step, and the speed tracking performance and oscillation damping capability of the proposed controller is demonstrated by some experiments and compared with a conventional PI controller which is the most widely used controller in engineering.
Optimal Hierarchical Management of Shipboard Multibattery Energy Storage System Using a Data-Driven Degradation Model

The lifetime of shipboard energy storage systems (ESSs) has great impacts on the operating cost of all-electric ships (AESs) since their high investment costs. Additionally, those ESSs are designed to have multiple battery packs with high capacity redundancy to cope with various navigation scenarios and distributed in different electric zones onboard to avoid operating risks. To extend the battery lifetime while fully addressing the redundant capacity and distributed locations, an optimal hierarchical management model for shipboard multibattery ESS is proposed, which consists of three levels. In the first level, a practical quadratic degradation cost model is reformulated from a nonlinear data-driven model, which considers both the depth of discharge (DoD) and mean state of charge (MSOC). Then, in the second level, the obtained quadratic model is implemented into the shipboard generation scheduling that views the multibattery ESS as a “single battery pack. ”In the third level, a multibattery management strategy is implemented to split the “single battery pack’s” power to each battery group by iteratively limiting their MSOCs. To bring the proposed energy management method into practical usage, a real-time simulation experiment is designed to test its validity. The case study shows that the proposed method is suitable for real-time usage and reveals that iteratively regulating the MSOC of batteries will greatly facilitate their lifetime. As a result, the operating cost of AESs can be reduced due to battery lifetime extension.
Uniqueness of Space of Hermite Type Splines

The smoothness of functions is quite essential in applications. This smoothness can be used in functional calculations, in the construction of the finite element method, in the approximation of those or other numerical data, etc. The interest in smooth approximate spaces is supported by the desire to have a coincidence of smoothness of exact and approximate solutions. A lot of papers have been devoted to this problem. The continuity of the function at a point means equality of the limits on the right and left; generalization of this situation is the equality of values of two linear functionals (at the prescribed function) with supports located on opposite sides of the mentioned point. Such generalization allows us to introduce the concept of generalized smoothness, which gives the ability to cover various cases of singular behavior functions at some point. The generalized smoothness is called pseudo-smoothness, although, of course, we can talk about the different types of pseudo-smoothness depending on the selected functionals mentioned above. Splines are often used for processing numerical information flows; a lot of scientific papers are devoted to these investigations. Sometimes spline treatment implies to the filtration of the mentioned flows or to their wavelet decomposition. Often a discrete flow appears as a result of analog signal sampling, representing the values of a function, and in this case, the splines of Lagrange type are used. In some cases, there are two interconnected analog signals, one of which represents the values of some function, and the second one represents the values of its derivative. In this case, it is convenient to use splines of the Hermite type of the first height for processing. In all cases, it is highly desirable that the generalized smoothness of the resulting spline coincides with the generalized smoothness of original signal. The concepts, which are introduced in this paper, and the theorems, which are proved here, allow to achieve this result. The paper discusses the existence and uniqueness of spline spaces of the Hermite type of the first height (under condition of fixing the spline grid and the type of generalized smoothness). The purpose of this paper is to prove the uniqueness of the Hermite type spline space of the first height (not necessarily polynomial) having the maximum pseudosmoothness. In this paper we use the necessary and sufficient criterion of the pseudo-smoothness obtained earlier.
Secured Energy Trading Using Byzantine-Based Blockchain Consensus

To mitigate the problems of demand-supply mismatch in the future grid the solution of renewable energy source (RES) integration results in a bidirectional flow of information and transactions, which are prone to different kinds of cyber attacks, especially in energy trading where the security of financial transactions is of most concern. Electric vehicle (EV) having the advantage of mobility can play a significant role in maintaining demand-supply balance at any location unlike their peers (conventional compensator). For deciding entire system security, securing EVs charging-discharging transactions at all charging stations or connecting points is most important. The system can be made more secure against cyber-attacks with the introduction of the blockchain framework. Hence, in view of secured transactions, the paper focuses on the energy trading process between EVs and distribution network (DN) in a Byzantine based blockchain consensus framework. During peak load period DN initiates the energy trading process by demanding additional power from the EVs. This process of energy trading results in energy and information exchange which needs to be secured through blockchain from vulnerable attacks and threats. Possible scenarios of various cyber-attacks on different nodes of the system are visualized in the form of false data. To highlight the application of blockchain, the Byzantine general problem framework is used which states that for successful attack 33% of information is to be manipulated, in other words, decreasing the probability of attack confirms the system security. Numerical results based on various operating scenarios for the standard IEEE 33 bus system are in agreement with the Byzantine consensus problem indicating improvement in system security.
Rendering Scheduling Framework in Edge Computing: A Congestion Game-based Approach

Mobile gaming has experienced explosive growth in the past few years. Meanwhile, the gaming scenes become more complicated, but the mobile devices have limited computing resources and inadequate energy, which can not satisfy the increasing requirements of mobile games. In recent years, edge computing flourishes as a promising solver to offload computation-intensive tasks to edge devices. However, how to schedule the various rendering tasks to heterogeneous edge servers is challenging because of the variety and unknown workload of the rendering tasks. To overcome these challenges, we design a rendering scheduling framework in edge computing and formulate the rendering scheduling problem as a congestion game, whose objective is to minimize the maximal completion time. We also design a rendering scheduling algorithm based on our congestion game model and derive the Nash Equilibrium and convergence. In addition, we explore a case study called redundant object elimination in rendering scenarios and adopt machine learning tools for workload estimation. Finally, we conduct extensive simulations based on our algorithm, which show significant performance compared with alternative strategies.
Quick breast cancer detection via classification of evoked EEG potentials in the mammologist's brain

Electroencephalography is a method of testing the electrical activity of the brain by jointly processing electrical signals registered at several points on the surface of the skull. It was originally invented to study mechanisms by which human behavior is generated, in particular, for brain diseases diagnosis. However, in the past decades, electroencephalography has become the basis of many brain-computer interfaces, which decode neural response to different stimuli into commands that, for instance, operate external devices. Our research is concerned with another purpose of analyzing responses of a multi-channel electroencephalogram (EEG) to outward stimuli. It is assumed that the person whose EEG is processed is an experienced mammologist able to reliably distinguish between X-ray mammograms of women with breast cancer and those of healthy women. These studies pursue the aim to essentially improve productivity of rare pronounced experts by way of, first, accelerating the screening of mammographic images up to ten pictures per second, and, second, immediately detecting the eventual potentials evoked in the expert's EEG by a target (cancer) image among a crowd of nontarget ones before the expert becomes aware of this fact.