Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a Novel Variable Structure Nearest Level Modulation on Cascaded H-Bridge Multilevel Inverter

The output voltage of a Multilevel Inverter (MLI) can be controlled using various low and high frequency modulation strategy. Apart from meeting the load voltage demand, the modulation strategy also tries to reduce the harmonics present in the output voltage waveform. In this paper, a novel modulation strategy, a Variable Structure Nearest Level Modulation (VSNLM) has been proposed with the objective to reduce the Total Harmonic Distortion (THD) thus giving better output voltage quality and smoother control. The development of the mathematical model and subsequent analysis has been carried out. Moreover, the proposed modulation scheme has been implemented on 7, 9, 11, and 13 level Cascaded H Bridge inverter and a generalized mathematical model of the proposed scheme has been developed. A comparison of the proposed strategy with the conventional NLC is presented in the paper. The results confirm the superior performance of proposed strategy in terms of THD. Experimental results have also been presented for validation.
Impact of Amplification and Regeneration Schemes on the Blocking Performance and Energy Consumption of Wide-Area Elastic Optical Networks

This paper studies the physical layer’s impact on the blocking probability and energy consumption of wide-area dynamic elastic optical networks (EONs). For this purpose, we consider five network configurations, each named with a network configuration identifier (NCI) from 1 to 5, for which the Routing, Modulation Level, and Spectrum Assignment (RMLSA) problem is solved. NCI 1–4 are transparent configurations based on all-EDFA, hybrid Raman/EDFA amplifiers (with different Raman gain ratio <inline-formula> <tex-math notation="LaTeX">$\Gamma _{R}$ </tex-math></inline-formula>), all-DFRA, and alternating span configuration (EDFA and DFRA). NCI 5 is a translucent configuration based on all-EDFA and 3R regenerators. We model the physical layer for every network configuration to determine the maximum achievable reach of optical signals. Employing simulation, we calculate the blocking probability and the energy consumption of the different network configurations. In terms of blocking, our results show that NCI 2 and 3 offer the lowest blocking probability, with at least 1 and 3 orders of magnitude of difference with respect to NCI 1 and 5 at high and low traffic loads, respectively. In terms of energy consumption, the best performing alternatives are the ones with the worst blocking (NCI 1), while NCI 3 exhibits the highest energy consumption with NCI <inline-formula> <tex-math notation="LaTeX">$2\,\,\Gamma _{R}=0.75$ </tex-math></inline-formula> following closely. This situation highlights a clear trade-off between blocking performance and energy cost that must be considered when designing a dynamic EON. Thus, we identify NCI 2 using <inline-formula> <tex-math notation="LaTeX">$\Gamma _{R}=0.25$ </tex-math></inline-formula> as a promising alternative to reduce the blocking probability significantly in wide-area dynamic EONs without a prohibitive increase in energy consumption.
Distributed Event-Triggered Random Gradient-Free Optimization Algorithm For Multiagent Systems

Since the communication resources and computing ability of agents are limited, the traditional subgradient method is difficult to solve the distributed optimization problems with nonconvex objective function, this paper proposes a distributed event-triggered random gradient-free optimization algorithm to solve this problem. The convergence of this algorithm can be ensured by providing non-additive and square-summable stepsize and the proposed event-triggered control reduce the communication burden dramatically. Then, all agents can reach average consensus by setting diminishing and square-summable threshold through the convergence analysis. Finally the simulation results also verify that the proposed algorithm can reduce the number of triggers and obtain excellent convergence performance.
Scale Recovery Using Vanishing Points for Indoor Mobile Vehicles With Monocular Vision

This paper proposes a scale recovery method for indoor mobile vehicles with monocular vision. We aim to solve the scale ambiguity problem which is the main weakness of monocular SLAM. The method is based on the detection and modeling of the ground plane under the constant camera height assumption which is suitable for ground mobile vehicles. The ground plane detection relies on vanishing points estimation, and the ground plane modeling is accomplished by point clouds on the ground complemented with vanishing directions estimation. The method is evaluated both on the self-collected indoor dataset and the KITTI benchmark dataset. The experimental results verify the effectiveness of our scale recovery method.
Improved Deeplabv3 For Better Road Segmentation In Remote Sensing Images

Road extraction from remote sensing images (RSI) is one of the most important applications in semantic segmentation task. In this paper, we propose an improved DeepLab-V3model for better road segmentation in RSI. An improved Deeplab-V3 network model combined with U-Net fusion shallow features is constructed, and the collective loss function of DICE loss and BCE loss in network feedback learning is used to solve the problem of imbalance of two-class samples and effectively extracts roads in remote sensing scenes. The experiment on a challenging road segmentation dataset from Google Earth confirms that our method is better than that of Deeplab-V3 and U-Net, making Deeplab-V3 more practical for road extraction of RSI.
A Novel Method for Intrusive Stochastic Estimation of Geometric Tolerance Effects in Finite Element Electromagnetic Analysis

The robust design of microwave, millimeter-wave, or Terahertz components and structures incorporating manufacturing process tolerances would enhance the fabrication yield and thereby reduce the overall production cost. Based on the spectral stochastic finite element method (SSFEM) for material variations, a novel geometrical SSFEM (GSSFEM) is proposed to analyze dimensional uncertainty in 3-D microwave models. The Jacobian for Piola mapping is represented as a function of stochastic variables to capture mesh level uncertainties. Polynomial chaos expansion is used to approximate the electric field as a random process. The technique is validated by applying it to waveguide problems with uncertainty in geometric dimensions. GSSFEM results are compared with analytical formulations of the transmission coefficient for a physical insight and further with the Monte Carlo simulations for quantitative evaluation. The results using the proposed approach are in excellent agreement with conventional methods and are found to be faster than sparse grid stochastic collocation, while the scaling of the computational requirements with the number of degrees of freedom and stochastic dimension is as good as this efficient scheme. Furthermore, this approach has been employed to analyze uncertainty in multiple geometric parameters to compare their sensitivity. The proposed GSSFEM can be employed for analyzing the impact of fabrication tolerance of passive waveguide components at microwave frequencies and beyond.
Agent Pricing Strategy and Energy Using Optimization Based on Stackelberg Game

With the reform and opening up of the power market, the agent plays an important role in electricity management in the residential area. A bi-level model based on Stackelberg game is proposed in this paper, which compromises the interests of both agent and electric vehicle (EV) users. Firstly, the agent’s profit can be maximized by setting a reasonable electricity price to guide the EV charging. Meanwhile, EV users make charging strategies according to the electricity price to minimize the cost. Then, the Karush-Kuhn-Tucker (KKT) and duality theorem are used to transform the proposed model into a mixed integer programming problem, which can be easily solved by commercial solver YALMIP/CPLEX. Finally, the proposed model shows effectiveness and rationality in a specific example. Therefore, it can be expected to provide a novel method for saving the charging cost of EV users and for increasing the profit of the agent.
UGV autonomous driving system design for unstructed environment

Autonomous Unmanned Ground Vehicle (UGV) in complex environments is currently a popular research area. Due to the limitation of sensors and abundant environmental information, it is difficult for UGV to obtain their own status and environmental information in real time. This article proposes an autonomous UGV system based on LiDAR and camera in GNSS-denied scenarios. The camera and LiDAR are used to detect various obstacles in the environment separately. Then, we propose an obstacle detection fusion strategy, which eliminates false detection and improves accuracy of target detection. Finally the real-world experiments show that, based on our multi-sensor fusion detection algorithm, UGV can move autonomously and safely in unstructured environment, which consists of convex and concave obstacles.
EEG Based Eye Movements Multi-Classification Using Convolutional Neural Network

Several studies stated that more natural and direct interactive control methods are needed in pilot training especially in case of emergency. The expression of intention based on biological signal is a natural way. Eye movement plays an important role in human interaction with the environment which is suitable as a control instruction. Eye movement signals have certain temporal feature in Electroencephalogram (EEG) signals. We collected the eye movement signals by an EEG headset with dry electrodes with high performance. At the same time, deep learning has shown great success in EEG signal processing. In this work, a shallow 2D-Convolutional Neural Network (CNN) structure is proposed to classify the eye movements in 4 directions of upward, downward, leftward and rightward in EEG signals, which are used as 4 kinds of commands to control virtual aircraft. The data are fed to the network through the simulation training experiment designed by us. The average accuracy of the 4 eye movement directions was 88.13%, 86.59%, 87.78% and 87.85% respectively. Among the 10 subjects, the highest accuracy was 92.71%, the lowest was 84.88%, and the total average accuracy was 87.59%, which proves the effectiveness of the scheme.
A Two-Stage Heavy Hitter Detection System Based on CPU Spikes at Cloud-Scale Gateways

The cloud network provides sharing resources for tens of thousands of tenants to achieve economics of scale. However, heavy hitters caused by a single tenant will probably interfere with the processing of the cloud gateways, undermining the predictable performance expected by other cloud tenants. To prevent it, heavy hitter detection becomes a key concern at the performance-critical cloud gateways but faces the dilemma between fine granularity and low overhead. In this work, we present CloudSentry, a scalable two-stage heavy hitter detection system dedicated to multi-tenant cloud gateways against such a dilemma. CloudSentry contains a lightweight coarse-grained detection running 24/7 to localize infrequent CPU spikes. Then it invokes a fine-grained detection to precisely dump and analyze the potential heavy-hitter packets at the CPU spikes. After that, a more comprehensive analysis is conducted to associate heavy hitters with the cloud service scenarios and invoke a corresponding backpressure procedure. CloudSentry significantly reduces memory, computation and storage overhead compared with existing approaches. Additionally, it has been deployed world-wide in Alibaba Cloud for over one year, with rich deployment experiences. In a gateway cluster under an average traffic throughput of of 251Gbps, CloudSentry consumes only a fraction of 2%-5% CPU utilization with 8KB run-time memory, producing only 10MB heavy hitter logs during one month.
Design and Implementation of a Control and Monitoring Scheme for Spacecraft Obstacle Avoidance

In spacecraft control, in order to achieve fixed-point flight, target tracking, formation control and other tasks, the spacecraft is often required to have the ability to avoid obstacles. Especially in recent years, with the increase of space junk, the flight environment of spacecraft becomes more and more complex. Therefore, this paper studies the problem of obstacle avoidance in spacecraft flight, and designs a second-order sliding mode controller. By constructing an artificial potential function, the spacecraft can plan a safe path in the flight mission. And in order to better observe the state of the spacecraft, this paper designs a monitoring platform based on smart phones to realize the real-time monitoring of the state of the spacecraft. Finally, the effectiveness of the controller and the monitoring platform is verified by the physical experiment based on the spacecraft simulator.
Detection of Multiple Sclerosis using Deep Learning

It is essential to detect white matter lesions in 3D Magnetic Resonance Images (MRIs) of patients with Multiple Sclerosis for diagnosis and treatment evaluation of MS accurately. It is strenuous for the optimal treatment of the disease to detect early MS and estimate its progression. In this study, we propose efficient Multiple Sclerosis detection techniques to improvise the performance of a supervised machine learning algorithm and classify the progression of the disease. Detection of MS lesions become more intricate due to the presence of unbalanced data with a very small number of lesions pixel. Our pipeline is evaluated on MS patient's data from the Laboratory of Imaging Technologies. Fluid-attenuated inversion recovery (FLAIR) series are incorporated to introduce a quicker system alongside maintaining readability and accuracy. Our approach is based on convolutional neural networks (CNN). We have trained the model using transfer learning and used softMax as an activation function to classify the progression of the disease. Our study result show the effective use of MRI of MS lesions. Experiments on 30 patients and 100 healthy brain MRIs can accurately predict disease progression. Manual detection of lesions by clinical experts is complicated and time-consuming as a large amount of MRI data is required to analyze. Our approach exhibits a significant accuracy rate of up to 98.24%.
Privacy-preserving Leader-following Consensus with Continuous-time Dynamics

Leader-following consensus is an essential problem for tracking control. However, the state trajectories of agents may be exposed because of frequent exchange of information. Hence, this paper considers the secure leader-following consensus with continuous-time dynamics over directed fixed topologies. For each follower, introduce a virtual node that forms a directed node pair combining with the real node, i.e., the information can only transmit from virtual node to real node in a node pair. In this case, we design a control law utilizing neighboring information, and analyze convergence and privacy preservation of the leader-following multi-agent systems. Finally, a numerical simulation is provided to illustrate our results.
Palaute: An Online Text Mining Tool for Analyzing Written Student Course Feedback

Collecting student feedback is commonplace in universities. These surveys usually include both open-ended questions and Likert-type scale questions but the answers to open questions tend not to be analysed further than simply reading them. Recent research has shown that text mining and machine learning methods can be utilized to extract useful topics from masses of open student feedback. However, to our knowledge, not many off-the-shelf applications exist for processing open-ended student feedback automatically. Additionally, the use of text mining tools may not be available to all educators, as they require in-depth knowledge of text-mining, data analysis, or programming tools. To address this gap the current study presents a tool (Palaute) for analyzing written student feedback using topic modeling and emotion analysis. The utility of this tool is demonstrated with two real-life use cases: First, we analyze student feedback data collected from courses in a software engineering degree programme, and then feedback from all courses organized in a university. In our experiments, the analysis of open-ended feedback revealed that on certain software engineering course modules the workload is perceived as heavy, and on some programming courses the automatic code grader could be improved. The university-wide analysis produced indicators of good teaching quality, such as interesting courses, but also some concrete improvement points like the time given to complete course assignments. Therefore, the use of the tool resulted in actionable improvement points, which could not have been identified using only numeric feedback metrics. Based on the demonstrated utility, this paper describes the design and implementation of our open-source tool.
Evaluation of an Equivalent Circuit Model for Simulation of Surface Acoustic Wave Sensors

This paper presents a study of an equivalent circuit for surface acoustic wave (SAW) devices in the context of SPICE simulations, but valid for any other circuit simulation tool. We expand on previous derivations of the Foster Theorem approximation of impedances, providing normalized tables that evaluate LC pairs for different orders of approximation, also comparing their relative error. We propose a simple circuit block that can be used to represent external perturbations in a SAW delay line, while also including a parallel capacitance to model the electromagnetic coupling between input and output IDTs, an important effect when working with liquid media. Our results show close agreement with the measurements, along with interesting time-domain behavior not discussed by other works.
DA-Net: Density-Adaptive Downsampling Network for Point Cloud Classification via End-to-End Learning

Since a point cloud might contain large quantities of points in practical scenarios, it is desirable to perform downsampling before point cloud analysis. Classic downsampling strategies, such as farthest point sampling (FPS), tend to sample points regardless of subsequent tasks, which inevitably hampers the effectiveness of sampling results. Moreover, raw point clouds obtained from 3D sensors are usually noisy and unevenly distributed. As a consequence, the downsampling process might be affected seriously. In this paper, we propose a density-adaptive downsampling network (DA-Net) for point cloud classification task. The entire process of DA-Net is conducted in an end-to-end way, ensuring that the sampling operation is differentiable and optimal for downstream applications. One key process of DA-Net is density-adaptive K nearest neighboring (DAKNN), which is proposed to alleviate the adverse effects brought by density variation of point clouds. Another key process is the local adjustment (LA) of initial sampled points, which can further equip our model with noise immunity. Consequently, our network can enhance the effectiveness of sampled points and further improve the task performance. In point cloud classification task, our approach achieves significantly better results over existing downsampling alternatives. Extensive experiments are presented to demonstrate the advantages of our DA-Net.
A Comprehensive Evaluation of Metadata-Based Features to Classify Research Paper’s Topics

The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title & keywords combination outperforms other combinations with an F-measure score of 0.88.
A Non-fragile Approach to H∞ ILC of Linear Discrete-time Systems with Uncertain Learning Gain

This paper studies the problem of iterative learning control (ILC) with uncertain learning gain. The ILC controller to be designed with additive gain variations resulting from controller implementations. Resorting to 2D Roesser system manner and Lyapunov method, a new condition is developed to guarantee the desired stability with the prescribed H∞ performance. A sufficient conditions for the ILC controller with additive gain variations design in terms of solutions to a set of linear matrix inequalities (LMIs). To overcome the coupling nonlinearities between system parameters and Lyapunov variable, an effective strategy is employed. A notion of structured vertex separator is utilized to approach the problem of additive gain variations. A numerical example is given to illustrate the effect of the proposed method.
External Consensus of Multi-agent Systems Based on Equivalent Input Disturbance

In this paper, the external consensus problem of viscous nonlinearity multi-agent system via the controller design based on EID estimator has been discussed. A novel consensus control is developed by using a feedback controller based on disturbance rejection. Specifically, the disturbance rejection performance of the multi-agent system is improved according to the estimation of equivalent-input-disturbance. Furthermore, a robust feedback controller for each agent is designed such that the external consensus of the multi-agent system affected by valve stiction disturbance can be ensured. Finally, a simulation example is provided to demonstrate the validity of this method.
Global Clustering Method of Metagenome Sequences Based on Tetranucleotide Frequency

Metagenome assembly from next-generation sequencing data is a challenging process. Clustering short reads before assembly offers a unique opportunity for parallel downstream assembly of genomes. Our previous clustering software, SpaRC(Spark Read Clustering), is a metagenome sequences clustering tool based on Apache Spark, which provides a scalable solution for clustering billions of short reads. However, it suffers false negatives (under-clustering) problem. We previously proposed a global clustering method based on abundance to overcome this problem, but its only has good clustering effect on multi-sample and high coverage datasets. In this paper we proposed a new global clustering method based on tetranucleotide frequency(TNF) which has wider application and stability. The experiment results indicate that the improved global clustering has better clustering performance than the previously method.