Miracle-dz/UniFAR-FTUs huggingface dataset:
Prioritized Multi-agent Path Finding for Differential Drive Robots

Methods for centralized planning of the collision-free trajectories for a fleet of mobile robots typically solve the discretized version of the problem and rely on numerous simplifying assumptions, e.g. moves of uniform duration, cardinal only translations, equal speed and size of the robots etc., thus the resultant plans can not always be directly executed by the real robotic systems. To mitigate this issue we suggest a set of modifications to the prominent prioritized planner - AA-SIPP(m) - aimed at lifting the most restrictive assumptions (syncronized translation only moves, equal size and speed of the robots) and at providing robustness to the solutions. We evaluate the suggested algorithm in simulation and on differential drive robots in typical lab environment (indoor polygon with external video-based navigation system). The results of the evaluation provide a clear evidence that the algorithm scales well to large number of robots (up to hundreds in simulation) and is able to produce solutions that are safely executed by the robots prone to imperfect trajectory following. The video of the experiments can be found at https://youtu.be/Fer_irn4BG0.
Visual SLAM with RGB-D Cameras

This paper focuses on visual SLAM with RGB-D cameras (abbreviated as RGB-D SLAM), which has been an actively studied issue in the robotics community since RGB-D cameras can obtain depth information of environments simply. Firstly, two types of RGB-D cameras are introduced according to the principle of depth measurement. Secondly, the typical RGB-D SLAM algorithm framework is normally divided into four parts: visual odometry, optimization, loop closing and mapping. Thirdly, a series of landmark achievements on algorithm, open source libraries and tools, and performance evaluation of RGB-D SLAM are summarized. Finally, the advantages and the disadvantages, as well as the development trends of RGB-D SLAM are discussed.
Observer Selection for Source Identification on Complex Networks

We consider the problem of selecting an optimal observer set in order to maximize the probability of identifying the source of diffusion on complex networks with deterministic propagation delay. A maximum likelihood (ML) estimator is proposed to choose the most informative nodes for observation and a greedy algorithm is used to implement the estimation efficiently. Numerical simulations show the effectiveness of our algorithm.
Detection of IoT Botnet Based on Deep Learning

In this paper, we propose a deep learning based approach for IoT botnet detection. We use the damped incremental statistics to extract basic traffic features of IoT devices and apply the Z-Score method to normalize the features. After that, the mangle area maps (TAM) based multivariate correlation analysis (MCA) algorithm is employed to generate dataset. Then we design a convolutional neural network (CNN) to learn the dataset and utilize the trained CNN to detect the traffic. The final experiments show that our approach can distinguish benign traffic and different kinds of attack traffic effectively and reaches the accuracy of 99.57%.
Design and implementation of a butterfly robot system

This paper is meant to present the studying process and implementation of a bio-inspired four-winged flying butterfly robot. The prototype contains two servomotors that control the wings movement, a Bluetooth communication, an Inertial Measurement Unit (IMU MPU-9250) which contains a gyroscope, an accelerometer and a magnetometer, one development board and the insect body. First part of the study is dedicated to the butterfly body construction. In this regard, several prototypes, which differed in size, shape, weight and materials have been developed and analyzed. These artificial butterflies offer an overview of the dynamic of the insect, the wing motion and control for stable flight encourage to further developments of the system. The communication with the butterfly robot was realized by taking advantages of the Matlab software benefits. It is used to send commands to the robot over Bluetooth and the results can be analyzed through system data acquisition. The contribution can be also considered as a reference guide for those who want to create their own butterfly robot and to analyze its dynamics.
A Fault-Tolerant MPSoC For CubeSats

We present the implementation of a fault-tolerant MP-SoC for very small satellites (<100kg) based upon commercial components and library IP. This MPSoC is the result of a codesign process and is designed as an ideal platform for software-implemented fault-tolerance measures. It enforces strong isolation between processors, and combines fault-tolerance measures across the embedded stack within an FPGA. This allows us to assure robustness for a satellite on-board computer consisting of modern semiconductors manufactured in fine technology nodes, for which traditional fault-tolerance concepts are ineffective. We successfully implemented this design on several Xilinx UltraScale and UltraScale+ FPGAs with modest utilization. We show that a 4-core implementation is possible with just 1.93 $W$ of total power consumption, which for the first time enables true fault-tolerance for very small spacecraft such as CubeSats. For critical space missions aboard heavier satellites, we implemented an MPSoC-variant for the space-grade XQRKU060 part together with the Xilinx Radiation Testing Consortium. The MPSoC was developed for a 4-year ESA project. It can satisfy the high performance requirements of future scientific and commercial space missions at low cost while offering the strong fault-coverage necessary for platform control for missions with a long duration.
Speed Consensus and Reference Tracking for a Swarm of Second-order Agents

In this paper a second-order model for a swarm of agents is proposed to face the team reference tracking problem. The described model is a double integrator where each agent dynamic is ruled by a control input formed by attractive parts to the reference position and speed, and by a repulsive part that rules the interactions among agents. It will be proved that following the proposed model, the swarm centroid will asymptotically reach the reference trajectory and the agents’ speeds will reach a consensus on the reference speed while their positions will be fixed in a reference frame that moves according to the reference trajectory. The main novelty of the proposed work is the interaction term among agents, the effect of which can be easily tuned by modifying an interaction matrix that modulates the effect of agents interactions on their various components. Properly choosing this matrix, aggregation properties can be modified to ensure that all the agents enter and remain in a defined zone around the reference trajectory, moving according to it.
Algorithm of Path Planning Based on Time Window for Multiple Mobile Robots in Warehousing System

Automation of goods transportation in warehousing system is essential to improve productivity and reduce costs for the modern logistic enterprises. Under this background, automatic warehousing is becoming a key to logistic systems due to its ability of scheduling and optimizing delivery tasks of robots and, consequently, increasing productivity and decreasing costs. This paper firstly describes briefly problems needed to be solved for working of multiple mobile robots in automatic warehousing systems. Then, we build a map model based on topological graph for the warehouse environment and choose a storage representation of adjacent matrix for it. This paper details the algorithm of task scheduling and path planning for multiple mobile robots in warehousing system. The presented algorithm applies the ${\mathrm {A}}^{*}$ shortest-path method to solve path planning problem of single robot and, further, introduces time window approach to solve traffic jams and collisions of multiple robots. Computer simulation tests were conducted to compare the efficiency of the ${\mathrm {A}}^{*}$ algorithm with other shortest-path methods of path planning for single robot. For logistics scenario of multiple robots, routing simulations were also carried out to verify performance of the purposed algorithm under different working conditions. Test results showed that the algorithm could generate collision-free and optimized paths for multiple mobile robots.
A Noval Approach For Predicting The Academic Performance Of Student

Predicting academic performance of a student is critical for educational institutions because strategic programs can be planned in improving or maintaining students’ performance during their period of studies in the institutions. The academic performance of a student is measured by the marks obtained in senior secondary level, their EQ and IQ. An evaluation is done on a student’s academic performance and this performance analysis helps educational managers to offer them additional support as well. The IQ and EQ level are diverse in various people. Both have important roles in influencing the development of students learning and academic. In this paper the prediction of the performance of students is done by comparing and classifying their EQ and IQ by several set of algorithms in Weka tool for getting strong results in future, Which can be done easily by using a set of data. The product is supported by data mining concepts, tools and algorithms. A series of questions are to be answered by the students within a stipulated time to calculate these two factors. The factors are calculated along with their senior secondary level marks which is then generated to be given as the input in the prediction phase. The prediction phase then classify the students into clusters and determine whether the students are likely to fail or pass. Also, this is tested and analyzed finally in the testing phase to determine the systems accuracy.
Sparse Time-Frequency Distribution Calculation with an Adaptive Thresholding Algorithm

Time-frequency distributions (TFD) are powerful tools for the analysis of non-stationary signals; however they are heavily under-used since most of the TFD calculation methods introduce the unwanted artifacts, so called cross-terms. A recently investigated approach of the TFD cross-terms removal enforces the sparsity constraint to the resulting TFD, ultimately leading to a high resolution sparse TFD with heavily suppressed cross-terms. In this paper, we apply a sparse reconstruction algorithm, which combines the fast intersection of the confidence intervals (FICI) rule and the two step iterative shrinkage/thresholding algorithm (TwIST), denoted as the FICI-TwIST algorithm, to modeled and real-life teleseismic signals. The obtained results have been compared, in terms of the resulting TFD concentration and the algorithm execution time, to the state-of-the-art sparse reconstruction algorithms.
An Integrated Accuracy Enhancement Method for Cable-driven Flexible Continuum Robot*

This paper focuses on an accuracy enhancement method for a cable-driven continuum robot with a flexible backbone by integrating a machine learning technique into the kinematic modeling. The continuum robot employs a segmented cable-driven scheme, which can be considered as a modular manipulator composed by a number of consecutive cable-driven segments. Based on the unique backbone structure which merely allows bending movements, a two-variable Product-of-Exponential (POE) formula is employed to formulate the kinematic model of the cable-driven continuum robot. However, such an analytic kinematic model is inaccurate due to the compliance of the backbone structure. In this paper, a machine learning approach based on the Gaussian Process Regression (GPR) is proposed to compensate the positioning errors resulting from the inaccurate kinematic model. Compared with other machine learning methods, GPR has the advantages of requiring less learning parameters and training points, which makes the learning process computationally efficient. In order to validate the effectiveness of the proposed method, computer simulations have been conducted. Simulation results indicate that the integrated approach can reduce the position error by 70.49% and the orientation error by 65.76%.
Cloud Federations: Economics, Games and Benefits

Sharing economy is a game-changing business paradigm that is currently permeating several industrial sectors. This paper aims to build a fundamental theory of the sharing economy of the computational capacity resource of Cloud Service Providers (CSPs). CSPs aim to cost-efficient serve geographically dispersed customers that often request computational resource-demanding services. The formation of CSP federations arises as an effective means to manage these diverse and time-varying service requests. In this paper, we introduce innovative federation models and policies for profitable federations that also achieve adequate QoS for their customers. Taking in account the flexible cloud computing service model, we abstract the virtualized infrastructure of each CSP to an M/M/1 queueing system, we formulate the CSP revenue and cost functions, and we study the task forwarding-based (TF) and the capacity sharing-based (CS) federation approaches. Under TF, each CSP may forward part of its workload to other federated CSPs, while under CS each CSP may share parts of its computational infrastructure with others. For both approaches, we propose two operation modes with different degree of CSPs’ cooperation: $(i)$ the joint business mode, where the CSPs fully cooperate: they jointly decide on the federation policies that maximize the total federation profit which is shared fairly among them; $(ii)$ the reward-driven mode, where self-interested CSPs participate in a game: they adjust their responses to federation policies aiming to maximize their individual profits. The results reveal that our policies lead to effective federations, which are beneficial both for CSPs and for customers.
Gas Spectroscopy at 222 – 270 GHz Based on SiGe BiCMOS using a Multi-Pass Ring Cell

We present broadband gas spectroscopy based on SiGe BiCMOS technology. A bandwidth of about 222 -270 GHz is achieved by a combination of two frequency bands on single transmitter and receiver chips. The antennas of transmitter and receiver for both bands are closely placed on a single chip such that coupling into the same optical system can be realized. We coupled the beams into a compact custom-made multi-pass ring cell with an optical path length of 1.9 m. We demonstrate sensitive gas spectroscopy capabilities by a full bandwidth spectrum of methanol.
A Framework to Solve the Energy, Makespan and Lifetime Problems in Reliability-Driven Task Scheduling

Traditional reliability-driven task scheduling (RTS) is able to increase or maintain reliability by using techniques (e.g., replication, rollback recovery, and frequency elevation) that either tolerate the occurred transient faults or decrease the transient fault rate. The reliability-aware techniques however would inevitably lead to soaring energy consumption, longer makespan as well as high temperature which results in a shortened device lifetime. Previous work on RTS often neglects these problems. This paper proposes a framework for reliability-aware real-time MPSoCs, in which three novel RTS mechanisms are developed to solve the energy, makespan, and temperature-induced lifetime problems. Extensive simulation-based experiments are carried out to verify the effectiveness of the proposed framework. Results have shown that the proposed scheme has a better performance in saving energy, reducing makespan, and improving availability while maintaining high reliability as compared to several representative existing approaches.
Optimization of Power System Stabilizer Parameters for Damping of Power System Oscillations

The design problem of an interconnected power system implemented with a power system stabilizer (PSS) can be formulated as a simple optimization problem. This paper describes two systematic approaches to optimization issues by tuning the controller parameters utilizing hybrid HPSS, obtained by combining phase compensation technique with integral of squared error (ISE) criterion and genetic algorithm GPSS with integral of time multiply absolute error (ITAE) technique as objective functions. The two control methodologies have been developed for synchronous machine infinite bus (SMIB) power system. The HPSS and GPSS computes and searches for the optimal parameter settings and simultaneously, enhance the damping of low frequency oscillations and stabilize the system. The transfer functions of the dynamic model, inclusive of excitation system and PSS have been deduced using Mason’s graph to evaluate the performance indices and carry out the search processes. Simulations were carried out under different plant conditions employing MATLAB and GEATbx for use with MATLAB. Simulation result shows the viability and effectiveness of the designed methodologies under perturbed and parametric conditions.
A Fusion Framework using Integrated Neural Network Model for Non-Intrusive Load Monitoring

Smart grid has been developed greatly over the years and the users are paying more attention to detailed energy consumption information. Non-Intrusive Load Monitoring (NILM) technique enables the users to get the power of each appliance by analyzing aggregated power. In this paper, we propose a fusion framework and use an integrated neural network model to estimate the power of each appliance. First, we detect the working state change events in aggregated power by using cumulative sum method. To correlate each event with the corresponding appliance, we then calculate the possibilities of working state for each appliance based on different event features. Then we use the fusion framework to decide which appliance has caused this event. Next, we generate a reference power curve for each appliance and refine this curve by an integrated neural network model using aggregated power. The experimental results show that the proposed method can achieve good performance on a variety of indicators even on low sampling-rate data.
Welding robotic hand-eye calibration method based on structured light plane

The calibration of the relationship between the welding robot hand and the camera is an important issue in the process of industrial welding intelligentization. For the welding robot with end-mounted line structured light, this paper proposes a novel method of robot hand-eye calibration based on structured light plane. Firstly, move the the position of robot to make structured light plane intersect with the marker points multiple times and the image coordinates of the marker points are obtained. Then construct and solve linear equations to obtain the least squares solution of the calibration matrix. Finally, use the algorithm of integrated learning method bagging to obtain the average of the least squares solution. Compared with the traditional complicated calibration method, this method doesn’t need a specific standard workpiece or calibration board which makes the calibration process more convenient. The results of experiment show that the calibration three-axis measurement accuracy is within ±0.20mm, which meet the requirements of industrial field welding precision.
Retrieving the Complex Vibrational Susceptibility with Interferometric Stimulated Raman Scattering

Coherent Raman Scattering (CRS) [1] is a third-order optical process, which drives a nonlinear polarization in the sample, proportional to the third order nonlinear vibrational susceptibility $\chi^{(3)}(\omega) = \chi_{\mathrm{R}}^{(3)}+\chi_{\mathrm{NR}}^{(3)}$. This is a mixture of a complex resonant term, responsible for the different vibrational transitions, and a purely real, non-resonant and frequency-independent term, due to electronic transitions of the target molecules and the surrounding medium. The two most common CRS techniques, in their standard implementation, do not allow the retrieval of the full complex χ(3) signal: the CARS signal is a mixture of its real and imaginary parts, while SRS only retrieves the imaginary part of the nonlinear vibrational susceptibility. Measurement of the complex χ(3) signal would offer instead a richer spectroscopic information to be used, e.g., to distinguish molecules with similar amplitude response but different spectral phase [2]. Numerical methods [3, 4] can be used to reconstruct the full vibrational χ(3), but their main limitation is the requirement of the entire vibrational spectrum, while most CRS-based microscopies can reach very high imaging speed working with narrowband configurations. Interferometric techniques can overcome this limitation and have been demonstrated for CARS [5].
Distributed Nash Equilibrium Seeking Algorithms for Two-Layer Constrained Non-Cooperative Games

This paper studies the Nash equilibrium seeking problem for two-layer constrained non-cooperative games. In particular, the non-cooperative game among upper-layer clusters is played by lower-layer players therein subject to set constraints. In each cluster, a player is appointed as an administrator who is responsible for interacting with other clusters. A KKT condition is first determined to formulate the underlying generalized Nash equilibrium. Based on such a KKT condition, a distributed seeking algorithm using a projection operator is proposed. In particular, under the assumption that each player has access to the administrators in other clusters, the proposed seeking algorithm via intra-cluster interaction guarantees that each player is capable of seeking its corresponding component of the generalized Nash equilibrium. An illustrative example effectively verifies the proposed theoretical results.
Fermi-Pasta-Ulam-Tsingou Recurrence in Spatial Optical Dynamics

Celebrated as the Fermi-Pasta-Ulam-Tsingou (FPUT) problem, the reappearance of initial conditions in unstable and chaotic systems is one of the most controversial phenomena in nonlinear dynamics. Integrable models predict recurrence as exact solutions [1], but the difficulties involved in upholding integrability for a long dynamics has not allowed a quantitative experimental validation. Evidences of the recurrence of states have been reported from deep water waves [2] to optical fibers [3]. However, the observation of the FPUT dynamics as predicted by exact solutions of an underlying integrable model remains an open challenge. Here, we report the observation of the FPUT recurrence in spatial nonlinear optics and provide evidence that the recurrent behavior is ruled by the exact solution of the Nonlinear Schrodinger Equation [4].
Pattern Recognition for Prosthetic Hand User’s Intentions using EMG Data and Machine Learning Techniques

In this paper, we propose a simplified pipeline system for hand gesture pattern recognition. This system is based on surface electromyography of the upper forearm, obtained from a commercial sensor, the Myo armband, developed by Thalmic Labs. The pipeline involves data acquisition, pre-processing, feature extraction, classification, post-processing and interfacing. Implementations and improvements of each stage, including a new post-processing method are discussed. The evaluation of the pipeline system is conducted with 10 subject’s electromyographic data whilst performing the 5 default classes of gestures packaged with the proprietary Myo system. Comparing our results with results found in literature and the Myo system, we have determined that our pipeline performs effectively (94.8% accuracy with 5 seconds of recording per gesture), particularly with comparison to the default Myo system (83% accuracy).