Miracle-dz/UniFAR-FTUs huggingface dataset:
Affine Formation Stabilization of multiple UAVs via Active Disturbance Rejection Control

This paper studies affine formation control problem for multiple UAVs with unknown nonlinear dynamics and external disturbances. To deal with the uncertainties, we employ the active disturbance rejection control (ADRC) technique. In this scheme, all unknown quantities can be observed via the extended state observer (ESO). Based on the outputs of the ESO, a class of affine formation control protocol is designed using the stress matrix, under which the predefined time-varying formation can be achieved. It is shown that the affine formation stabilization errors as well as the estimation errors can be arbitrarily small by choosing appropriate control gains. Finally, simulation results are presented to validate the effectiveness of the proposed control strategy.
Research and Design of CAS Protocol Identity Authentication

With the rapid development of science and technology today, in the face of a variety of different systems, the use of a secure, unified and efficient authentication system can not only simplify the workload of administrators, but also simplify the memory of users and ensure the security of user data. We studied the advantages and disadvantages of several existing authentication methods. In order to facilitate multiple systems to achieve unified authentication login, we choose to use CAS to realize SSO’s single sign-on function. It enables multiple subsystems to share a safe and reliable login method, and users can switch access freely among multiple subsystems. At the same time, in order to avoid the disadvantages caused by user’s simple password, we improved the CAS encryption method with a small part of the custom encryption method. Some opinions and suggestions are put forward on the problem that single point of logout does not destroy session and users can log in after logout.
Enabling UTBB Strained SOI Platform for Co-integration of Logic and RF: Implant-Induced Strain Relaxation and Comb-like Device Architecture

For the first time, ion implant was used to partially relax the tensile strain by half in the fully-depleted (FD) strained SOI (SSOl) so that SiGe pFETs with a higher compressive strain can be realized at a fixed Ge composition. This enables the co-integration of highly tensile-strained Si nFETs and compressive-strained SiGe pFETs on the same substrate, achieving significant improvement in electrical performance over the unstrained counterpart verified by both experiment and simulation results. We also propose a Comb-like strained SOI architecture to further boost RF performance, demonstrating peak $G_{\mathrm{m}}$ improved by 47% over unstrained n-type FinFET SOI, as well as an improvement of 22% and 36% for $f_{\mathrm{T}}$ and $f_{\max}$, respectively, over n-type FinFETs SSOI.
Finite State Machine based Vehicle System for Autonomous Driving in Urban Environments

In autonomous driving systems, many approach have been developed based on high-precision sensors, high computing power, development of various networks in academia and industry. The key point of an autonomous car is that it needs to know what is happening in the driving situation and an action corresponding to this situation must be created and operated. In this paper, we propose the system structure and method of a autonomous driving car consisting of three main parts:perception, planning, control. Each main part is designed to recognize the real-time driving environment, make action plan based on finite state machine, and activate kinematic model based control. The proposed vehicle system copes with six major situations of urban environment driving, and we verified system through the simulation.
Communication and Intelligent Control in a Power Grid Using Open Source IoT Technology

Industrial Internet of Things is one of the opportunities for online state monitoring, effective control and economical profitability of selected systems. The majority of electricity generated in the Czech Republic comes from conventional fossil power plants - 54% in 2018. Same fact applies to heating where approximately 82% of heat in 2018 was generated from fossil fuels. Despite the fact that energy is being saved and the efficiency on the final customer side is improving, our demand of energy is rising. Over the last few years the online measurement of operation parameters plays a big role in performance optimization and energy management. Following article describes an Open source / Matlab IIoT platform used for monitoring and possible optimization of a district heating network in a typical Czech residential district and electrical island operation of a electrical power system.
Analyzing The Trajectories Of Customers By Using LCSS Approach

To understand the customer behavior, we need to analyze the movements of customers inside the mall, because it helps to organize the aisles and shelves according to the customer’s needs. There are a lot of approaches of clustering data, but the most useful for paths is the longest common subsequence LCSS; it is an efficient method that groups similar trajectories in the same cluster. In this paper, we detect the localizations of customers by using Bluetooth Low Energy Beacons, and we generate the trajectories. Then, we use Longest Common Subsequence method to group the trajectories in order to find hot areas and extract major trajectories of the customers.
Data flow analysis from UML/MARTE models based on binary traces

The design of increasingly complex embedded systems requires powerful solutions from the very beginning of the design process. Model Based Design (MBD) and early simulation have proven to be capable technologies to perform initial design space analysis to optimize system design. Traditional MBD methods and tools typically rely on fixed elements, which makes difficult the evaluation of different platform configurations, communication alternatives or models of computation. Addressing these challenges require flexible design technologies able to support, from a high-level abstract model, full design space exploration, including system specification, binary generation and performance evaluation. In this context, this paper proposes a UML/MARTE based approach able to address the challenges mentioned above by improving design flexibility and evaluation capabilities, including automatic code generation, trace execution collection and trace analysis from the initial UML models. The approach focuses on the definition and analysis of the paths data follow through the different application components, as a way to understand the behavior or the different design solutions.
Distributed Load-Frequency Control in Power Systems

This document presents a methodology to design and implement a Distributed Load Frequency Control (D_LFC) scheme in power systems. The proposal is based on a control law that feed backs internal rotor angle deviation to the governor of every generator. As a result, the governor acts as a distributed controller for frequency and transferred power, which uses local measurements. Although information exchange between governors is not performed, they describe a synchronized behaviour because of the natural coupling between the generators' speed. Control response times are lower than centralized schemes such as the Automatic Generation Control (AGC) due to the fact that control actions can be carried out locally near the disturbance. Simulation in the IEEE 39-Bus System on PowerFactory Digsilent permit us to compare the performance of D_LFC with respect to AGC.
Multi-Purpose Intelligent Drudgery Reducing Ecobot

These days, Many agriculture tasks are mechanized and numerous programmed hardware and robots accessible industrially. Two significant issues in present day agribusiness are water shortage and high work worth. The above issues are settled utilizing agribusiness task mechanization it is planned to configuration to diminish work cost [1]. ECOBOT is a robot extraordinarily intended for farming purposes. This diminishes the human work and yields the creation development with low venture of seeds. Agrobot goes about as an Internet of Things gadget which gathers the information from various sensors and passes the data to the client by means of Wi-Fi. This robot for the most part manages burrowing of land, seeding, furrowing, giving water, preparing, splashing medicinal, collecting and so forth. What's more, Microcontrollers like Arduino and Node-MCU is utilized to control and gathers the sensors data.
Digital Socio-Political Communication and its Transformation in the Technological Evolution of Artificial Intelligence and Neural Network Algorithms

The study aims to analyze the specifics of determining the subjects of digital social and political communication in the context of the development of artificial intelligence technologies and neural network algorithms. The work uses a case-study design. As a research methodology, the method of critical analysis of the digital communications practice in the socio-political sphere, as well as discourse analysis of modern scientific research in the field of the development of artificial intelligence and neural network algorithms, are used. It is concluded that the implementation of technological solutions based on artificial intelligence and neural network algorithms into the processes of socio-political communications creates a problem of defining the subject of communication acts in the socio-political sphere. Society may face such communication practices in which hybrid subjectness is realized. In the conditions of hybrid subjectness, both real subjects and programmed neural network algorithms acting as real subjects (but only imitating own subjectivity) interact in common communication space. The originality of the work lies in the formulation of the author's hypothesis about the emergence of the phenomenon of hybrid subjectness in the space of modern socio-political communications and its potential in the aspect of influencing the mass consciousness of citizens.
Coexistence for LTE-Advanced and FSS Services in the 3.5GHz Band in Colombia

The 3.5GHz band is an optimal candidate for 5G networks due to its propagation characteristics and massive bandwidth. However, services like the Fixed Satellite Service (FSS) are using this band in several countries. Therefore, this paper presents a coexistence study of the Long Term Evolution - Advanced (LTE-A) and FSS services in the 3500–3700 MHz in Colombia. Simulations were done in realistic scenarios in the city of Bogota, Colombia. Preliminary results show that critical scenarios are the ones from the LTE eNodeB (eNB) and Users Equipment (UE) nodes to the FSS earth stations. The study includes the analysis of Guard Bands (GB) and arrival angles into the Protection Distances (PD). Results show that the PD is highly dependent on the angle of the interfering signal and the GB used. The PD for a cochannel interference in a suburban scenario is higher than 250km, in the worst-case scenario, and could be reduced down to 17.5 km if a 25 MHz GB is included and the angular difference of the interfering LTE-A signal is 42□. Moreover, our results show that the PD needed for interference from UE are 100 times less compared to the eNB ones.
Realization Algorithms of Major Types of Linguistic Processors Based on the Systems with Predefined Semantics

This paper discusses the main types of applications for natural language (NL) processing. Depending on the purpose of the semantic system, a natural language model is built. Based on this model an application is developed. The model built is called a linguistic processor. Certain models imply the use of specific algorithms which typically take a lot of computing and information resources. This article describes a new approach to building language processors – the use of systems with predefined semantics. The definition of key concepts in such systems is given: seme, semantic pattern, recognition tree, semantic spectrum, elementary and compound solutions. Also, the main algorithms for implementation of these systems are reviewed. The system capabilities are checked by a software prototype. The test results have shown the following results:- almost all main features of NL are taken into account (polysemy, homonymy, synonymy, the dependence of meaning of the text on the context, etc.);- the system is practically universal, since it allows to implement applications designed for any purpose;- the suggested approach is tested by the implementation of such applications as command systems, text classification systems and text generating systems (e.g., question-and-answer systems);- the system takes significantly less information and computing resources than those used by the already created applications for NL text processing;- the only disadvantage of the suggested approach is the need for time-consuming learning of the system being built for it to be able to operate within a certain domain.
A Novel Inverted Sine PWM Carrier for Multistage Inverter with Lesser Number of Switches

Multilevel inverters is a Power rated Power electronic device, which has been largely used for maximum power and lofty potential difference applications because of its capability to synthesize output voltage waveforms with a preferable harmonic spectrum and reach higher voltages with a maximum device rating. Inverter works in the opposite principal of converters, which are employed for changing AC into DC. Inverters have been extensively utilized in many high power applications, Since it posses lesser Total Harmonic Distortion, electromagnetic interference, and huge dc link voltages but it has some drawback which may be a higher count of components, complex controlling technique and Potential difference-balancing situation. Therefore to overcome the drawbacks of the traditional two step inverter, a novel strategy with a reversing potential difference component has been suggested to enhance the performance of Inverter. The Proposed strategy needs reduced number of components compared to already available inverters (especially in higher voltage levels) and needs less controlling signals. Therefore, the cost along with complexity of the new proposed technique is appropriately reduced, mainly for multilevel output voltage. At last, Simulation of the twenty five level proposed strategy has been built and analysed by various modulation techniques to express the performance of the inverter by simulation results using MATLAB-SIMULINK. By analysing the output results of different Pulse Width Modulation strategies, it is noted that Inverted Sine Phase Disposition PWM lends less Total Harmonic Distortion and COPWM strategy offers higher fundamental Root Mean Square of the output voltage.
Extended Range AUV Localization and Navigation Aided by Gravity Anomalies and Bathymetry

Localization and navigation of autonomous under-water vehicles (AUVs) is critical to underwater operations as measurements collected by their sensors are of little value if they are not geo-referenced to an acceptable certainty. This paper presents contributions to address these challenges for long en-durance AUV missions. First, is a flexible high fidelity underwater navigation testbed to verify navigation and localization strategies. Second, a navigation algorithm that builds on state-of-the-art underwater inertial navigation aided by prior gravity anomaly maps. The algorithm is verified in the underwater navigation testbed. The presented case shows that the proposed algorithm can decrease localization error by 75%.
Impact of CuO NPs to Alga Microcystis Aeruginosa: Effect of Dissolved Organic Matter

With the rapid development and versatile applications of nanotechnology, a large quantity of nanoparticles (NPs) has been released into the environment. It has been one of the most popular studies that concerning the toxic effect of NPs to the algae in aquatic ecosystem. The membrane integrity of algal cells was assessed using different methods when algae were treated by CuO NPs in the absence and presence of Suwannee river fulvic acid (SRFA), one of DOM. We found SRFA could increase the toxicity of CuO NPs to cell membrane due to the more stable NPs suspension in the presence of SRFA. Results additionally indicate that maybe oxidative damage caused by CuO NPs was the primary reason that led to membrane structure and function failed.
Students' Academic Performance Prediction using Regression: A Case Study

Performance of students in an academic program depends upon several aspects of their previous academic performance and family background. In the present study, multiple linear regression is adopted to predict students' performance and to determine the most influencing features of students' performance. Students' data are collected through a questionnaire-based survey from an engineering institution located in South India. Factor analysis is conducted on the collected dataset for dimensionality reduction and to evaluate the features that most correlate with students' performance. The proposed regression model yields prediction of academic performance in the second semester of the academic program in the case institution with a deviation of 15%.
Operator-Based Robust Nonlinear Control for Calorimetric Power Loss Measurement System Using Peltier Device

This paper presents a method of operator-based nonlinear temperature control for a calorimeter using a Peltier device. The Peltier device has nonlinear characteristic and so it is not easy to design a controller which satisfies desired performance. Based on the concept of the Lipschitz operator and the robust right coprime factorization condition, nonlinear temperature controllers are designed for the model, and the closed-loop system's robust stability is guaranteed. Moreover, a tracking operator is designed to ensure the temperature tracking performance. Finally, simulation and experimental results are presented to show the effectiveness of the proposed design method.
Learning-based Low Light Image Enhancement for Visual Odometry

For the low light environment, performing good visual odometry still remains an open challenge. The main difficulties in low light situations lie in that the limitations of the sensors and the buried features in the dark, which hamper to achieve successful detecting and tracking of interest points. Although there are some pioneering works proposed on image enhancement for low light situations, there are comparatively little works focusing on visual odometry in low light scenes. Here, we address this problem from a deep learning view, for which we train a deep neural network for image enhancement to obtain the enhanced image sequences for VO. In contrast to the end-to-end training scheme, the network is designed based on the physical Retinex model, and the L1 distance loss and structure similarity index measure (SSIM) loss are incorporated into network training. We validate the performance of the enhancement by evaluating the enhanced sequences by state-of-the-art VO algorithm, ORB-SLAM.
Multiband Microstrip Antenna Inspired by Metasurface

Using metamaterial unit cell, a homogeneous artificial structure can be formed, these two dimensional structure is called as Metasurface. Microstrip Patch antennas (MPA) are known for their smaller size, lesser weight, easier for fabrication, and low fabrication cost. But they have narrow bandwidth. A simple MPA was considered which resonate at 8.6GHz with return loss of -24.81dB, bandwidth of 530MHz.Introduced a metasurface array of size 5X5 which resonate at 8.5GHz along with MPA to study its effects. With the addition of the metasurface, the MPA resonate with dual bands at 8.1GHz and 14.7GHz with improved bandwidth from 530MHz to 1590MHz. Three different metasurfaces were proposed and it has been observed that there is slight difference in the gain for three structures. For circular shape metasurface gain at 8.1GHz is 8dBi, at 14.7GHz is 5.4dBi. For Square shape metasurface gain at 8.1GHz is 7.7dBi, at 14.7GHz is 5.3dBi and for plus shape metasurface gain at 8.1GHz is 6.7dBi, at 14.7GHz is 5.7dBi.
Using Reinforcement Learning to Allocate and Manage SFC in Cellular Networks

In this paper, we propose the use of reinforcement learning to deploy a service function chain (SFC) of cellular network service and manage the VNFs operation. We consider that the SFC is deployed by the reinforcement learning agent considering a scenario with distributed data centers, where the virtual network functions (VNFs) are deployed in virtual machines in commodity servers. The VNF management is related to create, delete, and restart the VNFs. The main purpose is to reduce the number of lost packets taking into account the energy consumption of the servers. We use the Proximal Policy Optimization (PPO2) algorithm to implement the agent and preliminary results show that the agent is able to allocate the SFC and manage the VNFs, reducing the number of lost packets.
Graphical Minimax Game and On-Policy Reinforcement Learning for Consensus of Leaderless Multi-Agent Systems *

In this paper, we study the adaptive optimal consensus control of leaderless multi-agent systems (MASs) with heterogeneous dynamics. First, the consensus control problem is converted into a graphical minimax game problem and the corresponding algebraic Riccati equation (ARE) for each agent is obtained. Then, an on-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without requiring the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed online learning algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed algorithm is demonstrated through a numerical simulation.
Improving Accuracy of an AoA-based Wi-Fi Indoor Localization using Kalman Filter

Indoor location-based system (Indoor LBS) has increasingly attracted attentions in research and industrial community in the recent years. However, the adoption of indoor LBS is still slow due to many obstacles, in particular, its low accuracy performance. Many real-world applications require challenging performance targets such as real-time operation, high accuracy, and energy efficiency. In order to meet the requirements, fast and accurate positioning methods are necessary. However, noise from interference and multipath in the indoor environment is one of the most important factors preventing accurate and stable positioning. Since it is difficult to make changes to the sensor technologies especially in the hardware, improving the accuracy of indoor positioning by removing noise from the positioning measurement offers an effective solution to the accuracy problem for indoor LBS. We propose a Kalman Filter method which could be applied to the measurements of the indoor LBS. The result from the experiment shows that the positioning accuracy has improved for over 40 percent for static positioning.