Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-based Guidance, Navigation and Control architecture for an Autonomous Underwater Vehicle

This article studies the design, modeling, and implementation of a model-based Guidance, Navigation, and Control (GNC) architecture for an Autonomous Underwater Vehicle (AUV). First, effective simulation modeling is developed using a theoretical six-degree-of-freedom (6DoF) dynamic model. Then, this study considers two GNC algorithms (simple and advanced). The simple GNC algorithm considers three different kinds of PID controllers (velocity, velocity-position, and position), and the advanced GNC algorithm enables path-following and data acquisition and processing from an underwater sensor. The path following is based on the position control using a unique PID controller and obtains its waypoints from a wall detection algorithm. This wall detection algorithm uses a mechanical imaging sonar as the main perception sensor. Finally, an implementation challenge in two control scenarios is addressed to validate the designed GNC architecture and to carry out model-verification of the position PID controller.
LISP-Based Integrated Control Plane Framework for Service Function Chaining in Distributed Edge Clouds

Service function chaining, which provides a sequential set of multiple services in an end-to-end communication path, is a key technology for providing better service quality to users and efficient management of the network. Recently, as the services traditionally deployed in the data center have become more proximate to the user using a distributed cloud environment, the management of the service chain has become more complex for connecting services between clouds at a distance. In particular, when a user moves and resumes end-to-end communication at a new location, these complexities may lead to an increase in service downtime, which results in network performance degradation. In this paper, we propose an integrated control system to quickly recover the path to the service chain along with mobility management. The proposed system is based on the locator/identifier separation protocol to provide cost-effective mobility management by integrating the location information of users and services into the service chain mapping system. Based on the analysis of the operating cost and handover delay, it was determined that the proposed system can quickly restore a service chain path between distributed clouds with less signaling exchange and minimum delay when compared to previous approaches.
Detection for Hybrid Beamforming Millimeter Wave Massive MIMO Systems

In this letter, we improve the error performance of hybrid beamforming millimeter wave (mmWave) massive multi-input multi-output (MIMO) systems by designing detectors for such systems. First, we discuss the effect of the mmWave channel parameters and hybrid beamforming settings on the equivalent channel, which consists of the precoder, mmWave channel, and combiner. Then, we propose a low-complexity near-optimal signal detection scheme for the equivalent channel. Using computer simulations, it is shown that the error performance can be significantly improved and computational complexity reduced compared to those of state-of-the-art MIMO detection schemes. We achieve this reduced complexity by exploiting the available structure in the equivalent channel.
Flood Water Depth Classification Using Convolutional Neural Networks

Floods cause havoc in many regions of India every year during the monsoon season. However, commuters really do not really think of the after effects caused by the floods. Commuting during water-logging is risky because the depth of logged water cannot be determined. This can lead to cars and bikes getting stuck inside the flooded zone. Thus travelling through such areas can lead to loss of life. Our paper focuses on the classification of floodwater depth. We propose a deep-learning model that classifies the depth of the flood water level into different categories according to the depth. In this paper, we have used the Convolutional Neural Network (CNN) to classify the images given as an input by the user/commuter. For better user accessibility we propose to integrate the model with a cross-platform app that would be convenient for the commuters; along with an in-app alert system, which notifies other users about the location where the water level is above threshold value.
Research on the Use of Big Data and Artificial Intelligence in Forecasting the Labor Force Balance in the Russian Federation

The relevancy of Big Data usage for the analysis and forecasting of labor force balance in the Russian Federation is substantiated in this article. This study takes into account the development of the digital economy in the Russian Federation, which increasingly includes telemediated working enforcing the organizations to process the required information quickly and efficiently to forecast correctly the labor force balance. The use of Big Data for labor force balance forecasting can improve communication between employers and prospective employees, increase the employee interest in professional development for the most lucrative jobs and provide the company that posts jobs with the best employees.The approbation of the main theoretical provisions of the proposed model and the practical implementation of the solution developed on its basis take place in the structural divisions of the Humanitarian and Pedagogical Academy of Sciences (branch) of V.I. Vernadsky Crimean Federal University in Yalta.
HGESO-based disturbance rejection path following control for underactuated hovercrafts

In this brief, a high gain extended observer (HGESO) based disturbance rejection yaw control law is proposed for path following of underactuated hovercrafts in the presence of unknown nonlinear time-varying uncertainties. First, a HGESO is first proposed to estimate the unknown nonlinear time-varying uncertainties for the underactuated hovercraft heading subsystem. Then, a HGESO-based backstepping yaw control law is designed to achieve the disturbance rejection yaw control for underactuated hovercrafts. Furthermore, the stability of the proposed control method is proven by the input-state-stability theory. Finally, a simulation case study of an underactuated hovercraft is made to support the proposed control law
Crack Detection and Localization with Real-Time Path Tracking using Stereo Vision for Autonomous Underwater Welding

Oil and gas organizations across the globe rely heavily on underwater pipelines and water transportation systems. Due to exposure of pipe and ship surfaces to hostile environments like turbulence, water currents, and underwater vegetation, it is not uncommon to observe cracks on exposed material. Thus, regular inspection and maintenance are imperative. Such tasks need to be extremely precise which render the task to be time-consuming and the hostility of the environment renders the task to be dangerous and expensive. This calls for a robotic solution to the problem. In this paper, we propose a 7 DOF autonomous underwater welding arm along with autonomous crack detection, localization, and welding of the crack. We propose a real-time crack identification and segmentation technique using computer vision algorithms to facilitate real-time path generation for the arm, trajectory following, and motion planning. This methodology has been tested by mounting the designed arm on an AUV in the UnderWater Simulator and moving it using the MoveIt! package. The paper also implements a closed-loop control mechanism with an adaptive motion planner using pose tracking to overcome external disturbances during welding operations. The results indicate that the path tracking problem can be solved using computer vision algorithms in the absence of dynamic models.
Zero-Copying I/O Stack for Low-Latency SSDs

Traditional buffered read for block devices is no longer effective in terms of latency with today's low-latency solid-state drives. This is because memory copy accounts for a large portion of I/O latency. Zero-copy read is an attractive approach to reducing the I/O latency, but has the inability to utilizing the page cache, thereby losing the opportunity to access reused data in DRAM-like speed. This study proposes an I/O stack that has the advantages of both zero-copy and the use of the page cache for modern low-latency SSD. In the proposed I/O stack, the page cache serves the read request by the application first. Upon a miss, the storage device transfers data to a user buffer directly. In the meantime, our scheme collects necessary information that is used later for prefetching data into the page cache in the background. Therefore, when an application accesses the data again, it can be served at DRAM-like speed. Our prototype in the Linux kernel shows up to 11 percent throughput improvement on the YCSB workload against the database applications.
Comparative analysis of EKF and Particle Filter performance for an acoustic tracking system for AUVs exploiting bearing-only measurements

This paper aims to compare different navigation state estimation approaches for the tracking of an Autonomous Underwater Vehicle (AUV) based on Direction of Arrival (DoA) measurements. The use of passive acoustic devices to compute the DoA information allows for a high rate (up to 1Hz in the proposed tests) of the measurements while the AUV moves underwater. The informative content of these data integrated with the synchronised data of the on-board sensors allows for a fine tracking of the vehicle under test for navigation performance evaluation. The paper addresses the issue of the selection of the proper data filtering strategy for the estimation of the navigation state of the AUV. In particular, it investigates whether the adoption of a Particle Filter (PF) has significant advantages with respect to a classical approach based on an Extended Kalman Filter (EKF). The two approaches exploit the same models and parameters to limit the comparison to the filtering strategy. The ground truth is the GPS signal provided by the receiver integrated within the antenna on the AUV under test. The comparative analysis considers both the estimation performance and the computation load. Results are extensively provided for a subset of all the processed dataset, all the remaining part of data confirm the reported analysis.
Collaboration of LoRaWAN and Underwater Acoustic Communications in Sensor Data Collection Applications

The Robotic Vessels as-a-Service (Robo VaaS) project aims to provide innovative services in a harbor scenario, exploiting new technologies to enhance different aspects of harbor activities. In this paper we analyze the data collection service from underwater sensor nodes. Specifically, we assess the End-To-End (E2E) communication, from sensor nodes deployed in the harbor to the gateway placed on the shore. The sensor data is collected by an autonomous underwater vehicle (AUV), and then forwarded to one or more surface buoys. Finally, the buoys convey the received data to the shore. Communication from the underwater sensors to the AUV and from the AUV to the surface buoys is performed through acoustic links, while the communication from the surface buoys to the shore gateway is performed via LoRaWAN. In this scenario we evaluate the performance of the E2E communication, by simulating both the underwater and the above water network.
Destination-Directed Trajectory Modeling, Filtering, and Prediction Using Conditionally Markov Sequences

In some problems, there is information about the destination of a moving object. An example is a flight from an origin to a destination. Such problems have three main components: an origin, a destination, and motion in between. We call such trajectories ending up at the destination <italic>destination-directed trajectories (DDTs)</italic>. Described by an evolution law and an initial probability density, a Markov sequence is not flexible enough to model DDT well. The future (including destination) of a Markov sequence is completely determined probabilistically by its initial density and evolution law. One class of conditionally Markov (CM) sequences, called the CM<inline-formula><tex-math notation="LaTeX">$_\text{L}$</tex-math></inline-formula> sequence (it includes the Markov sequence as a special case), has the following main components: a joint density of two endpoints and a Markov-like evolution law. This article proposes modeling DDT as CM<inline-formula><tex-math notation="LaTeX">$_\text{L}$</tex-math></inline-formula> sequences. We study the CM<inline-formula><tex-math notation="LaTeX">$_\text{L}$</tex-math></inline-formula> sequence, its dynamic model, and its realizations, all for DDT modeling. We demonstrate that CM<inline-formula><tex-math notation="LaTeX">$_\text{L}$</tex-math></inline-formula> sequences naturally model DDT, enjoy several desirable properties for DDT modeling, and can be easily and systematically generalized if necessary. In addition, we study DDT filtering and trajectory prediction based on a CM<inline-formula><tex-math notation="LaTeX">$_\text{L}$</tex-math></inline-formula> model and compare them with those based on a Markov model. Several simulation examples are presented to illustrate DDT modeling and inference.
Data-Driven Linear Quadratic Control Scheme for Three-Phase Microgrid System Optimized by Genetic Algorithm

This paper manifests Linear Quadratic (LQ) controller optimization techniques for a three-phase microgrid (MG) system. optimization has been done with the Genetic algorithm (GA). This paper demonstrates how data-driven optimization techniques can help designing an optimal controller. A comparative analysis has been drawn between the conventional proportional-integral-derivative (PID) controller scheme and the data-driven Linear Quadratic (LQ) control scheme. Two different LQ controller structures have been designed with GA optimization- Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) controller. It becomes evident from the results that the optimized LQ controller outperforms the conventional PID controller. Between the two LQ control scheme, LQG has shown better performance in reference tracking and handling system transients. The system has been designed and optimized in the Matlab/ Simulink platform.
Indolizine-Squaraine NIR Emissive Materials

Near-infrared (NIR) emissive organic materials are an emerging area of study with an array of applications for both military and civilian purposes including night vision technologies, secure communications, surveillance, homing, fluoresence imaging, secure displays as NIR OLED materials, heat-blocking coatings, in vivo fluoresence biological imagaing, and additional optoelectronics device applications. We have developed a series of organic NIR emissive materials based on an indolizine donor and squaraine acceptor. The novel-to-squaraine donor, indolizine, exhibits a remarkable increase in absorption maximum wavelength when compared with benchmark indoline-based squaraine dyes (700 nm vs. 625 nm) with molar absorptivities ranging from 100,000-262,000 M-1cm-1. Emission is observed at about 750 nm in chloroform, corresponding to a stokes shift of 50 nm, which is a substantial increase when compared with the 5 nm stokes shift commonly observed for indoline-squaraine dyes. Our molecular design was evaluated by computational analysis which reveals clues about the origin of this stokes shift and the water solubility observed for these compounds without appending water solubilizing groups.
EM wave propagation through the seawater-air interface in dynamic marine environment

Electromagnetic (EM) wave has a good performance of trans-media propagation. This characteristic is helpful for wireless data transmission from seawater to air. However, water movement can affect the EM wave propagation. This paper evaluates the performance of EM wave propagation in real dynamic marine environment. The theoretical model was built to describe the EM field in air produced by underwater magnetic dipole with orientation and position changes. Meanwhile, experiment on sea was implemented to verify the theoretical results. Both the theoretical and experimental results show that EM fields in air will be greatly affected by the changes of underwater antenna's orientation and position. The fluctuations of field intensity in air will reduce the stability of wireless data transmission from seawater to air.
An antifungal mechanism of semi purified fraction of Hypnea musciformis lies in membrane-targeted action within Candida Albicans

This study analyzes the antifungal properties of semi purified fraction of Hypnea musciformis and elucidates its mode of action against pathogenic fungi. Although antifungal effect of Hypnea musciformism has been reported, this is the first study for its mode of action underlying disruption of plasma membrane in Candida albicans. Semi purified fraction of Hypnea musciformis exerts activity against C. albicans by directly killing cells and causing others to undergo programmed cell death (apoptosis). Apoptosis is initiated at subinhibitory concentrations, suggesting that strategies to target this process may augment the benefits of antifungal agents.
CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes

We present CoMet, a novel approach for computing a group’s cohesion and using that to improve a robot’s navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of pedestrians from an RGB-D camera on-board a robot. Specifically, we detect characteristics corresponding to the proximity between people, their relative walking speeds, the group size, and interactions between group members. We use our cohesion-metric to design and improve a navigation scheme that accounts for different levels of group cohesion while a robot moves through a crowd. We evaluate the precision and recall of our cohesion-metric on pedestrian datasets. We highlight the performance of our social navigation algorithm on a Turtlebot robot and demonstrate its benefits in terms of multiple metrics: freezing rate (57% decrease), deviation (35.7% decrease), and path length of the trajectory(23.2% decrease).
Tracing the Factoids: the Anatomy of Information Re-organization in Wikipedia Articles

Wikipedia articles are known for their exhaustive knowledge and extensive collaboration. Users perform various tasks that include editing in terms of adding new facts or rectifying some mistakes, looking up new topics, or simply browsing. In this paper, we investigate the impact of gradual edits on the re-positioning and organization of the factual information in Wikipedia articles. Literature shows that in a collaborative system, a set of contributors are responsible for seeking, perceiving, and organizing the information. However, very little is known about the evolution of information organization on Wikipedia articles. Based on our analysis, we show that in a Wikipedia article, the crowd is capable of placing the factual information to its correct position, eventually reducing the knowledge gaps. We also show that the majority of information re-arrangement occurs in the initial stages of the article development and gradually decreases in the later stages. Our findings advance our understanding of the fundamentals of information organization on Wikipedia articles and can have implications for developers aiming to improve the content quality and completeness of Wikipedia articles.
Implementation of EMP in EIA Follow-Up of Oil and Gas Projects in the Niger Delta Region of Nigeria: A Case of Bayelsa and Rivers States

The Environmental Management Plan (EMP) is an off-shoot of environmental impact assessment (EIA) and is used during the implementation of a project to manage physical, socio-economic and health concerns identified during the assessment. Oil and gas production activities in Nigeria take place in a very delicate ecological region of the Niger Delta. Since the introduction of EMPs for projects in the oil and gas sector in Nigeria, the extent of their implementation according to best practices is still poorly understood. The apparent limited knowledge on the implementation of EMPs puts environmental sustainability at great risk. This study evaluated the implementation of the Environment Management Plan of oil and gas production projects in the Bayelsa and Rivers States in the Central Niger Delta sub-region. Twelve case studies were selected from the region using a multi-level selection method which involved both random and purposive sampling techniques. The two states were purposively selected since they have the highest number of EMPs and the oldest history of oil production in Nigeria. The implementation of the EMPs within cases was scored using a check list which included 18 indicators developed based on the best practice principles of EIA follow-up. Findings show that the implementation of the EMPs is inadequate with an average score of 46.3%. The study concludes that the implementation of EMP is poorly handled and does not adequately address the approval conditions. The study recommends that more analysis and similar studies should be undertaken in other sectors and jurisdictions in order to better understand the implementation of EMP.
THE EFFECT OF SPEECH STYLE ON VOT VALUES IN SERBIAN-ENGLISH INTERPHONOLOGY

The paper analyzes VOT values in Serbian-English interlanguage phonology, as well as in the production by native American English speakers, with a special focus on the effect of speech style formality on the values of VOT. In order to answer the proposed research questions, the investigation included 72 first-year students at the Faculty of Philology and Arts, University of Kragujevac, whose plosive production was recorded in three tasks differing in the degree of formality: a word list, paragraph reading and an interview. The same tasks were performed by the native speakers, too. The obtained corpus was analyzed from the perspective of experimental phonetics accompanied by descriptive and qualitative interpretation of results. Speech style proved to be a statistically significant predictor of variation in VOT for all the plosives both among native and non-native speakers. The differences detectable among speakers are best represented by the values of VOT for both voiced and voiceless plosives, most often interpreted as a result of the differences in phonological systems of Serbian and English. The results of the paper underscore important pedagogical implications and expand the options for future research in the field of interlanguage phonology.