Miracle-dz/UniFAR-FTUs huggingface dataset:
On-board Range-based Relative Localization: For Leader-Follower Flight of Micro Aerial Vehicles

In this paper a range-based relative localization solution is proposed and demonstrated in practice. The approach is based on wireless range measurements between robots, along with the communication of their velocities, accelerations, yaw rates, and height. It can be implemented on many robotic platforms without the need for dedicated sensors. With respect to previous work, we remove the dependency on a common heading reference between robots. The main advantage of this is that it makes the relative localization approach independent of magnetometer readings, which are notoriously unreliable in an indoor environment. A theoretical observability analysis shows that it may also have two disadvantages: the motion of the robots must meet more stringent conditions and the relative localization method becomes more susceptible to noise on the range measurements. However, simulation results have shown that in the presence of significant magnetic disturbances that are common to indoor environments, removing the heading dependency is beneficial. We conclude the paper by implementing the heading-independent method on real Micro Aerial Vehicles (MAVs) and performing leader-follower flight in an indoor environment. Despite the observability analysis showing leader-follower flight to be an especially difficult task, we still manage to successfully fly for over 3 minutes with two fully autonomous followers using only on-board sensing.
Fisher kernels for image descriptors: a theoretical overview and experimental results

Visual words have recently proved to be a key tool in image classification. Best performing Pascal VOC and ImageCLEF systems use Gaussian mixtures or k-means clustering to define visual words based on the content-based features of points of interest. In most cases, Gaussian Mixture Modeling (GMM) with a Fisher information based distance over the mixtures yields the most accurate classification results. In this paper we overview the theoretical foundations of the Fisher kernel method. We indicate that it yields a natural metric over images characterized by low level content descriptors generated from a Gaussian mixture. We justify the theoretical observations by reproducing standard measurements over the Pascal VOC 2007 data. Our accuracy is comparable to the most recent best performing image classification systems.
Development of Metamaterial EBG Absorbers for Application of Wireless Inter/Intrachip Communication Systems

First, the chapter presents a novel design of electromagnetic bandgap (EBG) absorber with the characteristics of broad bandwidth, low profile, and polarization‐independence to a normal incident electromagnetic wave. The absorber is composed of three consecu ‐ tive octagon or decagon loops, and highly‐resistive frequency selective surface (FSS) layers. Second, based on the feature of the designed absorber unit, a broadband, meta‐ material absorber‐bounded, wireless inter/intrachip (WIIC) communication channel is constructed at the center frequency of 60 GHz. Third, in order to validate the developed methodology used in WIIC analysis, a wired channel on a conventional PCB has been measured, simulated, and analyzed. Fourth, with the extracted S‐parameters of the WIIC system and wired PCB channel, the system impulse responses and transfer functions of the investigated channels have been further extracted, which are used for validation and BER analysis of the WIIC system. Finally, it has been shown that based on the derived BER results, the performance of the designed WIIC channel is close to that of an additive Gaussian white noise (AWGN) channel when the WIIC transceivers are built in with the functionalities of forward error control (FEC), channel estimation, and equalization.
Incidencia del juego de la rayuela en el desarrollo de la psicomotricidad

La psicomotricidad es la disciplina que estudia el movimiento del sujeto y su relación con el desarrollo cognitivo, el objetivo del estudio fue determinar la incidencia de la práctica del juego de la rayuela en el desarrollo de la psicomotricidad en niños de 4 a 5 años. Fue un estudio controlado aleatorizado, en donde luego de seleccionar por criterios de inclusión y exclusión en niños del Jardín Parvulitos de Minerva, 12 niños fueron del grupo control y 12 del grupo entreno, tuvo una duración de 12 semanas, se valoró mediante una ficha de observación, diseñada según los objetivos del ministerio de educación del Ecuador para el ámbito de expresión corporal y motricidad para ese grupo etario con parámetros de: Adquirido, En proceso e Iniciado. Los resultados en las diferentes pruebas permitieron concluir que existe incidencia en la psicomotricidad, con la aplicación del juego de la rayuela.
The Efficacy of an Extracurricular Inquiry Programme on Grade 8 and 9 South African Low Quintile Learners’ Science Learning

Physical sciences teachers in low-quintile South African schools avoid the use of inquiry on the grounds of inefficiency and ineffectiveness for test preparation. This avoidance is consistent with the view that the high cognitive load inquiry offers to extreme novices such as these learners may be prohibitive. Given the purported benefits of inquiry, this situation seems lamentable, but until empirical evidence is found to the contrary, the applicability of inquiry in this context appears an impractical ideal. In this study I begin the search for evidence of benefits of inquiry for the education of learners from such backgrounds, albeit within an educational setting atypical of the context. I investigate the effectiveness of a week-long inquiry holiday programme for 26 Grade 8 and 9 learners from low-quintile schools and the predictive value of their Natural Sciences marks for success in the programme. The findings show statistically significant learning gains from pre- to post-test scores and pre- to delayed post-test scores, as well as strong correlations between the learners’ Natural Sciences marks and the post-test scores. This shows that holiday inquiry programmes can be effective at promoting learning, measured by test-performance, even for learners with backgrounds of underprivilege, at least among higher achieving learners.
Resilient organizational capabilities in NTBFs. Concept and variables as dynamic and adaptive capabilities

In this paper we propose the concepts and variables that characterize resilient organizational capabilities (onwards ROC that can be viewed as dynamic capabilities. The concept of resilience highlights the ability to overcome trauma and the power to emerge more reinforced, considering the concept of “lessons learned” as the basis for its development. There are two key factors: attitudes and culture that can be transformed into a concept of resilience. In this work, the concept and characteristics of the ROC or “adaptive capabilities” generating innovation in the field of SMEs are presented. Also, the role of dynamic capabilities and intellectual capital that has been playing to date will be explained. In this research a methodological triangulation and a qualitative analysis to data on a Case Studies in new technology based firms (NTBFs) of the Scientific Park of Madrid and Colombian SMEs are realized. And will be completed with a quantitative analysis through the survey of these same two groups. The results of the comparative study show differences in analysed ROC, explained by cultural or economic themes, or by the sectors or sizes to which the companies of the reference groups belong, which is the basis for a future line of research.
Technical Guide to Information Security Testing and Assessment

(NIST) promotes the U.S. economy and public welfare by providing technical leadership for the nation's measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology (IT). ITL's responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems. This Special Publication 800-series reports on ITL's research, guidance, and outreach efforts in computer security and its collaborative activities with industry, government, and academic organizations. Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose.
Performance Analysis of AODV, DSDV and I-DSDV Routing Protocols in MANET

: Ad hoc networks are characterized by a lack of infrastructure, and by a random and quickly changing network topology; thus the need for a robust dynamic routing protocol that can accommodate such an environment. To improve the packet delivery ratio of Destination-Sequenced Distance Vector (DSDV) routing protocol in mobile ad hoc networks with high mobility, a message exchange scheme for its invalid route reconstruction is being used. Three protocols AODV, DSDV and I-DSDV were simulated using NS-2 package and were compared in terms of packet delivery ratio, end to end delay and routing overhead in different environment; varying number of nodes, speed and pause time. Simulation results show that I-DSDV compared with DSDV, it reduces the number of dropped data packets with little increased overhead at higher rates of node mobility but still can‟t compete with AODV in higher node speed and number of node.
Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli

A Master of Science thesis in Computer Engineering by Ali Alhaj Darwish entitled, "Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli," submitted in June 2013. Thesis advisor is Dr. Michel Pasquier. Available are both soft and hard copies of the thesis.
An Effective Word Arrangement Display for Co-Separating Supposition Targets and Assessment Words from Online Audits

The principle goal of this research is to enhance the topical relations by removing the sentiment focuses and also feeling words, and accomplish the higher execution utilizing word arrangement demonstrate concept. Partially Supervised Word Alignment Model (PSWAM) is utilized for word arrangement in existing framework. The Latent Dirichlet Allocation (LDA) model is utilized for finding conclusion word connection extraction in proposed framework. The proposed strategy accomplishes elite regarding affectability and specificity. The proposed framework is finished by utilizing Latent Dirichlet Allocation (LDA) which is utilized to build the execution for number of data set all the more proficiently.
The plant pathogenic genus Neocordana

Neocordana species mainly causes leaf spots on Musa spp. (banana and plantain) and on Canna denudata (an ornamental plant). Leaf spots on Musa spp. reduce the quality of the commodity and result in significant economic loss. Based on molecular and morphological data, the genus accommodates seven species at present with a worldwide distribution. Most of the taxonomic work has been conducted on Neocordana musae. In this study, updates on the diversity, distribution, and morpho-molecular taxonomy of Neocordana are provided. In addition, important aspects such as pathogenicity, disease control, antagonistic activity and association with other pathogenic fungi are discussed. The phylogenetic relationships of Neocordana with other genera in the family Pyriculariaceae based on LSU and ITS DNA sequence data are investigated. Our results support the monophyletic nature of Neocordana which is sister to Barretomyces. This study provides the first comprehensive account on diversity, taxonomy, and pathogenicity of Neocordana.
Secure Outsourcing of Large-scale Linear Programming

Linear programming (LP) is often used in reality production and life, such as the use of resources, human resource management, production arrangement. The client should pay out huge amounts of overhead to dispose huge data sets with its resource-constraint devices. Fortunately, Cloud computing can finish off this deficiency. The client outsources the computation to cloud servers. Then the servers accomplish the task and return the result to the client. But in the process, the correctness, verifiability and privacy should be emphasized. In our paper, based on the studies and analysis of previous protocols for secure outsourcing of linear programming in cloud computing, we find the obvious deficiencies in efficiency and safety. On the basis of the existing protocols, we propose a new protocol that combines the advantages of existing protocols and improves the shortcomings of existing protocols. We also analyze the security and efficiency of the new protocol and design a suitable simulation to verify the efficiency of this protocol.
Computationally Efficient Vision-based Robot Control

Video target tracking systems is a trending research topic, with a plethora of applications emerging from recent studies, with both visual object detection and tracking disciplines being the most notable, principally on embedded platforms. Not only are they employed in various fields, but also remarkably combines several branches of studies, such as control engineering, video processing, and more recently, machine learning and sensor fusion. Autonomous vehicles are a notable example, being equipped with a variety of sensors, including cameras, and widely apply image processing and sensor fusion techniques, thus providing more concise and high-level information, which increases robustness and more importantly, certainty, on decision making. However, such techniques must respect real-time constraints, especially in terms of timing, due the fact a delay might have a high cost under certain circumstances. Currently, there is a broad interest in processing images with neural networks, which are superior in terms of performance and robustness in comparison to traditional image processing algorithms. Although the rapid development of image sensors in combination with neural network technology, computational power of the underlying platform is still a bottleneck, especially for embedded applications. Moreover, the platform is commonly responsible for multiple tasks, which might include a user interface, data processing, (digital) filtering and high-level control, thus cannot be fully dedicated to the neural network itself. Finally, modern system-on-chips comprise hardware accelerators and multiple processing cores, which enable embedded systems to accomplish the desired throughputs, and achieve better results and efficiency in comparison to pure software implementations. Most of the time, these SoCs are completely customizable and interaction between software and hardware is facilitated. In this thesis, the focus is on both implementation and evaluation of a computational efficient robot control, based on neural networks to detect and localize a specific target (another robot), on an embedded platform. Sensor fusion and Kalman filtering are addressed, with the latter being used to post-process the output of the neural network, meanwhile the former combines encoder, accelerometer and gyroscope data which is used to derive the ego-motion information of the robot. Moreover, a specific approach for estimating the ego-motion impact in terms of pixels is proposed and discussed. The neural network, however, is not the focus, thus is only briefly discussed. During development, computational complexity, project extensibility and parallel tasks were the main concern, with the former being reduced whenever possible.
Geometrically nonlinear vibrations of thin visco-elastic periodic plates on a foundation with damping: non-asymptotic modelling

The objects under consideration are thin visco-elastic periodic plates with moderately large deflections. Geometrically nonlinear vibrations of these plates are investigated. In order to take into account the effect of microstructure size on behaviour of these plates a non-asymptotic modelling method is proposed. Using this method, called the tolerance modelling, model equations with constant coefficients involving terms dependent on the microstructure size can be derived. In this paper, only theoretical considerations of the problem of nonlinear vibrations of thin visco-elastic periodic plates resting on a foundation with damping are presented.
STOCHASTIC SHEDULING OF LINEAR CONSTRUCTION SITES

Essential basic data of scheduling linear construction sites are subject to stochastically variable influence factors, e.g., weather conditions. To evaluate economical low-cost alternatives which determine the production sequence of segments of a linear site through a projected construction time, a mathematical model is developed which is formulated as a two-stage stochastic linear programming problem and can be solved by standard linear programming methods. Criteria for interpretation and evaluation of model solutions in terms of a time-velocity diagram are derived and illustrated with a simple example problem. By means of stability analysis, risk factors and probability distributions of project duration and cost can be obtained for different construction alternatives to prepare a final decision. It is emphasized that the methodology presented is not seen as an approach to determine “the optimal decision,” but rather as a tool to facilitate decision making, since this ultimately relies on practical engineering judgment.
Disambiguation of Basic Action Types through Nouns’ Telic Qualia

Knowledge about semantic associations be-tween words is effective to disambiguate word senses. The aim of this paper is to investigate the role and the relevance of telic information from SIMPLE in the disambiguation of basic action types of Italian HOLD verbs ( prendere, ‘to take’, raccogliere , ‘to pick up’, pigliare ‘to grab’ etc.). We propose an experiment to compare the results obtained with telic information from SIMPLE with basic co-occurrence information extracted from corpora (most salient verbs modifying nouns) classified in terms of general semantic classes to avoid data sparseness.
Discrimination in machine learning algorithms

Machine learning algorithms are routinely used for business decisions which may directly affect individuals: for example because a credit scoring algorithms refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithm do not discriminate based on sensitive attributes (sex, race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases.
Design, Simulation and Performance of a Four Phases Linear Variable Reluctance Motor

The linear variable reluctance motor (LVRM) is a motor used in linear applications converting electromagnetic energy into linear movement and thrust. This paper aims at the analytical design of the machine and its validation through the finite element method. The design of a unilateral LVRM model of longitudinal flux type with passive translation movement is developed through the output power equation of the variable reluctance machine (VRM). The finite element analysis (FEA) is applied in order to check the values of inductance, the normal forces and propulsion. The FEA is also necessary to observe the undulation factor (ripple force), highly present in LVRM's, which produces vibration and acoustic noise. The analysis of the relationship between polar arcs and polar pitches plays an important role in the development of LVRM’s output force in the reduction of motor weight and volume. The LVRM model is developed from desired parameters, the maximum length of the stator, maximum speed and mass of the translator; through the design of a variable reluctance rotary motor (VRRM). The VRRM specifications are then converted into its linear equivalent.
Group Method of Data Handling: How does it measure up?

— Prediction is the method of determining future values based on the patterns deduced from a data set. This research compares various data mining techniques—namely, multiple regression analysis in statistics, Artificial Neural Networks (ANN), and the Group Method of Data Handling (GMDH), including both with and without feature selection. Currently, the literature suggests that GMDH, an inductive learning algorithm, is an excellent tool for prediction. GMDH builds gradually more complex models that are evaluated via a set of multi-input, single-output data pairs. ANNs are inspired by the complex learning that happens in the closely interconnected sets of neurons in the human brain and are also considered an excellent tool for prediction. This article is the beginning of a more detailed research project to investigate how well GMDH performs in comparison to other data mining tools.
Leveraging Data Analysis for Domain Experts: An Embeddable Framework for Basic Data Science Tasks.

In this paper, we describe a framework for data analysis that can be embedded into a base application. Since it is important to analyze the data directly inside the application where the data is entered, a tool that allows the scientists to easily work with their data, supports and motivates the execution of further analysis of their data, which would lead to new and interesting findings is desirable. If the analysis process is too complicated, tedious or not apparent, this could restrict scientists, especially in non-IT-related disciplines, from analyzing the data in depth. To enable this solution we first describe the requirements for an analysis tool and explain the steps we took to meet those requirements. Then we describe the steps that are necessary to integrate the analysis into a base application. We also explain how the analysis framework can be extended with new specific components that allow the users to add exactly the features they need for their analysis.
A Distributed Strategy for Near-Optimal Network Topology Design

In this paper, we propose a systematic strategy for distributedly maximizing the connectivity of a network. A graph process is introduced to formulate the problem in which edges are added to an existing graph in a step-wise manner to maximize the algebraic connectivity of underlying network topology. This problem turns into an intractable combinatorial optimization and hence we explore a heuristic rule to approximately solve it. In dependence upon global network structure information, we first provide a local criterion of near-optimization by involving the elements of the Fiedler eigenvector of graph Laplacian. To conduct the graph process in a distributed fashion, a decentralized estimation algorithm is devised for locally attaining the knowledge of the Fiedler eigenvector. The proofs of convergence and convergence rate are provided. Synthetically, a novel heuristic strategy is proposed to deal with the network optimization problem in a distributed and generalized fashion. The effectiveness of proposed methods are demonstrated via some examples.