Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex-valued Neural Networks: Utilizing High-dimensional Parameters

Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are in fact useful, containing characteristics bringing about many significant applications. Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters such as complex-valued neural networks, quantum neural networks, quaternary neural networks, and Clifford neural networks, which have been developing in recent years. Graduate students and researchers will easily acquire the fundamental knowledge needed to be at the forefront of research, while practitioners will readily absorb the materials required for the applications.
Phase Shift Induced Degradation of Polarization Caused by Bends in Inhibited-Coupling Guiding Hollow-Core Fibers

We report on a comprehensive analysis of the polarization-maintaining (PM) behavior of two different kinds of inhibited-coupling (IC) guiding hollow-core fibers and the influence of fiber bends. A phase shift induced degradation of the incident linear polarization state was observed due to a mismatch of the propagation constants of the orthogonally polarized LP01-modes of the fiber. This effect was predicted theoretically and is corroborated with numerical simulations and experimental results. It is shown that the phase shift is enhanced by a reduction of the bending radius of the fiber but can be circumvented by orienting the polarization of the injected beam either parallel or perpendicular to the plane of bending.
Performance Evaluation of Hadoop Distributed File System and Local File System

Hadoop is a framework which enables applications to work and petabytes of data on large clusters with thousand of nodes built of commodity hardware. It provides a Hadoop Distributed File System (HDFS) that stores data on the computed nodes, providing very high aggregate bandwidth across the cluster. In addition, Hadoop implements a parallel computational paradigm named map- reduce which divides the application into many small segments of work, each of which may be executed or re executed on any node in the cluster. In this project I would like to analyze performance of HDFS and LFS with respective read and write. To measure the performance I will set up a hadoop cluster and design an interface which gives us the size of the file, time taken for upload or download from Local File System(LFS) and Hadoop Distributed File System(HDFS). By literature survey the expected HDFS writing performance scales will on both small and big data set were it is however lower than on the small data-set. This work also draws a comparison between the HDFS (Hadoop Distributed File System) and LFS performances.
A model for biometric security using visual cryptography

The evolution of technologies has started for increasing the security level of data. Developer and users should understand that there is no perfect solution for a secured password and it has its own way of encryption and decryption. Passwords are generated temporarily and they may be cracked easily. Human are having their own specific identity which is unique. Biometric is a feature used to identify a particular person by its DNA structure, finger print or tongue. On the other hand this feature need to be secure from wrong hands because it may be misused by any attacker. Therefore, this paper proposing a model which is consisting combination of Visual Cryptography and Steganography with the association of QR codes. Here, we are using Visual Cryptography for creating two shares in which one of the share will be rotated in a clock wise direction about 180 degree & other about 270 degree and then we are implementing Steganography by using two's complement on both the share image. After the transformation of shares into steganoimages, we are converting one of the steganoimage into a QR code which will be kept secret with the user. During the verification time, the QR code will be required for the authentication of user.
Design of a high numerical aperture achromatic objective lens for endomicroscopy

Abstract. An achromatic objective lens with the object-space numerical aperture (NA) of 0.7 is designed for endomicroscopy applications for in vivo diagnosis of cancer. For these applications, it is required that the size of the optical components be no more than the diameter of a biopsy needle. The challenges associated with designing such an optical system are discussed. The clear aperture size for the lens is set to 1.5 mm. The lens is achromatic for the wavelength range from 452 to 623 nm. The system was designed and optimized using CODE V optical design software. The performance of the design is analyzed and found to be within the design requirements. Tolerance analysis is also performed to check the robustness of the design.
Identification sans loi de la réponse mécanique de matériaux à partir de mesures de champs hétérogènes via une approche « Data Driven Computational Mechanics »

Kirchdoerfer & Ortiz [1] recently introduced a new paradigm in compunational mechanics, where the constitutive law is replaced by a database of discrete strain-stress couples representing the mechanical behavior of the material. This original and promising approach nevertheless requires the availability of such a database for a wide range of strain and stress. In this work we propose a method, based on the work of Kirchdoerfer and Ortiz which produces such databases for elastic materials, without any assumption on an underlying constitutive law. From a set of heterogeneous DIC displacement fields [2, 3] for different loading conditions of a sample, we simultaneously identify the database and a statically admissible stress field in the sample for each loading condition. Mots clefs : Data-Driven Computational Mechanics, corrélation d’image, loi de comportement 23ème Congrès Français de Mécanique Lille, 28 au 1er Septembre 2017
Estudio Comparativo de Dos Formulaciones de Elementos Finitos en la Modelización de la Estricción en Probetas Rectangulares de Metales Tipo J2

Las simulaciones computacionales cada vez cobran mayor importancia en el ambito de la tecnologia. En el caso particular de grandes deformaciones plasticas de metales es necesario contar con ecuaciones constitutivas adecuadas que permitan obtener resultados fiables a partir de las simulaciones. Una herramienta adecuada y relativamente economica para calibrar las ecuaciones antes mencionadas, en presencia de grandes deformaciones en metales ductiles, es el ensayo de traccion simple. El presente trabajo es una extension de trabajos anteriores realizados por los autores. Se comparan y discuten los resultados obtenidos en las simulaciones numericas del mencionado ensayo. Se analiza el caso tridimensional de probetas rectangulares de metal tipo J2 empleando la misma discretizacion espacial y dos formulaciones diferentes de elementos finitos. La primera de ellas emplea una formulacion Lagrangiana Actualizada mientras que la segunda utiliza una formulacion Lagrangiana Total. En particular, se comparan los cambios geometricos en la forma de la seccion transversal al eje longitudinal de la probeta en la zona donde ocurre la estriccion. Ademas se comparan las distribuciones de deformacion plastica efectiva, tension efectiva; tension en direccion longitudinal y en direccion transversal en la zona antes mencionada.
A low‐profile wideband dual‐polarization patch antenna with antisymmetric feeding network

In this paper, a novel broadband dual‐polarization patch antenna is proposed. Antisymmetric Γ feeding network is applied to excite the radiating patch etched on the upper side of the horizontal substrate, which could minimize the undesired radiation from the probe and extend the impedance bandwidth. For verifying the proposed approach, a prototype is fabricated and measured, the simulated and measured results show the antenna has a wide impedance bandwidth of 48% (1.66‐2.71 GHz) for S11 < −10 dB, as well as stable radiation gain around 9.5 dBi with low cross‐polarization. In addition, the total height of the antenna is only 0.17 λ0 ( λ0is the free space wavelength of central frequency) and high port‐to‐port isolation is better than 30 dB. The characteristics of the proposed antenna illustrate it can be an indication for a micro base station in the mobile communication system.
On the time scales and structure of Lagrangian intermittency in homogeneous isotropic turbulence

We present a study of Lagrangian intermittency and its characteristic time scales. Using the concepts of flying and diving residence times above and below a given threshold in the magnitude of turbulence quantities, we infer the time spectra of the Lagrangian temporal fluctuations of dissipation, acceleration and enstrophy by means of a direct numerical simulation in homogeneous and isotropic turbulence. We then relate these time scales, first, to the presence of extreme events in turbulence and, second, to the local flow characteristics. Analyses confirm the existence in turbulent quantities of holes mirroring bursts, both of which are at the core of what constitutes Lagrangian intermittency. It is shown that holes are associated with quiescent laminar regions of the flow. Moreover, Lagrangian holes occur over few Kolmogorov time scales while Lagrangian bursts happen over longer periods scaling with the global decorrelation time scale, hence showing that loss of the history of the turbulence quantities along particle trajectories in turbulence is not continuous. Such a characteristic partially explains why current Lagrangian stochastic models fail at reproducing our results. More generally, the Lagrangian dataset of residence times shown here represents another manner for qualifying the accuracy of models. We also deliver a theoretical approximation of mean residence times, which highlights the importance of the correlation between turbulence quantities and their time derivatives in setting temporal statistics. Finally, whether in a hole or a burst, the straining structure along particle trajectories always evolves self-similarly (in a statistical sense) from shearless two-dimensional to shear bi-axial configurations. We speculate that this latter configuration represents the optimum manner to dissipate locally the available energy.
Classification of photographed document images based on deep-learning features

In this paper, we propose two new problems related to classification of photographed document images, and based on deep learning methods, present the baseline solutions for these two problems. The first problem is that, for some photographed document images, which book do they belong to? The second one is, for some photographed document images, what is the type of the book they belong to? To address these two problems, we apply “AexNet” to the collected document images. Using the pre-trained “AlexNet” on the ImageNet data set directly, we obtain 92.57% accuracy for the book-name classification and 93.33% accuracy for the book-type one. After fine-tuning on the training set of the photographed document images, the accuracy of the book-name classification increases to 95.54% and that of the booktype one to 95.42%. To our best knowledge, although there exist many image classification algorithm, no previous work has targeted to these two challenging problems. In addition, the experiments demonstrate that deep-learning features outperform features extracted with traditional image descriptors on these two problems.
Depth- and Potential-Based Supervised Learning

The task of supervised learning is to define a data-based rule by which the new objects are assigned to one of the classes. For this a training data set is used that contains objects with known class membership. In this thesis, two procedures for supervised classification are introduced. The first procedure is based on potential functions. The potential of a class is defined as a kernel density estimate multiplied by the class's prior probability. The method transforms the data to a potential-potential (pot-pot) plot, where each data point is mapped to a vector of potentials, similarly to the DD-plot. Separation of the classes, as well as classification of new data points, is performed on this plot, thus the bias in kernel density estimates due to insufficiently adapted multivariate kernels is compensated by a flexible classifier on the pot-pot plot. The proposed method has been implemented in the R-package ddalpha that is a software directed to fuse experience of the applicant with recent theoretical and computational achievements in the area of data depth and depth-based classification. It implements various depth functions and classifiers for multivariate and functional data under one roof. The package is expandable with user-defined custom depth methods and separators. The second classification procedure focuses on the centers of the classes and is based on data depth. The classifier adds a depth term to the objective function of the Bayes classifier, so that the cost of misclassification of a point depends not only on its belongingness to a class but also on its centrality in this class. Classification of more central points is enforced while outliers are underweighted. The proposed objective function may also be used to evaluate the performance of other classifiers instead of the usual average misclassification rate. The thesis also contains a new algorithm for the exact calculation of the Oja median. It modifies the algorithm of Ronkainen, Oja and Orponen (2003) by employing bounded regions which contain the median. The new algorithm is faster and has lower complexity than the previous one. The new algorithm has been implemented as a part of the R-package OjaNP.
Lexicographically optimal earliest arrival flows

A dynamic network introduced by Ford and Fulkerson is a directed graph in which each arc has a capacity and a transit time. The evacuation problem is one of the fundamental problems in a dynamic network. The goal of this problem is to find the minimum time limit Θ such that we can send all the supplies to the sinks within time Θ. An earliest arrival flow is an optimal flow for the evacuation problem such that the amount of supplies which have reached the sinks is maximized at every time step. It is known that in a dynamic network with multiple sinks, if the sinks have capacities, then an earliest arrival flow does not necessarily exist. In this paper, to cope with this issue, we first introduce a lexicographically optimal earliest arrival flow in a dynamic network with multiple sinks. Then we propose a pseudo‐polynomial‐time algorithm for finding a lexicographically optimal earliest arrival flow. Furthermore, we prove that if the transit time of every arc is zero, then we can find a lexicographically optimal earliest arrival flow in polynomial time.
Sung Note Segmentation for a Query-by-Humming System

Retrieval performance in query-by-humming (QBH) systems depends crucially on the accurate note segmentation and labeling of user queries. To facilitate note segmentation, querying is often restricted to the easily detected syllable "ta", which is not necessa rily the syllable most preferred by users. In this work, new acoustic feat ures based on the signal energy distribution as obtained from the singing pe rception and production points of view are investigated. Performance evalua tions on a manually labeled database of syllabic humming show that a specific mid-band energy combined with a biphasic detection function achieves high co rrect detection and low false alarm rates on the sonorant consonant syllables /da /, /la/ and /na/. The resulting onset detector is incorporated in the signal-proces sing front-end of an available QBH system (hitherto constrained to ta-syllable que ries only). QBH retrieval performance results are reported on a large dataset of user queries.
Sample Size Effect on Variance of Treatment Mean and Relative Efficiency in a Split-Plot Design

Sample size is an imperative feature of any empirical study in which the goal is to make inferences about a population from a sample. It is important that a study has an adequate sample size. It must be large enough that an effect of such magnitude as to be of scientific significance will also be statistically significant. It is just as important, however, that the study not be too large where an effect of little scientific importance is nevertheless statistically detectable (Russell, 2001). This is necessary to ensure that the study has a good chance of detecting a statistically significant result and also to ensure that adequate resources are allocated. A study that has an inadequate sample size will have a low probability of detecting a statistically significant result and therefore represents a waste of valuable resources and add nothing to scientific knowledge. In practice, the sample size used in a study is determined based on the expense of data collection, and the need to have sufficient statistical power (Cohen, 1977). Increasing sample size leads to increase in statistical power and decrease in error rate (Caimiao et al., 2004). A sufficiently large sample size leads to more precise information, avoids the bias obtained by choosing a non-representative subset of samples, and also increases the reliability of conclusions, thus reducing uncertainty in results (Russell, 2001). On the other hand, an insufficient sample size will give a result which may not be sufficient to detect a difference between the groups and the study may own out to be incorrect leading to a type II error (Jonathan et al., 2005). It also wastes time and money as the result will be variably inconclusive. The sample size is decided arbitrarily based on the researchers convenience, available time, and resources, resulting in lack of precision due to insufficient number of subjects studied (Barun, 2010). The variance of treatment means or error is the average of the square variations of each population mean from the grand mean. Braun (2012) described it the variation which exists between treatment means. When populations are grouped, it helps to decide if variability between and within each populations are significantly different as evaluated by Martin and Naomi (2014), and Anwesha (2012). Efficiency is a term used in the comparison of various statistical procedures and, in particular, it refers to a measure of the optimality of an estimator of an experimental design, or of a hypothesis testing procedure. The relative efficiency of two experimental designs is the ratio of their efficiency statistics, although this term is often used where the comparison is made between a given design and a notional ’best possible’ design (Nikulin, 2001). The relative efficiency of two designs theoretically depends on the available sample size. This implies that there is a significant effect of sample
Visualization for maritime situational awareness

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication
EXPERIMENTAL STUDY: DETERMINATION OF CONCRETE REINFORCEMENT CONDITIONS

This paper is about the determination of concrete reinforcement conditions. The concrete rein-forcement condition testing is a process by which different test are carried out such as cover depth, concrete resistivity, ½ - cell mapping and chloride content, to determine how each test shows the effects of corrosion in the reinforced concrete. Four different types of tests were conducted on two different reinforced concrete cu-bes to investigate the embedded bar conditions. The tests were cover depth, concrete resistivity, ½ cell map-ping and chloride content tests. The experimental testing was carried out in the concrete laboratory at the Uni-versity of Dundee.
The Structure of Constacyclic Codes of Length $2p^s$ over Finite Chain Ring

Let $R$ be a finite commutative chain ring with identity of characteristic $p^a$ that has maximal ideal $\langle z\rangle$. In this paper, we study $\lambda$-constacyclic codes of length $2p^s$ over the ring $R$, for any unit $\lambda$ of $R$. If the unit $\lambda$ is not a square, the rings $\mathcal{R}_{\lambda}=\frac{R[x]}{\langle x^{2p^s}-\lambda\rangle}$ is a local ring with maximal ideal $\langle x^2-r, z\rangle$, where $r\in R$ such that $\lambda-r^{p^s}$ is not invertible. When there exists a unit $\lambda_0$ of $R$ such that $\lambda=\lambda^{p^s}_0$, we prove that $x^2-\lambda_0$ is nilpotent with nilpotency index $ap^s-(a-1)p^{s-1}$. When $\lambda=\lambda^{p^s}_0+z\omega$, for some unit $\omega$ of $R$, we show that $\mathcal{R}_{\lambda}$ is also a chain ring with maximal ideals $\langle x^2-\lambda_0\rangle$. Moreover, when $\lambda=\lambda^{p^s}_0+z^2\omega$, it shown that $\mathcal{R}_{\lambda}$ is a chain ring with maximal ideals $\langle x^2-\lambda_0\rangle$. Furthermore, the algebraic structure and dual of all $\lambda$-constacyclic codes are obtained.
Enhancing dynamic operation optimization feasibility for constrained model predictive control systems

This paper addresses a novel model predictive control algorithm for tracking to achieve an optimal operation for discrete‐time linear systems. The soft constraints characterizing the desired system performance are adjusted to solve the infeasibility problems caused by the original constraints. In this method, based on the principle of steady‐state error and constraints violation minimization, we firstly optimize the transition steady‐state with refined terminal invariant set, and slack variables as well as consider the dynamic constraints of the state constraints. Two types of soft constraints and slack variables are introduced to adjust state constraints online so that the MPC is feasible. Then, a dynamic optimization problem is solved which employs the constraints with minimum slack variables and takes the resulted steady‐state as the temporary target in current control period. The proposed method is able to minimize the violation of constraints and provides more potential to improve the control performance. Besides, the recursive feasibility and closed‐loop stability can be guaranteed by the combination of the new soft constraints adjustment method and tracking scheme. Finally, simulations at different circumstances prove the efficiency of the proposed method.
Control programático de Drone DJI Phantom 3

La empresa de tecnología china DJI, actualmente la líder del mercado en drones comerciales y recreacionales, alcanzó esta posición principalmente gracias al Phantom 2. Este dispositivo es un drone relativamente económico, sumamente simple de utilizar, ideal para aficionados. Cuenta con una buena autonomía considerando su tamaño y costo. La existencia de este tipo de robots comerciales ahorra los costos y complejidades inherentes al desarrollo individual y permite a las personas realizar grabaciones y fotografía aérea sin la necesidad de tener las habilidades y conocimientos técnicos necesarios para generar sus propios drones. En particular el Phantom 2 fue el drone más vendido a nivel mundial hasta la fecha. (Kakaes, 2015)