Miracle-dz/UniFAR-FTUs huggingface dataset:
Acquisition of weak GPS signals using wavelet-based de-noising methods

Various factors cause GPS signals to be weakened in their path from satellites to receiver. This phenomenon leads to some problems in the normal stages of the positioning process, including acquisition stage. The wavelet transform is one of the tools that have been employed in several methods to improve receiver’s sensitivity in confronting weak GPS signals. Choosing an appropriate threshold plays a key role in the performance of this de-noising method. This study presents two methods for selecting an appropriate threshold. In the first method, the threshold is determined based on the statistical information extracted from the signal. On the other hand, the threshold in the second method is determined by type II fuzzy sets. The results show that employment of these two methods increases SNR gain as well as the range of acquirable SNR which makes signals acquisition with minimum post-correlation SNR of in a GPS receiver possible.
A Note on Type 2 Degenerate q-Euler Polynomials

Recently, type 2 degenerate Euler polynomials and type 2 q-Euler polynomials were studied, respectively, as degenerate versions of the type 2 Euler polynomials as well as a q-analog of the type 2 Euler polynomials. In this paper, we consider the type 2 degenerate q-Euler polynomials, which are derived from the fermionic p-adic q-integrals on Z p , and investigate some properties and identities related to these polynomials and numbers. In detail, we give for these polynomials several expressions, generating function, relations with type 2 q-Euler polynomials and the expression corresponding to the representation of alternating integer power sums in terms of Euler polynomials. One novelty about this paper is that the type 2 degenerate q-Euler polynomials arise naturally by means of the fermionic p-adic q-integrals so that it is possible to easily find some identities of symmetry for those polynomials and numbers, as were done previously.
Desarrollo de esquemas de control predictivo aplicado a convertidores estáticos de tipo multinivel.

Los convertidores electronicos de potencia se emplean en diferentes aplicaciones como la propulsion de vehiculos electricos, sistemas de alimentacion ininterrumpida, energias renovables entre otras. Estos convertidores generalmente son de dos niveles y son regulados por controles lineales. Sin embargo, entre sus desventajas se encuentran perdidas de energia y tener un alto contenido armonico. Por otro lado, el uso de convertidores de mas niveles reduce el contenido armonico y produce una senal de tension escalonada de una mayor cantidad de niveles discretos. Sin embargo, una mayor cantidad de niveles en el convertidor incrementa la cantidad de voltajes a balancear al igual que el numero de estados lo cual aumenta la complejidad para regular el sistema. Entre las diferentes tecnicas que se utilizan para el control de convertidores multinivel podemos mencionar modos deslizantes, PWM, LQR, space-vector y el control predictivo. La mayoria de estas tecnicas requieren conocer el estado del convertidor, el cual en ocasiones no esta disponible, por lo que se ha propuesto el uso de sensores matematicos conocidos como observadores.En esta tesis se desarrolla una estrategia de control predictivo para convertidores estaticos de tipo multinivel. La tecnica aplicada es el modelo de control predictivo con conjuntos de control finito (FCS-MPC). Dicha tecnica esta basada en el conocimiento del modelo de convertidor, su comportamiento hibrido (dinamica continua y discontinua) y el conocimiento de la solucion del sistema entre conmutaciones. El convertidor a analizar es un inversor de diodo anclado (NPC) de tres niveles que tiene un comportamiento lineal entre conmutaciones, por lo que es posible estimar su comportamiento futuro y mediante un criterio escoger la configuracion adecuada. Los FCS-MPC estudiados en la literatura disponen de los parametros y el estado del inversor, cuyo conocimiento podria presentar un comportamiento no adecuado. En este trabajo se disena un observador interconectado adaptativo para cada condensador que permita estimar los voltajes de los condensadores y los parametros de manera simultanea. El esquema propuesto, permite el seguimiento de la corriente de referencia y el equilibrio de los condensadores para determinar las entradas del sistema lo cual evita la necesidad de usar tecnicas de modulacion.
On Pata–Suzuki-Type Contractions

In this manuscript, we introduce two notions, Pata–Suzuki Z -contraction and Pata Z -contraction for the pair of self-mapping g , f in the context of metric spaces. For such types of contractions, both the existence and uniqueness of a common fixed point are examined. We provide examples to illustrate the validity of the given results. Further, we consider ordinary differential equations to apply our obtained results.
A Performance-Region-Based Approach to the H∞ Stochastic Consensus Problem

This brief considers the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> consensus problem of networked general linear agents subject to external disturbances over Markovian randomly switching communication topologies. To solve this problem, a class of observer-based consensus control protocols is proposed, which solely depend on the local information of each agent. Under the proposed protocol, it is interesting to find that the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> stochastic consensus problem of the multi-agent system can be cast into the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> robust control problems of some independent systems with the same dimension as a single agent. For the purpose of designing the proposed protocol, the notion of <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> consensus performance region is first introduced and then analysed. Based on the theoretical analysis, an algorithm is given to solve the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> stochastic consensus problem. Finally, it is pointed out via some numerical simulations that a small enough positive coupling strength can make the consensus protocol be robust to the variations of the communication topology.
Traversal, Case Analysis, and Lowering for C++ Program Analysis

Traversal, Case Analysis, and Lowering for C++ Program Analysis. (August 2009) Luke A. Wagner, B.S., Texas A&M University Chair of Advisory Committee: Dr. Bjarne Stroustrup To work effectively, programmers need tools to support their typical development activities, such as the creation, analysis, and transformation of source code. Analysis and transformation tools can be difficult to write for modern programming languages and, without a reusable framework, each tool must separately implement nontrivial algorithms like name lookup and type checking. This thesis describes an extension to one such framework, named Pivot, that focuses on programs written in C++. This extension, named Filter, assists the tool builder in traversal, case analysis, and lowering of the data structure representing C++ programs. Comparisons described in the thesis show a 2-4x code reduction when solving basic problems (e.g., searching for uses of a given declaration) using the extension and a performance overhead that drops below 2x for larger problems (e.g., checking C++ layout compatibility).
Decentralized Coalition Formation with Agent-based Combinatorial Heuristics

A steadily growing pervasion of the energy distribution grid with communication technology is widely seen as an enabler for new computational coordination techniques for renewable, distributed generation as well as for bundling with controllable consumers. Smart markets will foster a decentralized grid management. One important task as prerequisite to decentralized management is the ability to group together in order to jointly gain enough suitable flexibility and capacity to assume responsibility for a specific control task in the grid. In self-organized smart grid scenarios, grouping or coalition formation has to be achieved in a decentralized and situation aware way based on individual capabilities. We present a fully decentralized coalition formation approach based on an established agent-based heuristics for predictive scheduling with the additional advantage of keeping all information about local decision base and local operational constraints private. Two closely interlocked optimization processes orchestrate an overall procedure that adapts a coalition structure to best suit a given set of energy products. The approach is evaluated in several simulation scenarios with different type of established models for integrating distributed energy resources and is also extended to the induced use case of surplus distribution using basically the same algorithm.
Neural-network-based adaptive control for bilateral teleoperation with multiple slaves under Round-Robin scheduling protocol

A neural-network-based adaptive control scheme is developed for bilateral teleoperation systems with single-master-multiple-slaves in the presence of dynamic uncertainties and communication constraint. Discrete-time data transmitting communication network with bandwidth limitation and time-varying communication delays is considered and the Round-Robin scheduling protocol is used to orchestrate the data transmission from multiple slaves to the master. Stability criteria of the closed-loop system are established by constructing appropriate Lyapunov-Krasovskii functionals. Simulation studies are performed to illustrate the effectiveness of the proposed control algorithm.
Comparison of spatial classification rules with different conditional distributions of class label

In this paper spatial classification rules based on Bayes discriminant functions are considered. The novelty of this work is that the statistical supervised classification method is improved by extending the influence of spatial correlation between observation to be classified and training sample. Such methods are used for data containing spatially correlated noise. Method accuracy is tested experimentally on artificially corrupted images. This classification rule with distance based conditional distribution for class label shows advantage against other classification rule ignoring such influence and against other commonly used supervised classification methods.
Fine-grained ship classification based on deep residual learning for high-resolution SAR images

ABSTRACT As the resolution of Synthetic Aperture Radar (SAR) images increases, the fine-grained classification of ships has become a focus of the SAR field. In this paper, a ship classification framework based on deep residual network for high-resolution SAR images is proposed. In general, networks with more layers have higher classification accuracy. However, the training accuracy degradation and the limited dataset are major problems in the training process. To build deeper networks, residual modules are constructed and batch normalization is applied to keep the activation function output. Different fine tuning strategies are used to select the best training scheme. To take advantage of the proposed framework, a dataset including 835 ship slices is augmented by different multiples and then used to validate our method and other Convolutional Neural Network (CNN) models. The experimental results show that, the proposed framework can achieve a 99% overall accuracy on the augmented dataset under the optimal fine-tuning strategy, 3% higher than that in other models, which demonstrates the effectiveness of our proposed approach.
Neural adaptive global stability control for robot manipulators with time‐varying output constraints

In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time‐varying parameter that installed in the fore‐end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time‐varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time‐varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods.
Electroencephalography for Early Detection of Alzheimer’s Disease in Subjective Cognitive Decline

Background and Purpose Early detection of subjective cognitive decline (SCD) due to Alzheimer’s disease (AD) is important for clinical research and effective prevention and management. This study examined if quantitative electroencephalography (qEEG) could be used for early detection of AD in SCD. Methods Participants with SCD from 6 dementia clinics in Korea were enrolled. 18F-florbetaben brain amyloid positron emission tomography (PET) was conducted for all the participants. qEEG was performed to measure power spectrum and source cortical activity. Results The present study included 95 participants aged over 65 years, including 26 amyloid PET (+) and 69 amyloid PET (−). In participants with amyloid PET (+), relative power at delta band was higher in frontal (p=0.025), parietal (p=0.005), and occipital (p=0.022) areas even after adjusting for age, sex, and education. Source activities of alpha 1 band were significantly decreased in the bilateral fusiform and inferior temporal areas, whereas those of delta band were increased in the bilateral cuneus, pericalcarine, lingual, lateral occipital, precuneus, posterior cingulate, and isthmus areas. There were increased connections between bilateral precuneus areas but decreased connections between left rostral middle frontal area and bilateral frontal poles at delta band in participants with amyloid PET (+) showed. At alpha 1 band, there were decreased connections between bilateral entorhinal areas after adjusting for covariates. Conclusions SCD participants with amyloid PET (+) showed increased delta and decreased alpha 1 activity. qEEG is a potential means for predicting amyloid pathology in SCD. Further longitudinal studies are needed to confirm these findings.
Comparaison de groupes d'observations dans le cadre de l'approche PLS

Les modeles d'equations structurelles a variables latentes sont de plus en plus utilises dans de nombreux domaines. L'approche PLS permet d'estimer les parametres de ce type de modeles. A l'inverse de la methode par analyse de la structure de covariance (aussi appelee LISREL), on ne peut pas se rapporter a un test de qualite d'ajustement parametrique comme celui base sur la distribution du chi². Il est donc difficile de comparer des sous populations en se basant sur un modele dans le cadre de l'approche PLS. Nous presentons un processus afin de comparer deux echantillons en se basant sur un modele d'equations structurelles. Dans ce but, nous partons d'une comparaison globale pour aller jusqu'a la comparaison des coefficients structurels du modele. Puis, nous presentons des applications dans le cadre de l'analyse de la satisfaction des clients.
A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.
Influência do entorno de uma unidade de conservação sobre a pressão de caça: RPPN Estação Veracel como estudo de caso

A caca de animais silvestres e uma das atividades humanas mais antigas e, associada a aplicacao de tecnicas de caca modernas, pode levar rapidamente a uma superexploracao e ate a extincao de especies. A caca no Brasil e ilegal na maior parte dos casos, mas ainda e uma atividade comum. Por ser uma atividade ilegal, coletar e quantificar tais informacao e um desafio. O presente estudo apresenta uma oportunidade rara para isso, contando com 1274 indicios de cacadores e 984 vestigios de caca coletados na Reserva Particular do Patrimonio Natural Estacao Veracel (RPPN Veracel). Nesse contexto, o objetivo principal desse estudo foi avaliar se a pressao de caca na RPPN pode ser influenciada pela paisagem no entorno (i.e., presenca de assentamentos rurais, areas urbanas, estradas e proporcao de matriz). Alem disso, foram descritas as variacoes temporais nas atividades de caca na RPPN, bem como a influencia de operacoes de fiscalizacao conjuntas com forcas policiais nessas atividades. Foi notado que a proporcao de matriz e a distância aos assentamentos de reforma agraria foram os principais vetores de pressao de caca na unidade de conservacao. A detencao de cacadores pela policia parece ter sido a principal responsavel pela reducao entre anos nas atividades de caca na RPPN. Diante dos resultados apresentados nesse estudo nota-se a necessidade (i) do estabelecimento de parcerias com as instituicoes de fiscalizacao e inteligencia para controle da acao dos cacadores; (ii) da realizacao de um trabalho educativo sobre a importância da fauna silvestre junto as comunidades do entorno da RPPN e (iii) do desenvolvimento de atividades profissionalizantes que permitam a geracao e o incremento de renda para essas comunidades.
Unequally excited generalised random binned antenna arrays

It has been recently shown that random arrays paired with a proper excitation strategy can be used to shape the array factor. These random arrays were termed unequally excited totally random arrays (TRAs). This scheme allows to greatly enlarge the class of obtainable patterns, well beyond the classical single beam. However, its main disadvantage is a reduction of the achievable performance, the latter measured in terms of the deviation from the average array factor. In this contribution, a strategy for remedying (even only partially) to such a loss of performance by retaining the same flexibility in shaping the pattern is addressed. To this end, a new scheme for generating random arrays that merges unequal excitations and a suitable generalised binned procedure is introduced. It is shown that this new model of generalised binned arrays always outperforms unequally excited TRAs. In particular, the study is focused on symmetric arrays (for which the elements are deployed symmetrically with respect to the array aperture centre) which allows to get closed-form expressions without invoking some common assumptions that in general do not hold true in practice. The theoretical findings are assessed by several numerical examples for the case of multi-beam array factors.
An analysis of learners' learning behavior in Massive Open Online

In this paper we access to data of 2,954 learner’s learning behavior from Xinjiang University online electives are part of the massive online platform named “Erya”. Based on the data, we construct learner’s learning model, acquire rules of learning and predict learner’s academic record. In order to meet learner’s personal needs, the results are used to help educator to develop teaching design and strategies, meanwhile, to optimize online platform and design resources. Keywords— massive open online; learning behaviors; learner’s academic
Studies in electroencephalographic audiometry

This thesis investigates the application of steady state responses to averaged electroencephalographic audiometry (AEA). Steady state responses may be obtained using either amplitude modulated or frequency modulated stimulation. In both cases the stimulus variables are modulation frequency, modulation depth, carrier frequency and stimulus intensity. Other variables include the number of samples used to compile the average, the stimulus presentation (i.e. monaural or binaural), and the subject state (e.g. awake or under sedation). The effect of these parameters on the responses to both amplitude and frequency modulated stimulation has been investigated for normal hearing adults and children. For amplitude modulated stimulation the effect of modulation frequency exhibits a large degree of inter-subject variability, while the response behaviour as a function of the other parameters does not;. The optimal conditions for audiological assessment have been determined for all parameters with the exception of modulation frequency, which is a function of the individual. For frequency modulated stimulation, the inter-subject variability is small, and optimal conditions have been determined for all parameters. The steady state response thresholds to both amplitude and frequency modulated stimulation have been determined for adult and child clinical subjects. These thresholds were compared with the behavioural thresholds obtained using conventional audiometry. The responses to amplitude modulated stimulation may be obtained at 20 dB above behavioural threshold, and those to frequency modulated stimulation at 40 dB above behavioural threshold. Some of the relationships between the two forms of steady state response, the transient response, and the electroencephalogram have been investigated, and the advantages and disadvantages of the steady state responses as an audiological technique have been discussed.
Probing the Light Sterile Neutrino Through the Heavy Charged Higgs Decay at the LHC

We show the 13 TeV proton-proton collider simulation in a ν -two-Higgs-doublet-model ( ν -THDM). The heavy charged Higgs bosons are produced in pairs through the electroweak processes and decay to the light sterile neutrinos (lighter than the W/Z boson masses). The light sterile neutrino further decays into a jet-like object with a muon in it. This helps us discriminate the signal from the backgrounds with the standard model jets.
Probabilistic Approach for Location-Based Authentication; 1st International Workshop on Security for Spontaneous, IWSSI 2007 co-located with Ubicomp 2007; ; UbiComp 2007

This paper explores location-based product authentication in a situation where only the past locations of products that flow in a supply chain are known. We transform location-based authentication into a pattern recognition problem and investigate different solutions based on machine-learning techniques. The proposed solutions are studied with computer simulations that model the flow of genuine and counterfeit products in a generic pharmaceutical supply chain. The results suggest that machine-learning techniques could be used to automatically identify suspicious products from the incomplete location information.