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Time series streams are ubiquitous in many application domains, e.g., transportation, network monitoring, autonomous vehicles, or the Internet of Things (IoT). Transmitting and storing large amounts of such fine-grained data is however expensive, which makes compression schemes necessary in practice. Time series streams that are transmitted together often share properties or evolve together, making them significantly correlated. Despite the rich literature on compression methods, the state-of-the-art approaches do not typically avail correlation information when compressing times series. In this work, we demonstrate how one can leverage the correlation across several related time series streams to both drastically improve the compression efficiency and reduce the accuracy loss.We present a novel compression algorithm for time series streams called CORAD (CORelation-Aware compression of time series streams based on sparse Dictionary coding). Based on sparse dictionary learning, CORAD has the unique ability to exploit the correlation across multiple related time series to eliminate redundancy and perform a more efficient compression. To ensure the accuracy of the compressed time series, we further introduce a method to threshold the information loss of the compression. Extensive validation on real-world datasets shows that CORAD drastically outperforms state-of-the-art approaches achieving up to 40:1 compression ratios while minimizing the information loss.

Mosquitoes are the most important vectors of some human diseases in tropical countries. In particular, Aedes aegypti is the main vector for Chikungunya, Dengue, and Zika viruses in Brazil (Scavuzzo, 2018). The infection causes flu-like symptoms, and sometimes evolves into a life-threatening condition called severe dengue or hemorrhagic dengue. It is a widespread infection that occurs in all regions of the planet's tropical climate, and now because of the climate change, it may expand to some other regions. In recent years, transmission has increased predominantly in urban areas and has become a major public health problem.Recently, there has been an increasing trend on GeoHealth studies by use of remotely sensed data for mapping health risk and monitoring vector-borne diseases. Thus, the aim of this work is to generate risk maps integrating those environmental and climate indicators with adaptive capacity factors to define regions with high risk of vector-borne diseases in Brazil. Consequently, we present a multi-indicator study, similar to the one already developed in Vietnam by Nguyen and Liou (Nguyen and Liou, 2018) for the Aedes Albopictus, but the present study is in Brazil for the Aedes aegypti and adding the information of the Soil Moisture (SM) from the Barcelona Expert Center (BEC). Then, the study combines climatological data (e.g. soil moisture, temperature and precipitation), and demographic and socioeconomic data over the Brazilian territory for the 2013-2018 period. Dengue episodes distribution data for this period have been obtained from the Notifiable Diseases Information System (SINAN), developed by Ministry of Health.

The Internet of things is an ever-changing field both in terms of technology and in terms of the level of vision that leaves it unclear about the approach to be taken by developers to ensure the sustainability of their developments. In this article, we propose a model driven development approach around an architecture of models whose pivot is an IoT meta-model that is technologically neutral. From this meta-model, a developer or an expert derive his model of solution, written in a domain specific modeling language and generates a Java code, point of departure for his solution implementation. An approach based on a model driven development can only ensure a sustainability of this development. Furthermore, an Eclipse IDE supports our approach.

The purpose of this paper is to investigate alternative designs to deal with uncertain robotic system dynamics in general. We demonstrate the effectiveness of a recently proposed adaptive sliding mode control (ASMC) design with a novel switching gain dynamics, called integral/exponential adaptation law, to a five-link robot manipulator with uncertain dynamics. The proposed design does not require a priori knowledge of the upper bounds of perturbations. The simulation results show low chatter on control action and better tracking error performances.

Data quality is a challenging problem in many real world application domains. While a lot of attention has been given to detect anomalies for data at rest, detecting anomalies for streaming applications still largely remains an open problem. For applications involving several data streams, the challenge of detecting anomalies has become harder over time, as data can dynamically evolve in subtle ways following changes in the underlying infrastructure. In this paper, we describe and empirically evaluate an online anomaly detection pipeline that satisfies two key conditions: generality and scalability. Our technique works on numerical data as well as on categorical data and makes no assumption on the underlying data distributions. We implement two metrics, relative entropy and Pearson correlation, to dynamically detect anomalies. The two metrics we use provide an efficient and effective detection of anomalies over high velocity streams of events. In the following, we describe the design and implementation of our approach in a Big Data scenario using state-of-the-art streaming components. Specifically, we build on Kafka queues and Spark Streaming for realizing our approach while satisfying the generality and scalability requirements given above. We show how a combination of the two metrics we put forward can be applied to detect several types of anomalies — like infrastructure failures, hardware misconfiguration or user-driven anomalies — in large-scale telecommunication networks. We also discuss the merits and limitations of the resulting architecture and empirically evaluate its scalability on a real deployment over live streams capturing events from millions of mobile devices.

In this work, a Monte Carlo simulation was performed to investigate the neutron generation in different materials of entrance foil to a target assembly for [18F] FDG production. However, the aim is to study a materials that has, more or less, similar mechanical properties as the Havar® foil with less generation of secondary particles and without affecting, the yield of FDG production.

This paper proposes an alternative approach to adaptive sliding mode controller (ASMC) design for nonlinear systems with uncertainties of unknown bounds. A smooth adaptation tuning approach is developed to enhance the accuracy and suppress the chattering phenomenon without overestimation of the uncertainty magnitude. The robustness is proven using the Lyapunov stability criterion. Numerical simulation results are performed to demonstrate the effectiveness of the proposed algorithm.

In this paper, we present the design of an observer-based controller for a perturbed mechanical system using two independent adaptive sliding mode structures (ASMS). The purpose of the proposed method is to compensate for external disturbances, modeling uncertainties and unmodeled dynamics. Assuming that only position measurements are available, the proposed control approach is based on a reduced-order velocity observer. We investigate the stability of the overall closed-loop dynamics. Experiments using a rotary electromechanical system are developed to demonstrate the effectiveness of the proposed design. In addition, comparative results of a similar ASMS control design using a full state-feedback are discussed.

Real world applications that deal with time series data often rely on matrix decomposition techniques, such as the Singular Value Decomposition (SVD). The Centroid Decomposition (CD) approximates the Singular Value Decomposition, but does not scale to long time series because of the quadratic space complexity of the sign vector computation. In this paper, we propose a greedy algorithm, termed Scalable Sign Vector (SSV), to efficiently determine sign vectors for CD applications with long time series, i.e., where the number of rows (observations) is much larger than the number of columns (time series). The SSV algorithm starts with a sign vector consisting of only 1s and iteratively changes the sign of the element that maximizes the benefit. The space complexity of the SSV algorithm is linear in the length of the time series. We provide proofs for the scalability, the termination and the correctness of the SSV algorithm. Experiments with real world hydrological time series and data sets from the UCR repository validate the analytical results and show the scalability of SSV.

Image in nuclear medicine represents a primordial material in diagnostics and quantitative studies. However, its quality is influenced by factors such as attenuation, scatter and partial volume effects. In the present work we are interested in investigating quantitatively the scatter effect on the spatial resolution and the energy spectrum using the Monte Carlo simulation.

In this paper, we propose a modified approach of the infinite horizon optimal control for nonlinear systems. These systems are first expressed as formal power series in the indeterminate state variables. Then, the control design is based on Lyapunov functions (LF), vector power series (VPS) and the Kronecker product (KP) algebra. The asymtotic stability analysis and the domain of attraction (DA) of the given nonlinear statefeedback are discussed using the linear matrix inequality (LMI) formulation. Simulation results prove the effectiveness of the proposed method.

This paper investigates the design of an adaptive observer for a large class of nonlinear systems with linearly dependent parameters that are jointed with unmeasured regression dynamics. An exponential stability of both the state and parameter errors is developed under the persistent excitation (PE) condition. The calculus of the observer gain and the adaptation law parameters is cast as a linear matrix inequality (LMI) feasibility problem. A simulation example is shown to demonstrate the effectiveness of the theoretical design.

The measurement or evaluation and clinical significance of human sperm morphology has always been and still is a controversial aspect of the semen analysis for the determination of a male's fertility potential. The evaluation of sperm size, shape and morphological smear characteristics should be assesed by carefully observing a stained sperm sample under a microscope. In order to avoid subjectivity, numerous studies that incorporate image analysis techniques in the assessment of sperm morphology have been proposed. The primary step of all these methods is segmentation of sperm's parts. In this paper, we have proposed a new method for segmentation of sperm's Acrosome, Nucleus and Mid-piece. Sperm's Acrosome, Nucleus and Midpiece are segmented through a method based on a Bayesian classifier which utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the apriori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the Accuracy, Sensitivity and Specificity were calculated.

Segmentation is an important step for the diagnosis of multiple sclerosis. This paper presents a new approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. At first, Brain image is considered to be three parts, namely the dark, the gray, and the white part. Then, the fuzzy regions of their member functions are determined by maximizing fuzzy entropy through the Genetic algorithm. Finally, MS lesions and CSF areas are determined by applying a localized weighted filter to the Bright and Dark membership images. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with Gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here.

Sea target detection is an important goal for military purposes and navigation. Environmental noise and sea clutter are two major problems in sea target detection. A new novel Neural Network in Kernel Space (NNKS) is presented for classification of the target and non-target in color images, and it is followed with a new kernel neuron sea target detection system (KNNS) which is efficient especially in the critical color images with clutter, noise and complex background that relative methods are not effective. The neural network is trained with color characteristics of the target and non-target from the HSV color space.

This paper deals with the design of an adaptive observer that can estimate both the states and the parameters of a class of nonlinear systems. The purpose of the proposed method is to eliminate the restrictive dissipativity condition from the stability proof that is present in many previous works presented in the literature. The observer gain calculus is cast as a linear matrix inequality (LMI) feasibility problem. We aim to apply the proposed algorithm to estimate the unknown parameters of a second order electromechanical rotary model. The appeal of this proven theoretical design is further demonstrated numerically with a comparative design.

Vessel extraction in retina images is a primary and important step in studying diseases including vasculature changes. In this paper, a fuzzy clustering method based on Ant Colony Algorithm, inspired by food-searching natural behavior of ants, is described. Features of color retina images are extracted by eigenvalues analysis of Hessian matrix and Gabor filter bank. Artificial ants in the image use these features for searching and clustering processes. Experiments and results of proposed algorithm show its good performance in vessel extraction. This algorithm is tested on DRIVE database and its results are compared with other works using the same database. The accuracy of our method is 0.933 versus 0.947 for a second observer.

Software reuse is a crucial topic in the software community. It is considered as a promising way to improve software development productivity and quality. However, component reuse success is widely related to other activities such as the localization of components satisfying developer requirements. Current approaches for components retrieval are mostly limited to specific components (software components, source code, etc). To go beyond the limits of these approaches, we propose a structural and behavioral retrieval technique that takes in account the heterogeneity of the components repository (abstraction level, technology, domain, etc). The basic notion of our approach is to combine formal and semi-formal specification used to describe component and then to make the retrieval process more efficient.

This paper is devoted to a stabilization problem for a servo-system under the influence of friction. The proposed approach is based on a multi-objective control design using a reduced-order velocity observer, a generalized Maxwell-slip (GMS) model-based friction compensation and a linear time invariant (LTI) compensator simultaneously. Stabilization conditions on interconnected systems are investigated with strictly positive real (SPR) condition on linear closed-loop dynamics of hierarchical systems using linear matrix inequality (LMI) frameworks. The appeal of this proven theoretical design is further demonstrated by numerical results.

Program supervision aims at automating the use of complex programs, independently of any particular application domain. Program supervision systems offer original techniques to plan and control program processing activities. The distribution of such systems becomes essential because real applications imply more and more participants on various sites. Therefore, given distributed data, programs and knowledge, our aim is to assist distant users, who are not necessarily specialists in image processing, in the latter task (image processing) and then in their decision making according to some parameter values and to their experience while observing the resulting images. Our current application relates to medical assistance in the use of medical imaging programs for osteoporosis detection in bone radiographies. A distributed approach will allow executing distant physician requests using an existing system and will favor collaborative work and sharing of knowledge to build new knowledge-based systems. The objective of this paper is to present a distributed system of computer-aided diagnosis and to show how a distributed program supervision system can assist distant physicians in their diagnosis so that the management of their requests remains transparent to them.

This paper deals with an extended output- feedback positioning tracking of a servo-system with friction based on generalized Maxwell-slip (GMS) friction observer dynamics with fixed model parameters. We succeed in designing a linear time invariant (LTI) compensator which ensures exponential stabilization and additional multi-objective features (e.g. closed-loop pole location). These conditions are expressed in terms of linear matrix inequalities (LMIs). Numerous simulations results illustrate the effectiveness of the proposed compensator and state a comparison with the similar LuGre closed-loop friction compensator.

Absmucf- This paper presents an approach to design a nonlinear controller for a pneumatic cylinder system for which the connection port comprises a non-negligible restriction. The pressure chamber of this system is difficult to control because the servo valve and the connection port form a double restriction. This double restriction causes a singularity in the feedback linearization Controller. To avoid this singularity, a mowed feedback linearization controller is proposed. The Linear Matrix Inequality @MI) approach is then used to calculate the controller gains which ensure the stability of the tracking error and the non-singularity of the control law. Simulation results show good performance of the proposed controller.

The purpose of This work is to use bounded error estimation (BEE) approach based on interval analysis and set inversion in order to obtain guaranteed estimation interval for the LuGre friction model parameters. The method assumes that if the errors corrupting the measurements are available and bounded, then the set of all parameters, which are consistent with data output, and these errors (uncertainties) are computed. The main advantage of this method in comparison with the classical ones, as the least square (LS) approach, is that it provides not only the estimate of the parameters but also the precision with which the estimated values is obtained. Moreover, as it's global, it bypasses the problem of initialization. To validate the proposed approach, experimental data are collected from an actuating electro-pneumatic device, and are used to estimate the friction parameters.

In this paper, an implementation of a force control for a rodless pneumatic actuator is proposed. The increasing interest of pneumatic drives is due to numerous advantages as high degree of compliance and dexterity, force capabilities and ready availability of cheap air supply. We describe a feedback linearization technique applied on the analytical nonlinear structure, dependently affine on parameter uncertainties, that genetically characterizes the pneumatic plants. Farther parametric uncertainties and exogenous disturbances arise on the deduced linear structure, for which we require a multi-objective state-feedback control synthesis. The design objective is performed using a constrained H/sub /spl infin// minimization that is compared to peak-to-peak gain performance. Additionally, in both cases, the optimization objective is applied under some closed-loop pole location constraints. A linear matrix inequality (LMI) based parametrization of both H/sub /spl infin// and peak-to-peak gain suboptimal controllers is developed. And, experimental results are illustrated to validate the approach and to compare the performances.

This paper addresses an extended output feedback positioning tracking of a servo-system with friction. It is based on LuGre friction observer dynamics with fixed model parameters. Our design features a global asymptotic stability of the tracking error while sustaining desired transient specifications using dual conditions of strictly positive real (SPR) one and exponential stability margin. These latter have to be fixed with respect to specific pole placement limiting fast controller dynamics. Our full-order dynamic output feedback controller is composed of two components: the first deals with the tracking context and the second represents a correction term in the observer from the position and velocity errors. The subsequent linear state space controller matrices are found by using the linear matrix inequality (LMI) approach. Simulation results illustrate the effectiveness of the proposed compensator.

This paper proposes a metamodel for a metatool able to describe and manage components. A good reuse-oriented information system development environment must be composed of an appropriate component repository. Repositories must support organization and management of components, selection and comprehension of relevant components and components heterogeneity. The components repository designer should be able to design a components repository scheme, which could be used to structure and organize the components collection. So, the meta-model must be able to support heterogeneous components repositories. The meta-tool allows the development process using conceptual components and used in the different stages of the information system life cycle. The resulting meta-model is able to model components descriptions formalisms and component models and to define relationships. Therefore the metatool supports the developers (designer or programmer) by providing conceptual design and software solutions for the reuse of the selected components (conceptual or software).

This article deals with a robust control scheme for a pneumatic servo system containing unknown possibly nonlinear uncertainties. Added to neglecting spool valve dynamics, it will be hypothesized that the temperature may vary from its nominal value within the variable volume chamber, resulting in some parameters sensitively changing. Obtained by a simpler feedback linearization, the linear plant is investigated by multi-objective synthesis of a PI controller. The design objective will be a mix of H/sub /spl infin// performance minimization constrained on an appropriate closed-loop pole location. These conditions will be expressed in terms of linear matrix inequalities (LMIs). This formulation is therefore numerically tractable via LMI optimization. So, simulation results are included to demonstrate the effectiveness of the designed controller.

Causality assignment is an important task in physical modeling by bond graphs. Traditional causality assignment procedures have specific aims and particular purposes. The traditional SCAP and MSCAP are algorithms that may fail if the bond graph has loops or contains junction causality violations. The RCAP algorithm focuses on the generation of differential algebraic equations to take into account junction violations caused by nonlinear multi-port devices and is not suitable for general bond graphs. We present a formulation of the causality assignment problem as a constrained multiobjective optimization problem. We then solve the resulting constrained system by a simple yet effective multi-objective evolutionary technique.