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Articles 11911 - 11940 of 17339

Full-Text Articles in Computer Sciences

Inferring Memory Map Instructions, Paul T. Scheid, Ari J. Spilo, Ron K. Cytron Jan 2014

Inferring Memory Map Instructions, Paul T. Scheid, Ari J. Spilo, Ron K. Cytron

All Computer Science and Engineering Research

We describe the problem of inferring a set of memory map instructions from a reference trace, with the goal of minimizing the number of such instructions as well as the number of unreferenced but mapped storage locations. We prove the related decision problem NP-complete. We then present and compare the results of two heuristic approaches on some actual traces.

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Adaptive Resonance Theory And Diffusion Maps For Clustering Applications In Pattern Analysis, Donald C. Wunsch, David J. Morris, Rui Xu Jan 2014

Adaptive Resonance Theory And Diffusion Maps For Clustering Applications In Pattern Analysis, Donald C. Wunsch, David J. Morris, Rui Xu

Electrical and Computer Engineering Faculty Research & Creative Works

Adaptive Resonance is primarily a theory that learning is regulated by resonance phenomena in neural circuits. Diffusion maps are a class of kernel methods on edge-weighted graphs. While either of these approaches have demonstrated success in image analysis, their combination is particularly effective. These techniques are reviewed and some example applications are given.

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Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch Jan 2014

Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data. to forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). the clustering results improve the accuracy of the proposed method of wind …

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Event-Triggered Optimal Regulation Of Uncertain Linear Discrete-Time Systems By Using Q-Learning Scheme, Avimanyu Sahoo, S. Jagannathan Jan 2014

Event-Triggered Optimal Regulation Of Uncertain Linear Discrete-Time Systems By Using Q-Learning Scheme, Avimanyu Sahoo, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an event-triggered optimal adaptive regulation of an uncertain linear discrete time system is proposed. This scheme solves the optimal control in a forward in- time and online manner by using both dynamic programming and Q learning. First, the time varying action dependent value or the Q-function is estimated online by an adaptive value function estimator (VFE) with event-based state vector and a time dependent basis function. The estimated value function parameters are subsequently used to generate the optimal control gain matrix. Further, aperiodic tuning law for the VFE parameters is proposed not only to estimate the parameters …

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Near Optimal Boundary Control Of Distributed Parameter Systems Modeled As Parabolic Pdes By Using Finite Difference Neural Network Approximation, Behzad Talaei, Hao Xu, S. Jagannathan Jan 2014

Near Optimal Boundary Control Of Distributed Parameter Systems Modeled As Parabolic Pdes By Using Finite Difference Neural Network Approximation, Behzad Talaei, Hao Xu, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper develops a novel neural network (NN) based near optimal boundary control scheme for distributed parameter systems (DPS) governed by semi linear parabolic partial differential equations (PDE) in the presence of control constraints and unknown system dynamics. First, finite difference method (FDM) is utilized to develop a reduced order system which represents the discretized dynamics of PDE system. Subsequently, a near optimal control scheme is proposed for the discretized system by using NN based approximate dynamic programming (ADP). To relax the requirement of system dynamics, a NN identifier is utilized. Moreover, a second NN is proposed to estimate a …

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Fixed Final-Time Near Optimal Regulation Of Nonlinear Discrete-Time Systems In Affine Form Using Output Feedback, Qiming Zhao, Hao Xu, S. Jagannathan Jan 2014

Fixed Final-Time Near Optimal Regulation Of Nonlinear Discrete-Time Systems In Affine Form Using Output Feedback, Qiming Zhao, Hao Xu, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the fixed final-time near optimal output regulation of affine nonlinear discrete-time systems with unknown system dynamics is considered. First, a neural network (NN)-based observer is proposed to reconstruct both the system state vector and control coefficient matrix. Next, actor-critic structure is utilized to approximate the time-varying solution of the Hamilton-Jacobi-Bellman (HJB) equation or value function. To satisfy the terminal constraint, a new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. A NN with constant weights and time-dependent activation function is employed to approximate …

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A Model-Based Fault Detection And Prognostics Scheme For Takagi-Sugeno Fuzzy Systems, Balaje T. Thumati, Miles A. Feinstein, S. Jagannathan Jan 2014

A Model-Based Fault Detection And Prognostics Scheme For Takagi-Sugeno Fuzzy Systems, Balaje T. Thumati, Miles A. Feinstein, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel model-based fault detection (FD) and prediction scheme is developed for a class of Takagi-Sugeno (T-S) fuzzy systems. Unlike other FD schemes, in the proposed design, an FD observer with online fault learning capability is utilized to generate a residual which is obtained by comparing the system output with respect to the observer output. A fault is declared active if the generated residual exceeds an a priori chosen threshold. Subsequently, the fault magnitude is estimated online by using a suitable parameter update law. Upon detection, the online estimate of the fault magnitude is used in a …

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Adaptive Neural Network-Based Optimal Control Of Nonlinear Continuous-Time Systems In Strict-Feedback Form, H. Zargarzadeh, T. Dierks, S. Jagannathan Jan 2014

Adaptive Neural Network-Based Optimal Control Of Nonlinear Continuous-Time Systems In Strict-Feedback Form, H. Zargarzadeh, T. Dierks, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper focuses on neural network (NN) based optimal control of nonlinear continuous-time systems in strict-feedback form when the system dynamics are known by using an adaptive backstepping approach. A single NN-based adaptive approach is designed to learn the solution of the infinite horizon continuous-time Hamilton-Jacobi-Bellman (HJB) equation while the corresponding optimal control input that minimizes the HJB equation is calculated in a forward-in-time manner without using value and policy iterations. First, the optimal control problem is solved for a generic multi-input and multi-output nonlinear system with a state feedback approach. Then the approach is extended to a single-input and …

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Why The Data Train Needs Semantic Rails, Krzysztof Janowicz, Frank Van Harmelen, James A. Hendler, Pascal Hitzler Jan 2014

Why The Data Train Needs Semantic Rails, Krzysztof Janowicz, Frank Van Harmelen, James A. Hendler, Pascal Hitzler

Computer Science and Engineering Faculty Publications

While catchphrases such as big data, smart data, data intensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, i.e., statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability …

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Neural Network-Based Finite-Horizon Approximately Optimal Control Of Uncertain Affine Nonlinear Continuous-Time Systems, Hao Xu, Qiming Zhao, Travis Dierks, S. Jagannathan Jan 2014

Neural Network-Based Finite-Horizon Approximately Optimal Control Of Uncertain Affine Nonlinear Continuous-Time Systems, Hao Xu, Qiming Zhao, Travis Dierks, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper develops a novel neural network (NN) based finite-horizon approximate optimal control of nonlinear continuous-time systems in affine form when the system dynamics are complete unknown. First an online NN identifier is proposed to learn the dynamics of the nonlinear continuous-time system. Subsequently, a second NN is utilized to learn the time-varying solution, or referred to as value function, of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. Then, by using the estimated time-varying value function from the second NN and control coefficient matrix from the NN identifier, an approximate optimal control input is computed. …

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A Hybrid Approach Using Rup And Scrum As A Software Development Strategy, Dalila Castilla Jan 2014

A Hybrid Approach Using Rup And Scrum As A Software Development Strategy, Dalila Castilla

UNF Graduate Theses and Dissertations

According to some researchers, a hybrid approach can help optimize the software development lifecycle by combining two or more methodologies. RUP and Scrum are two methodologies that successfully complement each other to improve the software development process. However, the literature has shown only few case studies on exactly how organizations are successfully applying this hybrid methodology and the benefits and issues found during the process. To help fill this literature gap, the main purpose of this thesis is to describe the development of the Lobbyist Registration and Tracking System for the City of Jacksonville case study where a hybrid approach, …

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Capacity Augmentation Bound Of Federated Scheduling For Parallel Dag Tasks, Jing Li, Abusayeed Saifullah, Kunal Agrawal, Christopher Gill Jan 2014

Capacity Augmentation Bound Of Federated Scheduling For Parallel Dag Tasks, Jing Li, Abusayeed Saifullah, Kunal Agrawal, Christopher Gill

All Computer Science and Engineering Research

We present a novel federated scheduling approach for parallel real-time tasks under a general directed acyclic graph (DAG) model. We provide a capacity augmentation bound of 2 for hard real-time scheduling; here we use the worst-case execution time and critical-path length of tasks to determine schedulability. This is the best known capacity augmentation bound for parallel tasks. By constructing example task sets, we further show that the lower bound on capacity augmentation of federated scheduling is also 2 for any m > 2. Hence, the gap is closed and bound 2 is a strict bound for federated scheduling. The federated scheduling …

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Crystallization In Nano-Confinement Seeded By A Nanocrystal -- A Molecular Dynamics Study, Heng Pan, Costas Grigoropoulos Jan 2014

Crystallization In Nano-Confinement Seeded By A Nanocrystal -- A Molecular Dynamics Study, Heng Pan, Costas Grigoropoulos

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Seeded crystallization and solidification in nanoscale confinement volumes have become an important and complex topic. Due to the complexity and limitations in observing nanoscale crystallization, computer simulation can provide valuable details for supporting and interpreting experimental observations. In this article, seeded crystallization from nano-confined liquid, as represented by the crystallization of a suspended gold nano-droplet seeded by a pre-existing gold nanocrystal seed, was investigated using molecular dynamics simulations in canonical (NVT) ensemble. We found that the crystallization temperature depends on nano-confinement volume, crystal orientation, and seed size as explained by classical two-sphere model and Gibbs-Thomson effect.

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Placement Of Mode And Wavelength Converters For Throughput Enhancement In Optical Networks, Ruaa Abdulrahman Jan 2014

Placement Of Mode And Wavelength Converters For Throughput Enhancement In Optical Networks, Ruaa Abdulrahman

Electronic Theses and Dissertations

The success of recent experiments to transport data using combined wavelength division multiplexed (WDM) and mode-division multiplexed (MDM) transmission has generated optimism for the attainment of optical networks with unprecedented bandwidth capacity, exceeding the fundamental Shannon capacity limit attained by WDM alone. Optical mode converters and wavelength converters are devices that can be placed in future optical nodes (routers) to prevent or reduce the connection blocking rate and consequently increase network throughput. In this thesis, the specific problem of the placement of mode converters (MC) and mode-wavelength converters (MWC) in combined mode and wavelength division multiplexing (MWDM) networks is investigated. …

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Functional Scaffolding For Musical Composition: A New Approach In Computer-Assisted Music Composition, Amy K. Hoover Jan 2014

Functional Scaffolding For Musical Composition: A New Approach In Computer-Assisted Music Composition, Amy K. Hoover

Electronic Theses and Dissertations

While it is important for systems intended to enhance musical creativity to define and explore musical ideas conceived by individual users, many limit musical freedom by focusing on maintaining musical structure, thereby impeding the user's freedom to explore his or her individual style. This dissertation presents a comprehensive body of work that introduces a new musical representation that allows users to explore a space of musical rules that are created from their own melodies. This representation, called functional scaffolding for musical composition (FSMC), exploits a simple yet powerful property of multipart compositions: The pattern of notes and rhythms in different …

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Visual Analysis Of Extremely Dense Crowded Scenes, Haroon Idrees Jan 2014

Visual Analysis Of Extremely Dense Crowded Scenes, Haroon Idrees

Electronic Theses and Dissertations

Visual analysis of dense crowds is particularly challenging due to large number of individuals, occlusions, clutter, and fewer pixels per person which rarely occur in ordinary surveillance scenarios. This dissertation aims to address these challenges in images and videos of extremely dense crowds containing hundreds to thousands of humans. The goal is to tackle the fundamental problems of counting, detecting and tracking people in such images and videos using visual and contextual cues that are automatically derived from the crowded scenes. For counting in an image of extremely dense crowd, we propose to leverage multiple sources of information to compute …

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Faults Identification In Three-Phase Induction Motors Using Support Vector Machines, Rama Hammo Jan 2014

Faults Identification In Three-Phase Induction Motors Using Support Vector Machines, Rama Hammo

Master of Technology Management Plan II Graduate Projects

Induction motor is one of the most important motors used in industrial applications. The operating conditions may sometime lead the machine into different fault situations. The main types of external faults experienced by these motors are over loading, single phasing, unbalanced supply voltage, locked rotor, phase reversal, ground fault, under voltage and over voltage. The machine should be shut down when a fault is experienced to avoid damage and for the safety of the workers. Computer based relays monitor the machine and disconnect it during the faults. The relay logic used to identify these faults requires sophisticated signal processing techniques …

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Sketchart: A Pen-Based Tool For Chart Generation And Interaction., Andres Vargas Gonzalez Jan 2014

Sketchart: A Pen-Based Tool For Chart Generation And Interaction., Andres Vargas Gonzalez

Electronic Theses and Dissertations

It has been shown that representing data with the right visualization increases the understanding of qualitative and quantitative information encoded in documents. However, current tools for generating such visualizations involve the use of traditional WIMP techniques, which perhaps makes free interaction and direct manipulation of the content harder. In this thesis, we present a pen-based prototype for data visualization using 10 different types of bar based charts. The prototype lets users sketch a chart and interact with the information once the drawing is identified. The prototype's user interface consists of an area to sketch and touch based elements that will …

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Energy Efficient Routing Towards A Mobile Sink Using Virtual Coordinates In A Wireless Sensor Network, Rouhollah Rahmatizadeh Jan 2014

Energy Efficient Routing Towards A Mobile Sink Using Virtual Coordinates In A Wireless Sensor Network, Rouhollah Rahmatizadeh

Electronic Theses and Dissertations

The existence of a coordinate system can often improve the routing in a wireless sensor network. While most coordinate systems correspond to the geometrical or geographical coordinates, in recent years researchers had proposed the use of virtual coordinates. Virtual coordinates depend only on the topology of the network as defined by the connectivity of the nodes, without requiring geographical information. The work in this thesis extends the use of virtual coordinates to scenarios where the wireless sensor network has a mobile sink. One reason to use a mobile sink is to distribute the energy consumption more evenly among the sensor …

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Automatic 3d Human Modeling: An Initial Stage Towards 2-Way Inside Interaction In Mixed Reality, Yiyan Xiong Jan 2014

Automatic 3d Human Modeling: An Initial Stage Towards 2-Way Inside Interaction In Mixed Reality, Yiyan Xiong

Electronic Theses and Dissertations

3D human models play an important role in computer graphics applications from a wide range of domains, including education, entertainment, medical care simulation and military training. In many situations, we want the 3D model to have a visual appearance that matches that of a specific living person and to be able to be controlled by that person in a natural manner. Among other uses, this approach supports the notion of human surrogacy, where the virtual counterpart provides a remote presence for the human who controls the virtual character's behavior. In this dissertation, a human modeling pipeline is proposed for the …

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Exploring Sparsity, Self-Similarity, And Low Rank Approximation In Action Recognition, Motion Retrieval, And Action Spotting, Chuan Sun Jan 2014

Exploring Sparsity, Self-Similarity, And Low Rank Approximation In Action Recognition, Motion Retrieval, And Action Spotting, Chuan Sun

Electronic Theses and Dissertations

This thesis consists of 4 major parts. In the first part (Chapters 1-2), we introduce the overview, motivation, and contribution of our works, and extensively survey the current literature for 6 related topics. In the second part (Chapters 3-7), we explore the concept of "Self-Similarity" in two challenging scenarios, namely, the Action Recognition and the Motion Retrieval. We build three-dimensional volume representations for both scenarios, and devise effective techniques that can produce compact representations encoding the internal dynamics of data. In the third part (Chapter 8), we explore the challenging action spotting problem, and propose a feature-independent unsupervised framework that …

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Measuring Security: A Challenge For The Generation, Janusz Zalewski, Steven Drager, William Mckeever, Andrew J. Kornecki Jan 2014

Measuring Security: A Challenge For The Generation, Janusz Zalewski, Steven Drager, William Mckeever, Andrew J. Kornecki

Department of Electrical Engineering and Computer Science - Daytona Beach

This paper presents an approach to measuring computer security understood as a system property, in the category of similar properties, such as safety, reliability, dependability, resilience, etc. First, a historical discussion of measurements is presented, beginning with views of Hermann von Helmholtz in his 19th century work “Zählen und Messen”. Then, contemporary approaches related to the principles of measuring software properties are discussed, with emphasis on statistical, physical and software models. A distinction between metrics and measures is made to clarify the concepts. A brief overview of inadequacies of methods and techniques to evaluate computer security is presented, followed by …

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M-Fdbscan: A Multicore Density-Based Uncertain Data Clustering Algorithm, Atakan Erdem, Taflan İmre Gündem Jan 2014

M-Fdbscan: A Multicore Density-Based Uncertain Data Clustering Algorithm, Atakan Erdem, Taflan İmre Gündem

Turkish Journal of Electrical Engineering and Computer Sciences

In many data mining applications, we use a clustering algorithm on a large amount of uncertain data. In this paper, we adapt an uncertain data clustering algorithm called fast density-based spatial clustering of applications with noise (FDBSCAN) to multicore systems in order to have fast processing. The new algorithm, which we call multicore FDBSCAN (M-FDBSCAN), splits the data domain into c rectangular regions, where c is the number of cores in the system. The FDBSCAN algorithm is then applied to each rectangular region simultaneously. After the clustering operation is completed, semiclusters that occur during splitting are detected and merged to …

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Effects Of A Current Transformer's Magnetizing Current On The Driving Voltage In Self-Oscillating Converters, Güngör Bal, Seli̇m Öncü Jan 2014

Effects Of A Current Transformer's Magnetizing Current On The Driving Voltage In Self-Oscillating Converters, Güngör Bal, Seli̇m Öncü

Turkish Journal of Electrical Engineering and Computer Sciences

Magnetizing inductance is one of the parameters that affect the phase and amplitude error of the output current of current transformers (CTs). In this study, the linear circuit model of a CT is developed to be used for driving purposes in power electronics applications. A simulation of the CT and its linear model is achieved. In the model circuit, the effect of the magnetizing inductance on the driving voltage can be examined. The equivalent circuit simulation results and the linear model simulation results along with the calculated results show agreement with each other. These results are compared with experimental results.

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Design And Implementation Of An Observer Controller For A Buck Converter, Shenbaga Lakshmi, Sree Renga Raja Jan 2014

Design And Implementation Of An Observer Controller For A Buck Converter, Shenbaga Lakshmi, Sree Renga Raja

Turkish Journal of Electrical Engineering and Computer Sciences

An observer controller for a buck converter is presented. A state feedback gain matrix is derived in order to achieve the stability of the converter and to ensure the robustness of the controller. A load estimator is designed to estimate the unmeasurable variables and to obtain the zero output voltage error. A pulse-width modulation scheme is adopted to obtain the output voltage regulation. In order to improve the transitory response and dynamic constancy of the converter, the controller parameters are designed based on the current mode control. The design is evaluated and verified using MATLAB/Simulink. An experimental set-up is done …

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Impact Of Small-World Topology On The Performance Of A Feed-Forward Artificial Neural Network Based On 2 Different Real-Life Problems, Okan Erkaymaz, Mahmut Özer, Nejat Yumuşak Jan 2014

Impact Of Small-World Topology On The Performance Of A Feed-Forward Artificial Neural Network Based On 2 Different Real-Life Problems, Okan Erkaymaz, Mahmut Özer, Nejat Yumuşak

Turkish Journal of Electrical Engineering and Computer Sciences

Since feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values …

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Finding Evidence Of Wordlists Being Deployed Against Ssh Honeypots – Implications And Impacts, Priya Rabadia, Craig Valli Jan 2014

Finding Evidence Of Wordlists Being Deployed Against Ssh Honeypots – Implications And Impacts, Priya Rabadia, Craig Valli

Australian Digital Forensics Conference

This paper is an investigation focusing on activities detected by three SSH honeypots that utilise Kippo honeypot software. The honeypots were located on the same /24 IPv4 network and configured as identically as possible. The honeypots used the same base software and hardware configurations. The data from the honeypots were collected during the period 17th July 2012 and 26th November 2013, a total of 497 active day periods. The analysis in this paper focuses on the techniques used to attempt to gain access to these systems by attacking entities. Although all three honeypots are have the same configuration settings and …

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Using Internet Artifacts To Profile A Child Pornography Suspect, Marcus K. Rogers, Kathryn C. Seigfried-Spellar Jan 2014

Using Internet Artifacts To Profile A Child Pornography Suspect, Marcus K. Rogers, Kathryn C. Seigfried-Spellar

Journal of Digital Forensics, Security and Law

Digital evidence plays a crucial role in child pornography investigations. However, in the following case study, the authors argue that the behavioral analysis or “profiling” of digital evidence can also play a vital role in child pornography investigations. The following case study assessed the Internet Browsing History (Internet Explorer Bookmarks, Mozilla Bookmarks, and Mozilla History) from a suspected child pornography user’s computer. The suspect in this case claimed to be conducting an ad hoc law enforcement investigation. After the URLs were classified (Neutral; Adult Porn; Child Porn; Adult Dating sites; Pictures from Social Networking Profiles; Chat Sessions; Bestiality; Data Cleaning; …

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On Cyber Attacks And Signature Based Intrusion Detection For Modbus Based Industrial Control Systems, Wei Gao, Thomas H. Morris Jan 2014

On Cyber Attacks And Signature Based Intrusion Detection For Modbus Based Industrial Control Systems, Wei Gao, Thomas H. Morris

Journal of Digital Forensics, Security and Law

Industrial control system communication networks are vulnerable to reconnaissance, response injection, command injection, and denial of service attacks. Such attacks can lead to an inability to monitor and control industrial control systems and can ultimately lead to system failure. This can result in financial loss for control system operators and economic and safety issues for the citizens who use these services. This paper describes a set of 28 cyber attacks against industrial control systems which use the MODBUS application layer network protocol. The paper also describes a set of standalone and state based intrusion detection system rules which can be …

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Idiographic Digital Profiling: Behavioral Analysis Based On Digital Forensics, Chad M. Steel Jan 2014

Idiographic Digital Profiling: Behavioral Analysis Based On Digital Forensics, Chad M. Steel

Journal of Digital Forensics, Security and Law

Idiographic digital profiling (IDP) is the application of behavioral analysis to the field of digital forensics. Previous work in this field takes a nomothetic approach to behavioral analysis by attempting to understand the aggregate behaviors of cybercriminals. This work is the first to take an idiographic approach by examining a particular subject's digital footprints for immediate use in an ongoing investigation. IDP provides a framework for investigators to analyze digital behavioral evidence for the purposes of case planning, subject identification, lead generation, obtaining and executing warrants, and prosecuting offenders.

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