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Ljung L.,Linkoping University | Singh R.,MathWorks Inc.
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2012

Version 8.0 of MATLAB's System Identification toolbox is released with version R2012a of MATLAB in the spring of 2012. This release presents a re-engineered implementation of the code using the new MATLAB object-oriented programming. Two main features are (1) that the toolbox commands and plots are seamlessly integrated with the other MATLAB toolboxes that deal with linear dynamic systems and (2) several new features and model objects. The toolbox now supports multi-input-multi-output (MIMO) systems across all model objects, and more emphasis is placed on continuous-time models. Also a new model object, idtf covers MIMO transfer function models in both continuous and discrete time. © 2012 IFAC.

Jia T.,MathWorks Inc. | Duel-Hallen A.,North Carolina State University
IEEE Transactions on Communications | Year: 2012

Adaptive bit-interleaved coded modulation (ABICM) is attractive for rapidly varying mobile radio channels due to its robustness to imperfect channel state information (CSI). A novel ABICM method that exploits the expurgated bound to maintain the target bit error rate (BER) for diverse CSI conditions is proposed and evaluated for an adaptive mobile radio orthogonal frequency division-multiplex (OFDM) system aided by the long-range fading prediction. It is demonstrated that ABICM is much less sensitive to prediction errors than adaptive modulation techniques that do not employ interleaving. However, reliable fading prediction is still necessary for ABICM to achieve high spectral efficiency for practical channel conditions. © 1972-2012 IEEE.

Tan Y.,MathWorks Inc. | Venkatesh V.,North Carolina State University | Gu X.,IBM
IEEE Transactions on Parallel and Distributed Systems | Year: 2013

Large-scale hosting infrastructures have become the fundamental platforms for many real-world systems such as cloud computing infrastructures, enterprise data centers, and massive data processing systems. However, it is a challenging task to achieve both scalability and high precision while monitoring a large number of intranode and internode attributes (e.g., CPU usage, free memory, free disk, internode network delay). In this paper, we present the design and implementation of a Resilient self-Compressive Monitoring (RCM) system for large-scale hosting infrastructures. RCM achieves scalable distributed monitoring by performing online data compression to reduce remote data collection cost. RCM provides failure resilience to achieve robust monitoring for dynamic distributed systems where host and network failures are common. We have conducted extensive experiments using a set of real monitoring data from NCSU's virtual computing lab (VCL), PlanetLab, a Google cluster, and real Internet traffic matrices. The experimental results show that RCM can achieve up to 200 percent higher compression ratio and several orders of magnitude less overhead than the existing approaches. © 2013 IEEE.

Mahapatra S.,MathWorks Inc.
AIAA Modeling and Simulation Technologies Conference 2011 | Year: 2011

The use of simulation studies to better understand the dynamic behavior of a system under investigation is at the core of verifying your designs early in the development process. Despite the amount of data that such studies produce, a 3D representation of the system creates a more complete understanding of system behavior. This paper describes the use of 3D animation in simulation-centric workflows to augment early verification activities, such as those used in Model-Based Design. The evolution of technology and domain specialization in the simulation and 3D graphics fields presents several challenges for using 3D animation in simulation-centric studies. A set of examples specific to MATLAB and Simulink environment illustrate how to meet these challenges. © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Jia T.,MathWorks Inc. | Duel-Hallen A.,North Carolina State University | Hallen H.,North Carolina State University
IEEE Transactions on Vehicular Technology | Year: 2013

The long-range prediction (LRP) of fading signals enables adaptive transmission methods for rapidly varying mobile radio channels encountered in vehicular communications, but its performance is severely degraded by the additive noise and interference. A data-aided noise reduction (DANR) method is proposed to enhance the accuracy of fading prediction and to improve the spectral efficiency of adaptive modulation systems enabled by the LRP. The DANR includes an adaptive pilot transmission mechanism, robust noise reduction (NR), and decision-directed channel estimation. Due to improved prediction accuracy and low pilot rates, the DANR results in higher spectral efficiency than previously proposed NR techniques, which rely on oversampled pilots. It is also demonstrated that DANR-aided LRP increases the coding gain of adaptive trellis-coded modulation (ATCM). Finally, for low-to-medium signal-to-noise ratio (SNR) values, we show that LRP-enabled adaptive modulation performs better for realistic reflector configurations than for the conventional Jakes model (JM) data set. © 1967-2012 IEEE.

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