In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

https://beloglazov.info/papers/2011-energy-aware-fgcs.pdf

Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.

/pdf/image-reconstruction-in-circular-cone-beam-computed-4wndbkpv6w.pdf

Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization

The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd.

Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

/pdf/self-adaptive-software-landscape-and-research-challenges-4rt6vhdmq2.pdf

Self-adaptive software: Landscape and research challenges

This chapter describes how the B-spline registration algorithm may be extended to perform multimodal image registration by utilizing the statistically based mutual information (MI) similarity metric. Modifications to the algorithm structure and data flow presented in Chapter 2 are discussed in detail and strategies for accelerating these new algorithmic additions are explored. Specific attention is directed towards developing fast and memory efficient data parallel methods of constructing marginal and joint image intensity histograms, since these data structures are key to successfully performing this statistically based image registration method. The impact of the MI similarity metric on the analytic formalism driving the vector field evolution is covered in depth. Consequently, the partial volume interpolation method is also introduced, dictating how the image intensity histogram data structures evolve with the vector field evolution. Single-core CPU, multi-core CPU, and many-core GPU-based implementations are benchmarked for performance using synthetic image volumes. Finally, quality is assessed by example through a multimodal thoracic MRI to CT deformable registration.

Multimodal B-Spline Registration

Unmanned aerial vehicles (UAVs) represent an important class of networked robotic applications that must be both highly dependable and autonomous. This paper addresses sensor selection and placement problems for distributed failure diagnosis in such networks where multiple vehicles must agree on the fault status of another UAV. An integer linear programming (ILP) approach is proposed to solve these problems. The ILP models of interest are developed and solved using two different solvers. Experimental results indicate that the proposed models are tractable for medium-sized topologies

Sensor selection and placement for failure diagnosis in networked aerial robots

Beam-steering techniques using directional antennas are expected to play an important role in wireless network capacity expansion through ubiquitous small-cell deployment. However, integrating directional antennas into the existing wireless PHY and MAC stack of small cells has been challenging due to the added protocol overhead and lack of a robust antenna beam selection technique that can adapt well to environmental changes. This paper presents the design, implementation, and evaluation of LinkPursuit, a novel learning protocol for distributed antenna state selection in directional small-cell networks. LinkPursuit relies on reconfigurable antennas and a synchronous TimeDivision Multiple Access (TDMA) MAC to achieve simultaneous directional transmission and reception. Further, the system employs a practical antenna selection protocol based on the well known adaptive pursuit algorithm from the reinforcement learning literature. We implement a realtime prototype of LinkPursuit on the WARP platform and conduct extensive experiments to evaluate its performance. The empirical results show that appropriate use of directionality in LinkPursuit can result in higher network sum rates than omnidirectional transmission under various degrees of cross-link interference.

/pdf/reinforcement-learning-system-to-mitigate-small-cell-4e5l88yqjt.pdf

Reinforcement learning system to mitigate small-cell interference through directionality

In this paper a trained timing synchronization method using a matched filter and Carrier Frequency Offset synchronization method based on a modified correlation scheme is implemented in hardware for Orthogonal Frequency Division Multiplexing. MATLAB System Generator is used to target a Virtex-6 FPGA on the ML605 Xilinx evaluation board, with an optimized number of board resources utilized. A complex pseudo-noise sequence is used as a preamble for timing. The employed training sequence for frequency synchronization consists of only one pilot symbol, distinguishing this system from most approaches which rely upon multiple pilot symbols. By reducing the number of symbols required for the training period, it is possible to increase throughput. In addition, to ensure that the system has flexibility and is not protocol specific, the system allows for a variable number of FFT sizes and pilot symbol design parameters to be used. Performance of the system is shown to improve over an Additive White Gaussian Noise channel by implementing the design in comparison to not compensating for the distortion.

Hardware implementation of low-overhead data aided timing and Carrier Frequency Offset correction for OFDM signals

This paper describes a new software-defined radio (SDR) platform targeted for rapid prototyping of small-cell systems. The SDR hardware combines the signal processing power of Xilinx ML605 Virtex-6 FPGA board with the Nutaq Radio420X frequency-agile transceiver and reconfigurable antennas to form a highly versatile platform for spectrum sensing, spectrum access, and cooperative communications. We evaluate the platform with two example applications: an offline OFDM physical processing flow based on WARPLab, and a real-time online automatic gain control mechanism. The results show that our SDR platform can reliably handle both offline and online processing demands with the added benefit of frequency agility offered by a state-of-the-art radio transceiver.

Nagarajan Kandasamy

Papers

Multimodal B-Spline Registration

Sensor selection and placement for failure diagnosis in networked aerial robots

Reinforcement learning system to mitigate small-cell interference through directionality

Hardware implementation of low-overhead data aided timing and Carrier Frequency Offset correction for OFDM signals

Leveraging an Agile RF Transceiver for Rapid Prototyping of Small-Cell Systems