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

The paper develops a methodology for the online built-in self-testing of deep neural network (DNN) accelerators to validate the correct operation with respect to their functional specifications. The DNN of interest is realized in the hardware to perform in-memory computing using non-volatile memory cells as computational units. Assuming a functional fault model, we develop methods to generate pseudorandom and structured test patterns to detect hardware faults. We also develop a test-sequencing strategy that combines these different classes of tests to achieve high fault coverage. The testing methodology is applied to a broad class of DNNs trained to classify images from the MNIST, Fashion-MNIST, and CIFAR-10 datasets. The goal is to expose hardware faults which may lead to the incorrect classification of images. We achieve an average fault coverage of 94% for these different architectures, some of which are large and complex.

/pdf/built-in-functional-testing-of-analog-in-memory-accelerators-3w2knw48.pdf

Built-In Functional Testing of Analog In-Memory Accelerators for Deep Neural Networks

We present a hybrid wired-wireless time synchronization method targeting enterprise WLAN networks to enable future deployments of distributed wireless PHY protocols. Our method synchronizes all network APs on the Ethernet backhaul using the IEEE 1588 Precise Time Protocol, while all clients are synchronized to their home AP via reference broadcast synchronization. We implement this synchronization primitive on WARP software-defined radios and evaluate through a microbenchmark involving four network links. The results verify that our method achieves sub-microsecond synchronization accuracy among all network nodes and therefore serves as an effective building block for protocol development.

https://dl.acm.org/doi/pdf/10.1145/3131348.3131349

Sub-Microsecond Network Synchronization for Distributed Wireless PHY Protocols

Spike-based convolutional neural networks (CNNs) are empowered with on-chip learning in their convolution layers, enabling the layer to learn to detect features by combining those extracted in the previous layer. We propose ECHELON, a generalized design template for a tile-based neuromorphic hardware with on-chip learning capabilities. Each tile in ECHELON consists of a neural processing units (NPU) to implement convolution and dense layers of a CNN model, an on-chip learning unit (OLU) to facilitate spike-timing dependent plasticity (STDP) in the convolution layer, and a special function unit (SFU) to implement other CNN functions such as pooling, concatenation, and residual computation. These tile resources are interconnected using a shared bus, which is segmented and configured via the software to facilitate parallel communication inside the tile. Tiles are themselves interconnected using a classical Network-on-Chip (NoC) interconnect. We propose a system software to map CNN models to ECHELON, maximizing the performance. We integrate the hardware design and software optimization within a co-design loop to obtain the hardware and software architectures for a target CNN, satisfying both performance and resource constraints. In this preliminary work, we show the implementation of a tile on a FPGA and some early evaluations. Using 8 STDP-enabled CNN models, we show the potential of our co-design methodology to optimize hardware resources.

Hardware-Software Co-Design for On-Chip Learning in AI Systems

Obfuscation of the orthogonal frequency-division multiplexing (OFDM) physical layer is described in this paper as a means to enhance the security of wireless communication. The standardization of the communication channel between two trusted parties results in a variety of security threats, including vulnerabilities in WPA/WPA2 protocols that allow for the extraction of the software layer encryption key. Obfuscating the physical layer of the OFDM pipeline provides an additional layer of security in the event that the software layer key is compromised and allows for rolling updates of the physical layer key without altering the software layer key. The interleaver stage of the OFDM pipeline is redesigned to utilize a physical layer key, which is termed Phy-Leave. The Phy-Leave interleaver is evaluated through both MATLAB simulation and hardware prototyping on the Software Defined Communication (SDC) testbed using a Virtex6 FPGA. The implemented rolling physical layer key policy and Phy-Leave system resulted in a less than 1% increase in the area of a Virtex6 FPGA, demonstrating physical layer obfuscation as a means to increase the security of wireless communication without a significant cost in hardware.

Securing Wireless Communication via Hardware-Based Packet Obfuscation

A key-based interleaver for enhancement the security of wireless communication includes a physical layer communication channel key to provide security even when the software encryption key is compromised. A method of creating a secure communication link using a physical layer interleaving system includes implementing a key policy implementation that utilizes temporal dependency and interleaving bits using a flexible inter and intra-block data interleaver.

Nagarajan Kandasamy

Papers

Built-In Functional Testing of Analog In-Memory Accelerators for Deep Neural Networks

Sub-Microsecond Network Synchronization for Distributed Wireless PHY Protocols

Hardware-Software Co-Design for On-Chip Learning in AI Systems

Securing Wireless Communication via Hardware-Based Packet Obfuscation

Physical Layer Key based Interleaving for Secure Wireless Communication