Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

物件導向軟體之架構(Object-Oriented Software Construction)探討

The PASCAL Visual Object Classes Challenge

Power Aware Routing in Mobile Ad Hoc Networks

In this paper, we propose PDRAM, a novel energy efficient main memory architecture based on phase change random access memory (PRAM) and DRAM. The paper explores the challenges involved in incorporating PRAM into the main memory hierarchy of computing systems, and proposes a low overhead hybrid hardware-software solution for managing it. Our experimental results indicate that our solution is able to achieve average energy savings of 30% at negligible overhead over conventional memory architectures.

PDRAM: a hybrid PRAM and DRAM main memory system

This paper reviews the development of energy harvesting for low-power embedded structural health monitoring (SHM) sensing systems. A statistical pattern recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized followed by a discussion of SHM sensor network paradigms, power requirements for these networks, and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. The paper concludes by defining some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes. Finally, it is noted that many of the technologies discussed herein are applicable to powering any type of low-power embedded sensing system regardless of the application.

/pdf/energy-harvesting-for-structural-health-monitoring-sensor-40xb7fid8o.pdf

Energy Harvesting for Structural Health Monitoring Sensor Networks

Recent proposals have employed optical circuit switching (OCS) to reduce the cost of data center networks. However, the relatively slow switching times (10--100 ms) assumed by these approaches, and the accompanying latencies of their control planes, has limited its use to only the largest data center networks with highly aggregated and constrained workloads. As faster switch technologies become available, designing a control plane capable of supporting them becomes a key challenge.In this paper, we design and implement an OCS prototype capable of switching in 11.5 us, and we use this prototype to expose a set of challenges that arise when supporting switching at microsecond time scales. In response, we propose a microsecond-latency control plane based on a circuit scheduling approach we call Traffic Matrix Scheduling (TMS) that proactively communicates circuit assignments to communicating entities so that circuit bandwidth can be used efficiently.

/pdf/integrating-microsecond-circuit-switching-into-the-data-58q77bdiqn.pdf

Integrating microsecond circuit switching into the data center

Solar panels are frequently used in wireless sensor nodes because they can theoretically provide quite a bit of harvested energy. However, they are not a reliable, consistent source of energy because of the Sun's cycles and the everchanging weather conditions. Thus, in this paper we present a fast, efficient and reliable solar prediction algorithm, namely, Weather-Conditioned Moving Average (WCMA) that is capable of exploiting the solar energy more efficiently than state-of-the-art energy prediction algorithms (e.g. Exponential Weighted Moving Average EWMA). In particular, WCMA is able to effectively take into account both the current and past-days weather conditions, obtaining a relative mean error of only 10%. When coupled with energy management algorithm, it can achieve gains of more than 90% in energy utilization with respect to EWMA under the real working conditions of the Shimmer node, an active sensing platform for structural health monitoring.

/pdf/prediction-and-management-in-energy-harvested-wireless-25tfw4kisn.pdf

Prediction and management in energy harvested wireless sensor nodes

In deep submicron circuits, elevation in temperatures has brought new challenges in reliability, timing, performance, cooling costs and leakage power. Conventional thermal management techniques sacrifice performance to control the thermal behavior by slowing down or turning off the processors when a critical temperature threshold is exceeded. Moreover, studies have shown that in addition to high temperatures, temporal and spatial variations in temperature impact system reliability. In this work, we explore the benefits of thermally aware task scheduling for multiprocessor systems-on-a-chip (MPSoC). We design and evaluate OS-level dynamic scheduling policies with negligible performance overhead. We show that, using simple to implement policies that make decisions based on temperature measurements, better temporal and spatial thermal profiles can be achieved in comparison to state-of-art schedulers. We also enhance reactive strategies such as dynamic thread migration with our scheduling policies. This way, hot spots and temperature variations are decreased, and the performance cost is significantly reduced.

/pdf/temperature-aware-task-scheduling-in-mpsocs-4eciorrt9b.pdf

Tajana Rosing

Papers

PDRAM: a hybrid PRAM and DRAM main memory system

Energy Harvesting for Structural Health Monitoring Sensor Networks

Integrating microsecond circuit switching into the data center

Prediction and management in energy harvested wireless sensor nodes

Temperature aware task scheduling in MPSoCs