Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (4 results)

  • Article
  • Peer Reviewed
UC San Diego Previously Published Works (2020)
Social scientists, psychologists, and epidemiologists use empirical human interaction data to research human behaviour, social bonding, and disease spread. Historically, systems measuring interactions have been forced to choose between deployability and measurement fidelity—they operate only in instrumented spaces, under line-of-sight conditions, or provide coarse-grained proximity data. We introduce SociTrack, a platform for autonomous social interaction tracking via wireless distance measurements. Deployments require no supporting infrastructure and provide sub-second, decimeter-accurate ranging information over multiple days. The key insight that enables both deployability and fidelity in one system is to decouple node mobility and network management from range measurement, which results in a novel dual-radio architecture. SociTrack leverages an energy-efficient and scalable ranging protocol that is accurate to 14.8 cm (99th percentile) in complex indoor environments and allows our prototype to operate for 12 days on a 2000 mAh battery. Finally, to validate its deployability and efficacy, SociTrack is used by early childhood development researchers to capture caregiver-infant interactions.
    Cover page: SociTrack: Infrastructure-Free Interaction Tracking through Mobile Sensor Networks
    • Article
    Technical Reports (2009)

    This paper presents a novel energy attribution and accounting architecture for multi-core systems that can provide accurate, per-process energy information of individual hardware components. We introduce a hardwareassisted direct energy measurement system that integrates seamlessly with the host platform and provides detailed energy information of multiple hardware elements at millisecond-scale time resolution. We also introduce a performance counter based behavioral model that provides indirect information on the proportional energy consumption of concurrently executing processes in the system. We fuse the direct and indirect measurement information into a low-overhead kernel-based energy apportion and accounting software system that provides unprecedented visibility of per-process CPU and RAM energy consumption information on multi-core systems. Through experimentation we show that our energy apportioning system achieves an accuracy of at least 96% while impacting CPU performance by less than 0:6%.

      Cover page: Accurate Energy Attribution and Accounting for Multi-core Systems
      • Article
      • Peer Reviewed
      UC Riverside Previously Published Works (2012)
      Hardware specialization is often the key to efficiency for programmable embedded systems, but comes at the expense of flexibility. This paper combines flexibility and efficiency in the design and synthesis of domain-specific datapaths. We merge all individual paths from the Data Flow Graphs (DFGs) of the target applications, leading to a minimal set of required resources; this set is organized into a column of physical operators and cloned, thus generating a domain-specific rectangular lattice. A bus-based FPGA-style interconnection network is then generated and dimensioned to meet the needs of the applications. Our results demonstrate that the lattice has good flexibility: DFGs that were not used as part of the datapath creation phase can be mapped onto it with high probability. Compared to an ASIC design of a single DFG, the speed of our domain-specific coarse-grained reconfigurable datapath is degraded by a factor up to 2x, compared to 3-4x for an FPGA; similarly, our lattice is up to 10x larger than an ASIC, compared to 20-40x for an FPGA. We estimate that our array is up to 6x larger than an ASIC accelerator, which is synthesized using datapath merging and has limited or null generality. © 2012 EDAA.
        • Article
        • Peer Reviewed
        Journal of Systems Research, Volume 1, Issue 1 (2021)

        When designing their performance evaluations, networking researchers often encounter questions such as: How long should a run be? How many runs to perform? How to account for the variability across multiple runs? What statistical methods should be used to analyze the data? Despite their best intentions, researchers often answer these questions differently, thus impairing the replicability of their evaluations and the confidence in their results.

        In this paper, we propose a concrete methodology for the design and analysis of performance evaluations. Our approach hierarchically partitions the performance evaluation into three timescales, following the principle of separation of concerns. The idea is to understand, for each timescale, the temporal characteristics of variability sources, and then to apply rigorous statistical methods to derive performance results with quantifiable confidence in spite of the inherent variability. We implement this methodology in a software framework called TriScale. For each performance metric, TriScale computes a variability score that estimates, with a given confidence, how similar the results would be if the evaluation were replicated; in other words, TriScale quantifies the replicability of evaluations. We showcase the practicality and usefulness of TriScale on four different case studies demonstrating that TriScale helps to generalize and strengthen published results.

        Improving the standards of replicability in networking is a complex challenge. This paper is an important contribution to this endeavor; it provides networking researchers with a rational and concrete experimental methodology rooted in sound statistical foundations. The first of its kind.

          Cover page: [Tool] Designing Replicable Networking Experiments With TriscaleCreative Commons 'BY-NC' version 4.0 license
          Top