Clustering Fiber Traces Using Normalized Cuts.
Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred.
Ewing, B., Hillier, L., Wendl, M.C. & Green, P. Base-calling of automated sequencer traces using phred.
Ewing, B., Hillier, L., Wendl, M. C. & Green, P. Base-calling of automated sequencer traces using phred.
We also observe a 2× improvement in performance for memory-centric application traces using the MORE modeling system.
Therefore, this paper presents an instruction-level characterization for analyzing dynamic traces using a trace-driven simulator.
We first develop a characterization of write sharing (based on the notion of a write run), and then examine the traces, using metrics derived from the characterization.
Using the direct measurement methodology, we evaluate the T-SIMn framework by collecting traces using an iPhone, which is representative of a wide variety of one antenna devices.
To make our observations quantitative, we analyzed all the traces using the PyEMMA software package to generate a hidden Markov model (HMM) from the time series data of measured extensions (see Methods and Supplementary Figure 3).
Exercises cover network programming in a language of the student's choice, understanding and analyzing packet traces using tools like wireshark and mitmproxy, as well as applying security principles to analyze and determine network security.
We evaluate the performance of the protocol in simulation with real audio traces using both quantitative and mean opinion score (MOS) audio quality metrics, comparing to several standard voice codecs.
