Tracking accuracy results for the proposed method is presented on the basis of synthetic audio samples generated with the image method, whereas performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are published elsewhere.
The statistical performance of the fully validated HPLC method was compared to our developed sensitive spectrofluorimetric method, and the performance results of the proposed HPLC method were considerably satisfactory.
We also assess the performance of the package, in comparison with publicly distributed interface codes (the level set method), with published performance results (VOF and other methods) and with previous versions of front tracking.
While the Bayesian ML method achieves promising performance results for moderate channel lengths, computational complexity continues to be a drawback for longer channels.
Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets.
Case studies are used to validate the method and illustrate that performance results in the 60 90% range can be expected.
Applications in upscaling, tech transfer and commercial production are described, along with evaluation of the method performance by comparison with results from quantitative calibration models.
To illustrate the new method, we present the performance results of an LDPC code constructed over with quadrature phase shift keying (QPSK) modulated signals transmitted over a static channel, but affected by phase noise, which is modeled by the Wiener (random-walk) process.
The data of this series of validation studies are compared to the ENGL method performance requirements and the results are analysed to determine the relative contribution of the different aspects of the test procedure to the variation.
The calibration algorithms, methods and tools and performance results using LHC collision data are discussed.
