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The algorithm was developed not to identify UHR frequency in all cases but to identify it in easy cases.
The algorithm was developed to determine the home address of a runner based on his or her public running routes.
The tech powering the algorithm was developed by third year PhD student Rasmus Rothe, of the Computer Vision Lab at ETH Zurich, including using image data and attractiveness ratings supplied by Bling — the latter gleaned from the binary 'hi or bye' choices Blinq users make as they swipe through potential matches.
The algorithm was developed in the context of an interdisciplinary research project that aims at discovering fundamental principles of expressive music performance from large amounts of complex real-world data (specifically, measurements of actual performances by concert pianists).
The algorithm was developed on the basis of the so called Luus Jaakola (LJ) one, which was successfully used by several researchers to solve chemical and process engineering problems.
The algorithm was developed on the MIT-BIH Arrhythmia Database where a QRS complex sensitivity of 99.82% and positive predictivity of 99.83% and a PVC sensitivity of 92.36% and specificity of 95.54% were achieved.
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The algorithm is developed using fuzzy rules and directional techniques.
"Really because the early ideas of computing, the early ideas of the algorithm were developed there".
The algorithm is developed based on stochastic finite-element method.
The algorithm is developed by analyzing the data collected from extensive exhaustive searches.
The algorithm is developed by exploiting the special structure of our model.
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