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The second method exploits an evolving idea of QAM-to-QPSK transformation, and uses transformed symbols to estimate phase mismatch.
The method exploits an air-coupled ultrasonic probe that detects the ultrasonic waves generated by a high-power pulsed laser.
This latter method exploits an invaluable prior knowledge attained from an average voice model [3], and adapts this general model using an adaptation algorithm such as maximum likelihood linear regression (MLLR)[32], maximum a posteriori (MAP)[33], and cluster adaptive training (CAT [21].
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This method exploits a cheaper initial substrate, galactose, for donor UDP-galactose production.
The method exploits a priori information coming from large sample inspection of one or a few parts of the lot.
The method exploits a small random sample of realised FRFs obtained from nominally identical structures to predict corresponding FRF bounds for a substantially larger batch.
Our method exploits a stencil-based approach for identification of the Pareto front that does not rely on population-based metaheuristic algorithms, typically used for this purpose.
The method exploits a chemical Knoevenagel Doebner condensation (optimised to give complete conversions in a short reaction time, employing microwave irradiation) and a biocatalytic phenylalanine ammonia lyase mediated hydroamination (for the stereoselective addition of ammonia).
The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task.
The method exploits a cheap and easily available biomaterial not explored so far for the synthesis of metallic nanoparticles.
The proposed method exploits a spatial pyramid image representation building on the idea proposed in [37] in which a Pyramid Match Kernel is used to find an approximate correspondence between two sets of elements.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com