3/3/2023 0 Comments 2f system lens fourierWhile able to perform 100% inspection at a low cost, commonly used 2D machine vision systems are insufficient to assess all of the functionally relevant critical dimensions in such 3D products on their own. These make SSAW a suitable method for sequence analysis, especially, given the rapidly increasing volumes of sequence data required by most modern applications.ĭimensional surface metrology is required to enable advanced manufacturing process control for products such as large-area electronics, microfluidic structures, and light management films, where performance is determined by micrometre-scale geometry or roughness formed over metre-scale substrates. The running time was significantly better in most cases. SSAW demonstrates competitive or superior performance in terms of standard indicators, such as accuracy, F-score, precision, and recall. Using two different types of applications, namely, clustering and classification, we compared SSAW against the the-state-of-the-art alignment free sequence analysis methods. After these steps, the original sequence is turned into a feature vector with numeric values, which can then be used for clustering and/or classification. Then, the series of complex numbers formed are transformed into feature vectors using the stationary discrete wavelet transform. It extracts k-mers from a sequence, then maps each k-mer to a complex number field. SSAW stands for Sequence Similarity Analysis using the Stationary Discrete Wavelet Transform (SDWT). ResultsA new alignment-free sequence similarity analysis method, called SSAW is proposed. Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.
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