In this paper, a PMMA (polymethylmethacrylate) microfluidic device with filtration features fabricated by hot embossing and thermal bonding was used to separate RBCs (red blood cells) from whole rat blood. The filtration features are composed of 20 µm deep and 300 µm wide main channels, 15 µm high and 25 µm wide micro-dams which were fabricated in main channels and an array of orthogonal side channels for perfusion flow to collect RBCs. As rat blood advances through the main channels, a perfusion flow through the side channels washes away RBCs which are sufficiently small to enter the gaps between the micro-dams and the cover plate. A silicon mold fabricated by dry etching was used to produce three-dimensional filtration features on PMMA substrates. Oxygen plasma treatment was used to increase the adhesive ability of PMMA surfaces, which enables thermal bonding at 86 • C and 0.75 MPa. The distortion of microchannels and micro-dams has been minimized, which makes the value of the gap between the micro-dam and the cover plate appropriate for cell filtration.

SummaryNetwork traffic classification is a fundamental research topic on high-performance network protocol design and network operation management. Compared with other state-of-the-art studies done on the network traffic classification, machine learning (ML) methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines (SVMs) based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real-time accurate SVM training model named SPP-SVM is proposed. An SPP-SVM is deducted from the scaling dataset and employs principal component analysis (PCA) to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP-SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP-SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two-and multi-class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP-SVM's two-and multi-class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.

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