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In this work we fabricate and validate two optofludic devices for the manipulation and analysis of single cells. The chips are fabricated by femtosecond laser micromachining exploiting the 3D capabilities of the technique and the inherent perfect alignment between microfluidic channels and optical networks. Both devices have been validated by probing the mechanical properties of different cancer cell lines, which are expected to show different elasticity because of their different metastatic potential.

According to reports, lung cancer is gradually becoming the first cancer that threatens human life. The early stage of lung cancer is in the form of pulmonary nodules. The key issue in computer-aided diagnosis of lung tumors is to correct and accelerate rapid segmentation of diseased tissue. Therefore, this paper proposes a robust fuzzy c-mean clustering algorithm for pulmonary nodules segmentation, which can effectively improve the adaptive degree of local domain pixels. Since the information of the domain pixels does not necessarily have a positive correlation with the central pixels, the reference mechanism of domain window pixel information needs to be redefined. The robust fuzzy c-means clustering algorithm redefines the grayscale of the spatial pixel points in the domain and selects different fuzzy factors according to the reference standard. Based on this, the weights of different fuzzy factors are updated according to the characteristics of pixel points and gray fluctuation in pixel domain. The experimental results show that this method is superior to other typical algorithms in the segmentation of pulmonary nodules.

The advance of the signal processing, this paper implement a more accurate digital measurement algorithm, Smart DFT(SDFT) in Micro Phasor Measurement Unit(μPMU), which is based on Discrete Fourier Transforms(DFT) to estimate frequency information from Taiwan power system. μPMU, the sensor we plan to acquire the information from the power system by using the signal of outlet voltage-level 110V in Taiwan, such as frequency, voltage and angle. The performance of SDFT implemented in μPMU represents a more precise frequency information when frequency fluctuation occurred just as the frequency of power system. We offer the results of simulations, stable frequency generated from waveform generator and real frequency from main electricity to compare SDFT with DFT method which implemented in μPMU.