Design and fabrication of polarization-independent micro-ring resonators

Origin of polarization sensitivity of photonic wire waveguides (PWWs) is analysed and the effective refractive indices of two different polarization states are calculated by the three-dimensional full-vector beam propagation method. We find that PWWs are polarization insensitive if the distribution of its refractive index is uniform and the cross section is square. An MRR based on such a polarization-insensitive PWW is fabricated on an 8-inch silicon-on-insulator wafer using 248-nm deep ultraviolet lithography and reactive ion etching. The quasi-TE mode is resonant at 1542.25 nm and 1558.90 nm, and the quasi-TM mode is resonant at 1542.12 nm and 1558.94 nm. The corresponding polarization shift is 0.13 nm at the shorter wavelength and 0.04 nm at the longer wavelength. Thus the fabricated device is polarization independent. The extinction ratio is larger than 10 dB. The 3 dB bandwidth is about 2.5 nm and the Qvalue is about 620 at 1558.90 nm.

Temperature-independent fiber Bragg grating strain sensor using bimetal cantilever

A new packaged fiber Bragg grating using bimetal cantilever beam as the strain agent is presented. The grating is two-point attached on one specific surface of the bimetal beam which consists of two metallic material with different thermal-expansion coefficient. Thereby the grating can be compressed or stretched along with the cantilever beam while temperature varies and temperature compensation can be realized. At the same time, grating chirping can be avoided for the particular attaching method. Experiment results demonstrated that the device is able to automatically compensate temperature induced wavelength shift. The temperature dependence of Bragg wavelength reduced to -0.4 pm/degrees C over the temperature range from -20 to 60 degrees C. This fiber grating package technique is cost effective and can be used in strain sensing. (c) 2005 Elsevier Inc. All rights reserved.

Digits speech recognition based on geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems.We observe that this may be true for a recognition tasks based on geometrical learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions via the Hilbert transform. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy, Experiments show method based on ICA and geometrical learning outperforms HMM in different number of train samples.

Cancer subtypes recognition and its gene expression profiles analysis based on geometrical learning

The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.