Extraction of intrinsic frequency response of p-i-n photodiodes

This letter presents a new method for extracting the intrinsic frequency response of a p-i-n photodiode (PD) from the measured frequency response of the PD at different bias voltages. This method is much simpler than the conventional calibration method, since only the measured scattering parameters are required, and there is no need to calibrate the test fixtures and the lightwave source. Experiment shows that the proposed method is as accurate as the calibration method.

Extraction of intrinsic response from S-parameters of laser diodes

We describe a new method for extracting the intrinsic response of a laser diode from S-parameters measured using a calibrated vector network analyzer. The experimental results obtained using the new method are compared with those obtained using the optical modulation method and the frequency response subtraction method. Good agreement has been obtained, confirming the new method validity and accuracy. The new method has the advantages of obtaining the intrinsic characteristics of a laser diode with conventional measurements using a network analyzer.

Viruses' complete genomes classification based on geometrical learning

In this paper, we proposed a method of classification for viruses' complete genomes based on graph geometrical theory in order to viruses classification. Firstly, a model of triangular geometrical graph was put forward, and then constructed feature-space-samples-graphs for classes of viruses' complete genomes in feature space after feature extraction and normalization. Finally, we studied an algorithm for classification of viruses' complete genomes based on feature-space-samples-graphs. Compared with the BLAST algorithm, experiments prove its efficiency.

Transmission electron microscopy study of triple-ribbon contrast features in a ZnTe epitaxial layer

A transmission electron microscopy study of triple-ribbon contrast features in a ZnTe layer grown epitaxially on a vicinal GaAs (001) substrate is reported. The ribbons go through the layer as threading dislocations near the [<(11)over bar 2>](111) or [112](<(11)over bar 1>) directions. Each of these (with a 40 nm width) has two narrow parts enclosed by three partial dislocations (with a 20 nm spacing). By contrast analysis and contrast simulation, the ribbons have been shown to be composed of two partially overlapping stacking faults. Their origin is attributed to a forced reaction between two crossing perfect misfit dislocations.

Wavelet denoising and feature extraction of seismic signal for footstep detection

Seismic sensors are widely used to detect moving target in ground sensor networks. Footstep detection is very important for security surveillance and other applications. Because of non-stationary characteristic of seismic signal and complex environment conditions, footstep detection is a very challenging problem. A novel wavelet denoising method based on singular value decomposition is used to solve these problems. The signal-to-noise ratio (SNR) of raw footstep signal is greatly improved using this strategy. The feature extraction method is also discussed after denosing procedure. Comparing, with kurtosis statistic feature, the wavelet energy feature is more promising for seismic footstep detection, especially in a long distance surveillance.

Biologically Inspired Tensor Features

According to the research results reported in the past decades, it is well acknowledged that face recognition is not a trivial task. With the development of electronic devices, we are gradually revealing the secret of object recognition in the primate's visual cortex. Therefore, it is time to reconsider face recognition by using biologically inspired features. In this paper, we represent face images by utilizing the C1 units, which correspond to complex cells in the visual cortex, and pool over S1 units by using a maximum operation to reserve only the maximum response of each local area of S1 units. The new representation is termed C1 Face. Because C1 Face is naturally a third-order tensor (or a three dimensional array), we propose three-way discriminative locality alignment (TWDLA), an extension of the discriminative locality alignment, which is a top-level discriminate manifold learning-based subspace learning algorithm. TWDLA has the following advantages: (1) it takes third-order tensors as input directly so the structure information can be well preserved; (2) it models the local geometry over every modality of the input tensors so the spatial relations of input tensors within a class can be preserved; (3) it maximizes the margin between a tensor and tensors from other classes over each modality so it performs well for recognition tasks and (4) it has no under sampling problem. Extensive experiments on YALE and FERET datasets show (1) the proposed C1Face representation can better represent face images than raw pixels and (2) TWDLA can duly preserve both the local geometry and the discriminative information over every modality for recognition.