Coherent Features of Resonance-Mediated Two-Photon Absorption Enhancement by Varying the Energy Level Structure,Laser Spectrum Bandwidth and Central Frequency

The femtosecond pulse shaping technique has been shown to be an effective method to control the multi-photon absorption by the light–matter interaction. Previous studies mainly focused on the quantum coherent control of the multi-photon absorption by the phase, amplitude and polarization modulation, but the coherent features of the multi-photon absorption depending on the energy level structure, the laser spectrum bandwidth and laser central frequency still lack in-depth systematic research. In this work, we further explore the coherent features of the resonance-mediated two-photon absorption in a rubidium atom by varying the energy level structure, spectrum bandwidth and central frequency of the femtosecond laser field. The theoretical results show that the change of the intermediate state detuning can effectively influence the enhancement of the near-resonant part, which further affects the transform-limited （TL）-normalized final state population maximum. Moreover, as the laser spectrum bandwidth increases, the TL-normalized final state population maximum can be effectively enhanced due to the increase of the enhancement in the near-resonant part, but the TL-normalized final state population maximum is constant by varying the laser central frequency. These studies can provide a clear physical picture for understanding the coherent features of the resonance-mediated two-photon absorption, and can also provide a theoretical guidance for the future applications.

Application of Empirical Mode Energy to the Analysis of Fluctuating Signals

After an aerial object enters the water, physical changes to sounds in the water caused by the accompanying bubbles are quite complex. As a result, traditional signal analyzing methods cannot identify the real physical object. In view of this situation, a novel method for analyzing the sounds caused by an aerial object’s entry into water was proposed. This method analyzes the vibrational mode of the bubbles by using empitical mode decomposition. Experimental results showed that this method can efficiently remove noise and extract the broadband pulse signal and low-frequency fluctuating signal, producing an accurate resolution of entry time and frequency. This shows the improved performance of the proposed method.

Skin Lesion Classification System Using Shearlets

The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is proposed.Atfirst,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and 16.From the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for training.SVM classifier is used for the classification of skin lesion images.The dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic lesions.The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)curves.The SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.

Wavelet Energy Feature Extraction and Matching for Palmprint Recognition

According to the fact that the basic features of a palmprint, includingprincipal lines, wrinkles and ridges, have different resolutions, in this paper we analyzepalmprints using a multi-resolution method and define a novel palmprint feature, which calledwavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energydistribution of the principal lines, wrinkles and ridges in different directions at differentresolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses thediscriminabilities of each level WEF and, according to these discriminabilities, chooses a suitableweight for each level to compute the weighted city block distance for recognition. The experimentalresults show that the order of the discriminabilities of each level WEF, from strong to weak, is the4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation andtranslation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprintverification and palmprint identification, respectively. These results demonstrate the power of theproposed approach.

An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing(CAIR)is an excellent technology used widely for image retarget.It can also be used to tamper with images and bring the trust crisis of image content to the public.Once an image is processed by CAIR,the correlation of local neighborhood pixels will be destructive.Although local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise.Therefore,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this paper.Firstly,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR.Secondly,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection.Then,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train classifier.Finally support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not.The candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are created.The experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other methods.It can achieve a better performance than the state-of-the-art approaches.

Research on remote hydro-generator sets diagnosis system

A type of remote monitoring and diagnosis system is brought forward which based on Matlab Web Server.Firstly,wavelet packet decomposition is introduced to acquire energy features of which reflect hydrogenerator sets performance to be Feature Parameter.Then these Feature Parameters can be adopted as BP Neural Network input variable to realize fault diagnosis.Most of all,it is the first time to adopt Matlab Web Server to hydro-generator sets faults diagnosis field to implement distributed remote monitoring and diagnosis system.Therefore,remote diagnosis application is independent from the OS used on server side.There is no need for software maintenance by clients.And clients can finish remote diagnosis by Web Browser and without installation of Matlab-software.Client users can monitor and diagnose hydro-generator sets by Browser.Finally,further research work is pointed out such as hydro-generator sets fault modeling,accelerating BP Neural Network learning speed and convergence property,improving data transfer speed of Matlab Web Server to meet the needs of real-time diagnosis for hydropower generator sets.

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Road visual navigation relies on accurate road models.This study was aimed at proposing an improved scale-invariant feature transform(SIFT)algorithm for recovering depth information from farmland road images,which would provide a reliable path for visual navigation.The mean image of pixel value in five channels(R,G,B,S and V)were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm,for achieving precise location of the feature points and ensuring the uniformity and density of the feature points.The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45ºin eight directions was then treated as the feature vector,and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points.In order to achieve the depth information of farmland road images,the energy method of feature points was used for eliminating the mismatched points.Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48%and 5.6 s,respectively,with the accuracy for depth recovery of-7.17%-2.97%in a certain sight distance.The mean uniformity,time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%,5.0-6.5 s,and higher than 88%,respectively,indicating that performance for achieving the feature points(e.g.,uniformity,matching accuracy,and algorithm real-time)of the improved SIFT algorithm were superior to that of conventional SIFT algorithm.This study provides an important reference for navigation technology of agricultural equipment based on machine vision.