Bacterial blight of rice, which is a vascular bundle disease caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the most serious diseases of rice worldwide, and leads to the yield of rice reduced greatly. The dominant gene Xa14 is highly resistant to Philippine race 5 of bacterial blight. The gene was located at the distal end on the long arm of chromosome 4 by Taura et al. Near-isogenic line in the background of IR24, namely IRBB14 carrying the gene Xa14 has been developed at International Rice Research Institute (IRRI). In order to construct the high-resolution likage maps for the Xa14 region on chromosome 4 for finally clone by positional cloning, two F 2 populations were used to estimate linkage based on marker genotype and reaction to disease inoculation with Xanthomonas oryzae pv. oryzae. Using 145 highly susceptible individuals from a total of 775 plants of F 2 population of the cross between IRBB14 and IR24, the gene Xa14 was located in a 0.68 cM region on the nearby end of chromosome 4, which was flanked by the molecular markers HZR970-8 and HZR988-1, with the distance of 0.34 cM between the flanked markers and Xa14, respectively, and completely cosegregated with the SSR markers HZR645-4, HZR669-2, HZR669-5, and HZR669-7 in this population. Using 158 highly susceptible individuals from a total of 763 plants of F 2 population of the cross between IRBB14 and ZZA, the gene Xa14 locus was mapped to the interval between HZR648-5 and RM280, and was 1.90 cM away from HZR648-5 which was the closest marker flanking the Xa14 locus. Combining recombination frequencies for the two populations together, the gene Xa14 was mapped to 3 BAC clones spanned approximately 300 kb in length between SSR markers HZR970-8 and HZR988-1.

Because the underwater environment is complex, autonomous underwater vehicles (AUVs) have difficulty locating their surroundings autonomously. In order to improve the adaptive ability of AUVs, this paper presents a novel obstacle localization strategy based on the flow features. Like fish, the strategy uses the flow field information directly to locate the object obstacles. Two different localization methods are provided and compared. The first method, which is named the Method of Spatial Distribution (MSD), is based on the spatial distribution of the flow field. The second method, which is named the Method of Amplitude Variation (MAV), is provided by the amplitude variation of the flow field. The flow field around spherical targets is obtained by a numerical method, and both methods use the parallel velocity component on the virtual lateral line. During the study, different target numbers, detective ratios, spacing ratios, and flow velocities are taken into account. It is demonstrated that both methods are able to locate object obstacles. However, the prediction accuracy of MAV is higher than that of MSD. That implies that MAV is more robust than MSD. These new findings indicate that the object obstacles can be directly located based on the flow field information and robust flow sensing is perhaps not based on the spatial distribution of the flow field but rather, on its fluctuation range.

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：In order to improve the environment adaptive ability of autonomous underwater vehicle (AUV), a method of form recognition and position location of underwater target is studied based on the lateral line sensing mechanism. The flow field structure of equilateral triangle is studied by numerical simulation. The pressure signal on the "lateral line" is extracted as identification information. And a support vector machine (SVM) recognition model is trained and established based on the data.The penalty factor and a kernel function parameter in SVM model is determined by two-step network method. The model test shows that the form of targets can be identified based on the pressure coefficient. The relative detection distance is analyzed and fitted by extracting the characteristic values of pressure coefficient waveform. The results show that the relative position of target can be calculated effectively based on the pressure amplitude. Therefore, it is proved that the pressure signal and SVM can be used to identify and locate underwater targets. The method provides a new idea for improving AUV environment adaptive ability.

The flow field is difficult to evaluate, and underwater robotics can only partly adapt to the submarine environment. However, fish can sense the complex underwater environment by their lateral line system. In order to reveal the fish flow sensing mechanism, a robust nonlinear signal estimation method based on the Volterra series model with the Kautz kernel function is provided, which is named KKF-VSM. The flow field signal around a square target is used as the original signal. The sinusoidal noise and the signal around a triangular obstacle are considered undesired signals, and the predicting performance of KKF-VSM is analyzed after introducing them locally in the original signals. Compared to the radial basis function neural network model (RBF-NNM), the advantages of KKF-VSM are not only its robustness but also its higher sensitivity to weak signals and its predicting accuracy. It is confirmed that even for strong nonlinear signals, such as pressure responses in the flow field, KKF-VSM is more efficient than the commonly used RBF-NNM. It can provide a reference for the application of the artificial lateral line system on underwater robotics, improving its adaptability in complex environments based on flow field information.

Fish can sense their surrounding environment by their lateral line system (LLS). In order to understand the extent to which information can be derived via LLS and to improve the adaptive ability of autonomous underwater vehicles (AUVs), a novel strategy is presented, which directly uses the information of the flow field to distinguish the object obstacle. The flow fields around different targets are obtained by the numerical method, and the pressure signal on the virtual lateral line is studied based on the chaos theory and fast Fourier transform (FFT). The compounded parametric features, including the chaotic features (CF) and the power spectrum density (PSD), which is named CF-PSD, are used to recognize the kinds of obstacles. During the research of CF, the largest Lyapunov exponent (LLE), saturated correlation dimension (SCD), and Kolmogorov entropy (KE) are taken into account, and PSD features include the number, amplitude, and position of wave crests. A two-step support vector machine (SVM) is built and used to classify the shapes and incidence angles based on the CF-PSD. It is demonstrated that the flow fields around triangular and square targets are chaotic systems, and the new findings indicate that the object obstacle can be recognized directly based on the information of the flow field, and the consideration of a parametric feature extraction method (CF-PSD) results in considerably higher classification success.

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