To provide the reliability of power supply and ensure the personal security, cables in urban city are usually paved underground in nowadays. However, according to the statistical report from State Grid, around 53.4% of cable breakages are caused by external damages from 2009 to 2011. Among these external cable vandalisms, construction equipments are the main damage sources, including impact hammer, cutting machine, grab excavator, etc. Thus, designing a surveillance system which can automatically detect such potential destroy is highly desired. In this paper, we investigate the automatic recognition system based on classifying the acoustic waves generated by the three equipments. In the proposed recognition framework, the linear prediction cepstral coefficients (LPCC) for acoustic waves generated by the three equipments are extracted in small frames. These LPCC features are then fed to the support vector machine classifier for training and testing. To show the efficiency of the recognition system, real recorded acoustic waves are collected in varies construction using a cross microphone array for experiments. Experiments show that the recognition algorithm we developed is efficiency.

Chlorophyll content of the flag leaf is an important trait for drought resistance in wheat under drought stress. Understanding the regulatory mechanism of flag leaf chlorophyll content could accelerate breeding for drought resistance. In this study, we constructed a recombinant inbred line (RIL) population from a cross of drought-sensitive variety DH118 and drought-resistant variety Jinmai 919, and analyzed the chlorophyll contents of flag leaves in six experimental locations/years using the Wheat90K single-nucleotide polymorphism array. A total of 29 quantitative trait loci (QTLs) controlling flag leaf chlorophyll were detected with contributions to phenotypic variation ranging from 4.67 to 23.25%. Twelve QTLs were detected under irrigated conditions and 18 were detected under dryland (drought) conditions. Most of the QTLs detected under the different water regimes were different. Four major QTLs (Qchl.saw-3B.2, Qchl.saw-5A.2, Qchl.saw-5A.3, and Qchl.saw-5B.2) were detected in the RIL population. Qchl.saw-3B.2, possibly more suitable for marker-assisted selection of genotypes adapted to irrigated conditions, was validated by a tightly linked kompetitive allele specific PCR (KASP) marker in a doubled haploid population derived from a different cross. Qchl.saw-5A.3, a novel stably expressed QTL, was detected in the dryland environments and explained up to 23.25% of the phenotypic variation, and has potential for marker-assisted breeding of genotypes adapted to dryland conditions. The stable and major QTLs identified here add valuable information for understanding the genetic mechanism underlying chlorophyll content and provide a basis for molecular marker–assisted breeding.

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