A method of line spectrum extraction based on target radiated spectrum feature and its post-processing

To improve the ability of detecting underwater targets under strong wideband interference environment,an efficient method of line spectrum extraction is proposed,which fully utilizes the feature of the target spectrum that the high intense and stable line spectrum is superimposed on the wide continuous spectrum.This method modifies the traditional beam forming algorithm by calculating and fusing the beam forming results at multi-frequency band and multi-azimuth interval,showing an excellent way to extract the line spectrum when the interference and the target are not in the same azimuth interval simultaneously.Statistical efficiency of the estimated azimuth variance and corresponding power of the line spectrum band depends on the line spectra ratio(LSR)of the line spectrum.The change laws of the output signal to noise ratio(SNR)with the LSR,the input SNR,the integration time and the filtering bandwidth of different algorithms bring the selection principle of the critical LSR.As the basis,the detection gain of wideband energy integration and the narrowband line spectrum algorithm are theoretically analyzed.The simulation detection gain demonstrates a good match with the theoretical model.The application conditions of all methods are verified by the receiver operating characteristic(ROC)curve and experimental data from Qiandao Lake.In fact,combining the two methods for target detection reduces the missed detection rate.The proposed post-processing method in2-dimension with the Kalman filter in the time dimension and the background equalization algorithm in the azimuth dimension makes use of the strong correlation between adjacent frames,could further remove background fluctuation and improve the display effect.

Inhibition of human esophageal squamous cell carcinomas by targeted silencing of tumor enhancer genes: an overview

Esophageal cancer has been reported as the ninth most common malignancy and ranks as the sixth most frequent cause of death worldwide. Esophageal cancer treatment involves surgery, chemotherapy, radiation therapy, or combination therapy. Novel strategies are needed to boost the oncologic outcome. Recent advances in the molecular biology of esophageal cancer have documented the role of genetic alterations in tumorigenesis. Oncogenes serve a pivotal function in tumorigenesis. Targeted therapies are directed at the unique molecular signature of cancer cells for enhanced efficacy with low toxicity. RNA interference(RNAi) technology is a powerful tool for silencing endogenous or exogenous genes in mammalian cells. Related results have shown that targeting oncogenes with siRNAs, specifically the mRNA, effectively reduces tumor cell proliferation and induces apoptotic cell death. This article will briefly review studies on silencing tumor enhancer genes related to the induction of esophageal cancer.

Extraction and application of the low dimensional dynamical component from underwater acoustic target radiating noise

Signal processing in phase space based on nonlinear dynamics theory is a new method for underwater acoustic signal processing. One key problem when analyzing actual acoustic signal in phase space is how to reduce the noise and lower the embedding dimen- sion. In this paper, local-geometric-projection method is applied to obtain fow dimensional element from various target radiating noise and the derived phase portraits show obviously low dimensional attractors. Furthermore, attractor dimension and cross prediction error are used for classification. It concludes that combining these features representing the geometric and dynamical properties respectively shows effects in target classification.

Local-linear-prediction analysis for underwater acoustic target radiated noise

Local-linear-prediction in phase space is performed for the underwater acoustic target radiated noise. Relation curve of average prediction error versus neighboring points' number is calculated. The result is used in judging the nonlinearity of radiated noise time series, and obtaining the appropriate form and coefficients of predicting model. The line and continuous spectral component are predicted respectively. Choice of some model parameters minimizing the prediction error is also discussed.