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A novel dual-band algorithm for detecting contaminants with low visibility on chicken carcass surface based on hyperspectral image was proposed. Firstly, The 675 nm band image, in which the identity of the intensity within ROI (Region of Interest) is the best and the spectrum difference between ROI and the edge of the ROI is the biggest, was chosen from the hyperspectral data for binarization and the mask was extracted by using region growing on the biggest connected area. Then the "and" operation between the mask and the 400 nm band image with the largest discriminability of contaminants was carried out. The max ROI which can self adapt according to the position and shape of the chicken carcass was obtained. Finally, the labeling method was used to recognize if there are contaminations within the segmented ROI. The results showed that through the proposed method, the max ROIs which could self adapt to the position and shape of the chicken carcass were extracted and the average size of the ROI was bigger than 176% compared to that by existing methods. The average correct identification rate of contaminations such as blood, bile and feces was 81.6%.

Moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) are two important sensors on TERRA satellite. The authors can have more spectral and multi-angular observations on the land surface objects by combining these two datasets. In the present paper, both MODIS and MISR observations were combined to estimate leaf area index (LAI) of land surface. The adjoining model and trust-region optimal algorithm were introduced into the framework of physical model inversion to speed up the running of the model inversion algorithm. And the algorithm allows the prior knowledge on the retrieved parameters to be input into the inversion procedure. The uncertainty and sensitivity matrix (USM) based analysis is helpful for selecting the observed data subset with more information and less noise to retrieve LAI. The measured LAI in situ and estimated LAI from ETM data were scaling-up to MODIS/MISR LAI product scale, and were taken as the ground truth to evaluate the new approach. The result suggests that combining two sensors datasets can improve the accuracy of LAI estimation, and the developed inversion method in this paper can be applied to the large area remote sensed image data effectively.

Airborne hyperspectral identification of red tide organism dominant species can provide technique for distinguishing red tide and its toxin, and provide support for scaling the disaster. Based on support vector machine(SVM), the present paper provides an identification model of red tide dominant species. Utilizing this model, the authors accomplished three identification experiments with the hyperspectral data obtained on 16th July, and 19th and 25th August, 2001. It is shown from the identification results that the model has a high precision and is not restricted by high dimension of the hyperspectral data.

The spectral characteristics of the coastal waters in East China Sea was studied using in situ measurements, and the multiband algorithms of remote sensing for bloom waters was discussed and developed. Examples of red tide detection using the algorithms in the East China Sea were presented. The results showed that the algorithms could provide information about the location and the area coverage of the red tide events.

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Single-view 3D reconstruction based on deep learning is a research hot spot at present. In order to discover more high-frequency details, SDF-SRN algorithm introduces positional encoding, but neural network is easy to overfit without accurate supervision, and reconstructs uneven surface. To solve the problem, this paper proposed the network model based on sparse feature. The model enabled the network that preferred to overfitting to predict high-frequency residual by residual learning. The feature extraction network extracted sparse features and the global features. Then one hypernetwork took the sparse features as input and generated prediction shallow head. This shallow head predicted low-frequency part of signed distance function. Another hypernetwork took global features as input and generated another shallow head. This shallow head predicted high-frequency residual. It fused two predictions of shallow heads into final signed distance function. Spectrum analysis shows that the design purpose of network is achieved. Compared with other smooth surface reconstruction schemes, the network can achieve smoother surface reconstruction with enough details. It overcomes the overfitting of SDF-SRN. The qualitative and quantitative comparison with other advanced single-view reconstruction approaches show the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Aiming at the urgent demands of intelligent fault diagnosis for the key components of helicopter main reducers， the dynamic response characteristics of the system under the typical fault state are analyzed from the perspective of dynamic modeling， which is conducive to deepening the understanding of fault mechanism and advanced diagnosis algorithm. The fault dynamic models considering multi-nonlinear factors are developed for the typical components of a helicopter main reducer using the lumped-parameter method， and the multi-body software is applied for simulation modeling and verification. The results show that the modulation phenomenon between meshing frequency and local fault characteristic frequency is clear and the circumferential responses of the planet gear are obviously greater than the translational responses. these models can effectively reveal the vibration characteristics under typical faults and provide guidance for fault diagnosis of the helicopter transmission system. [ABSTRACT FROM AUTHOR]

A hyperspectral reflectance can be expected as an alternative method to quickly predict the concentration of heavy metals in soil at present. However, excessive band counting of hyperspectral data can lead to information redundancy during the quantitative inversion of surface parameters, and thereby to make it difficult to extract valid data from the spectrum. In this study, two aspects were addressed to solve spectral redundancy during the inversion of Pb content in soil: One is to extract the characteristics of iron oxides from the soil spectrum, according to the adsorption mechanism of Pb element in soil, in order to reduce data redundancy, while enhance the mechanism of inversion. Another is to propose an Improved Genetic Algorithm (IGA), in order to deal with the premature convergence of traditional Genetic Algorithm (GA), further to improve the extracting ability of algorithm for the required spectrum. Taking the Xiong’an New Area near Beijing, a national new-district in China, as the research region, the field experiment was carried out to quickly collect the Pb content of soil in the farmland, resulting from the rapid construction and developing facilities in recent years. 70 soil samples from the Xiong’an farmland were selected to establish the inversion model of Pb content. SVC HR1024I type ground object spectrometer was used to measure the raw hyperspectral reflectance of soil samples according to the national standard procedure in field farmland. After that, the collected soil samples were transferred to the laboratory for chemical analysis, particularly for the Pb content. Before modeling, the methods, including Savitzky-Golay filter, envelope removal and first-order difference, were used to pretreat with segmented the curves of soil reflectance. Partial Least Squares Regression (PLSR) was selected to serve as inversion model, in order to well deal with the strong correlation between hyperspectral bands. The models were also constructed in terms of full spectrum and characteristic bands of iron oxides, to verify the effectiveness of extracting method from characteristic bands of iron oxides during the inversion process. In full spectrum, the average values of R2, RPD, RMSE in the IGA-PLSR models were 0.440, 1.366, and 4.012 mg/kg, respectively. In the characteristic bands of iron oxides, the average values of R2, RPD, RMSE in the IGA-PLSR models were 0.822, 2.377, and 2.221 mg/kg, respectively. It infers that the models using the characteristic bands of iron oxides were much better than those using the full spectrum, indicating that the extraction for the characteristic bands of iron oxides can be contributed to reduce the redundancy of spectral information, and thereby to significantly improve the accuracy of the inversion model. Consequently, a final inversion model was selected from the optimal model for the improvement of inversion algorithm, based on the utilization of the characteristic bands of iron oxides to construct the GA-PLSR and IGA-PLSR models with different iterations. In the GA-PLSR models, the average values of R2, RPD, RMSE in the optimal model were 0.788, 2.140, and 2.447 mg/kg, respectively, whereas, the IGA-PLSR model achieved the highest accuracy, where the average values of R², RPD, RMSE were 0.837, 2.403, and 2.063 mg/kg, respectively. Therefore, the optimal model of IGA-PLSR was chosen to estimate the Pb content in soil for the Xiong’an farmland area. The IGA-PLSR method has demonstrated better performance in the inversion of Pb content in soil, indicating that the proposed method was valid to improve genetic algorithm. At last, a visualized profile can be conducted for the changes of RMSE during the 1000 iterations of two models. The visualization revealed that the RMSE in the GA-PLSR model decreased monotonously during the operation process, whereas, the RMSE in the IGA-PLSR model fluctuated greatly at the beginning of the iteration, showing a certain probability to jump out from the local solution area to a better solution area at the later stage, while meaning that the problem of “premature convergence” was solved properly. This study revealed that two aspects, including the extraction of iron oxides spectral features associated with Pb absorption, and the improved algorithm of bands selection, both can be used to enhance the accuracy of Pb content estimation in soil. The proposed algorithm can provide a sound reference to real-time monitor the variation of heavy metal content in soil for Xiong’an area, China. [ABSTRACT FROM AUTHOR]

This paper proposed a new speech concealment algorithm for many speech concealment algorithms with small hidden capacity and bad transparency. Firstly, it compressed and encoded the concealed information by G. 729A, and then applied the lifting wavelet transform and singular spectrum analysis to the audio information hiding. Experiments show that the audio file embedded in the hidden information by the algorithm not only has a significant improvement in the hidden information capacity, but also has good transparency, and can also resist certain attacks. The algorithm in concealed capacity, transparency and robustness reaches a better balance. [ABSTRACT FROM AUTHOR]

Tomato has become the most cultivated and consumed vegetable crop in the world, and China has become one of the largest producers and consumers of tomatoes in the world. The pectin content in cell wall of tomato during ripening is closely related to fruit hardness, which is one of the important indicators to determine the maturity and reflect the quality of tomato. The requirement of tomato maturity classification and evaluation promotes the development of non-destructive, fast and accurate detection methods of tomato hardness.Hyperspectral imaging integrates spectroscopy and imaging technology in an analysis system, which transfers tomato maturity assessment from subjective, manual classification and measurement methods. Hyperspectral imaging has been widely used in the rapid acquisition of information to classify, detect or identify the quality of various fruits. A novel method for tomato hardness detection based on hyperspectral imaging and modified interval Random Frog was proposed in this paper. Firstly, hyperspectral images of 120 tomato samples in different mature periods were captured by hyperspectral imaging system covering near-infrared region (865.11nm-1 711.71nm). And the hardness data of tomato was obtained by texture analyzer. Secondly, the spectral data were pretreated by multiplicative scatter correction (MSC) and normalized preprocessing to eliminate noise and improve signal-to-noise ratio. The validity of the characteristic wavelength plays a crucial role in the prediction performance of the model. Therefore, we need an effective method to extract the effective wavelength to improve the accuracy of the model. Interval random frog (iRF) algorithm considers all possible spectral wavelengths and ranks all the wavelengths based on selected probability. But one of the disadvantages of this method is large number of iterations and slow convergence. In view of above disadvantages, the traditional iRF algorithm was optimized in terms of constructing initial variable subset method. A modified interval Random Frog (miRF)was proposed to extract the characteristic wavelength effectively. Finally, a prediction model was developed based on partial least squares regression (PLSR) method to detect tomato hardness. The results indicated that the convergence efficiency and accuracy of miRF has a significantly improvement compared with the iRF method. The iRF has selected 100 feature bands, accounting for 40.65% of the full band, and its runtime was 32.1min. miRF has selected 47 feature bands, accounting for 19.1% of the full band, and its runtime was 1.6 min. It can be seen that miRF greatly reduces the running time of the algorithm. The characteristic wavelengths selected by iRF and miRF methods were mainly distributed in 1 582 nm-1 655 nm, followed by 1 160 nm-1 190 nm and 1 353 nm-1 383 nm, indicating that above regions were sensitive bands to tomato hardness. In order to prove the effectiveness of the proposed algorithm, the results of miRF-PLSR were compared with those of iRF-PLSR and SPA-PLSR. The prediction set correlation coefficients (RP) of the SPA-PLSR model and the iRF-PLSR model were 0.803 9 and 0.936 6 respectively. And the RP of miRF-PLSR model was 0.968 5. The root mean square error (RMSEP) of the SPA-PLSR model and the iRF-PLSR model were 0.007 7 kg/mm2 and 0.004 4 kg/mm2respectively. And the RMSEP of miRF-PLSR model was 0.004 0 kg/mm2. The experiments results show that the miRF-PLSR model has the best prediction results in all models. [ABSTRACT FROM AUTHOR]

In this paper, in order to realize the quick, non-destructive and accurate diagnosis of rice nutritional status, we use hyperspectral imaging techniques as an approach for nitrogen content prediction of rice leaves in cold region. The experiments were carried out for two years (2014 and 2015) at Fangzheng country, Heilongjiang province, China. Longdao 20 was chosen as the test cultivar. 6 nitrogen fertilization rates were applied in our experiments, i.e., N0 (0 kg/hm2), N1 (60 kg/hm2), N2 (90 kg/hm2), N3 (120 kg/hm2), N4 (150 kg/hm2), and N5 (180 kg/hm2). The hyperspectral reflectance and nitrogen content of rice leaves under different nitrogen levels at jointing stage were separately measured using American Headwall imaging spectrometer and German AA3 analyzer. The hyperspectral images of 240 rice leaf samples in the spectral range of 400-1 000 nm were acquired. Average spectrum was extracted from the region of interest (ROI) of each sample. Several regression analysis (RA) estimate models have been built based on different characteristic spectral parameters using different algorithms which include successive projections algorithm (SPA) and segmented principal components analysis (SPCA) combined with correlation analysis (CA) for testing and screening. The first method, a nitrogen content value estimation model based on multiple stepwise regression analysis (MSRA) in the whole wavelength region of 400~1000nm has been built and been predicted. Wavelengths 899, 890 nm were retained as the model independent variables. The second method, 8 characteristic wavelengths i.e., 454, 460, 475, 504, 525, 685, 700 and 735 nm were chosen by SPA and selected as modeling variables of MSRA. Wavelengths 735, 525 nm were chosen as the model independent variables. The third method, we divided the whole wavelength into 5 parts which are 400-504, 505-670, 671-697, 698-724 and 725-1 000 nm using correlation coefficient matrix method. The principal component analysis (PCA) was carried out on each part, and 7 sensitive bands were selected according to the contribution rate of each component. The correlation between the nitrogen content of rice leaves and the characteristic spectral parameters which consist of sensitive bands was analyzed, and 11 characteristic spectral parameters, i.e., single band index (SI(558), SI(866)), red edge position index (REPI(709)), ratio index (RI(866,670)), difference index (DI(730,715), DI(730,558)), double difference index (DDI(730,715,685), DDI(866,685,558)), normalized difference index (NDI(866,670), NDI(866,685)) and green normalized difference index (GNDI(730,558)) were selected to establish simple regression analysis (SRA) models. 4 kinds of characteristic spectral parameters with the highest coefficients of determination (RC2 and RP2) were selected to establish multiple regression analysis (MRA) models. We predicted all the estimate models so that testing its accuracy and stability. The results indicated that, reflectance of rice leaves decreases in the visible region, and increases in the near infrared region, with the raise of nitrogen level. From the calibration performance index, the whole wavelength model based on MSRA is the best with a coefficient of determination (RC2=0.821) and root mean square error (MRSEC) of 0.079. From the prediction performance index, the multivariate SI(866), DI(730,715), DDI(730,715,685), DDI(866,685,558) based on SPCA-CA combined MRA is the best with a coefficient of determination (RP2=0.869) and the root mean square error (RMSEP) of 0.085. The study provides technical support and theoretical basis for the rapid detection of nitrogen content in rice leaves and the precise fertilization management during rice growth. [ABSTRACT FROM AUTHOR]

Traditional vision detection methods are not suitable for plastic films mingled in ginned cotton, for their color is transparent or very similar to cotton.As an emerging technique, hyperspectral imaging integrates spectroscopy and traditional imaging, can acquire spectral and spatial information at the same time. Therefore, hyperspectral reflectance images of plastic films were captured using hyperspectral imaging technique and a hyperspectral image segmentation method for plastic films by the combination of spectrum analysis and image processing technology was proposed.Eight regions of interest(ROIs) with different sizes in different parts of each sample were randomly selected to extract spectral data.The mean spectra of each sample were obtained by calculating the average spectra of all the 8 ROIs.The noisy spectral data at the two ends of the spectral curves were removed and 750 bands of spectral data in 450.8~1034.9 nm range were selected.In order to decrease noise disturbance, Savitzky-Golay(SG) polynomial smoothing was used to eliminate random noise, and standard normal variate(SNV) transformation was used to correct the errors caused by spectrum scattering.The preprocessed spectral data matrix was selected as independent variables to establish the partial least squares regression model.Then, the regression model was used to conduct partial least squares regression analysis, and the first three loading vectors of the loading matrix were chosen to plot the curves.Significant difference in the peaks and valleys of the three loading vectors from zero was observed, therefore, the wavelengths corresponding to the peaks and valleys could be considered as effective wavelengths.Then 4 wavelengths at 560.3,3.9,6.9 and 798.8 nm were selected as the effective wavelengths.Afterwards, arithmetic operations were used for image fusion.Images at the 4 wavelengths were extracted and the addition of three images at 673.9,6.9 and 798.8 nm was performed.The result showed that the main part of the plastic films was retained through a variety of image fusion methods.Hence, the fused image was used for segmentation by Otsu’s method and then median filtering was performed on the segmentation results to reduce noise.In order to obtain the edge information of plastic films, a subtraction of the doubled images at 560.3 nm from the aforementioned fused image was adopted to obtain the fused images.Otsu’s method and median filtering were performed again to get the segmentation results and remove noise.Finally, the results obtained with the abovementioned two fusion methods were integrated again and small targets were removed to get the final results.Segmentation results showed that the plastic films were well segmented and the edge details were complete.To better reflect the effectiveness of the used image fusion method, principal components analysis(PCA) and minimum noise fraction(MNF) were conducted on the 4 images at the effective wavelengths and Otsu’s method was used to segment the first principal component(PC1) image and first minimum noise fraction(MNF1) image.Then the median filtering and small targets removal were performed.Observation revealed that the over-segmentation of PC1 and MNF1 images was serious and many background areas were divided into target areas, which indicated that the proposed image fusion method was better than both PCA and MNF.To validate the segmentation performance of the proposed method, area overlap measure(AOM), misclassified error(ME) and recognition accuracy were used to evaluate the segmentation effects objectively.The results showed that the proposed method got the best AOM value of 0.6636 and ME value of 0.4226, with the accuracy reached 91.07%.This study reveals that the proposed method is able to well segment the images of plastic films and can lay a good foundation for the subsequent feature extraction and automatic recognition. [ABSTRACT FROM AUTHOR]

Rice blast is one of the most serious rice diseases and significantly impacts rice yields. In recent years, it is a hotspot to use hyperspectral imaging technology for the nondestructive identification of rice blast. However, nutrient deficiency in rice(such as nitrogen, potassium, etc.) will probably result in the chlorosis similar to rice blast. Therefore, to differentiate between them is very important for field management.In this study, field trials of rice blast and nitrogen stress were carried out in Fangzheng, Harbin, and 2 rice varieties with weak resistance were involved. From 8 to 10 in July, 2015,4 types of rice leaves from both 2 varieties, including 60 in group of health, 60 in group of nitrogen deficiency, 60 in group of mild infection and 60 in group of severe infection, were collected and their hyperspectral images were captured with the HeadWall hyperspectral imaging system, and then the average reflectance spectrum of interest region of different leaves were acquired using the environment for visualizing images. In order to explore 4 types of spectral characteristics, the average spectrum of each type sample data, which was smoothed with polynomial convolution smoothing(Savitzky-Golay, SG), were calculated as a spectral curve of each category. Significant differences were found at the following three positions:the range around 560 nm in the reflection peak of green wavelength region; the range from 620 nm to 670 nm in red wavelength region; and particularly remarkable in the range around 760 nm in high reflectance of the near infrared region.The models of rice leaf blast recognition were established by taking advantage of a partial least squaresdiscriminate analysis method(PLS-DA) and the principle component analysis plus support vector machine(PCA-SVM), and using three different data pretreatment methods to preprocess original reflectance spectrum data, i.e., SG, standard normal variate transformation(SNV) and multiplicative scatter correction(MSC).The models were tested with the cross-validation strategy. The key of PLSDA model is to select appropriate number of factors, 20 of which were determined by repeated testing, and the PLS-DA models were established by Fisher method.The prediction effects of the three models with preprocessing spectrum were all greater than 96.3%, and better than the original reflectance spectra.The PLSDA model with SNV pretreatment got the best discrimination results and the prediction accuracy rate was 100%.The PCA-SVM models were constructed by taking the first 15 principal components as inputs and by adopting radial basis function as kernel function with the penalty coefficient was 10000 and kernel radius was 0.00599.The prediction accuracy of the three models with preprocessing spectrum were all equal to or greater than 95%, also better than the original reflectance spectra, and the discrimination results of PCA-SVM model with MSC pretreatment was 97.5%.This study provides a new idea and method for the nondestructive detection and identification of rice leaf blast, and also lays a foundation for a wide range of remote sensing and monitoring for rice blast. [ABSTRACT FROM AUTHOR]

The decomposition efficiency of mixed pixels is an important subject in the research of remote sensing application. Current mature extraction algorithms of the endmember are pure pixel index (PPI), N-FINDR, vertex component analysis (VCA), sequential maximum angle convex cone (SMACC), alternating volume maximization (AVMAX), minimum volume enclosing simplex (MVES), etc. Extracting pure spectra from all pixels of an image by those algorithms has relatively slow processing and low accuracy. Therefore, in this paper, we introduced an integration of simple linear iterative clustering (SLIC) super pixel segmentation of hyperspectral mixed pixel decomposition algorithm. The number of pixels in a hyperspectral image was one of the main reasons that affected pixel unmixing (especially endmember extraction). Super pixel segmentation techniques can compose adjacent pixels with similar characteristics into image blocks, and retain useful information for further image processing, thus significantly reducing the number of pixels involved in endmember extraction and solving the problems of low processing and low accuracy in an effective way. In this paper, endmember extraction method was modified. We used Simple Linear Iterative Clustering (SLIC) algorithm to segment hyperspectral image pixels into super pixel sets, and conducted experimental comparisons on impact of different dimension reduction methods such as principal components analysis (PCA), minimum noise fraction (MNF), correspondence between different RGB (six kinds), different color spaces (RGB, LAB), different data formats（JPG, BIN）and different algorithms’ parameters K on hyperspectral image super pixel segmentation results. Furthermore, the impact of SLIC super pixel segmentation results on two typical endmember extraction algorithms was analyzed. The results showed that, with the increase of K value, the decomposition of mixed pixels gradually increased. The reason for such change was that as the K value increased, the number of pixels increased, and the size of the image was reduced and was close to a normal pixel. The JPG (lossy compression) data format of time was shorter than BIN (lossless compression) data format because a lossy compression can reduce the image size occupied in memory or disk space. The root-mean-square error (RMSE) of SLIC+MVES was slightly higher than that of MVES, and lower than that of AVMAX, with the time much less than actual MVES. In other words, relative to MVES, SLIC+MVES greatly increased processing speed, at the expense of a small amount of sacrifice in accuracy. AVMAX consumed little time itself because it did not need to solve linear programming problems repeatedly. So after adding super pixel segmentation, the time shortened for AVMAX was not significant, but the time for SLIC computing had increased. When increasing K, the computation time increased gradually, and the RMSE was flat or deceasing. When K was large enough, the results of SLIC+MVES was approximate to that of MVES. The difference in the RGB color space and Lab color space was not obvious. SLIC algorithm used Lab color space because it did not rely on the advantages of the device, but also had its own advantages, i.e., a wide color gamut. In most cases, dimensionality reduction by MNF was better than that of PCA. As to the MNF dimensionality reduction method, the data in JPG format, with Lab color space were more favorable for unmixing results. In addition, the value of SLIC parameters from 5 to10 was more appropriate. The SLIC super pixel segmentation algorithm was simple and improved the efficiency of unmixing, with a great practical value. [ABSTRACT FROM AUTHOR]

Hyperspectral data involve a huge amount of information, and how to select the best combination band to build high-accuracy spectrum model, is the key work for the developing of remote sensing inversion model of vegetation parameter. Maximum correlation coefficient (MCC) is a method of extracting combination bands by using the correlation coefficient between vegetation parameter and hyperspectral band as the selection index of feature combination band. Due to simple calculation and easier operation, MCC is widely used in dimensionality reduction and extracting the useful bands of hyperspectral data. However, the method only considers to extract the maximum correlation bands of vegetation parameter variable, which may lead to overlook the indirect effect of other factors. Optimum index factor (OIF) is the extraction method of feature combination bands based on the basic idea that the amount of information is proportional to the sum of mean square deviation, and inversely proportional to the sum of correlation coefficient for each band. Although OIF can obtain feature combination bands of abundant information and small redundancy, the highest correlation between vegetation parameter and the selected band can't be ensured, which may cause the decreasing of estimation ability of the model which is built by the extraction bands. Obviously, OIF method and MCC method in the extraction of feature combination bands have complementary advantages. In this study, by using the evaluation method with entropy coefficients, OIF and MCC are endowed with objective weight respectively, and based on this the optimal combination band is obtained, which is named as optimum index factor and correlation coefficient (OIFC). The feature combination bands extracted by OIFC, have the highest correlation with the corresponding vegetation parameters, as well as with ensured rich information. In order to demonstrate OIFC's practicality, feature combination bands of winter wheat leaf chlorophyll which was extracted by OIFC were given as an example. The 760, 1860 and 1970 nm were considered as feature combination bands and selected by OIFC. Then, hyperspectral model of wheat chlorophyll content was built by partial least squares. Compared with the models from vegetation indices such as normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) and OIF method, the precision of the model built by OIFC was the highest and the coefficients of determination was 0.7390. The determination coefficient of linear fitting between predicted values by OIFC and measured chlorophyll contents was 0.827, and the root mean square error was 5.440. The results show that the extracted bands of winter wheat chlorophyll based on OIFC has higher modeling precision. It also proves that OIFC can reliably and effectively extract feature combination bands of vegetation parameters from hyperspectral data. The method of OIFC can also provide theoretical basis and technical support for further improving the accuracy of hyperspectral estimation model of physical and chemical parameters of vegetation. [ABSTRACT FROM AUTHOR]

Estimating the total nitrogen (TN) content of soil accurately and rapidly is the guarantee to promote formula fertilization development. This research selected 30 point locations randomly from different regions. Then the topsoil layer (0-30 cm), subsoil layer (>30-48 cm) and ground layer (>48-60 cm) of each point were chosen to get soil samples. And these samples were used for all the subsequent experiments. The near infrared spectral absorbance of soil samples with different nitrogen contents was measured using the Fourier spectrum analyzer MATRIX-I. At the same time, the TN content of each sample was measured using Kjeldahl method in the laboratory. Then the absorbance spectral characteristics of soil samples from different layers were analyzed including the change laws of soil moisture and TN content from layer to layer. The first order differential processing was conducted among the 90 soil samples' original spectral absorbance. Then the correlation analyses were done between the TN content and the original or differential spectral data respectively. From the results of correlation coefficient between differential spectra and TN content, 1387, 1496, 1738, 1875, 2116 and 2314 nm were selected as sensitive wavebands finally. The sensitive wavebands were used to establish the multiple linear regression (MLR) model, the model based on back propagation (BP) neural network and the BP neural network prediction model optimized by the genetic algorithm to predict the soil TN content. The results were showed as below. For MLR model the accuracy of calibration process was high, while in predicting process, the modeling accuracy decreased with the increase of soil depth. For the model based on the BP neural network, it had good universality in predicting the TN content in the different layers of soil. To some extent, this method improved the prediction ability under the background of high moisture, while the model accuracy was yet lower. For the BP neural network model optimized by the genetic algorithm, the R² of the calibration process reached 0.883, and the root mean square error (RMSE) of calibration was 0.0278 mg/kg. The R² of prediction for topsoil layer reached 0.716, and the RMSE of prediction was 0.031 mg/kg. The R² of prediction for subsoil layer was 0.801, and the RMSE of prediction was 0.030 mg/kg. The R² of prediction for ground layer reached 0.667, and the RMSE of prediction was 0.033 mg/kg. Compared with the MLR model and the BP neural network model without optimization, the BP neural network model optimized by the genetic algorithm showed a significant improvement in both calibration and predicting accuracy for each soil layer. Therefore, the BP neural network prediction model optimized by the genetic algorithm has obvious advantages for soil TN content prediction. [ABSTRACT FROM AUTHOR]

The application of hyperspectral analysis techniques can extract hyperspectral characteristic parameters and identify soil types. In contrast to the traditional methods, this application is more convenient and time-saving. This paper took two types of Typic Hapli-Stagnic Anthrosols (the hydromorphic alluvial sandy mud paddy and the hydromorphic tide sandy mud paddy) in Xingguo County as the research objects to analyze the hyperspectal characteristics and to extract the effective hyperspectral absorption characteristic parameters (SAFP) for the two soils. In the research, we used three hyperspectral methods including the hyperspectral resample process, the continuum removal, and the first-order differential transform of hyperspectral reflectivity. The results showed that the wavelength position of peaks and valleys can be used to effectively identify the two different Anthrosols. The wave peak width, the wave valley width and the wave valley tilt angle were the parameters to distinguish the two types of Anthrosols. In addition, the analysis of the hyperspectral characteristics parameters after the hyperspectral resample process showed that the difference between the two types of soils was the wave peak reflectivity, and the wave peak reflectivity was the most effective parameter. The hydromorphic alluvial sandy mud paddy belonged to the high reflectivity mud type. The declining rate of the hydromorphic alluvial sandy mud paddy hyperspectral reflectivity was larger and the hyperspectral curve absorption width was significantly larger than that of the hydromorphic tide sandy mud paddy. Moreover, the analysis of the hyperspectral characteristics parameters after the continuum removal showed that the wave valley width can be used to distinguish the two types of Typic Hapli-Stagnic Anthrosols. The wave valley widths of the two soils were 26 and 178 nm, respectively in the vicinity of the wavelength 480 nm. Finally, the analysis of the hyperspectral characteristics parameters after the first-order differential transform of hyperspectral reflectivity showed that the wave valley tilt angle was the other effective parameter to distinguish the two types of Anthrosols. The wave valley tilt angle of the hydromorphic alluvial sandy mud paddy and the hydromorphic tide sandy mud paddy were -0.00573° and 0.00573°, respectively at the wavelength 471 nm. This showed that the hyperspectral reflectivity increase trend of the hydromorphic alluvial sandy mud paddy was negative in the vicinity of the wavelength 471 nm, while the hyperspectral reflectivity increase trend of the hydromorphic tide mud paddy was positive in the vicinity of the wavelength 471 nm. This paper can provide a reference for the application of hyperspectral analysis techniques to identify different Stagnic Anthrosols soil. [ABSTRACT FROM AUTHOR]

A sensing-based spectrum sharing network with multi-antenna is suggested, which is consisted of a single-input single-output (SISO) primary user (PU) pair and a multi-input single-output (MISO) secondary user (SU) pair. In proposed network model, PU sends message to the SU when PU occupies the channel. So the SU calculates total interference power constraint by received PU message and adaptively adjusts its transmit power in order to avoid harmful interference to PU. If the licensed band is empty, the SU immediately occupies the channel with maximum transmitted power. Simultaneously, in order to maximize the system achievable rate, the optimal power allocation strategies among transmitter antennas are derived under the primary interference power constraint. Finally, the derived optimal power control policies are validated through simulations. The results show that, compared to the opportunistic spectrum access and sensing-based spectrum sharing, derived power control policies in suggested system model exhibits better performance. [ABSTRACT FROM AUTHOR]

To effectively balance the contradiction between the access channels sensed time and total channel throughput in cognitive radio secondary user network system, this paper proposes a spectrum access strategy which only senses partial channel channels. The strategy optimizes the total number of sensing channels and sensing time of individual channels jointly, and solves the result through optimal stopping algorithm. The algorithm can maximize savings while achieving larger average throughput in the sensed time. The simulation analysis and comparison with HC-MAC(Hardware Constrained- Media Access Control) algorithm show that the algorithm costs shorter sensed time and has greater throughput in the same given conditions. [ABSTRACT FROM AUTHOR]

Spectrum prediction is a promising technique to infer future spectrum state from historical spectrum data by exploiting the correlation of spectrum states in time domain. To realize reliable prediction of spectrum state under quick-changing channel occupancy, this article proposes a support vector regression (SVR)-based spectrum prediction algorithm. By adopting three typical distribution models of spectrum state evolution, this article compares the prediction accuracy performance of the proposed algorithm and the existing back propagation ( BP) neural network-based spectrum prediction algorithm. Simulation results show that the proposed algorithm has the capability of small sample learning and obtains better prediction accuracy with less training samples. On the basis of the previous study, research on the imperfect training spectrum sample which considering the probability of detection and the probability of false alarm are analyzed. The analysis shows that the prediction accuracy performance of the SVR-based spectrum prediction algorithm still achieves satisfactory result with the imperfect training samples. [ABSTRACT FROM AUTHOR]

An orthogonal projection based SLNR joint transceiver design algorithm is proposed for cognitive MIMO system to realize effective spectrum sharing between cognitive user network and primary user network. The proposed algorithm can be implemented by two steps. Firstly, in order to eliminate the interference to primary user, a pre-processing that orthogonally project the transmitting signal to the null space of interfering channel was used at cognitive base station. Then, aimed at improving performance of the cognitive user network, an alternate iteration method was exploited to jointly design transceiver utilizing the modified SLNR as optimizational target. Simulation results demonstrate that proposed algorithm can efficiently suppress the co-channel interference and improve BER and sum-rate capacity performance of cognitive user network without disturbing the normal communication of primary user network. [ABSTRACT FROM AUTHOR]

In this paper, based on the difference between the maximum eigenvalue and the minimum eigenvalue (DMM) cooperative spectrum sensing algorithm, a new threshold decision rule is proposed via analyzing the limiting distribution of the minimum eigenvalue of the covariance matrix of the received signals from multiple cognitive users (CUs) by means of random matrix theory (RMT). The proposed scheme could effectively overcome the noise uncertainty and need neither the prior knowledge of the signal transmitted from primary user(PU), nor the noise power in advance. Simulation results show that the proposed algorithm has lower decision threshold for a target false alarm probability, and it can get better sensing performance compared with the previous sensing schemes with low SNR and small sampling. [ABSTRACT FROM AUTHOR]

In order to improve the generality of the water content hyperspectral calibration model for different varieties of cold fresh pork, a new hyperspectral signal correction algorithm called a variety sensitive wavelength selection combining with piecewise direct standardization (VSWS-PDS) is proposed. The variety sensitive wavelengths were first selected, based on a regression coefficient of the partial least square regression (PLSR) model, then the piecewise direct standardization(PDS) algorithm was utilized to correct the selected wavelengths and to eliminate the impact of variety difference on model prediction results. The detailed process of the VSWS-PDS algorithm is illustrated as follows: (1) The samples of the "master" variety were divided into calibration set mC and prediction set mP by utilizing the Kennard and Stone(KS) algorithms. (2) A PLSR model named PLSR1 was built with a calibration set mC. (3) A small quantity of representative "slave" variety samples selected from "slave" variety samples by utilizing sample set partitioning based on a joint X-Y distances (SPXY) algorithm were added to mC,and then a new PLSR model named PLSR2 was built. (4) The variety sensitive wavelengths were selected on the basis of the relative difference between the regression coefficients of model PLSR1 and PLSR2. (5) The standardization samples mTC were selected among the calibration samples mC by utilizing the KS algorithm, then the Euclidean distances between the physical or chemical reference values of the sample set mTC and all of the "slave" variety samples were calculated one by one, the "slave" variety samples with the minimum distance from mTC were chosen as standardization samples, yielding sTC, and the rest, yielding sP. (6) The average spectrum of mTC and mTC was calculated, yielding mTmean and sTmean. If wavelength i was variety sensitive, a new spectrum matrix Zi was reconstructed from sTmean in a small window from i-k to i+k, a multiple linear regression (MLR) model was built between the spectral intensity mTmeani and the corresponding spectrum matrix Zi, then the regression coefficient vector bi was obtained. If wavelength i was not variety sensitive, then bi was equal to 1. (7) The regression coefficient vectors were placed in a banded diagonal transfer matrix F. (8) The "slave" variety samples sP are chosen to evaluate the correction effect of the VSWS-PDS algorithm. First of all, spectrum matrix was obtained from sP, yielding XStest. Subsequently, matrix XStest was multiplied by the transfer matrix F, yielding XStesttrans, and then the "master" variety calibration model PLSR1 was utilized to predict the physical or chemical values corresponding to XStesttrans directly. The prediction results of PLSR1 were used to evaluate the correction effect of the VSWS-PDS algorithm. The No. 0 indigenous pork was selected as the "master" variety and the Enshi mountain pork was selected as the "slave" variety. The spectra of the "slave" variety samples sP were corrected by the VSWS-PDS algorithm, water content was then predicted by the "master" variety calibration model PLSR1 with the cross validation determination coefficients (R2cv) of 0.91, cross validation root mean square error (RMSECV) of 0.29% ,and cross validation residual prediction deviation (RPD) of 3.3, with prediction determination coefficients (R2p) of 0.84, prediction root mean square error(RMSEP) of 0.50%, and prediction RPD of 2.58. Meanwhile, the experimental results without the VSWS-PDS correction process were R2p of 0.20, RMSEP of 1.42%, and prediction RPD of 0.91. Obviously, the VSWS-PDS algorithm can significantly improve the prediction ability of the model built with No. 0 indigenous pork samples to predict water content of the Enshi mountain pork samples. Finally, a comparative study between direct standardization (DS), PDS, and the VSWS-PDS is conducted. It is shown that the transfer results obtained with the proposed method VSWS-PDS were better than those obtained by DS or PDS. [ABSTRACT FROM AUTHOR]

Since the performance of cooperative spectrum sensing in cognitive radio (CR) will decrease in the fading channel, a clustering cooperative spectrum sensing algorithm based on the cooperation in cluster and multi-clusters combination is proposed in this paper. In the proposed algorithm, all the CR users are divided into several clusters, and the CR user in each cluster with the shortest distance from the fusion centre is regarded as the cluster head. The cluster head collects the local sensing information from the other CR users in the cluster, obtains the cooperative sensing result of the cluster, and then sends the result to the fusion centre which will combine the results from all the clusters by "OR rule" in order to obtain the final decision on the presence of the primary user. The simulation indicates that when the channels are perfect, the performances of the proposed algorithm and traditional algorithm without clustering are nearly the same, however, when the channels are in deep fading, the performance of the proposed algorithm is improved remarkably, and it can be increased by the decreasing of the number of clusters. [ABSTRACT FROM AUTHOR]

To solve the problem of frequency spectrum, a method using all-phase FFT is presented in this paper. Based on the theory of all-phase FFF and OFDM, the mathematical expression, model and algorithm of modulation and demodulation in all-phase OFDM system were derived. The all-Phase FFT can adjudicate the phase and amplitude by using the function of spectrum analysis. The simulation results show that All-Phase OFDM system has less phase error, spectrum leakage and lower BER than OFDM. [ABSTRACT FROM AUTHOR]

Spectral domain analysis (SDA) is presented for solving complex propagation constant of on-chip multi-layer interconnects. As SDA is a full wave solution and is usually computational expensive, a novel bell-shaped window function is introduced to speed up the integral equation (IE). This novel bell-shaped window function has low-pass-filter feature in the spectral domain. Also it has simple closed-form formula which favorites the computational complexity. Based on this window's special feature research, a new accelerating algorithm is derived to speed up the SDA integration. Both the accuracy and the speed of the proposed algorithm are proved to be very promising. [ABSTRACT FROM AUTHOR]

A BPSK carrier phase recovery algorithm is proposed that combines discrete Fourier transform with digital phase locked loop. For coarse frequency estimation, the algorithm adopts square algorithm and DFT to BPSK signal, and then initializes the DPLL. frequency by the coarse frequency estimation, which satisfies that DPLL begins at pull-in range. The DPLL. locks in through about one period and supplies the carrier signal to the modulation accurately. Simulation results show that the algorithm qualifies the fast and accurate synchronization, and avoids the shortage of longer time to lock that was needed by using traditional automatic frequency controller and PLL algorithm. Meanwhile, the proposed algorithm is easy for DSP chips to realize all digital structure. [ABSTRACT FROM AUTHOR]

Making use of the good stability of QR decomposition and good computing ability of lifting wavelet, a blind robust digital audio watermarking method based on QR decomposition and lifting wavelet transform is proposed. To protect the security of the original binary watermarking image, chaotic sequence is used to spread its spectrum and watermarking signal for embedding is generated. In the watermark embedding procedure, first, QR decomposition is executed after rising dimension of the original host audio signal; then, as R component is an upside-triangle matrix and the element in its first row is nonzero, lifting wavelet transform is taken on its first row to obtain the selected wavelet coefficients; last, the watermarking signal is embeded into the selected wavelet coefficients using linear instantaneous mixing model to generate the watermarked audio signal. In the watermark extraction procedure, independent component analysis algorithm is utilized to extract the watermarking signal from the watermarked audio signal for detecting. The watermarking image can be obtained after post-processing. Experimental results show that this method can achieve blind extraction of watermark and has good transparency and robustness. [ABSTRACT FROM AUTHOR]

In order to shorten the acquisition time of multichannel combined acquisition, a new parallel acquisition algorithm is proposed. Several code sequences are added as a new code sequence. The input signal is correlated with the new code sequence. After detection of input signal, each channel would be detected again. Analysis and simulation results showed that the new parallel acquisition algorithm can effectively shorten the acquisition time without increasing resources. But the new parallel acquisition algorithm decreases correlation signal noise ratio (SNR) slightly which is suitable for multichannel signals' fast acquisition [ABSTRACT FROM AUTHOR]

In order to improve the compensation ability of FIR-ALE (Finite Impulse Response-Adaptive Linear Enhancer) system for phase noises, an improved method based on LMS algorithm is discussed in this article. The simulation of 8PSK modulation signals with Matlab is also illustrated. These two kinds of methods are compared through analyses of power spectrums, BER and constellation. Simulation results prove that the improved LMS system can reduce the phase jitter by almost 25 dB when the jitter amplitude is 5° and the frequency is 120 Hz, and the system can reduce the phase jitter by 35 dB when the jitter amplitude is 15° and the frequency is 25 Hz. Moreover, the compensation ability of the improved LMS method is enhanced with the increasing jitter amplitude compared with that of the FIR-ALE method. [ABSTRACT FROM AUTHOR]

It is first reported here that the MR approach may not always increase accuracy for a real-valued signal. Some exceptional examples presented here demonstrate that, under some special circumstance, the results from the MR approach are even worse than the plain readings from the FFT spectral lines. Two approaches were proposed. The first one makes use of the ratio of complex spectrum (RCS), and the second one, composite RCS based approach, which interpolates the right-side and left-side results of the spectral peak from the first approach. The noise-free examples show that the composite RCS approach is significantly less sensitive to the negative frequency component than the MR approach. The Monte Carlo simulation shows that it is superior to the Quinn's algorithm. [ABSTRACT FROM AUTHOR]