不同语速对听力正常者和听障者言语辨别率的影响

目的研究不同语速对听力正常者和听障者言语辨别率的影响。方法选择30例轻到中度感音神经性听力下降者作为观察组,在志愿者中随机抽取30例听力正常者作为对照组。两组均用自行录制的不同语速CD进行测试,比较两组在语速变化情况下言语辨别率的变化。结果正常语速时对照组的言语辨别率为82.53%,1.5倍正常语速时为74.66%,2.0倍正常语速时为73.22%;观察组在正常语速时的言语辨别率为74.35%,1.5倍正常语速时为66.73%,2.0倍正常语速时为50.18%,两组相比差异具有统计学意义(P<0.05)。结论从正常语速到2.0倍正常语速时听障者的言语辨别率较听力正常者低,表明感音神经性听力下降者言语辨别率与语速有关。

不同语速对听力正常者和听障者言语辨别率的影响

目的 :探讨感音神经性听力损失与不同语速汉语言语测试之言语辨别率的关系。方法 :实验组为 2 0例 (2 0耳 )轻到中度 (30～ 6 0dBHL)感音神经性听力下降者 ,对照组为 2 0 (2 0耳 )听力正常者。两组均用自制的不同语速言语录音CD进行测试 ,比较两组在不同语速下言语辨别率的变化。结果 :对照组在正常语速时的言语辨别率为 80 5 % ,1 5倍正常语速时为 76 5 % ,2 0倍正常语速时为 74 %。实验组在正常语速时的言语辨别率为 73 2 % ,1 5倍正常语速时为 6 7 5 % ,2 0倍正常语速时为 4 8 2 %。结论 :感音神经性听障者在 2 0倍正常语速时的言语辨别率较正常语速时的变化较听力正常者的两者变化有明显区别 (P <0 0 1)。提示感音神经性听力下降者的时向分辨功能降低 ,与快语速时言语辨别率的降低有相关性。

人虽老,耳可聪

经常听到有些老人无奈地说:“人老啦,耳朵背啦,听不清喽,你说话得大点声儿！”“耳背”让很多老人感到生活不便,觉得自己因为听不清别人说话让人“烦”,被排除在丰富多彩的生活之外。真的,有哪个老人不希望自己能够“60岁的人,30岁的听力”呢?”3月3日是全国爱耳日,让我们来关心一下老年人的耳聋问题吧。

基于重组超矢量的GMM-SVM说话人辨认系统

在传统的高斯混合模型-支持向量机(Gaussian Mixture Model-Support Vector Machine,GMM-SVM)说话人辨认系统中,SVM利用从GMM矢量空间中得到的超矢量(Super Vector)直接对说话人进行建模与识别,由于没有考虑到超矢量内各均值矢量之间的关联性,识别性能有限。为此,提出了基于重组超矢量构建文本无关的GMM-SVM说话人辨认系统。该系统充分利用各相邻高斯分量的均值矢量的高度关联性,保证了重组后的超矢量能充分反映说话人身份的内在细节,使得系统具有充分利用SVM处理高维小数据性能的优越特点。验证实验结果表明,与传统的GMM-SVM系统相比,重组超矢量GMM-SVM说话人辨认系统显著地缩短了系统建模的时间,同时有效地提高了说话人的辨别率。

A WEIGHTED FEATURE REDUCTION METHOD FOR POWER SPECTRA OF RADAR HRRPS

Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using High-Resolution Range Profiles (HRRPs). Several existing feature reduction methods in pattern recognition are analyzed, and a weighted feature reduction method based on Fisher's Discriminant Ratio (FDR) is proposed in this paper. According to the characteristics of radar HRRP target recognition, this proposed method searches the optimal weight vector for power spectra of HRRPs by means of an iterative algorithm, and thus reduces feature dimensionality. Compared with the method of using raw power spectra and some existing feature reduction methods, the weighted feature reduction method can not only reduce feature dimensionality, but also improve recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.

Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product （collection 6_L2） obtained from the Moderate Resolution Imaging Spectroradiorneter （MODIS） sensors on board the Terra （MOD06） and Aqua （MYD06） satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 （12 years） with a spatial resolution of 5 km × 5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.

A new identification method for five species of oysters in genus Crassostrea from China based on high-resolution melting analysis

The high phenotypic plasticity in the shell of oysters presents a challenge during taxonomic and phylogenetie studies of these economically important bivalves. However, because DNA can exhibit marked differences among morphologically similar species, DNA barcoding offers a potential means for oyster identification. We analyzed the complete sequences of the cytochrome oxidase subunit I （COI） of five common Crassostrea species in China （including Hong Kong oyster C. hongkongensis, Jinjiang oyster C. ariakensis, Portuguese oyster C. angulata, Kumamoto oyster C. sikamea, and Pacific oyster C. gigas） and screened for distinct fragments. Using these distinct fragments on a high-resolution melting analysis platform, we developed an identification method that does not rely on species-specific PCR or fragment length polymorphism and is efficient, reliable, and easy to visualize. Using a single pair of primers （Oyster- COI-1）, we were able to successfully distinguish among the five oyster species. This new method provides a simple and powerful tool for the identification of oyster species.

A Thin Cloud Removal Method from Remote Sensing Image for Water Body Identification

In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.

Hazard and population vulnerability analysis: a step towards landslide risk assessment

In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlfin, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility, magnitude （area-velocity ratio） and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources （Google Earth, aerial photographs and historical information）. Estimations of landslide susceptibility were determined by combining four statistical techniques： （i） logistic regression, （ii） quadratic discriminant analysis, （iii） linear discriminant analysis, and （iv） neuronal networks. A Digital Elevation Model （DEM） of lo m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief. These factors, in addition to land cover, lithology anddistance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then, due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment （SAVE） model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments. Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.

Fingerspelling Recognition by Hand Shape Using Higher-Order Local Auto-Correlation Features

The fingerspelling recognition by hand shape is an important step for developing a human-computer interaction system. A method of fingerspelling recognition by hand shape using HLAC （higher-order local auto-correlation） features is proposed. Furthermore, in order to use HLAC features more effectively, the use of image processing techniques： reducing an image resolution, dividing an image, and image pre-processing techniques, is also proposed. The experimental results show that the proposed method is promising.

AUTOMATIC TARGET RECOGNITION OF RADAR HRRP BASED ON HIGH ORDER CENTRAL MOMENTS FEATURES

The paper addresses the problem of target recognition using High-resolution Radar Range Profiles(HRRP).A novel approach of feature extraction and dimension reduction based on extended high order central moments is proposed in order to reduce the dimension of range profiles.Features extracted from radar HRRPs are normalized and smoothed,and then comparative analysis of the similar approaches is done.The range profiles are obtained by step frequency technique using the two-dimensional backscatters distribution data of four different aircraft models.The template matching method by nearest neighbor rules,which is based on the theory of kernel methods for pattern analysis,is used to classify and identify the range profiles from four different aircrafts.Numerical simulation results show that the proposed approach can achieve good performance of stability,shift independence and higher recognition rate.It is helpful for real-time identification and the engineering implements of automatic target recognition using HRRP.The number of required templates could be reduced con-siderably while maintaining an equivalent recognition rate.

Biological object recognition approach using space variant resolution and pigeon-inspired optimization for UAV

Recognizing the target from a rotated and scaled image is an important and difficult task for computer vision. Visual system of humans has a unique space variant resolution mechanism(SVR) and log-polar transformations(LPT) is a mapping method that is invariant to rotation and scale. Motivated by biological vision, we propose a novel global LPT based template-matching algorithm(GLPT-TM) which is invariant to rotational and scale changes; and with pigeon-inspired optimization(PIO) used to optimize search strategy, a hybrid model of SVR and pigeon-inspired optimization(SVRPIO) is proposed to accomplish object recognition for unmanned aerial vehicles(UAV) with rotational and scale changes of the target. To demonstrate the efficiency, effectiveness and reliability of the proposed method, a series of experiments are carried out. By rotating and scaling the sample image randomly and recognizing the target with the method, the experimental results demonstrate that our proposed method is not only efficient due to the optimization, but effective and accurate in recognizing the target for UAV.

Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition

Clinical data have shown that survival rates vary considerably among brain tumor patients,according to the type and grade of the tumor.Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1H NMRS) can provide important information on tumor biology and metabolism.These metabolic fingerprints can then be used for tumor classification and grading,with great potential value for tumor diagnosis.We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies,including two astrocytomas (grade I),12 astrocytomas (grade II),eight anaplastic astrocytomas (grade III),three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS 1H NMRS.The results were correlated with pathological features using multivariate data analysis,including principal component analysis (PCA).There were significant differences in the levels of N-acetyl-aspartate (NAA),creatine,myo-inositol,glycine and lactate between tumors of different grades (P<0.05).There were also significant differences in the ratios of NAA/creatine,lactate/creatine,myo-inositol/creatine,glycine/creatine,scyllo-inositol/creatine and alanine/creatine (P<0.05).A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%.HRMAS 1H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.