Characterisation of the optical properties of InGaN MQW structures using a combined SEM and CL spectral mapping system

We demonstrate the ability of a combined scanning electron microscope and cathodoluminescence （CL） spectral mapping system to provide important spatially resolved information. The degree of inhomogeneity in spectral output across a multi-quantum well sample is measured using the SEM-CL system as well as measuring the efficiency roll-off with increasing carrier concentration. The effects of low energy electron beam modification on the InGaN/GaN multi quantum wells have also been characterized.

JOINT RESOURCE ALLOCATION FOR WLAN&WCDMA INTEGRATED NETWORKS BASED ON SPECTRAL BANDWIDTH MAPPING

Next wireless network aims to integrate heterogeneous wireless access networks by sharing wireless resource.The spectral bandwidth mapping concept is proposed to uniformly describe the resource in heterogeneous wireless networks.The resources of codes and power levels in WCDMA system as well as statistical time slots in WLAN are mapped into equivalent bandwidth which can be allocated in different networks and layers.The equivalent bandwidth is jointly distributed in call admission and vertical handoff control process in an integrated WLAN/WCDMA system to optimize the network utility and guarantee the heterogeneous QoS required by calls.Numerical results show that,when the incoming traffic is moderate,the proposed scheme could receive 5%-10% increase of system revenue compared to the MDP based algorithms.

Spectral Analysis of Hyperion Data for Mapping the Spatial Variation of in a Part of Latehar &Gumla District, Jharkhand

Reflectance and emittance spectroscopy in the near-infra red and short-wave infra red offers a rapid, Inexpensive, non-destructive tool for determining the mineralogy of rock and soil samples. Hyperspectral remote sensing has the potential to provide the detailed physico-chemistry (mineralogy, chemistry, morphology) of the earth’s surface. This information is useful for mapping potential host rocks, alteration assemblages and mineral characteristics, in contrast to the older generation of low spectral resolution systems. In the present study EO-1, hyperion data has been used for the delineation of AL+OH minerals. The requirements for extracting bauxites from Hyperion images is to first compensate for atmospheric effects using cross track illumination correction & the log residual calibration model. MNF transformation was applied to reduce the data noise and for extracting the extreme pixels. Some pure pixel end member for the target mineral and the backgrounds were used in this study to account for the spectral angle mapping & matched filtering and the results were validated with the respect of field study.

Lithological Mapping from OLI and ASTER Multispectral Data Using Matched Filtering and Spectral Analogues Techniques in the Pasab-e-Bala Area, Central Iran

Using satellite data for geological mapping beside saving time and reducing coast leads to increased accuracy. In this study, the result of remote sensing techniques has been compared for manifesting geological units. The study area is limited to 1:25,000 rectangle of Pasab-e-Bala which is located in the northeast of Isfahan and West of Qom-Zefreh fault. This region mainly consists of Devonian and Quaternary sedimentary units. In this study, ASTER and OLI satellite data has been corrected atmospherically and radiometrically. Spectral Analogues method and OLI band combination (652) in RGB image were powerful in distinguishing various rock units. Finally, a new geologic map of the Pasab-e-Bala area was created by integrating the results of remote sensing, previous geological maps and field inspection. It is concluded that the workflow of Landsat 8 image processing, interpretation and ground inspection have a great potential to identify geological formations. According to field data originality, accuracy of the produced map was evaluated through calculating kappa index and overall accuracy and a thematic accuracy of 86% was achieved for geological formations.

Lithological mapping with multispectral data–setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan

In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library(pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern Pakistan.The spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data tosupportthespatial delineation of outcropping rock sequences instratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basisto further analyze the lithological spotin numerous regions in the Hindu Kush.

谱负MAP波动理论的一个注记

谱负MAP是应用概率论领域的重要内容之一.利用Asmussen-kella鞅推广了谱负MAP(X,J)的波动理论,给出谱负MAP在与之独立且服从Erlang分布的随机时刻点上水平与极值的联合变换所满足的等式,进而由Erlangization方法,给出谱负MAP(X,J)的水平与极值的联合变换的瞬时趋近算法.

ON THE SPECTRAL CHARACTERIZATION OF A STRONGLY CONTINUOUS PERTURBED SEMIGROUP

In this paper, we get a sufficient condition to estimate the essential type of a strongly continuous sendgroup in a Banach space, using oh condition we prove that the essential type of the strongly continuous semigroup don't increase under the A-smoothing perturbation and establish the spectral mapping theorem for the asymptotic parts of the spectrum of generator and semgroup.

PL Mapping技术检测大直径SI-GaAs晶片中缺陷分布

采用PL Mapping技术,检测6英寸SI-GaAs晶片的均匀性,从而得到样品中的缺陷分布状况。本文主要通过光荧光谱获得样品表面和内部丰富信息,利用光谱图中颜色的不同来分析样品缺陷的不均匀分布等特点;对比样品局部发光强度信号和发光峰位的变化曲线图及化学腐蚀图片,进一步讨论由于缺陷而造成的样品均匀性的变化。

Application of Different Image Processing Techniques on Aster and ETM+ Images for Exploration of Hydrothermal Alteration Associated with Copper Mineralizations Mapping Kehdolan Area (Eastern Azarbaijan Province-Iran)

The Kehdolan area is located at 20 kilometers to the?south-east of Dozdozan Town (Eastern Azarbaijan Province). According to structural geology, volconic rocks are situated in Alborz-Azarbyjan zone, and faults?are?observed?in?the?same direction to this system with SE-NW trend. The results show that kaolinite alteration trend with Argilic and propylitic veins?is the?same direction with SW-NE faults in this area. Therefore, these faults with these trends can be considered as the mineralization control for determination of the alterations. Different image processing techniques,?such as false color composite?(FCC), band ratios, color ratio composite?(CRC), principal component?analysis?(PCA), Crosta technique, supervised spectral angle mapping?(SAM), are used for?identification of the alteration zones associated with copper mineralization. In this project ASTER?data are process and spectral analysis to fit for recognizing intensity and kind of argillic, propylitic,?philic, and ETM+ data?which?are process and to fit for iron oxide and relation to metal mineralization of the area. For recognizing different alterations of the study area, some chemical and mineralogical analysis data from the samples showed that ASTER data and ETM+ data were?capable of hydrothermal alteration mapping with copper mineralization.?Copper mineralization in the region is in agreement with argillic alteration. SW-NE trending faults controlled the mineralization process.

An improved algorithm for mapping burnt areas in the Mediterranean forest landscape of Morocco

The identification of burnt forests and their monitoring provide essential information for the suitable management and conservation of these ecosystems. This research focuses on the use of remote sensing with MODIS sensor data in a Mediterranean environment, precisely in the Rif region known for its high occurrence of forest fires and the largest burnt areas in Morocco. It mapped the burnt areas during the summer of 2016 using spectral indices from MODIS images, namely the Normalized Burn Ratio (NBR) and the Burnt Area Index for MODIS (BAIM). Two field surveys were used to calibrate spectral indices and validate the maps. First, a monotemporal analysis using a single pre-fire image determined the appropriate threshold of the spectral indices (BAIM and NBR) for burn detecting. Secondly, a multitemporal method was applied based on dBAIM and dNBR images which represented pre-fire and postfire differences of the BAIM and NBR images, respectively. The results show that separate use of monotemporal postfire and multitemporal methods produced an overestimation of the burnt areas. Finally, we propose a new algorithm combining both methods for burnt area mapping that we name Burnt Area Algorithm. MCD45A1 and MCD64A1 MODIS burnt area products were compared to the proposed algorithm. Validation of the estimated burnt areas using reference data of the Moroccan High Commission for Water, Forests and Fight against Desertification showed satisfactory results using the proposed algorithm, with a determination coefficient of 0.68 and a root mean square error of 44.0 ha.

Production of Multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product

Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.

On the basis of the morphology of the T-wave alternans: A Poincare mapping method research

Presently T-wave alternans (TWA) has become a clinical index of non-invasive diagnosis for heart sudden death prediction, and detecting T-wave alternate accurately is particularly important. This paper introduces an algorithm for detecting TWA using Poincare mapping method which is a technique for nonlinear dynamic systems to display periodic behavior. Sample series of beat to beat cycles were selected to prepare Poincare mapping method. Vector Angle Index (VAI), which is the mean of the difference between θi (the angle between the line connecting the i point to the origin and the X axis) and 45 degrees was used to present the presence or absence of TWA. The value of 0.9 rad ≤ VAI ≤ 1.03 rad is accepted as a level determinative for presence of TWA. VAI via Poincare mapping method (PM) is used for correlation analysis with T-wave alternans voltage (Vtwa) by way of the spectral method (SM). The cross-correlation coefficient between Vtwa and VAI is γ = 0.8601. The algorithm can identify the absence and presence of TWA accurately and provide idea for further study of TWA-PM.

双分支复频谱下多特征聚合的轻量化语音增强方法

针对目前多种改进的卷积循环网络(CRN)在单掩蔽或单映射的编解码结构下提取特征单一、捕获全局特征不强、参数量较大等问题,提出一种多特征聚合卷积模块与高效Transformer融合注意力机制结合的复频谱联合掩蔽和映射的单通道语音增强高效网络。在编解码层设计一种双分支门控协作单元(DGCU),提取复频谱多层次特征后交互、聚合以弥补特征提取单一问题;中间层设计一种通道时频注意力融合模块,聚焦语音的时频、空间局部细节特征。最后在THCHS30数据集上进行消融和对比实验,实验结果表明,该网络以最低参数量、较低计算量实现了轻量化,在匹配和不匹配噪声下PESQ分别提升了10.5%~50.6%、16.3%~94.5%,客观、主观指标都优于其他对比的网络模型,表现出较高的降噪性能和网络泛化能力。

数字经济赋能乡村振兴研究热点与趋势——基于CiteSpace的可视化分析

数字经济是信息通信技术应用与发展所创造的新生产物,已成为全球经济发展和社会进步的重要源动力。在国家推进“数字经济赋能乡村振兴”的大背景之下,本文采用CiteSpace软件对2000—2022年中国知网(CNKI)所收录的CSSCI来源期刊有关“数字经济赋能乡村振兴”研究505篇文献,根据研究热点、聚类变化和演进趋势等特征分别进行了关键词共现、聚类分析、时间区图和时间线图等知识图谱的绘制。结合对知识图谱的分析和文献梳理,对数字化转型背景下我国乡村领域的研究现状和研究内容、热点问题、演进脉络及研究趋势等进行了深入分析,并对数字经济赋能乡村振兴的实践提出了三点对策建议:加快乡村信息化转型,开创城乡经济融合的新格局及孵化创新乡村产业模式,以期为乡村振兴数字经济领域研究提供参考。

复谱映射下融合高效Transformer的语音增强方法

针对卷积神经网络(Convolutional Neural Network,CNN)过去在语音增强中表现优异但对全局特征捕获不足,以及Transformer近年展现出长序列间依赖优势但又存在局部细节特征丢失、参数量大等问题,该文为了充分利用CNN与Transformer的优势并弥补各自不足,提出了一种在复频谱映射下的新型卷积模块与高效Transformer融合的单通道语音增强网络。该网络由编码层、传输层与双分支解码层组成:在编解码部分设计了一种协作学习模块(Collaborative Learning Block,CLB)来监督交互信息,在减少参数量的同时提高主干网络对复特征的获取能力;传输层中则提出一种时频空间注意Transformer模块分别对语音子频带和全频带信息建模,充分利用声学特性来模拟局部频谱模式并捕获谐波间依赖关系。将该模块进一步与通道注意分支相结合,设计了一种可学习的双分支注意融合(Dual-branch Attention Fusion,DAF)机制,从空间-通道角度提取上下文特征以加强信息的多维度传输;最后,在此基础上搭建一种高斯加权渐进网络作为中间传输层,通过堆叠DAF模块进行加权求和后输出以充分利用深层特征,使得解码过程更具鲁棒性。分别在英文VoiceBank-DEMAND数据集、中文THCHS30语料库与115种环境噪声下进行消融以及综合对比实验,结果表明,该文方法仅以最小0.68×10^(6)的参数量,相比于大部分最新相关网络模型取得了更优的主、客观指标,具有较为突出的增强性能与泛化能力。

Automated Burned Scar Mapping Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.

Selective sampling with Gromov–Hausdorff metric:Efficient dense-shape correspondence via Confidence-based sample consensus

Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

Comparison of Spatio-Spectral Properties of Zen-Meditation and Resting EEG Based on Unsupervised Learning

This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a detached brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce beta buzz. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.

平滑注意力与谱上采样细化的非等距三维点云模型对应关系计算

为了解决非等距3维点云模型对应关系计算易受模型大尺度形变影响而导致对应失真、准确率低且平滑性差的问题,该文提出一种结合平滑注意力与谱上采样细化的非等距3维点云模型对应关系计算新方法。首先,利用点所在表面的几何特征信息设计平滑注意力机制与平滑感知模块,提高特征对大尺度形变区域非刚性变换的感知能力;其次,将深度函数映射模块与平滑正则化约束相结合,提升函数映射计算结果的平滑性;最后,在谱上采样细化模块中,以多分辨率重建的方式得到最终的逐点映射结果。实验结果表明,与已有算法相比,本算法在FAUST、SCAPE和SMAL数据集上构建的对应关系测地误差最小,处理大尺度形变模型时,能够提升逐点映射的平滑性和全局准确率。

Lithological Mapping Using Landsat 8 OLI in the Meso-Cenozoic Tarfaya Laayoune Basin (South of Morocco): Comparison between ANN and SID Classification

In the Saharian domain, the Tarfaya-Laayoune coastal basin developed in a stable passive margin, where asymmetrical sedimentation increase from East to West and reach a sediment stack of about 14 kilometers. However, the morphology of the studied area corresponds to a vast plateau (hamada) presenting occasional major reliefs. For this purpose, remote sensing approach has been applied to find the best approaches for truthful lithological mapping. The two supervised classification methods by machine learning (Artificial Neural Network and Spectral Information Divergence) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest geological maps and RGB images were used for pseudo-color groups to identify important areas and collect the ROIs that will serve as facilities samples for the classifications. The results obtained showed a clear distinction between the various formation units, and very close results to the field reality in the ANN classification of the studied area. Thus, the ANN method is more accurate with an overall accuracy of 92.56% and a Kappa coefficient is 0.9143.