“图感”:开启儿童直接智慧之门的钥匙

＂图感＂作为一种直观的感知与一种直觉灵动的智慧顿悟,在学生学习＂图形与几何＂的过程中发挥着极其重要的作用。它有助于培养学生的几何直观与数学直觉,发展学生的创造性思维,开启学生的直接智慧之门。教师要为学生创造培育＂图感＂的条件,丰富学生的视觉意象,在＂图导＂与＂图构＂的过程中提高学生对图形的直观感觉与敏锐程度,提升学生数学学习的直接智慧。

基于无线传感器网络定位技术的图书馆智能管理创新

"互联网+"时代,信息技术的发展对图书馆的智能化管理工作提出了更高的要求。文章概述了无线传感器网络与定位技术,重点讨论了如何将WSN定位技术运用到图书智能管理,通过定位技术与大数据、云计算等信息技术的结合,推动图书馆智能系统建设和智能化发展。

Oriented Bounding Box Object Detection Model Based on Improved YOLOv8

In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.

Automatic road extraction framework based on codec network

Road extraction based on deep learning is one of hot spots of semantic segmentation in the past decade.In this work,we proposed a framework based on codec network for automatic road extraction from remote sensing images.Firstly,a pre-trained ResNet34 was migrated to U-Net and its encoding structure was replaced to deepen the number of network layers,which reduces the error rate of road segmentation and the loss of details.Secondly,dilated convolution was used to connect the encoder and the decoder of network to expand the receptive field and retain more low-dimensional information of the image.Afterwards,the channel attention mechanism was used to select the information of the feature image obtained by up-sampling of the encoder,the weights of target features were optimized to enhance the features of target region and suppress the features of background and noise regions,and thus the feature extraction effect of the remote sensing image with complex background was optimized.Finally,an adaptive sigmoid loss function was proposed,which optimizes the imbalance between the road and the background,and makes the model reach the optimal solution.Experimental results show that compared with several semantic segmentation networks,the proposed method can greatly reduce the error rate of road segmentation and effectively improve the accuracy of road extraction from remote sensing images.

Hot spot tracking of flood remote sensing research over the past 22 years:abibliometric analysis using CiteSpace

Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.

Design,Analysis,and Optimization of a CMOS Active Pixel Sensor

A three-transistor active pixel sensor and its double sampling readout circuit implemented by a switch capacitor amplifier are designed. The circuit is embedded in a 64 × 64 pixel array CMOS image sensor and success-fully taped out with a Chartered 0.35μm process. The pixel pitch is 8μm × 8μm with a fill factor of 57%, the photo-sensitivity is 0.8V/（lux · s） ,and the dynamic range is 50dB. Theoretical analysis and test results indicate that as the process is scaled down, a smaller pixel pitch reduces the sensitivity. A deep junction n-well/p-substrate photodiode with a reasonable fill factor and high sensitivity are more appropriate for submicron processes.

SOME ALGORITHMS FOR NC MACHINING SIMULATION AND VERIFICATION

This paper presents an approximation method to display realistic pictures of numerical control (NC) machining simulation very quickly. T he tool movement envelope is divided into many small regions and the normal to t hese small regions is calculated. The system saves the calculated result in a fi le before starting animation display. When the system starts displaying machinin g animation, it does not need to calculate small triangular facets normal to the workpiece surface. It only needs to find out what part of the cutter cuts the w orkpiece surface and to read the normal from the file. A highly efficient NC cod e verification method is also presented in this paper. The method first detects the error in z direction. If some points are reported to be out of the tolerance , the system divides neighborhood of these points into smaller grids and calcula tes the normal surface at each grid intersection and the error in the normal ve ctor direction.

A Low-Power-Consumption 9bit 10MS/s Pipeline ADC for CMOS Image Sensors

A low-power-consumption 9bit 10MS/s pipeline ADC,used in a CMOS image sensor,is proposed. In the design, the decrease of power consumption is achieved by applying low-power-consumption and large-output-swing amplifiers with gain boost structure, and biasing all the cells with the same voltage bias source, which requires careful layout design and large capacitors. In addition,capacitor array DAC is also applied to reduce power consumption,and low threshold voltage MOS transistors are used to achieve a large signal processing range. The ADC was implemented in a 0.18μm 4M-1 P CMOS process,and the experimental results indicate that it consumes only 7mW, which is much less than general pipeline ADCs. The ADC was used in a 300000 pixels CMOS image sensor.

Effect of initial microstructure on hot workability of 7085 aluminum alloy

The hot workability of 7085 aluminum alloys with different initial microstructures （as-homogenized and as-solution treated） was studied by isothermal compression tests at the deformation temperature ranging from 300 to 450 ℃ and the strain rate ranging from 0.0001 to 1 s 1. The strain rate sensitivity of the alloy was evaluated and used for establishing the power dissipation maps and instability maps on the basis of the flow stress data. The results show that the efficiency of power dissipation for the as-homogenized alloy is lower than that of the as-solution treated alloy. The deformation parameters of the dynamic recrystallization for the as-homogenized and as-solution treated alloy occur at 400 ℃, 0.01 s i and 450 ℃, 0.001 s-1, respectively. The flow instability region of the as-homogenized alloy is narrower than that of the as-solution treated alloy. These differences of the alloys with two different initial microstructures on the processing maps are mainly related to the dynamic precipitation characteristics.

Research on detection of lane based on machine vision

To prevent a vehicle from departing the lane in assistant or automatic steering, real-time vision-based detection of lane is studied. The system architecture, detecting principle and lane model are described. Then the detecting algorithm of the lane image is discussed in detail. In this algorithm, several proper sub-windows in one image are first selected as the processing regions. To every sub-window, by means of such steps as appropriate pre-processing, edge detection and Hough transform, etc., the lane description features are extracted. Experimental results reveal that this detection method is of good real-time, high recognition reliability and strong robustness, etc., which can provide the decision-making foundation for the following automatic or assistant steering to some extent.

Principle and Analysis of Novel Gate-Induced Noise in Pixel MOSFET of CMOS Imagers

A detailed principle and a rigorous analysis of a new noise,the gate-induced noise,in pixel MOSFET of CMOS imagers are provided.The gate-induced noise of the MOSFET is more notable in the strong reversion region than that in the subthreshold region when the applied gate voltage is low.However,the applied gate voltage being up to 3V,the gate-induced noise is more notable with the ω/ω T increasing when the MOSFET operates in the subthreshold region than that in the strong reversion region.Between the photocurrent I D and the root mean square value of the gated-induced noise,current i 2 d presents the relation of i 2 d∝I D in the saturation region of the strong reversion and approximately i 2 d∝I D in the subthreshold region.A deta iled and rigorous study of the gate-induced noise in the reset MOSFET for the p hotodiode APS and improved photodiode APS are provided.The improvement of logari thmic response APS is analyzed and the simulation results show that the gate-in duced noise can be reduced.

Image quality assessment based on perceptual grouping

To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.

Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction

To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.

Numerical Simulation and Analysis of Bipolar Junction Photogate Transistor for CMOS Image Sensor

A new photodetector--bipolar junction photogate transistor is presented for CMOS image sensor and its analytical model is also established.With the technical parameter of the 0.6μm CMOS process,the bipolar junction photogate transistor is analyzed and simulated.The simulated results illustrate that the bipolar junction photogate transistor has the similar characteristics of the traditional photogate transistor.The photocurrent density of the bipolar junction photogate transistor increases exponentially with the incidence light power due to introducing the injection p+n junction.Its characteristic of blue response is rather improved compared to the traditional photogate transistor that benefits to increase the color photograph made up of the red,the green,and the blue.

A New CMOS Image Sensor with a High Fill Factor and the Dynamic Digital Double Sampling Technique

A single CMOS image sensor based on a 0.35μm process along with its design and implementation is introduced. The architecture of an active pixel sensor is used in the chip. The fill factor of a pixel cell can reach 43%,higher than the traditional factor of 30%. Moreover, compared with the conventional method whose fixed pattern noise （FPN） is around 0.5%, a dynamic digital double sampling technique is developed, which possesses simpler circuit architecture and a better FPN suppression outcome. The CMOS image sensor chip is implemented in the 0.35μm mixed signal process of a Chartered by MPW. The experimental results show that the chip operates welt,with an FPN of about 0.17%.

Face hallucination via compressive sensing

Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compressive sensing based face hallucination method is presented,which is comprised of three steps:dictionary learning、sparse coding and solving maximum a posteriori(MAP)formulation.In the first step,the K-SVD dictionary learning algorithm is adopted to obtain a dictionary which can sparsely represent high resolution(HR)face image patches.In the second step,we seek the sparsest representation for each low-resolution(LR)face image paches input using the learned dictionary,super resolution image blocks are obtained from the sparsest coefficients and dictionaries,which then are assembled into super-resolution(SR)image.Finally,MAP formulation is introduced to satisfy the consistency restrictive condition and obtain the higher quality HR images.The experimental results demonstrate that our approach can achieve better super-resolution faces compared with other state-of-the-art method.

On the mode of CRI English assisting college English teaching

The English News of CRI possesses many advantages such as abundance of information and being easily accessible for English learners within the mainland of China. Unfortunately, it has been ignored by most teachers of English and students. Based on the input hypothesis, affective filter hypothesis and schemata theory, this paper probes the mode of CRI English assisting English teaching in college.

A New Fusion Technique of Remote Sensing Images for Land Use/Cover

In China, accelerating industrialization and urbanization followinghigh-speed economic development and population increases have greatly impacted land use/coverchanges, making it imperative to obtain accurate and up to date iufbimation on changes soas toevaluate their environmental effects. The major purpose of this study was to develop a new method tofuse lower spatial resolution multispectral satellite images with higher spatial resolutionpanchromatic ones to assist in land use/cover mapping.An algorithm of a new fusion method known asedge enhancement intensity modulation (EEIM) was proposed to merge two optical image data sets ofdifferent spectral ranges. The results showed that the EEIM image was quite similar in color tolower resolution multispectral images, and the fused product was better able to preserve spectralinformation. Thus, compared to conventional approaches, the spectral distortion of the fused imageswas markedly reduced. Therefore, the EEIM fusion method could be utilized to fuse remote sensingdata from the same or different sensors, including TM images and SPOT5 panchromatic images,providing high quality land use/cover images.

Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis

With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.

MULTI-SOURCE REMOTE SENSING IMAGE FUSION BASED ON SUPPORT VECTOR MACHINE

Remote Sensing image fusion is an effective way to use the large volume ofdata from multi-source images. This paper introduces a new method of remote sensing image fusionbased on support vector machine (SVM), using high spatial resolution data SPIN-2 and multi-spectralremote sensing data SPOT-4. Firstly, the new method is established by building a model of remotesensing image fusion based on SVM. Then by using SPIN-2 data and SPOT-4 data, image classificationfusion is tested. Finally, an evaluation of the fusion result is made in two ways. 1) Fromsubjectivity assessment, the spatial resolution of the fused image is improved compared to theSPOT-4. And it is clearly that the texture of the fused image is distinctive. 2) From quantitativeanalysis, the effect of classification fusion is better. As a whole, the re-suit shows that theaccuracy of image fusion based on SVM is high and the SVM algorithm can be recommended forapplication in remote sensing image fusion processes.