如何处理好历史学习的拓展

进行课外拓展是新课程理念的要求,也是时代发展的要求。但是教师的拓展要避免随意性,遵循以文本为基础,遵循适度的原则。并且对文本的拓展要能够引起学生的共鸣,还要遵循遵循适时性原则。从而在丰富课堂学习的同时,起到优化教学的效果。

生本课堂,期待教师的“三导”

生本课堂要体现“以生为本”的理念,但要达成这一目标,必须完成教师角色的转换,使其从台前转入幕后,充分发挥教师的主导作用。首先要做到“导”得适时,其次要“导”得适处,最后要“导”得适度。从而实现生本课堂聚焦核心素养、培养关键能力、塑造必备品格的终极目标。

ADAPTIVE EXPONENT SMOOTHING GRADIENT ALGORITHM

A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s LMS algorithm. It is based on the fact that LMS algorithm has properties of time delaying and low pass filtering. This paper shows that the algorithm, on the domain of {Ω 1:α∈(0,1)}×{Ω 2:β(0,∞)} , unbiasedly and asymptotically converges to the Winner solution when the signal is a stationary Gauss stochastic process. The convergent property and the performance misadjustment are analyzed in theory. And calculation method of the algorithm is also suggested. Numerical results given by computer simulations show that the algorithm is effective.

MICROPROCESSOR BASED MODEL FOLLOWING ADAPTIVE CONTROL SYSTEM FOR CSIM DRIVES

A model following adaptive control system for CSIM is presented in this paper. A dynamic mathematical model of slip control based system is obtained. With the help of model reducing technique, full order model is reduced to simplify the design without degrading much of the performance. Model following adaptive control laws in discrete form are derived. These laws satisfy the hyperstability condition for taking care of the load and machine parameter changes of the drive. A microprocessor 8098 is used to develop the speed controller. The implementation of the control system uses only available variables of the reference model and the controlled plant. Experimental results are given to demonstrate the good performance of the system.

ALGORITHM FOR THE DETECTION AND PARAMETER ESTIMATION OF MULTICOMPONENT LFM SIGNALS

A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. The key problem lies in the chirplet estimation. Genetic algorithm is employed to search for the optimization parameter of chirplet. High estimation accuracy can be obtained even at low Signal-to-Noisc Ratio(SNR). Finally simulation results are provided to demonstrate the performance of the proposed algorithm.

BLIND ADAPTIVE MULTIUSER DETECTION BASED ON A CONSTRAINED MMSE CRITERION

The proposed blind adaptive multiuser detector utilizes the signature waveform and time information of the desired user. With each received sample vector, the proposed algorithm updates the detector and gives the symbol estimate in the current time slot. Such property facilitates it to track time-varying channels.

PRECONDITIONED METHODS FOR SPACE-TIME ADAPTIVE PROCESSING

This paper introduces the preconditioned methods for Space-Time Adaptive Processing(STAP).Using the Block-Toeplitz-Toeplitz-Block(BTTB)structure of the clutter-plus-noise covari-ance matrix,a Block-Circulant-Circulant-Block(BCCB)preconditioner is constructed.Based on thepreconditioner,a Preconditioned Multistage Wiener Filter(PMWF)which can be implemented by thePreconditioned Conjugate Gradient(PCG)method is proposed.Simulation results show that thePMWF has faster convergence rate and lower processing rank compared with the MWF.

BACKSIDE EFFECT OF BISTATIC AIRBORNE CLUTTER CHARACTERISTICS AND ITS APPLICATIONS IN GMTI

An interesting clutter characteristic of bistatic radars is presented, which is named as backside effect. In such an effect, the range-dependent ground clutter spectrum can be easily aligned, and Space-Time Adaptive Processing (STAP) is to be more applicable and effective for the Ground Moving Target Indication (GMTI) in bistatic systems. The backside effect is proved by the numerical calculation method and explained in point of the geometry. At last a new spectrum aligning method is induced, i.e., ADC and Rotation (ADCR), which can gain a further performance improvement on GMTI.

Modified Laser Speckle Imaging with Adaptive Window

One of the advantages of laser speckle is detecting microvascular through image processing. This paper proposes a new image processing method for laser speckle, adaptive window method that adaptively processes laser speckle images in the space. Disadvantage of conventional fixed window method is that it uses the same window size regardless of target areas. Inherently laser speckle contains undesired noise. Thus a large window is helpful for removing the noise, but it results in low resolution of image. Otherwise a small window may detect micro vascular but it has limits in noise removal. To overcome this trade-off, the concept of adaptive window method is newly introduced to conventional laser speckle image analysis. In addition, the modified adaptive window method applied to other selection images. We have compared conventional Laser Speckle Contrast Analysis （LASCA） and its variants with the proposed method in terms of image quality and processing complexity, Moreover compared the result of the accompamed changing sdection images have also been compared.

Adaptable Processing： Stage Development

The development and evolution of Microsoft Office and Microsoft Windows shells are based in general on the special methodology of software creation and implementation such as macros, subroutine, custom commands, and specialized features. Microsoft Office for Mac has for long been criticized for its lack of support of Unicode and BiDi languages, notably Arabic and Hebrew. This has not changed in the Office 2008 version. Microsoft Office 2010 （also called Office 2010 and Office 14） is the current version of the Microsoft Office productivity suite for Microsoft Windows, and the successor to Microsoft Office 2007. With Office 2010, users are in control, getting things done and producing amazing results however and wherever they work best. Ms Office 2010 is the last version of Microsoft Office with support for Windows XP, Windows Server 2003, Windows Vista, and Windows Server 2008 due to Office 2013 requiring Windows 7, Windows Server 2008 R2, Windows 8, Windows Server 2012, or Windows RT. Adaptive hardware （Ugurdag, 2006） reflects the capability of a system to maintain or improve its performance in the context of internal or external changes. Adaptation at hardware levels increases the system capabilities beyond what is possible with software-only solutions. Algorithms, techniques, and their implementation in hardware are developed over a diverse variety of applications. The methodology of the On-Off-Line adaptable processors （Todoroi ＆ Micusha, 2012） support development of adaptable software and hardware. Automatic creation of the Off-Line adaptable processors are proved. Development of the On-Line and On-Off-Line adaptable processors based on Off-Line processing creation method is proposed （Todoroi, Micu^a, ＆ Todoroi, 2009; Todoroi, 2008a, 2008b）

A method of remote sensing image water segmentation based on adaptive morphological elliptical structuring elements

The use of visible and infrared remote sensing images to calculate the water area is an effective means to grasp the basic situation of water resources,and water segmentation is the premise of statistics.Generally,the edge features of the water in the remote sensing images are complex.When the traditional morphology is used for image segmentation,it is easy to change the image edge and affect the accuracy of image segmentation because the fixed structuring elements are used to perform morphological operations on the image.To segment water in the remote sensing image accurately,a remote sensing image water segmentation method based on adaptive morphological elliptical structuring elements is proposed.Firstly,the eigenvalue and eigenvector of the image are estimated by linear structure tensor,and the elliptical structuring elements are constructed by the eigenvalue and eigenvector.Then adaptive morphological operations are defined,combining the close operation to eliminate the influence of dark detail noise on water without overstretching the water edge,so that the water edge can be maintained more accurately.Finally,on this basis,the water area can be segmented by gray slice.The experimental results show that the proposed method has higher segmentation accuracy and the average segmentation error is less than 1.43%.

A Robust Method for Adaptive Center Extraction of Linear Structured Light Stripe

In the non-contact measurement using the linear structured light(LSL),the extraction precision of the light stripe center directly affects the measurement accuracy of the whole detection system.To solve the problem that general algorithms cannot accurately extract the center of the light stripe with the uneven width and unstable greyvalue distribution,an adaptive optimization method is proposed.In this method,the stripe region is firstly segmented,and the widths of the laser stripe are calculated by boundary detection.The initial stripe center points are computed by the quadratic weighted grayscale centroid method based on the self-adaptive stripe width.After that,these center points are optimized according to the determined slope threshold.The sub-pixel coordinates of these center points are recalculated.Detailed analysis is also performed in line with the proposed evaluation index of the extraction algorithm.The experimental results show that the mean square error of extracted center points is only 0.1 pixel,meaning that the accuracy of laser stripe center extraction is improved significantly by the method.Furthermore,the method can run effectively at a relatively low computational time cost,and can demonstrate great robustness as well.

Adaptive sampling for mesh spectrum editing

A mesh editing framework is presented in this paper, which integrates Free-Form Deformation (FFD) and geometry signal processing. By using simplified model from original mesh, the editing task can be accomplished with a few operations. We take the deformation of the proxy and the position coordinates of the mesh models as geometry signal. Wavelet analysis is em- ployed to separate local detail information gracefully. The crucial innovation of this paper is a new adaptive regular sampling approach for our signal analysis based editing framework. In our approach, an original mesh is resampled and then refined itera- tively which reflects optimization of our proposed spectrum preserving energy. As an extension of our spectrum editing scheme, the editing principle is applied to geometry details transferring, which brings satisfying results.

Adaptive preprocessing algorithms of corneal topography in polar coordinate system

New adaptive preprocessing algorithms based on the polar coordinate system were put forward to get high-precision corneal topography calculation results. Adaptive locating algorithms of concentric circle center were created to accurately capture the circle center of original Placido-based image, expand the image into matrix centered around the circle center, and convert the matrix into the polar coordinate system with the circle center as pole. Adaptive image smoothing treatment was followed and the characteristics of useful circles were extracted via horizontal edge detection, based on useful circles presenting approximate horizontal lines while noise signals presenting vertical lines or different angles. Effective combination of different operators of morphology were designed to remedy data loss caused by noise disturbances, get complete image about circle edge detection to satisfy the requests of precise calculation on follow-up parameters. The experimental data show that the algorithms meet the requirements of practical detection with characteristics of less data loss, higher data accuracy and easier availability.

Removal of Different Dyes by Pseudomonas fluorescens

The effluents of textile dyes are highly colored, and disposal of the wastes into receiving waters cause damage to the environment, therefore dyes removal is of great importance. In this research, removal of Orange II, Remazol Blue, Methyl red, Malachite green and Safranin dyes by Pseudomonas fluorescens was studied in batch system as function of temperature, pH and initial dye concentration. The rate of removal dyes was studied by using pectrophotometer. The optimum value was determined as pH 7 for all dyes tested about 87.8-72.7%, and optimum temperature for removal of all tested dyes was 30℃ about 86.6-60.8%. Higher removal of dyes observed at 25 mL for all tested dyes about 91.9-72,7%. In general, the increase in dye concentration inhibited the growth of bacteria. Pseudomonasfluorescens showed higher removal of Orange II among the dyes tested.

Strategies for h-Adaptive Refinement for a Finite Element Treatment of Harmonic Oscillator Schrodinger Eigenproblem

A Schrodinger eigenvalue problem is solved for the 219 quantum simple harmonic oscillator using a finite element discretization of real space within which elements are adaptively spatially refined. We compare two competing methods of adaptively discretizing the real-space grid on which computations are performed without modifying the standard polynomial basis-set traditionally used in finite element interpolations; namely, （i） an application of the Kelly error estimator, and （ii） a refinement based on the local potential level. When the performance of these methods are compared to standard uniform global refinement, we find that they significantly improve the total time spent in the eigensolver.

Space discriminative function for microphone array robust speech recognition

Based on W-disjoint orthogonality of speech mixtures, a space d,scnmlnative tunetlon was proposer1 to enumerate and localize competing speakers in the surrounding environments. Then, a Wiener-like postfiherer was developed to adaptively suppress interferences. Experimental results with a hands-free speech recognizer under various SNR and competing speakers settings show that nearly 69 % error reduction can be obtained with a two-channel small aperture microphone array against the conventional single microphone baseline system. Comparisons were made against traditional delay-and-sum and Griffiths-Jim adaptive beamforming techniques to further assess the effectiveness of this method.

Effect of mixed moderately thermophilic adaptation on leachability and mechanism of high arsenic gold concentrate in an airlift bioreactor

A refractory gold concentrate with 19% arsenic was treated by a mixed moderately thermophiles in an airlift bioreactor through an adaptation protocol. The moderately thermophiles could respond well to 20%(w/v) pulp density with less than 10% loss of productivity, and resist arsenic up to 15 g/L. There were a lot of jarosite, arsenolite and sulfur, but not scorodite and ferric arsenate in the bioleached residue. Jarosite passivation and lower sulfur-oxidizing activity of the cells due to the toxicity of the high concentrations of soluble arsenic and iron ions at low p H value should mainly response for the incomplete extraction at high pulp density. The initial bacterial community did not change in nature except for new found P aeruginosa ANSC, but sulfur-oxidizing microorganisms have been dominant microorganisms after a long time of adaptation. Pseudomonas aeruginosa originating from the gold concentrate should be closely relative to the metabolism of the organic matters contained in the refractory gold concentrate.

A GAUSSIAN MIXTURE MODEL-BASED REGULARIZATION METHOD IN ADAPTIVE IMAGE RESTORATION

A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.

Improved Real-time Implementation of Adaptive Gassian Mixture Model-based Object Detection Algorithm for Fixed-point DSP Processors

Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model （AGMM）. Although ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint arithmetic so that it should pay for expensive computational cost. Thus, direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement. This paper presents a novel rcal-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs. In the proposed implementation, in addition to changes of data types into fixed-point ones, magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.