Disclosed is a method for establishing a prediction model based on a multidimensional texture of brain nuclear magnetic resonance images. Images are segmented using a region growing method, a contourlet transform method is used to extract an edge texture feature parameter of ROIs, a multidimensional database is established, and a prediction model is established using various data mining methods, comprising a Gaussian process, a support vector machine, a random forest, a Lasso regression and a semi-supervised support vector machine. The ROIs comprise a hippocampus region and an entorhinal cortex region.

In aided diagnosis of early Alzheimer disease (AD), it is important to identify features of the ROIs (reactive oxygen intermediates including entorhinal cortex and hippocampus) in the magnetic resonance imaging (MRI) images. However, the MRI technology can only use the hippocampus atrophy as one of the indicators to distinguish a patient from a healthy person. The explanations of doctors to the MRI images are easily affected by subjectivity and lack of consistency. Moreover, it is hard for the MRI technology to evaluate correctly how serious the symptom of the AD is.
1, the Prior Image Processing Technology
Contourlet Transform
The Contourlet transform inherits the anisotropic scale relationship of Curvelet transform. In a certain sense, the Contourlet transform is another implementation of the Curvelet transform. The basic idea of Contourlet transform is to capture edge point discontinuities using multiscale decomposition similar to wavelet firstly, and then to converge the point discontinuities with similar location to form contour segments according to direction information.
The Contourlet transform may be divided into two parts: Laplacian Pyramid (LP) filter structure and 2-D directional filter bank (DFB). The LP decomposition first produce a lowpass sampling approach of the original signals and a difference image of the original image and a lowpass predication image, and further decompose the lowpass image obtained to gain a lowpass image and a difference image of the next level. Multiresolution decomposition of image is obtained in this gradually filtering. The DFB adopts conjugate mirror filters with a sector structure, and transforms one level binary tree structure of a directional filter into a structure with 21 parallel passages.
The Contourlet transform is a new two-dimensional transform method for image representations, which has properties of multiresolution, localization, directionality, critical sampling and anisotropy. Its basic functions are multiscale and multidimensional. The contours of original images, which are the dominant features in natural images, can be captured effectively with a few coefficients by using Contourlet transform.
However, when the new transform method is used to process MRI images of different body parts, it needs to reconstruct new algorithm and select suitable parameters using basis functions. Therefore, there still are many theoretical issues to be studied. The Contourlet transform has been successfully used to solve actual problems such as image fusion. However, there are few references on extraction of image texture features using the Contourlet transform. No one has disclosed to use the second generation wavelet transform and Contourlet transform to extract textures of AD brain MRI images.
2, the Prior Typical Prediction Model Includes Supervised Machine Learning Model and Semi-Supervised Machine Learning Model
Supervised Machine Learning Model
Gaussian Process (GP):
The Gaussian process classification algorithm is a supervised learning algorithm, which is established under the Bayesian framework, and it is first used to solve the problem of nonlinear value prediction. Provided there is a data set D={(xi, yi)|yi=±1, i=1, 2, . . . , n}, the essence of binary classification is to find a suitable mapping f(x) to ensure that the sample can be classified correctly using y=f(x). The basic idea of classification using Gaussian regression algorithm is to find a suitable f(x) aiming at maximizing the posterior probability in Bayesian rule under the assumption that the f(x) is a Gaussian process. It is firstly assumed that there is an implicit function f(x)=Ø(x)Tw, which defines the mapping relationship between input properties and output properties (class tags). It is further assumed that the class tags are independent identically distributed, and are subjected to conditional probability p(y|f(x)). Thus, for the binary classification, the probability that the sample xi belongs to class yi is:p(yi|fi)=σ(yifi)
wherein, fi=f(xi) is an implicit function, σ(·) is a Sigmoid class function, such as a logistic function or a cumulative Gaussian function. Since training samples are independent to each other, the corresponding likelihood probability is:
      p              (              y        |        f            )        =                    ∏                  i          =          1                n                                                    p                          (                                    y              i                        |                          f                                            (                                  x                  i                                )                                              )                      =                  ∏                  i          =          1                n                                                    σ                          (                                    y              i                        |                          f              i                                )
wherein, for the sample set D={(xi, yi)|yi=±1, i=1, 2, . . . , n}, the Gaussian process classifier assumes that the f(x)˜GP(0, k) is a Gaussian process with mean value equivalent to zero to simplify calculation, i.e. p(f|x) is a multidimensional Gaussian density function:
      p              (              f        |        x            )        =            1                                    (                          2                            π                        )                                L                                      /                                    2                          |        k                          |                      1                                      /                                    2                                    exp              {                        -                      1            2                                            f          T                                  K                      -            1                                  f            }
wherein, K is a covariance matrix, which is a kernel function. Therefore, the posterior probability can be obtained according to Bayesian formula:
      p              (                        f          |          y                ,        X            )        =                    p                          (                      y            |            f                    )                                  p                          (                      f            |            X                    )                            p                    (                  y          |          X                )
wherein, p(y|f) is a likelihood function, p(y|X)=∫p(y|f)p(f|x)df is a marginal probability density.
The training to the Gaussian process classifier is namely the estimation to the posterior probability. The estimated value q(f|y, X) of the posterior probability p(f|y,X) can be calculated by Laplace method. Therefore, the probability that the class tag of a given testing data point x* belongs to positive class is:π*=p(y*=±1|X,y,x*)=∫Ø(f*)q(f*|X,y,x*)df*
According to the principle of Gaussian process classification, the numeric data set obtained after feature extraction is inputted into the Gaussian process classifier. The estimated value q(f|y, X) of the posterior probability can be calculated after hyper-parameter optimization. Then, prediction results related to testing set can be obtained.
1) Support Vector Machine (SVM):
Support vector machine method is a machine learning method based on VC dimension theory of statistical learning theory and structure risk minimum principle. Its principle is to find an optimal classification hyperplane which satisfies the requirements of classification, so that the hyperplane ensures classification accuracy and the margin of the hyperplane is largest.
In theory, the support vector machine is capable of realizing the optimal classification of linear classifiable data. Taking the classification of two kinds of data as an example, it is provided that the training sample set is (xi, yi), i=1, 2, . . . , l, xε{±+1}, and the hyperplane is written as (w·xi)+b=0. In order to ensure that the classification plane is correct to all the samples and has a classification space, the following restriction should be satisfied: yi[(w·xi)+b]≧1, i=1, 2, . . . , l. It can be calculated that the classification space is 2/∥w∥. Thus, the problem of constructing an optimal hyperplane has been transformed to the problem of solving the following formula under the restriction formula:minØ(w)=½∥w∥2=½(w·w)
In order to solve this problem of restriction optimization, the Lagrange function is introduced:L(w,b,a)=½∥w∥−α(y(w·x)+b)−1
wherein, α>0, and it is a Lagrange multiplier. The solution of the problem of restriction optimization is determined by the saddle point of the Lagrange function. Moreover, the partial derivative of the solution of the problem of restriction optimization at the saddle point with respect to w and b is zero. Thus, the QP (quadratic programming) problem is transformed to a corresponding dual problem:
            max                                              Q                          (          α          )                      =                            ∑                      j            =            1                    l                                                                    α          j                    -                        1          2                                              ∑                          i              =              1                        l                                                                                ∑                              j                =                1                            l                                                                                α                i                                                          α                j                                                          y                i                                                                            y                  j                                                                  (                                                            x                      i                                        ·                                          x                      j                                                        )                                                                                s        .        t        .                                                                ∑                          j              =              1                        l                                                                                α              j                                                  y              j                                          =      0        ,          j      =      1        ,    2    ,    …                      ,    l
the optimal solution α*=(α1*, α2*, . . . , αl*)l is obtained.
Calculating the optimal weight vector w* and optimal bias b*, which are:
            w      *        =                  ∑                  j          =          1                l                                                              α          j          *                                  y          j                                  x          j                                b      *        =                  y        i            -                        ∑                      j            =            1                    l                                                                                y            j                                                        α              j              *                                                  (                                                x                  j                                ·                                  x                  i                                            )
Wherein, the subscript jε{j|αj*>0)}. Thus, the optimal classification hyperplane is obtained: (w*·x)+b*=0, and the optimal classification function is:
            f                    (        x        )              =                  sgn                          {                                    (                                                w                  *                                ·                x                            )                        +                          b              *                                }                    =              sgn                          {                                                    ∑                                  j                  =                  1                                l                                                                                α                  j                  *                                                                                y                    j                                                                          (                                                                  x                        j                                            ·                                              x                        i                                                              )                                                                        +                          b              *                                }                      ,      x    ∈          R      n
For the linear unclassifiable case, the main idea of SVM is to map the input vector to a high dimensional eigenvector space, and construct the optimal classification plane in the feature space.
Doing transform Ø to x from the input space Rn to the feature space H, it can be obtained:x→Ø(x)=(Ø1(x),Ø2(x), . . . Øl(x))T
Replacing the input vector x with the eigenvector Ø(x), the optimal classification function can be obtained:
      f              (      x      )        =            sgn                    (                              w            ·                          ∅                                            (                x                )                                              +          b                )              =          sgn                    (                                            ∑                              j                =                1                            l                                                                                α                i                                                          y                i                                                          ∅                                                  (                  x                  )                                                              +          b                )
2) Random Forests (RF):
Random forest is a classification and predication model proposed by Leo Breiman, which has been applied in many fields.
The basic idea of constructing random forests is continuously generating training sample and testing sample by the technique of bootstrap resampling. The training sample generates a plurality of classification tree constituting the random forests. The classification result of the testing data is determined according to the vote of classification tree.
It is provided that the content of original sample is N, the variables representing various genes are x1, x2, . . . , xm. Randomly sampling with replacement new self-service samples with the number of b, and hence forming classification trees with the number of b. The samples that are not sampled each time form the data out of bag (OOB) with the number of b. the data out of bag acting as testing sample can be used to evaluate the importance of the various variables in the classification. The realization process is as follows:
Using the self-service samples to form each tree classifier, and classifying the corresponding OOB at the same time, it can hence obtain the voting scores of each sample in the OOB of the self-service samples with the number of b, which can be written as vote1, vote2, . . . , votes.
Randomly changing the order of the value of the variables xi in the OOB samples with the number of b, it forms new OOB testing samples. Then, classifying the new OOB by the constructed random forests and obtaining the voting scores of each sample according to the number of sample that is judged to be correct.
3) Lasso:
The basic idea of Lasso (the least absolute shrinkage and selection operator) method is to minimize the sum of squared residuals under the restriction that the sum of absolute regression coefficient is less than a constant, thus generating some regression coefficient rigidly equaling to zero. Although this is a biased estimation to decrease the variance of the predicted value, the prediction accuracy of the model will be increased accordingly and the model obtained is easier to be explained. For the case with a plurality of independent variables, we always hope to determine a model with fewer variables to present the best effect. The selection criteria of Lasso is to minimize the penalty function
                    1        n                                    ∑                      i            =            1                    n                                                                                (                                          Y                i                            -                                                X                  i                                                                  β                  i                                                      )                    2                      +                  ∑                  j          =          1                λ                                                                      β          j                              ,and adopts the least-angle regression algorithm.
The regression steps of Lasso are as follows:
a) Provided the estimated initial value of β is β(0), fixing λ and selecting the least-squares estimation {tilde over (β)}LS as the initial value of β. That is to say, β(0)={tilde over (β)}LS=(X′X)−1X′Y;
b) Obtaining the estimation to β(written as {tilde over (β)}) when fixing λ according to the estimated initial value β(0) and the following iterative formula:β(1))=β(0)−[∇2QLSβ(0)+nΣλβ(0)]−1[∇QLSβ(0)+nUλβ(0)]
wherein, ∇2QLSβ(0)=X′X, ∇QLSβ(0)=−2X(Y−Xβ(0)), Σλβ(0)=λdiag(1/|β0|, . . . 1/|βp|), if βj=0, the value of 1/|βj| can be evaluated to be a very large number. Uλβ(0)=Uλβ(0)β(0);                c) Changing the value of λ in the range of 0˜1, under the restriction of λ, constructing generalized cross validation
      GCV              (      λ      )        =                                                    Y            -                          X                                                                                β                  ~                                λ                                                              2                              (                      1            -                                          e                                                  (                  λ                  )                                            /              n                                )                2              .  The penalty function that minimizes the cross validation is the optimal selection penalty parameter λ, λ=arg min GCV(λ), and hence the Lasso estimation to β is {circumflex over (β)}Lasso={circumflex over (β)}Lasso.                (1) Semi-Supervised Support Vector Machine (S3VM):
The semi-supervised learning is a key problem studied in the field of pattern recognition and machine learning in recent years, which mainly considers the problem of how to train and classify with a few tag samples and a plurality of samples without tag. The semi-supervised learning algorithm based on support vector machine is a model first proposed by Kristin, which is realized by mixed integer programming method. Later, Joachims, Demirez et al. proposed some similar methods one after another among which the transductive support vector machine (TSVM) algorithm is more typical.
Given that a training set T={(x1, y1), . . . , (xl, yl), xj+1, . . . , xj+k}, wherein, xfεX=Rn, the top l samples are samples with classification tag, i.e. when i=1, 2, . . . , l, yiεY={−1,1}; the down k samples are samples without classification tag. Finding a decision function f(x)=sgn(ωTφ(x)+b) at X=Rn to deduce the class (positive class or negative class) of x of any pattern (wherein, ω is weight vector, φ(·) is a mapping function, b is a constant, and the kernel function K(xi, xj)=<φ(xi), φ(xj)>). Accordingly, the solving process is actually to find a principle to divide the points in Rn into two parts.
Obviously, it exists ωεRn, bεR, to ensure that it exists yi(ωTφ(xi)+b)+τi≧1, ξi≧0, i=1, . . . , l for each sample with classification tag xi(i=1, . . . , l). It exists (ωTφ(xj)+b)+rj≧1, ωTφ(xj)+b−sj≦−1, rj, sj≧0, j=l+1, . . . , l+k.
Currently, such a classification learning machine is constructed by maximizing soft margin and slack variables ξ=(ξ1, ξ2, . . . , ξl), r=(rl+1, rl+2, . . . , rl+k), s=(sl+1, sl+1, . . . , sl+k) are introduced. Obviously, ξ, r, s reflect the case that the training set is wrongly classified. Thus, it can construct the degree that describes the training set wrongly classified, and it may as well to adopt Σi=1lξi2, Σj=1l+krjsi as a mensuration to degree that describes the training set wrongly classified. At the same time, it is expected that the wrongly classified degree Σi=1lξi2, Σj=1l+krjsi is as small as better, that the optimal classified hyperplane can be solved out by minimizing ½(ωTω+b2), and that the affection to the classification result is limited. In order to fully take the three aims into account, the penalty parameters c1 and c2 are introduced as a weight to balance the three aims. That is to say, the aim function is minimized as:
            1      2                  (                                    ω            T                              ω                +                  b          2                    )        +            c      1                          ∑                  i          =          1                l                                                    ξ        i        2              +            c      2                          ∑                  j          =          1                          l          +          k                                                                      r          j                                  s          i
Thus, the problem of solving the optimal classified hyperplane is transformed into the problem of optimization:
                                      min                  ω          ,          b          ,          ξ          ,          r          ,          s                                  (                                            1              2                                                  (                                                                    ω                    T                                                      ω                                +                                  b                  2                                            )                                +                                    c              1                                                                  ∑                                  i                  =                  1                                l                                                                                ξ                i                2                                              +                                    c              2                                                                  ∑                                  j                  =                  1                                                  l                  +                  k                                                                                r                  j                                                                  s                  i                                                                    )                                                                        s            .            t            .                                                                                y                i                                                          (                                                                            ω                      T                                                                                φ                                                                    (                                                  x                          i                                                )                                                                              +                  b                                )                                              +                      ξ            i                          ≥        1            ,              i        =        1            ,      …                                ,      l                                                                                ω              T                                                  φ                                            (                                  x                  j                                )                                              +          b          +                      r            j                          ≥        1            ,                                                  ω              T                                                  φ                                            (                                  x                  j                                )                                              +          b          -                      s            j                          ≤                  -          1                    ,              r        j            ,                        s          j                ≥        0            ,                                                                  j        =                  l          +          1                    ,      …                                ,              l        +        k                                    Given              L                    (                  ω          ,          b          ,          ξ          ,          r          ,          α          ,          β          ,          γ          ,          u          ,          v                )              =                            1          2                                  (                                                    ω                T                                          ω                        +                          b              2                                )                    +                        c          1                                              ∑                          i              =              1                        l                                                                                ξ            i            2                              +                        c          2                                              ∑                          j              =              1                                      l              +              k                                                                                r              j                                                  s              i                                          -                        ∑                      i            =            1                    l                                                                                α            i                                          [                                                            y                  i                                                                  (                                                                                ω                        T                                                                                φ                                                                          (                                                      x                            i                                                    )                                                                                +                    b                                    )                                            +                              ξ                i                            -              1                        ]                              -                        ∑                      j            =                          l              +              1                                            l            +            k                                                                                β            j                                          (                                                            ω                  T                                                                  φ                                                        (                                          x                      j                                        )                                                              +              b              +                              r                j                            -              1                        )                              -                        ∑                      j            =                          l              +              1                                            l            +            k                                                                                γ            j                                          (                                                            -                                      ω                    T                                                                                φ                                                        (                                          x                      j                                        )                                                              -              b              +                              r                j                            -              1                        )                              -                        ∑                      j            =                          l              +              1                                            l            +            k                                                                                u            j                                          r            j                              -                        ∑                      j            =                          l              +              1                                            l            +            k                                                                                v            j                                          s            j                                                            Thus      ,                                    ∂            L                                ∂            ω                          =                  0          =                                    >              ω                        =                                                            ∑                                      i                    =                    1                                    l                                                                                y                    i                                                                          α                    i                                                                          φ                                                              (                                              x                        i                                            )                                                                                +                                                ∑                                      j                    =                                          l                      +                      1                                                                            l                    +                    k                                                                                (                                                                  β                        j                                            -                                              γ                        j                                                              )                                                                          φ                                                              (                                              x                        j                                            )                                                                                ,                                                                                ∂            L                                ∂            b                          =                  0          =                                    >              b                        =                                                            ∑                                      i                    =                    1                                    l                                                                                y                    i                                                                          α                    i                                                              +                                                ∑                                      j                    =                                          l                      +                      1                                                                            l                    +                    k                                                                                β                  j                                            -                              γ                j                                                        ,                                                                                ∂            L                                ∂            b                          =                  0          =                                    >                              ξ                i                                      =                                          1                                  2                                                                                c                    1                                                                                α                i                                                        ,                                                                                ∂            L                                ∂            b                          =                  0          =                                    >                              s                j                                      =                                                            β                  j                                +                                  u                  j                                                            c                2                                                        ,                                                                                ∂            L                                ∂            b                          =                  0          =                                    >                              r                j                                      =                                                            γ                  j                                +                                  v                  j                                                            c                2                                                        ,
It can be obtained with substitution:
            min              ω        ,        b        ,        ξ        ,        r        ,        s                        (                                    1            2                                                        ∑                                                i                  1                                =                1                            l                                                                                ∑                                                      i                    2                                    =                  1                                l                                                                                y                                      i                    1                                                                                y                                      i                    2                                                                                α                                      i                    1                                                                                α                                      i                    2                                                                                K                                                        (                                                                  x                                                  i                          1                                                                    ,                                              x                                                  i                          2                                                                                )                                                                                +                              1                          2                                                                                c                1                                                                                ∑                              i                =                1                            l                                                                                α              i              2                                      +                              1            2                                                        (                                                                    ∑                                          i                      =                      1                                        l                                                                                y                      i                                                                                α                      i                                                                      +                                                      ∑                                          j                      =                                              l                        +                        1                                                                                l                      +                      k                                                                                (                                                                  β                        j                                            -                                              γ                        j                                                              )                                                              )                        2                          +                              1            2                                                        ∑                                                j                  1                                =                                  l                  +                  1                                                            l                +                k                                                                                ∑                                                      j                    2                                    =                                      l                    +                    1                                                                    l                  +                  k                                                                                (                                                            β                                              j                        1                                                              -                                          γ                                              j                        1                                                                              )                                                                  (                                                            β                                              j                        2                                                              -                                          γ                                              j                        2                                                                              )                                                                  K                                                        (                                                                  x                                                  j                          1                                                                    ,                                              x                                                  j                          2                                                                                )                                                                                +                              ∑                          i              =              1                        l                                                                                ∑                              j                =                                  l                  +                  1                                                            l                +                k                                                                                y                i                                                                            α                  i                                                                  (                                                            β                      j                                        -                                          γ                      i                                                        )                                                                          K                                                  (                                                            x                      i                                        ,                                          x                      j                                                        )                                                                    +                              1                          c              2                                                                    ∑                              j                =                                  1                  +                  k                                                            l                +                k                                                                                (                                                      β                    j                                    +                                      u                    j                                                  )                                                          (                                                      γ                    j                                    +                                      v                    j                                                  )                                                    -                              ∑                          i              =              1                        l                                                                                α            i                          -                              ∑                          j              =                              1                +                k                                                    l              +              k                                                                                (                                          β                j                            +                              γ                j                                      )                              )                                            s        .        t        .                                                    α          i                    ,              β        j            ,              γ        j            ,              u        j            ,                        v          j                ≥        0.

Method for establishing prediction model based on multidimensional texture of brain nuclear magnetic resonance images


A lighting device for a vehicle, the lighting device including: a lens; a light source device configured to emit light; a first reflection unit configured to reflect light emitted from the light source device toward a second side of the lens; a reflective phosphor configured to (i) convert a wavelength of light reflected by the first reflection unit and (ii) reflect light which wavelength is converted toward the lens; a second reflection unit configured to reflect a portion of light that is reflected by the reflective phosphor toward the second side of the lens; a sensing unit configured to sense light reflected by the second reflection unit; and a controller configured to control the light source device based on sensing information sensed by the sensing unit, is disclosed.

A lighting device, such as a lamp, is installed in a vehicle so as to assist a driver to secure a field of vision by increasing surrounding illumination intensity or notify a current driving state of the vehicle to the outside.
The lighting device installed in the vehicle (hereinafter, referred to as a lighting device for a vehicle) may include a head lamp which emits light toward the front of the vehicle, and a rear lamp which indicates the direction of travel of the vehicle or notifies the operation or non-operation of a brake.
The lighting device for a vehicle may form a low beam or a high beam to ensure a driver's field of vision during night driving. Recently, the use of light-emitting diodes (LEDs) having high power efficiency and a long lifespan tends to increase.
Meanwhile, a laser diode with an irradiation distance longer than that of an LED can be used as a light source of the lighting device for a vehicle.


A method is provided for depositing a uniform thin film on a substrate using laser ablation of a target material in a deposition chamber. The laser beam may be pulsed and focused to strike the target along a line to produce a broad plume of ablated material expanding in a propagation direction outward from the target. The plane of the substrate deposition surface is oriented generally parallel to the propagation direction of the plume of ablated material. The deposition chamber includes a low background pressure of inert or reactive gas to facilitate lateral diffusion of ablated atoms, ions, and molecules to be deposited on the substrate surface. Particles ejected from the target material, which have trajectories generally parallel to the propagation direction of the plume, are too heavy to have significant lateral diffusion and have little chance of lodging on the deposition surface. The substrate may be rotated about an axis perpendicular to the deposition surface (and generally perpendicular to the propagation direction of the plume) to provide uniform deposition over the entire surface. The method may also be used to deposit thin films on both sides of a substrate simultaneously, and on multiple substrates by stacking them in the plume in space-apart orientation.

Laser ablation of a target of source material is a known technique that is useful in the field of thin film deposition on a substrate. Typically, a pulsed laser beam is employed to produce a plume of ablated material in a process known as Pulsed Laser Deposition, or PLD.
Many different materials, such as simple oxides, ferroelectrics, and high T.sub.c superconductors, for example, have been deposited by pulsed laser deposition. Compared with traditional technologies such as electron beam deposition, chemical vapor deposition (CVD), and various sputtering techniques, for example, PLD generally allows better control over stoichiometry of the deposited material, provides capability for a more reactive environment, and produces evaporants with desired energies for improved film crystallinity. Secondary advantages of PLD include small target size (typically 1/2 inch in diameter), ability to grow multilayer heterostructures, introduction of dopants by plasma interaction, and the absence of inert sputtering gas. Despite these advantages, however, PLD has remained primarily a research tool. The evolution of PLD into a production compatible process has been hampered by two major problems: the lack of large area uniformity, and the rough surface morphology that results from particle inclusion.
The ablation plume produced by PLD is a result of adiabatic jet expansion, which is not Lambertian (i.e., not a Cos .THETA. distribution). The angular distribution of PLD ablation plumes follows a Cos.sup.n .THETA. law, where n can vary from about 2 to 11. This narrow angular distribution and the close target-to-substrate distance results in poor thickness uniformity of the deposited film. The best known data on large area uniformity that has been reported was obtained using off-center substrate rotation to achieve a 10% variation over a one inch square area. Other techniques include rastering the laser beam over a large area target or using an aperture to skim the ablation plume while moving the substrate in the x-y direction.
In addition to nonuniformity, the inclusion of particles in a thin film degrades the quality of the film and the performance of the end device. For example, particle inclusion causes light scattering in optical applications and can cause electrical shorts in ferroelectric thin film memory devices. The poor surface morphology produced by laser deposition is a problem associated with the intense interaction between laser and target, which causes splashing from mechanisms such as exfoliation, subsurface boiling, and repulsion of the surface molten layer by recoil pressure of the expanding ablative plasma. With the exception of a few materials, such as yttria stabilized zirconia (YSZ), SrTiO.sub.3, MgO, La.sub.0.5 Sr.sub.0.5 CoO.sub.3, and a few other materials, nearly all laser ablated materials suffer from this problem. Some materials, such as Ge, produce "particle-free"films if a molten target is used. Other techniques include the use of a mechanical velocity filter, synchronous pulses from a second laser, synchronous pulsed oxygen nozzle beams, and careful preparation of the target surface. All of these techniques, however, are only marginally successful in improving surface morphology of the deposited films. If pulsed laser deposition is to be successful in transitioning from the laboratory to large scale fabrication operations, techniques must be developed to eliminate particle inclusion and provide large area uniformity of the deposited film.

Method of laser ablation for uniform thin film deposition


Apparatus is provided for forming a foreign language file from a native language file so that the display for a given equipment can be rendered in the foreign language rather than the native language. The apparatus is comprised of a native language file containing instructions for the format of the display, words to be translated, and contextual information related to the latter words, a translation tool for reading the native language file and forming an image of the native words to be translated, the size and shape of the space allotted to them and a blank space into which equivalent foreign words can be inserted, means for inserting the foreign words, and means for storing in a file the foreign words and the instructions for the format to be used.

In many types of programmed apparatus, interaction with a user is obtained by displays on a cathode ray a native language of menus, actions to be taken, and messages for helping the user when he requests aid. All of this information, along with instructions as to the format of the display, are stored in a native language file that is scanned by a screen generator that controls the cathode ray tube.
For the apparatus to be used by someone who is not familiar with the native language, a translation of the displayed words into suitable foreign equivalents is required. This has been obtained by recording the various displays and format descriptions on paper and sending it to a translator after the words to be translated have been designated in some way, as for example with a magic marker. After the designated words have been translated into foreign equivalents and recorded on the paper, it is used to create a foreign language file that is sent with apparatus sold to people familiar with that language. Whereas this may seem to be a simple and straightforward procedure, experience has shown that much confusion and delay results from the use of paper recording in this manner, e.g., the translator may provide words that are not apt because he is not familiar with the application or he may fail to observe the format instructions and use a foreign word that is too long.

Software process and tools for development of local language translations of text portions of computer source code


A beam is joined to a column by the steps of setting upright a square sleeve formed by a steel material in the shape of a box and/or square cylinder to the center of the upper surface of a base plate formed by a steel material in the shape of a plate in a posture of extending an axis in a vertical direction to connect the square sleeve to the base plate by welding, placing the base plate to the upper surface of a constructed footing through anchor bolts, inserting the lower end of a square column pipe formed by a steel material in the shape of square pipe into the square sleeve on the upper surface side of the base plate, tying the square column pipe with the connecting bolts inserted into bolt holes provided in the square column pipe and also bolt holes provided at predetermined positions of the wall surface of the square sleeve, then bringing an end plate formed by a steel material into contact with the wall surface of the square sleeve by making it condition that the end plate is preliminarily welded as one body to the longitudinal end surface of the beam, and fastening the end plate to the square sleeve with the connecting bolts.

1. Field of the Invention
This invention relates to the improvement on a construction method of joining a column and a beam both serving as main parts of steel frame structural material in the case of constructing a building structure on the basis of heavy-weight steel frame construction.
2. Description of the Prior Art
A so-called diaphragm construction method of joining a column and a beam by the steps of setting up each column on a footing by making it condition that an auxiliary joint member formed by a steel plate to join the end of a beam thereto is preliminarily mounted to the column by welding, and then placing the beam over between the auxiliary joint members to join the columns and the beam at their laps with connecting bolts is known as a construction method of joining a column and a beam both serving as main parts of steel frame structural material in the case of constructing a building structure on the basis of heavy-weight steel frame construction.
Since the conventional construction method (the diaphragm construction method) of joining the column and the beam as described above requires the column mounted with the auxiliary joint member, the column delivered after being assembled in a factory needs to be mounted with the auxiliary joint member before the delivery. Thus, the deformation or damage easily occurs to the auxiliary joint member mounted to the column in the course of transportation of the columns. Further, the auxiliary joint member mounted to the column makes it hard to load a truck with the columns in an orderly manner, resulting in troublesome loading.
Further, since each beam placed over between the columns takes the form having a joint portion as an inevitable consequence, a straight beam cannot be in use.

Construction method of joining column and beam in building structure based on heavy-weight steel frame construction


A solid fuel nozzle tip for issuing a flow of mixed solid fuel and air to a boiler includes a tip body having an inlet and an outlet defining a longitudinal axis therebetween. The tip body includes a slot on an inlet side of the tip body extending in a direction parallel to the longitudinal axis. The slot includes an opening facing the inlet side of the tip body and a recess formed at an angle with respect to the longitudinal axis to at least partially retain a pivot pin. A solid fuel nozzle assembly for issuing a flow of mixed solid fuel and air to a boiler includes a solid fuel nozzle tip with a tip body, as described above, and a locking plate operatively connected to the tip body to assist in retaining a pivot pin within the slot of the tip body.

1. Field of the Invention
The present invention relates to solid fuel nozzles, and more particularly to solid fuel nozzle tips for tangentially fired boilers.
2. Description of Related Art
A variety of systems and devices are known for delivering solid fuel for combustion in a boiler. Many such devices are directed to coal nozzles for delivering solid coal particles to coal fired boilers or furnaces, for example. Coal powered plants require an efficient means of supplying coal as fuel to produce heat power. Coal nozzles can include coal tips. Conventional coal tips require removal of the complete coal nozzle assembly from the boiler in order to replace the coal tip.
Such conventional methods and systems have generally been considered satisfactory for their intended purpose. However, there is still a need in the art for improved coal tips that allow for improved ease of manufacture and assembly. The present invention provides a solution for these problems.


In a processor, when a conditional branch instruction is encountered, a software prediction for the conditional branch is made as a function of the specific condition register field used to store the branch condition for the conditional branch instruction. If a specified condition register field is not used, the software prediction may be made dependent upon the specific address at which the branch instruction is located.

A conventional high performance superscalar processor typically includes an instruction cache for storing instructions, an instruction buffer for temporarily storing instructions fetched from the instruction cache for execution, a number of execution units for executing sequential instructions, a Branch Processing Unit (BPU) for executing branch instructions, a dispatch unit for dispatching sequential instructions from the instruction buffer to particular execution units, and a completion buffer for temporarily storing instructions that have finished execution, but have not been completed.
As is well known in the art, sequential instructions fetched from the instruction queue are stored within the instruction buffer pending dispatch to the execution units. In contrast, branch instructions fetched from the instruction cache are typically forwarded directly to the branch processing unit for execution. In some cases, the condition register (CR) value upon which a conditional branch depends can be ascertained prior to executing the branch instruction, that is, the branch can be resolved prior to execution. If a branch is resolved prior to execution, instructions at the target address of the branch instruction are fetched and executed by the processor. In addition, any sequential instructions following the branch that have been pre-fetched are discarded. However, the outcome of a branch instruction often cannot be determined prior to executing the branch instruction. When a branch instruction remains unresolved at execution, the branch processing unit utilizes a prediction mechanism, such as a branch history table, to predict which execution path should be taken. In conventional processors, the dispatch of sequential instructions following a branch predicted as taken is halted and instructions from the speculative target instruction stream are fetched during the next processor cycle. If the branch that was predicted as taken is resolved as mispredicted, a mispredict penalty is incurred by the processor due to the time required to restore the sequential execution stream following the branch instruction. Similarly, for the mispredicted branches that have been predicted not-taken, the instructions that were fetched after the branch instruction are discarded and a mispredict penalty is incurred by the processor due to the time required to restore the target execution stream following the branch.
A high performance processor (CPU) achieves high instruction throughput by fetching and dispatching instructions under the assumption that branches are correctly predicted and allows instructions to execute without waiting for the completion of previous instructions. This is commonly known as speculative execution, i.e., executing instructions that may or may not have to be executed. The CPU guesses which path the branch is going to take. This guess may be a very intelligent guess (as in a branch history table) or very simple guess (as in always guess path not taken). Once the guess is made, the CPU starts executing that path. Typically, the processor executes instructions speculatively when it has resources that would otherwise be idle, so that the operation may be done at minimum or no cost. Therefore, in order to enhance performance, some processors speculatively predict the path taken by an unresolved branch instruction. Utilizing the result of the prediction, the fetcher then fetches instructions from the speculative execution path prior to the resolution of the branch, thereby avoiding a stall in the execution pipeline if the branch is resolved as correctly predicted. Thus, if the guess is correct, there are no holes in the instruction fetching or delays in the pipeline and execution continues at full speed. If, however, subsequent events indicate that the branch was wrongly predicted, the processor has to abandon any result that the speculatively executed instructions produced and begin executing the path that should have been taken. The processor "flushes" or throws away the results of these wrongly executed instructions, backs itself up to get a new address, and executes the correct instructions.
Prior art handling of this speculative execution of instructions includes U.S. Pat. No. 5,454,117 which discloses a branch prediction hardware mechanism. The mechanism performs speculative execution based on the branch history information in a table. Similarly, U.S. Pat. No. 5,611,063 discloses a method for tracking allocation of resources within a processor utilizing a resource counter which has two bits set in two possible states corresponding to whether or not the instruction is speculative or when dispatched to an execution unit respectively. Also, Digital Equipment Corporation's Alpha AXP Architecture includes hint bits utilized during its jump instructions. However, as the name implies, these bits are hint only and are often ignored by the jump mechanism.
Most operations can be performed speculatively as long as the processor appears to follow a simple sequential method, such as those in a scalar processor. For some applications, however, speculative operations can be a severe detriment to the performance of the processor. For example, in the case of executing a load instruction after a branch instruction (known as speculative load because the load instruction is executed speculatively without knowing exactly which path of the branch would be taken), if the predicted execution path is incorrect, there is a high delay penalty incurred when the pending speculative load in the instruction stream requests the required data from the system bus. In many applications, the rate of mispredicted branches is high enough that the cost of speculatively accessing the system bus is prohibitively expensive. Furthermore, essential data stored in a data cache may be displaced by some irrelevant data obtained from the system bus because of a wrongful execution of a speculative load instruction caused by misprediction.
A need, therefore, exists for improvements in branch prediction. Presently, most prediction mechanisms operate as hardware prediction. These predicted paths, when mispredicted, tend to corrupt the hardware memory with the results of the speculatively executed instructions. However, certain classes of branches should not be predicted by hardware when the software can tell with a particular degree of certainty which path to take. Consequently, a system and method for software controlled branch prediction mechanism is desired.
It would therefore be desirable to provide a method and system for combining software and hardware branch prediction in a high performance processor. It is further desirable to provide a method and system which allows a developer or compiler of a software code (or program) which has a pre-determined and/or desired path during branch prediction to control the actual path predicted by manipulating the hardware prediction mechanism with a software input.
For many applications, the compiler can often determine how a conditional branch should be predicted by the hardware at run-time. For some applications, the software branch prediction can be highly accurate. The software branch prediction can be very useful for microprocessors that do not have a hardware branch prediction mechanism. It is also useful for improving the hardware branch prediction accuracy for some application, by combining the software branch prediction with the hardware branch prediction mechanism through mechanisms such as an agree/disagree prediction algorithm which works as follows.
Ordinarily the Branch History Table (BHT) stores the information about the branch's outcome. For example, in a 2-bit per entry BHT implementation, each entry indicates whether the associated BHT entry should be predicted taken (1×) or not-taken (Ox). When a branch is executed, if it is found to be taken, the entry is incremented (if it is already "11", then there is no change). If it is found to be not-taken, the entry is decremented (if it is already "00", then there is no change).
For agree/disagree prediction, instead of storing the taken/not-taken information in the BHT, the information stored is whether the branch outcome at execution was in agreement with the software branch prediction or not. If the software predicted taken and the branch is actually found to be taken when it is executed, then the branch "agrees" with the software prediction. Similarly, if the software prediction is not-taken and the branch is actually found to be not-taken during execution, then also the branch is considered to have "agreed" with the software prediction. Otherwise, the branch "disagrees" with the software prediction. When a branch is executed, its associated entry in the BHT is updated based on whether the branch "agrees" or "disagrees" with the software prediction. If the branch agrees, then the entry is incremented (no change, if it is already "11"). If the branch disagrees, then the entry is decremented (no change, if it is already "00"). When a branch is fetched, if its associated entry in the BHT is "1x", then the branch is predicted to agree with the software prediction, that is predict whatever the software says. On the other hand, when a branch is fetched, if its associated entry in the BHT contains "0x", then the prediction made is opposite of what the software predicted.
The primary advantage of agree/disagree prediction is that, for many applications, it decreases the harmful effects of aliasing in the BHT. That is, if two branches are mapped to the same entry in the BHT, it is highly likely that both will predict "agreed", if the software prediction accuracy is good (even though, one of the branches prediction may be "taken" and the others may be "not-taken").
In many architectures, the branch instructions do not have any unused or reserved bit that can be used to provide branch prediction hint by the software. Such hints can communicate to the hardware how the software thinks the branch should be predicted. For these architectures (which includes PowerPC), this invention describes a way of providing software branch prediction hints to the hardware.

Use of software hint for branch prediction in the absence of hint bit in the branch instruction

International Business Machines Corporation


An injection-moulding device is disclosed, comprising at least first and second mould parts, defining a mould cavity, wherein at least one of the mould parts comprises heating means, for heating the mould part in the vicinity of a mould cavity surface, said heating means comprising an inductive coil having a plurality of windings and being powered by an oscillator. The mould part comprises a top member, at the mould cavity surface, and, beneath the top member, a carrier member comprising grooves for taking up said coil windings, wherein the top member resistivity is lower than 1.5*10−6 Ωm, the top member relative magnetic permeability is lower than 10, the carrier member resistivity is higher than 20*10−6 Ωm, and the carrier member relative magnetic permeability is higher than 50.

Known injection-moulding devices, for producing objects of different kinds, usually comprise two main mould parts for defining a cavity. A first, fixed part has an injection nozzle, injecting the liquid moulding compound, typically a warm resin such as polycarbonate, into the cavity. A second mould part is movable in relation to the fixed mould part, such that it can be moved to open the cavity. The volume of the mould cavity, for forming the object, may be arranged in either or both of the mould parts. In so-called injection compression injection-moulding devices there may also be a third part defining the cavity.
When forming an object, liquid resin from an extruder is injected into the mould cavity through the nozzle when the two mould parts are in a closed or nearly closed state. As soon as an adequate amount of resin has been injected, the mould parts may be closed by pushing the movable part towards the fixed part, which allows a fine structure to be formed on an object surface, the structure being an inverse of a structure formed on one of the mould parts. E.g. if a CD (Compact Disc) is produced, this structure comprises a digital pattern intended, after several post-processing steps, to be readable by means of a laser pick-up system.
The mould parts are then cooled, usually by means of cooling water flowing in integrated cooling ducts, such that the resin in the cavity is solidified. Subsequently, the mould parts are separated by retracting the movable mould part, and the finished object can be removed.
The injected resin should be warm enough to allow the resin to completely fill the cavity and any desired surface structure to be reproduced before the resin reaches its glass transition region. This imposes limitations upon the temperature of the moulded resin when injected into the cavity as well as upon the temperature of the cooling water. When injected into the cavity, the resin temperature must be substantially higher than the upper range of the glass transition region. When the injection phase is completed and the cooling phase follows, at least some parts of the mould need be cooled from a starting temperature which is quite high. This is done with cooling water, the temperature of which must not be so low that the resin is solidified too early. The cooling water temperature may be 115° C. in a typical DVD-R manufacturing process. Therefore, the time needed for cooling is quite long. The total cycle time needed to injection mould e.g. a DVD-R (Digital Versatile Disc, Recordable) substrate is about 3 seconds. The cooling water flows continuously, and its temperature is carefully regulated.
Attempts have been made to reduce the total cycle time by providing auxiliary heating to the mould parts. When the mould parts are not just heated by the in-flowing hot resin, the temperature of the cooling water can be lowered, since the auxiliary heating compensates, during the injection phase, for the lower cooling water temperature. It can therefore still be ensured that the resin fills the cavity completely and that any surface structure is replicated before the resin reaches its glass transition region.
Therefore, due to the lower cooling water temperature, the cooling time needed to ensure that the resin has been solidified may be reduced.
GB, 2081171, A describes an injection moulding machine where the mould parts are heated by induction heating. An inductor is placed in a mould part in the vicinity of the mould cavity surface and is fixed in a hard epoxy resin. A high frequency oscillator is connected to the inductor, such that the inductor heats the mould part during production.
U.S. Pat. No. 4,563,145, A describes a moulding block for manufacturing flat information carriers from thermoplastic material. The moulding block comprises a glass plate, wherein a copper pipe is embedded. The copper pipe can be used both as a cooling channel and an inductive coil. On top of the glass plate, a ferromagnetic material layer is placed, which absorbs some of the energy emitted from the coil. On top of the ferromagnetic layer, a copper layer is placed, having high thermal conductivity. The copper layer conducts the generated heat to a surface layer in the mould part.
Thus, injection moulding machines with inductively heated mould parts have been known for some years, but have not come to a widespread use, as far as the inventors know. There is a trade-off between increased complexity and decreased cycle times. Known solutions have not been efficient enough to compensate for the increased complexity.

Method and apparatus for injection molding having an inductive coil heater

Thermal Cyclic Technologies TCTech I Stockholm AB


A coil control device of a magnetic resonance imaging system includes a DC-DC switching converter and a controller. The DC-DC switching converter is configured for switching and converting a DC power supply to a DC current or a DC voltage. An input end of the DC-DC switching converter is connected in parallel to the DC power supply. The controller is configured to control the DC-DC switching converter to switch and provide the DC current or the DC voltage. In some embodiments of the coil control device described herein, two power supplies (e.g., +15 V and −32 V) may be reduced to one power supply (e.g., +15 V), thereby saving energy and foregoing a water-cooling system.

Magnetic resonance imaging (MRI) is a technique using magnetic resonance phenomena for imaging. In magnetic resonance phenomena, a nucleus containing a single proton (e.g., the proton of a hydrogen nucleus prevalent in the human body) has a spinning movement similar to a small magnet. The spin axes of these small magnets do not follow a certain rule. If an external magnetic field is applied, these small magnets will be rearranged according to the magnetic force lines of the external magnetic field. For example, the small magnets may be arranged in two directions that are parallel or antiparallel to the magnetic force lines of the external magnetic field. The direction parallel to the magnetic force lines of the external magnetic field is called a positive longitudinal axis. The direction antiparallel to the magnetic force lines of the external magnetic field is called a negative longitudinal axis. The nucleus has only a longitudinal magnetization component, and the longitudinal magnetization component has both direction and amplitude. Nuclei in the external magnetic field may be excited by a radio-frequency (RF) pulse with a specific frequency to make the spin axes of the nuclei deviate from the positive longitudinal axis or the negative longitudinal axis, thereby producing resonance and giving rise to a magnetic resonance phenomenon. After the spin axes of the excited nuclei deviate from the positive longitudinal axis or the negative longitudinal axis, the nuclei have a transverse magnetization component. After stopping transmission of the radio-frequency pulse, the excited nuclei transmit echo signals to release the absorbed energy piecemeal in the form of electromagnetic waves. The phase and energy level of the electromagnetic waves both recover to the state prior to excitation. The image may be reconstructed after the echo signals transmitted by the nuclei are subjected to further processing (e.g., space encoding).
A magnetic resonance imaging system may operate with a number of various radio-frequency (RF) antennae (hereinafter, coils). A radio-frequency antenna is used to transmit and receive radio-frequency pulses so as to excite the atom nucleus to radiate magnetic resonance signals and/or to collect the induced magnetic resonance signals. The MRI system includes various coils, such as a body coil that covers the whole body area, a local coil that covers only a part of the body, or the like. The magnetic resonance system may have a large integrated coil (e.g., body coil) that is permanently fixed in a magnetic resonance scanner. The integrated coil may be arranged in a cylindrical manner around a patient sampling and collecting cavity (e.g., using a structure referred to as a nest configuration). In the patient sampling and collecting cavity, the patient is supported on a bed (e.g., a patient positioning table) during measurement. Since the coverage area of the body coil may be large, a higher transmitting power is used. The signal-to-noise ratio of an obtained image is relatively low, and the signal-to-noise ratio throughout the image is non-uniform. By contrast, the coverage area of a local coil may be small (e.g., the knee area covered by a knee coil, the head covered by a head coil, a wrist covered by a wrist coil, etc.). Thus, the local coil receives only limited radio-frequency signals within the radio-frequency excitation range. In order to distinguish the radio-frequency signals received by the local coil from the radio-frequency signals of the transmission stage, the radio-frequency signals received by the local coil are hereinafter referred to as magnetic resonance signals. The signal-to-noise ratio of an obtained image from a local coil may be high, and the signal-to-noise ratio throughout the image may be substantially uniform.
FIG. 1 shows a schematic diagram of a conventional coil control device of a magnetic resonance imaging system. In order to protect the security of the patient and the reliability of the coil itself, as shown in FIG. 1, the coil control device switches between a linear DC power supply (e.g., driven by a 15 V voltage VCC) and a negative voltage VSS (e.g., −32 V) for control. However, there are too many energy losses associated with conventional designs. A load at one side of the coil is a diode. A sink current ICS passing through the diode is several hundreds of milliamperes with regard to the coil and several amperes with regard to the body coil. The energy loss produced from a control circuit may be obtained according to the following formula,PDISS=ICS(VCC−VF),where YF is a forward voltage passing through the diode (e.g., about 0.7 V). In this example, the energy loss rate may be shown by the following formula:η=PDISS/PTTL*100%=ICS(VCC−VF)/(ICSVCC)*100%,where PTTL is the whole energy. In the example where VCC is 15 V and VF is 0.7 V, the energy loss rate η reaches 93.5%.
As shown above, in such a coil control circuit, most of the energy is wasted, thereby resulting in over-heating of the coil control circuit. As a result, a water-cooling system is used with the coil control circuit, thereby occupying space and increasing costs. In addition, the coil control circuit uses two power supplies that are positive and negative, thereby further increasing system complexity and costs.

Magnetic resonance imaging system and coil control device thereof


Methods and systems for continuously monitoring the gaze direction of a driver of a vehicle over time. Video is received, which is captured by a camera associated with, for example, a mobile device within a vehicle, the camera and/or mobile device mounted facing the driver of the vehicle. Frames can then be extracted from the video. A facial region can then be detected, which corresponds to the face of the driver within the extracted frames. Features descriptors can then be computed from the facial region. A gaze classifier derived from the vehicle, the driver, and the camera can then be applied, wherein the gaze classifier receives the feature descriptors as inputs and outputs at least one label corresponding to one or more predefined finite number of gaze classes to identify the gaze direction of the driver of the vehicle.

Vehicle accidents are often caused by driver drowsiness and inattention. In-vehicle cameras and sensors coupled with computer vision techniques can be employed to automatically monitor driver behavior and enhance safety and reduce accidents. In-vehicle video analytics can also be employed in the context of evidentiary support, for example, when adjudicating traffic violations or accidents. Such monitoring devices are typically found only in high-end vehicles and rely upon sophisticated image-capturing and processing afforded by specialized hardware that interacts with built-in vehicle telematics. Such systems, however, are expensive to implement and not currently very reliable.
Several approaches have been suggested for monitoring driver behavior. One technique involves, for example, the use of a mobile device such as a smartphone or portable camera that can be temporarily mounted within the vehicle for the purpose of driver monitoring. Such mobile monitoring devices can potentially offer a cost-effective service for users who do not rely upon dedicated built-in systems (e.g., systems implemented by automobile manufacturers). An inertial sensor and components such as an accelerometer, a gyroscope, and/or a magnetometer associated with the mobile device can be utilized to obtain data such as the position, speed, acceleration, deflection angle, etc., of the device and relate this information to driver behavior. Such an approach can monitor some aspects of driving behavior; however, it cannot predict significant events such as driver drowsiness, inattention, or other distractions that may lead to unsafe driving conditions.
Another approach involves the use of a mobile monitoring device placed on a windshield with a rear camera facing the road. This application can monitor the distance to nearby vehicles along with lane departure data and the vehicle speed via Global Positioning System (GPS). Such an approach however, does not capture any data about the state of the driver and thus cannot accurately monitor driver attention or fatigue. Another approach employs dual video captured from a driver-facing (or front) and road-facing (or rear) camera that automatically switches between front and rear cameras based on detection of various events. Such an approach provides only a coarse estimate of gaze and is not accurate.
Based on the foregoing, it is believed that a need exists for an improved method and system for estimating and monitoring the gaze direction of a driver in a vehicle for real time execution via a portable mobile device, as will be described in greater detail herein.

Method and system for estimating gaze direction of vehicle drivers


A photo printing system for transmitting image information including photo frame image data to a plurality of printers (P) through signal paths to print images, with a memory (41) for loading image information transmitted from an image input device (7), and a print order managing unit (50) for managing said image information on an order-by-order basis. The print order managing unit (50) includes a distributing unit (53) for applying printer IDs identifying the printers used, as attribute data, to the image information in the respective orders, a printer control unit (51) for checking an operating status of each of said printers, and a data output unit (54) for transmitting, based on a signal from said printer control unit, photo frame image data in an image file to be printed from said memory to one of said printers specified by the attribute data of said image file.

1. Field of the Invention
This invention relates to a photo printing technique using a processing device for transmitting image information to a plurality of printers through signal paths to print images.
2. Description of the Related Art
In a photo printing technique employing the above construction, a processing device capable of image processing may be connected to a plurality of printers through signal paths such as communication cables, and image information may be transmitted from the processing device to the printers. In printing images with this system, the printers are selectively used and image information is transmitted thereto by manual operations carried out at the side of the processing device.
In making numerous prints, a processing time may be reduced by distributing the image information to the plurality of printers. However, where the distribution is done manually, the operator must distinguish the states of the printers to determine which printer has finished printing, and transmit image information to that printer. This operation must be repeated until all required prints are made. Not only is this operation too time-consuming and troublesome, but also a proper distribution process may not be performed. For example, a delay could occur in the distributing operation, or the operator could make an error in the order of priority for a printing process. Thus, there is room for improvement.
Japanese Patent Laying-Open Publication H10-78620, for example, discloses a technique for varying an order of printing according to requested delivery times after receiving a plurality of orders for prints. However, this publication does not describe a manner of managing image data in the plurality of orders to be transmitted to printers for printing.
In particular, photo prints may be made from image information acquired with digital cameras fast becoming widespread today. When this printing process is performed at a photo processing shop, a storage medium storing image information acquired with a digital camera may be brought in to make prints. Even though the photo processing shop may have a plurality of printers, it is not only time-consuming to select printers manually, but also difficult to make a proper distribution of image information in a proper order, in handling a large number of storage media received. In addition, when prints are made from such storage media, it is necessary to manage, as a unit, a plurality of image data stored in each storage medium by allocating an order number. Such a complex process may not allow the printing process to be completed in a short time.

Photo printing method and system using a plurality of printers


An image signal provided by an image pickup device, such as a CCD, is converted into a video signal by a signal processor. An extraction means extracts a candidate object region from the current video signal and an object region determined a certain time before by a decision means is read from a memory. For example, the decision means compares an overlapping region in which the candidate object region and the object region read from the memory overlap each other and provides a new object region a size larger than the overlapping region and replace the object region with the new object region to update the contents of the memory. On the other hand, a calculating means calculates the features of the object region including the position of the centroid of the object region on a screen. A control means, such as a microcomputer, controls the image pick up device on the basis of information about the features so that a region including the target object substantially in its central part is cut out as a video signal. Consequently, the target object can be surely tracked regardless of objects other than the target object included in a scene.

The present invention relates to an image pickup apparatus, such as a video camera, or an automatic target tracking video camera system including a video camera and, more particularly, to a video camera capable of automatic target tracking and automatic zooming.
Active research and development efforts concerning the automation of the operation of cameras have been made in recent years with the progressive spread of video cameras and camcoder, i.e., video cameras integrally provided with a VCR. Techniques relating to an automatic iris, automatic white balance control and automatic focusing developed through the research and development efforts have been put to practical uses. However, the automation of operations for directing the camera toward a particular object, controlling the direction of the camera to keep the particular object within the angle of view of the camera and zooming to adjust the angle of view has not been practically realized and these operations must be manually carried out.
It has been thought that a detecting means for detecting the position of an object and a camera moving means for directing a camera toward an object are necessary to track a target object and to pick up the image of the target object.
A method relating to the detecting means, disclosed in Japanese Patent Laid-open (Kokai) No. 59-208983 detects a target object on the basis of the movement of the target object determined on the basis of a difference signal representing the positional difference between images taken at a fixed interval. However, when detecting the target object from its movement, the camera will fail to move correctly if an object other than the target object moves because the object other than the target object will be mistakenly detected.
Apparatus, in which an electrically powered pedestal and a camera unit are movable, relating to the camera moving means are proposed in Japanese Patent Laid-open (Kokai) Nos. 59-16357 and 63-72271. These apparatus, however, need a mechanism for moving the camera unit including the pedestal to change the direction of the camera, which inevitably enlarges the scale of the apparatus and increases power consumption. Although these apparatus are capable of tracking a target object in a wide tracking range, these apparatus have a large moment of inertia and are unable to operate at a high response speed. A method disclosed in Japanese Patent Laid-open (Kokai) No. 4-329773 employs an externally drivable optical decentering device, such as a prism having a variable apex angle. A method disclosed in Japanese Patent Laid-open (Kokai) No. 2-210982 controls the mode of driving a solid-state image pickup device electrically. Although this method of controlling the mode of driving the solid-state image pickup device can be practiced by a small-scale apparatus at a high response speed and at a low power consumption, this method is not capable of tracking a target object in a wide tracking range. Thus, each of these previously proposed methods has its merits and demerits and none of them is perfectly satisfactory.
Accordingly, it is an object of the present invention to solve the foregoing problems and to provide an automatic target tracking camera system capable of smoothly and accurately tracking a target object that moves about in a wide range.

Video camera capable of automatic target tracking