Proposed Method. Training: The trainining process (see top half of flowchart in Figure 1) is di- vided in four stages: registration, preprocessing of unlabeled data, feature extrac- tion and learning. The first step is to coarsely align all the volumes, labeled and unlabeled, to a template brain scan. The first volume in the dataset was arbitrar- ily chosen to be the template. This alignment makes it possible to use position features in the posterior classification. ITK (xxx.xxx.xxx ) was used to optimize an affine transform using a mutual information metric and a multi-resolution scheme. Using a nonlinear registration method could make the classifier rely too much on the registration through location features, making the method less robust. The next step is to preprocess the unlabeled volumes. The brain is first seg- mented using BET and FreeSurfer. The binary outputs of the two methods are then “softened” using a signed distance transform (positive if inside the brain, negative if outside). The distance map is mapped to the template space using the transforms from the registration step. The warped maps are used to calculate preliminary brain masks in the unlabeled scans by averaging the two maps for each volume and thresholding the result at zero, and they will also be used in the posterior semi-supervised learning step. { } The third step in the training stage is feature extraction. A pool of 58 image features is used in this study: (x, y, z) position, Gaussian derivatives of order up to two at five different scales (σ = 1.0, 2.0, 4.0, 8.0, 16.0 , in mm), and gradient magnitudes at the same scales. A subset of voxels from the training volumes is randomly selected for training purposes under the constraints that: 1) all scans contribute the same number of voxels; 2) 50% of the voxels have to be positives according to the annotated boundary (for the labeled scans) or the preliminary mask from the previous step (for the unlabeled); and 3) 50% of the voxels have to lie within 5mm of the boundary and 75% within 25mm. The features are normalized to zero mean and unit variance. Finally, a classifier can be trained using the labeled and unlabeled data. Xxxxxxx’s random forests[12] were used as the base classifier because they
Proposed Method. The architecture mainly comprises of two units: TA and vehicle nodes. TA is responsible to authenticate and allow to register all vehicles in the VANET. As It is a trustable unit and not liable to attacks. Vehicle has to register itself with TA before join the network. Moreover, a wired connection network is being used to connect RSUs and LE. TA is also responsible to answer queries coming from any object. The vehicle nodes in VANET consists of LE, TV and MV, all are equipped with OBU or OBE. LEs are the authorized vehicles, acts as mobile TA. A MV is also considered as TV once it gets authenticated successfully with either LE. Figure 1 shows the process of becoming MV into TV. Let, three vehicles are present in a VANET, LE and 2 MVs with integrated OBUs (𝑂𝐵𝑈𝑖 and 𝑂𝐵𝑈𝑘 ). First vehicle completed the authentication process with LE to become TV and obtain adequate authorized parameters to authorize MVs, i.e., it acts like LE temporarily to authenticate the vehicle 𝑂𝐵𝑈𝑘. IEEE standard 1609.2 protocol is used by vehicle nodes to establish communication with LE, RSU and TVs preserve identification and few variables received from TS. It also, stores public/ private keys along with session keys used for communication with other VANET infrastructure components. Authentication OBU𝑖 (Mistrustful) OBU𝑘 (Mistrustful) Authentication LE(trustful) OBU𝑘 (Mistrustful) LE(trustful) OBUi (trustful) OBU𝑘 (trustful) OBUi (trustful) LE(trustful)
Proposed Method. We propose Graph Agreement Models (GAM), a novel approach that aims to resolve the main limitation of label propagation methods while leveraging their strengths. Instead of using the edge weights as a fixed measure of how much the labels of two nodes should agree, GAM learns the probability of agreement. To achieve this, we introduce an agreement model, g, that takes as input the features of two nodes and (optionally) the weight of the edge between them, and predicts the probability that they have the same label. The predicted agreement probabilities are then used when training the classification model, f , to prevent overfitting.
Proposed Method. We can deal with the first problem using a method developed by Xxxxxx et al. (1996) and described in more detail by Xxxxx (2004). These authors were looking at agreement in the detection of signals. They had no way of knowing how many signals were undetected by all observers. They could not, therefore, use kappa statistics to describe the agreement. Instead, they estimated the probability that if one observer detected a signal another observer would also detect the signal. In the present application, we can estimate the probability that if one review selects a paper, another review will also select the paper. To estimate this probability, all we need are the numbers of reviews selecting a primary study for each study selected in any of the reviews. Denote the numbers of reviews by n, and the number of reviews selecting study i by ri. For each review selecting study i, there are n–1 other reviews, ri–1 of which select the study. Hence the proportion of other reviews selecting the study is (ri–1)/(n–1). This proportion will be the same for all the ri selections of study i. The total number of selections of primary studies is Σri and the average proportion of further reviews which select a study, averaged over all selections, is
Proposed Method calculation of residuals As the goal of this work is the identification of strong motion stations showing distinctive features which deviate significantly form the average behavior, as predicted by the common empirical ground motion models, the analysis has been carried out station by station. In other words, for each of the 206 stations considered in this analysis, the residuals between the measured and predicted logarithmically-transformed spectral accelerations SA( T ) at 21 periods from T=0.03 s to T=2s are analyzed. The analysis is period-dependent, thus allowing one to study the dependence of variability of earthquake ground motion on the considered spectral range. The residual rp,q ( T ) for the pth station and the qth event is defined as: r ( T ) = Log[SAobs ( T )@− Log[SAGMPE ( T )@ for p = 1...N , q = 1...N (3.3a) p,q p,q p,q S E where SAobs ( T ) and SAGMPE ( T ) are, respectively, the observed and predicted spectral p,q p,q acceleration for the station p and earthquake q. Subsequently, it was decided to normalize the residuals computed as follows: r N p,q rp,q ( T ) =
