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Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics

This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage.

Introduction
Hand biometrics is receiving an increasing attention at present because of their huge applicability in daily scenarios and the relation between user acceptance and identification/verification rates [1,2].
The characteristics of this biometric technique in terms of non-invasiveness and acceptability highlight the fact that hand biometrics could be a proper and adequate biometric method for verification and identification in devices like PC or mobile phones, since hand biometrics system requirements are easily met with a standard camera and hardware processor.
However, as applications requiring hand biometrics tends to contact-less, platform-free scenarios (e.g., smartphones [3]), hand acquisition (capturing and segmentation) is being increased in difficulty. In other words, hand biometrics is evolving from constrained and contact-based scenarios [4,5] to opposite approaches where less collaboration is required from individuals [3,6], providing non-invasive characteristics to this biometric technique, and thus, improving its acceptability.
Consequently, image pre-processing becomes compulsory to tackle with this problem, by providing an accurate segmentation algorithm to isolate hand from background, whatever its nature, and independent from environment and illumination conditions. Thus, a segmentation method is proposed able to isolate hand from different background, regardless the environmental and illumination conditions.
The proposed approach is based on multiscale aggregation, gathering pixels along scales according to a given similarity Gaussian function. This method produces an iterative clustering aggregation, providing a solution for hand image segmentation with a quasi-linear computational cost and an adequate accuracy for biometric applications.
The method has been tested with a synthetic image database, with around 408,000 images considering different backgrounds (e.g., soil, skins/fur, carpets, walls or grass) and illumination environments, and compared to two competitive approaches in literature in terms of image segmentation. These approaches are named Lossy Data Compression (LDC) [7] and Normalized Cuts (NCut) [8].
Finally, the layout of the paper remains as follows: Section 2 provides and overview on the current literature, describing the proposed method under Section 3. The database involved in evaluation is presented in Section 4, together with the results, presented in Section 5, providing conclusions and future work in Section 6.
In fact, the overall performance in terms of identification accuracy relies strongly on the result provided by the segmentation and pre-processing procedure.
Concerning hand-based biometrics, segmentation has received little attention in early works, provided that initial approaches carry out the acquisition procedure in a constrained and homogeneous background [4,18]. This background was selected so that hand segmentation is a trivial task by simple thresholding.
However, as hand biometrics is evolving from contact and peg-based approaches to completely contact-less, peg-free and platform independent scenarios, hand segmentation is increasing its difficulty and complication [6,19,20].
Several approaches in literature tackle with this problem by providing non-contact, platform-free scenarios but with constrained background, usually employing a monochromatic color, easily distinctive from hand texture by means of simple image thresholding [21][22][23]. More realistic environments propose a color-based segmentation, detecting hand-like pixels either based on probabilistic [24], clustering methods [25] or edge detection [4,5,20].
The most common applications of this approach consider image segmentation and boundary detection based on texture [29,31], providing accurate results when compared to human segmentation and other competitive approaches in literature [32].
The results obtained by multiscale aggregation in the fields of unsupervised image segmentation are certainly promising [32], and the application of this method for hand segmentation has been recently proposed [3].
Nonetheless, several aspects must be improved in terms of computational cost and memory usage efficiency [3,30,32]. In fact, these methods are strongly dependent on the number of pixels in an image, and only small images are supported. This limitation was partially solved [3,30], providing a quasi-linear segmentation method, described in detail in the following section.

Gaussian Multiscale Aggregation
The proposed approach attempts to provide an accurate segmentation of a colour hand image. The algorithm strategy consists of aggregating similar nodes according to a specific criteria along different scales until a given goal is met, ensuring that aggregated nodes within segments verify certain properties.
First step of the algorithm consists of providing a particular structure to the amount of elements within the image. Likewise to other methods [30], the proposed algorithms assumes that a given image I can be represented by a graph G = (V, E) where nodes in V represent pixels in the image and edges in E stands for the structure provided to the set of nodes.
In this approach, the structure on the first scale is assumed to be a 4-neighbourhood strategy, while for subsequent scales, structure is provided by means of Delaunay triangulation [33].
In addition, each node is represented by a similarity function denoted by φ where v i ∈ V designates a node in graph G and s indicates the scale the element v i belongs to. This similarity function is described in terms of relative measures with respect to intensity average and standard deviation.
More in detail, φ [s] v i is represented by a gaussian distribution N (µ, σ) where µ and σ specify the average and standard deviation neighbour intensity, provided the 4-neighbourhood structure.
Thus, similarity functions leads to the concept of likelihood between nodes in connecting edges, providing a definition of weights within graph G.
Given a graph G = (V, E), the similarity among pair of nodes is provided by means of weights W, which are defined for each scale s as: where v i , v j ∈ V, ∀i, j and φ v j represent the similarity function for nodes v i and v j , respectively. In addition, α stands for the selected colour space, which in this paper corresponds to the a layer of the CIELAB (CIE 1976 L*,a*,b*) colour space, due to its ability to describe all visible colors by the human eye [9].  i,j associated (striped region).
Therefore, graph G = (V, E, W) contains not only structural information on a given scale s but also relational details about the similarity of each node neighbourhood.
Furthermore, W i,j can be regarded as the weight associated to edge e i,j , so that W i,j = W(e i,j ). Notice that weights are not defined for each pair of nodes in V, but only for those pairs of nodes with correspondence in edge set E.
Some properties can be extracted from the definition of W i,j ∈ W as the similarity between two nodes v i and v j , then W i,j satisfies ∀i, j: Property (1) results from the definition given by Equation (1), since the integration of two non-negative functions provides a non-negative result. Similarly, property (2) is derived from the commutative product of a function product. Property (3) indicates that maximum value of weight is obtained if and only if nodes v i and v j have the same similarity distribution.
These former properties stand for each scale s, although for the sake of simplicity this index was not included on previous notation. Furthermore, each node v i ∈ V contains also information on the location within the image in terms of positions, which will be useful in posterior scale aggregation steps.
On the other hand, the essence of this algorithm relies on aggregation, which consists of grouping and clustering those similar nodes/segments in subgraphs, according to some criteria along scales.
The proposed method bases the aggregation procedure on the weights in W, given the fact that, those pairs of nodes/subgraphs with higher weights are more similar than those with lower weights, and therefore, those former pairs deserve to be aggregated under a same segment/subgraph. Thus, a function must be defined to provide some order in set W, so that posterior subgraphs in subsequent scales contain nodes with high weights and, therefore, high similarity.
Let Ω be an ordering function, which orders edges in E according to W, as follows: In other words, let e = {e 1 , . . . , e m } be a set of edges.
If Ω is applied to previous set e, then it is satisfied that Ω(e) i ≥ Ω(e) j , with i ≤ j, ∀ i, j, being Ω(e) i the ith element in the ordered set Ω(e). Concretely, Ω W represent the weight set W after Ω is applied.
Once the concept of ordering function is introduced, the algorithm aggregates pair of nodes based on this former weight ordering, ensuring that the dispersion of each segment remains bounded. This aggregation criteria is represented by the Equation (3): where σ i,j represent the dispersion of aggregating nodes v i and v j . Despite of selecting the geometric mean as the comparison criteria in previous equation, other methods are possible such as arithmetic mean, generalized mean or harmonic mean. The selection of geometric mean was carried out based on experimental results. Once pairs of nodes have been ordered and an aggregation criteria have been stated, the Gaussian Multiscale Algorithm aggregates pair of nodes with previous criteria Equation (4), considering the fact that G In other words, GMA approach aggregates a pair of nodes in scale s under the same existing segment when at least one of both is already assigned to a segment. In case none has previously assigned to any segment, a new segment is provided. In all previous cases, aggregation is carried out as long as δ i,j holds, otherwise, different segments are assigned to previous pair of nodes.
In addition, the number of assigned graphs in scale s is given by p, whose description is provided in Equation (5), which depends onδ i,j = 1 − δ i,j as follows: where function ξ i,j is defined as This assignment is done for each value in the ordered set Ω W , until whether every element in Ω W is evaluated or every node in V is assigned a segment in subsequent scale. Gaussian Multiscale Aggregation assures that every node in scale s−1 is assigned a segment/subgraph in scale s.
After aggregation, nodes in scale s are gathered into p subgraphs, with p < N [s] , being N [s] the number of nodes in scale s. Each subgraph contains a set of nodes, whose number is unknown a priori. These subgraphs must be compared in subsequent scales, and thus the similarity function in subgraphs is defined in Equation (7).
Consequently, let G Notice that the definition of the similarity functions φ G [s+1] n has sense also for individual nodes in V, considering nodes as graphs of one element. This is essential during the aggregation in first scale, where graphs gathers nodes instead of subgraphs. In this case, function φ [0] is represented by a gaussian function of mean and deviation corresponding to the average and dispersion intensity of their neighbour nodes, as stated before.
Therefore, similarity functions can be completely defined as in Equation (8) φ where N (µ, σ) stands for the gaussian distribution given an average µ and a σ, both of them corresponding to their respective neighbour properties. For clarity sake, first scale (s = 0) is obtained based on nodes v ∈ V and subsequent scales are obtained by gathering subgraphs. Concerning location, the position of subgraphs is obtained by averaging the position of the nodes contained on each subgraph. This is essential in order to provide a neighbourhood structure, since after aggregation every scale s collects a scatter set of subgraphs. This structure is given by means of Delaunay triangulation.
A Delaunay graph for a set S = {p 1 , . . . , p n } of points in the plane has the set S as its vertices. Two vertices p i and p j are joined by a straight-line (representing an edge) if and only if the Voronoi regions V (p i ) and V (p j ) share an edge. In addition, for a set of points in R 2 , knowing the locations of the endpoints permits a solution in O(nlogn) time. Therefore, Delaunay triangulation is a suitable method to provide a neighbourhood structure to previous aggregated subgraphs.
This operation represents the final step in the loop, since at this moment, there exist a new subgraph represents a node, and edges E [s+1] are provided by Delaunay triangulation, and weights W [s+1] are obtained based on Equations (1) and (8).
The whole loop is repeated until only two subgraphs remain, as stated at the beginning of this section. However, due to the constraints provided to aggregate (Equation (9)), the method could not aggregate more segments, without achieving the goal of dividing image into two subgraphs. Therefore, Equation (3) is in practice relaxed and stated as follows in Equation (9): being k [s] a factor able to avoid aggregation method from being stuck in the loop. This factor can be dynamically increased or decreased, according to previous method necessities. However, this value is initially set to k [s] = 0.01, for each scale s. The capability of k [s] to adapt the necessities of the algorithm remains as future work. The computational cost of this algorithm is quasi-linear with the number of pixels, since each scale gathers nodes in the sense that nodes in subsequent scales are reduced by (in practice) a three times factor ( Figure 4). Therefore, time to process the first scale (which contains the highest number of nodes) is greater than the rest of times to process subsequent scales, and the total time is approximately comparable to two times the processing time to aggregate first scale. This statement will be supported within the results of Section 5.

Database
After presenting the algorithm, next section describes the creation of the database involved in evaluation.
This section describes the creation of a synthetic database containing a total of 408,000 images of hands with a wide range of possible backgrounds like carpets, fabric, glass, grass, mud, different objects, paper, parquet, pavement, plastic, skin and fur, sky, soil, stones, tiles, tree, wall and wood.
The main aim of this database is twofold: • First, the main purpose is to provide a comparative evaluation frame for segmentation algorithm, where existing approaches in literature could be compared. In other words, this database makes it possible to assess to what extent the segmentation algorithm can satisfactory perform a hand isolation from background on real scenarios.
• In addition, this database contains the ground-truth result for each image, providing a possible supervised evaluation criteria. These ground-truth images were obtained, given that hands were taken with a blue-coloured background, so that hand can be easily extracted by simple thresholding [22].
The creation of the synthetic database (named GB2S Database) considers the hands extracted in former database and the set of the aforementioned different textures, which were obtained from the website http://mayang.com/textures/.
First of all, a straightforward segmentation was carried out with a threshold-based segmentation [22], obtaining two binary masks: M h , corresponding to those pixels representing hand, and M b with pixels corresponding to background.
Afterwards, both masks are laid one over each other, with M b containing pixels associated to a specific texture, resulting in an image with the hand over a desired background (grass, water, wood and so forth).
In order to ensure there is no considerable difference in illumination between hand and background, each image is converted from RGB to YCbCr color space [9] carrying out a histogram equalization in terms of illumination (Y), performing afterwards the inverse transform from YCbCr to RGB color space. Finally, a morphological operation consisting on an opening operator with a structural element of a disk of small size (5 pixels radio) is considered to fade the boundary between hand and background, so that hand is integrated within background.
All these former operations attempt to ensure a fair scenario, simulating the conditions provided in real situations.
For each hand image, a total of 5 × 17 (five images and 17 textures) synthetic images are created, collecting a total of 120 × 2 × 20 × 5 × 17 = 408,000 images (120 individuals, two hands, 20 acquisitions per hand, five images and 17 textures) to properly evaluate segmentation on real scenarios. Some visual examples of this database are provided in Figure 2.
This presented database is publicly available at http://www.gb2s.es/. Once the database has been presented, the following section comes out with the evaluation of the algorithm and the obtained results.

Results and Discussion
This section contains the results of the comparative evaluation of the proposed approach to LDC [7] and NCut [8]. First, the evaluation criteria is stated in order to provide a comparative frame, providing afterwards the results obtained in the evaluation.

Evaluation Criteria
Although there exist some unsupervised evaluation methods for image segmentation [32,34,35], we have preferred a supervised segmentation, since the synthetic database GB2S contains the corresponding ground-truth associated to each image. Segmentation results will be compared to this ground-truth image.
The proposed evaluation method is based on F-measure, [36], defined as follows: where P (Precision or Confidence) stands for the number of true positives (true segmentation, i.e., classify a hand pixel as hand) in relation to the number of true positives and false negatives (false hand segmentation), and R (Recall or Sensitivity) represents the number of true positives in relation to the number of true positives and false positives (false background segmentation, i.e., consider background as hand). F-measure is within the [0, 1] interval, so that 0 states a bad segmentation, while on the contrary 1 represents the best segmentation result. Aiming a fair comparison, the propose algorithm is compared to two competitive segmentation methods existing in the literature, namely Lossy Data Compression (LDC) [7] and Normalized Cuts (NCut) [8].

Gaussian Multiscale Aggregation Evaluation
The evaluation of a segmentation method involves different aspects concerning accuracy, computational cost and parameters dependency.
First aspect is related to what extent the algorithm is able to properly detect or isolate a specific object within an image. Concretely in this paper, accuracy is understood as the capability of the proposed algorithm to properly isolate hand from background. Table 1 shows the results in terms of F-measure of the proposed methods in comparison to LDC approach and Normalized Cuts. Although the results obtained by the proposed method (first column) can be improved, they overcome the other two schemes. Reader may notice that scenarios with textures similar to hand (e.g., soil) decrease the performance of the segmentation algorithms, but the proposed method still provides F-measure rates of more than 88%.
In addition, accuracy can be also visually evaluated. Figure 3 presents a comparative frame for segmentation evaluation, comparing the results obtained for the LDC method, Normalized Cuts and the proposed method. Reader can compare the obtained results (columns 4-6) to the ground-truth (column 2). The results obtained by the proposed approach conserve more precisely the shape of the hand even in scenarios with similar textures like parquet (row 5) or wood (last row).  [7] and Normalized Cuts [8], respectively.
Original Image Ground-truth Synthetic Image Proposed LDC approach Normalized Cuts Secondly, concerning computational cost, Table 2 presents the segmentation time in relation to the number of pixels of the images. This temporal evaluation was carried out in a PC computer @2.4 GHz Intel Core 2 Duo with 4 GB 1,067 MHz DDR3 of memory, considering that the proposed method was completely implemented in MATLAB. The results provided in Table 2 shows that the proposed algorithm is faster than the compared approaches. In addition, the proposed method can segment images of higher sizes, but LDC and NCut cannot handle higher sizes images without running out of memory.
Finally, this section will study the dependency of two parameters strongly related to algorithm performance, namely k factor and aggregation linearity (Equations (9) and (10)).
Factor k controls the aggregation capability of the overall method. Within these experiments, factor k was experimentally fixed to k = 0.01, ensuring that the number of segments in the last scale is two: hand and background. However, extending the proposed approach to other applications in image processing would imply to provide a dynamic factor k, depending whether the algorithm standstills in a certain scale. The proposal of a dynamic factor k remains as future work. Figure 4 presents the relation between number of segments along scales using different values of k. Notice that k = 0 implies no stopping criteria, and therefore aggregates scales until only one segment is obtained. Figure 4. Dependency of the aggregation process on parameter k. The lower k, the lower the constraints to aggregate segments. Notice that k = 0 means no stopping condition. During the explanation of the method, the algorithm is said to be quasi-linear with the number of pixels. This statement is supported by Table 2, but for clarity sake, we provide a chart ( Figure 5) indicating which proportion of time is required for each scale. The most demanding scale is the first one, whose proportion is higher than the other parts, concluding that the algorithm has indeed a quasi-linear behaviour in relation to the number of pixels.

Conclusions and Future Work
The application of hand biometrics to unconstrained and contact-less, platform-free environments implies an increase in difficulty in the pre-processing and segmentation procedure in hand acquisition. Therefore, an unsupervised segmentation algorithm has been proposed based on Gaussian multiscale aggregation. This method gathers iteratively those pixels similar in texture and color under segments, until a certain number of clusters/segments is provided as a result.
This method is able to isolate hand from a wide range of backgrounds (carpets, fabric, glass, grass, mud, different objects, paper, parquet, pavement, plastic, skin and fur, sky, soil, stones, tiles, tree, wall and wood), simulating real situations and unconstrained background scenarios.
Besides, the evaluation of the proposed approach has been carried out based on a publicly available synthetic database, containing 408,000 hand image acquisitions with different background textures. The evaluation consisted of a comparison of the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) [7] and Normalized Cuts (NCuts) [8].
The results obtained point out that the performance of the proposed algorithm outcomes existing segmentation algorithms in literature, regarding not only accuracy and computational cost, but also memory usage, since the proposed algorithm is quasi-linear in relation to the number of pixels.
As future work, we consider to implement the method with a dynamic k parameter, so that the algorithm can be adapted to any image, providing segmentations of more complex images. In addition, we aim a faster implementation of the method considering both software and hardware optimized implementation, together with a more complete evaluation with other publicly available databases.


Optional SVM for Fault Diagnosis of Blast Furnace with Imbalanced Data

Fault diagnosis for blast furnace is actually a multi-class classification problem because the blast furnace may appear usually many kinds of abnormal states. Moreover, those abnormal states should be monitored and diagnosed timely and what can help workers take effective measures. Support vector machine (SVM) is state-of-the-art for many classification problems currently. But many classification tasks involve imbalanced training examples in practice. Imbalanced dataset learning is an important practical issue in machine learning, especially in support vector machine (SVM). Fault diagnosis for blast furnace is such an imbalanced data problem. A novel algorithm named optional support vector machine is proposed to solve this imbalanced data classification by pruning training sets and adding the unlabeled data and applying edited nearest neighbor (ENN) rules. Firstly, training sets of majority class are pruned in order to reduce the training time. Secondly, the algorithm selects some useful unlabelled training data and adds them to the training sets. Those samples are used to replenish the lack of training samples so that the training sets are representative. However, they may contain some noisy examples. Finally, the edited nearest neighbor rule is removed the noisy examples. The algorithm adds the unlabelled (testing) samples to balance the number of samples between the minority class and the majority one. The real-time producing data of blast furnace are used to running experiment. In order to more accurately diagnose which kinds fault happened, a binary tree multi-class classification method is adopted based on blast furnace characteristics. Simulation results show that the proposed algorithm is feasible and effective.

Introduction
Fault diagnosis for blast furnace is actually a multi-class classification problem because the blast furnace may appear usually many kinds of abnormal states, such as heating, over-development of brim gas flow, hanging, cooling, chimney, lack of brim gas flow, low stock line, slip and etc other common disorders and abnormalities. Moreover, those abnormal states should be monitored and diagnosed timely so that workers may take effective measures and serious accidents will be avoided and many economic losses will be to a minimum. Fault diagnosis for blast furnace is an imbalanced data classification problem that there are a lot of normal samples and a few abnormal samples in reality. Moreover, in many other real-world classification problems, a classifier has to deal with imbalanced training examples. Learning for imbalanced class problems is encountered in a large number of practical applications of machine learning. Various classification methods are used in the study of imbalanced data classification problems. They include decision tree methods, neural networks, linear discriminant analysis, C5.0, multi-layer perceptron (MLP) and support vector machine and so on. [1][2][3] But some classifiers require more examples to learn, such as linear discriminant analysis, decision trees, and neural networks. And neural networks are not applicable for the problem with relative high number of features and fewer examples. Among the other classifiers: multi-layer perceptron (MLP) and support vector machine (SVM), it was found that compared to C5.0, MLP was less sensitive and SVM was not sensitive at all. [1][2][3] Japkowicz and Stephen (2002) 1) compared the performance of three methods for dealing with class imbalances: over-sampling, under-sampling, and cost-modifying (the weighting methods), with C5.0 as the classifier. The cost-modifying method was found to be most effective among all. Sun et al. (2009) 4) make a comparative study that is the effectiveness of the imbalanced classification strategies. The support vector machine (SVM) is currently one of the state-of-the-art classification techniques and it exhibits good predictive performance due to its ability to model nonlinearities. [2][3][4] The class imbalance problem could hinder the performance of standard machine learning methods so that standard SVM is not ideal to it. Li et al. (2008) 5) introduced SVM with uneven margins (SVMUM) that it modified the standard SVM.
It is very important for data imbalance to design new and more adaptive classifiers than before. And these new classifiers should not only study the data distribution but also © 2011 ISIJ consider different the number of the samples. If data distribution of training samples is different to one of testing samples, there is a lower performance. Therefore, a perfect training process is important to deal with it.
The rest of the paper is structured as follows. Section 2 introduces support vector machines. Section 3 focuses on active learning and imbalanced data classification. Section 4 presents an algorithm of optional SVM for imbalanced data classification. Section 5 describes ironmaking process of blast furnace and some faults of blast furnace and discusses simulation experiments to evaluate the algorithms on the datasets for blast furnace faults, with a particular emphasis on measuring the usefulness of the proposed algorithms. A fixed binary tree model based on one-against-all method for the blast furnace faults will approach the multi-class classification problem. In the end, Section 6 summarizes our findings and presents some discussions.

Support Vector Machine
The SVM is suitable for processing a binary classification problem. Given a training set of data points with input data and corresponding binary class labels and testing data , . The SVM is tried to find the hyperplane <w, b> and the labels of testing data . The nonlinear function φ (⋅) maps the input space to a high (possibly infinite)-dimensional feature space. In this feature space, a hyperplane may be constructed that is discriminating between the two classes. The hyperplane should be maintained to maximize the margin with minimum error. Its objective function is the following convex optimization problem. 6,7) ................. (1) Here and the variable w′ denotes the transpose of w, and C is a tradeoff between maximizing the margin and minimizing the training error, and nonzero slack variables ξi quantify SVM training error. Equation (1)

Active Learning with SVM for Unlabeled Data
SVM active learning is an SVM-specific algorithm, 7,8) which uses the margin (or the distance) from the unlabelled example to the classification hyperplane as a measure of the example's importance for learning. Active learning is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. Active learning entails the control of the learning algorithm over the input data on which it learns, [7][8][9] and more specifically, focusing on the problem areas. Due to the complexity of the annotation task, there is a challenge of obtaining sufficient training data. In addition, training samples are not representative. A good idea is to use active learning which is usually implemented as a module in the learning system. Active learning can select an unlabelled example based on its properties as the current model. Then the selected example is labeled and put in the training set.

Evaluation Criteria of Imbalanced Data Classifica-
tion Literature 2,10) has a detailed introduction of some evaluation criteria , such as under the ROC curve, geometric mean of accuracies, F1-measure, class weighted accuracy, sensitivity, specificity, precision, and overall accuracy, which are used to evaluate the classifier performance on imbalanced data. In this study, precision and F1-measure will be described as following.
In brief, precision and recall are calculated by and . Where TP is the number of positive sample true classified and FP is the number of positive sample false classified and FN is the number of negative sample false classified. In order to facilitate those instructions, a confusion matrix is shown in Table 1. F-measure criterion takes into account both precision and recall that is made by the following formula (5).

Training Set and Generalization Performance of SVM Classifier
If a training set is representative of the whole data set, in general, a SVM classifier with a larger margin on the training set would have a better generalization performance. Figure 1 shows an ideal SVM classifier about a binary classification problem with a two-dimensional data. However, if the training set is unrepresentative, a maximal margin classifier learnt from an unrepresentative training set may have poor generalization performance, as can be seen in Fig. 2. This figure also describes a binary-class classification on imbalanced data sets that it is different between ideal classifier and actual classifier. The difference is mainly caused by which few positive examples can not correctly reflect the class distribution of change in the imbalanced training data.
In fact, unfortunately, many imbalanced classification problems have only a small number of positive training examples what leads to an SVM classifier with poor generalization capability. And redundancy negative training examples waste much more training time. Therefore, actual classifier must be modified further by the corresponding adjustment.

Edited Nearest Neighbor Rule
Edited nearest neighbor (ENN) was originally proposed by Wilson (1972). 11) It works by removing noisy examples from the original set. An example is deleted if it is incorrectly classified by its k-nearest neighbors (k = 3, in general). A new method may apply ENN to remove each majority/minority class example from the data set what does not have at least two of its three nearest neighbors from the same class.

Pruning Training Sets
Imbalanced data conclude superabundant majority samples that are referred to as negative class. Some of majority samples are farther than the others to hyperplane. They are less influence of classification and may be gradually pruned. The training observations corresponding to nonzero Lagrange multipliers αi are called support vectors and they are located close to the decision boundary. The distribution of the support vectors will follow the data distribution. More support vectors will be found in dense input areas and less in more sparse ones. Therefore, selecting some ratio majority samples firstly run experiment and next pruned the others.

Algorithm of Optional SVM
Herein a new method is described for classifying imbalanced data. According to the usual practice, minority class is positive and majority class is negative one. They n+ and n-denote the number of minority class and majority class respectively and are satisfied to the inequality n+ < n-. The two variables n+ and n-do not need to calculate or optimize because they are certain number with respect to a provided training set.
The proposed algorithm proceeds as follows: (1) Randomly choose n+ majority samples and minority ones respectively as the initial training set S0. That is the same number of samples between minority class and majority class. Train the SVM classifies on S0 with those training samples.
(2) Calculate the distances mean of the minority class and the majority class, and denote f+(x) and f-(x).
(3) Select some ratio R1 negative training samples and run an experiment and prune the negative training samples that their distances to <w, b> = 0 are f(x) > f-(x).
(4) Select some ratio R2 of unlabelled (testing) documents and apply the SVM classifiers to select the positive class samples with their distances to <w, b> = 0 that they satisfy to the inequality 0 < f(x) ≤ f+(x). Label them and add them to the training set. Meanwhile, selecting the negative class samples with their distances to <w, b> = 0 that they meet to the inequality 0 < f(x) ≤ f-(x). Label them and add them to the training set, too.
(5) Apply ENN to remove each noisy example from the data set.
(6) Retrain the SVM classifiers with the changed training set.
(7) Repeat steps (2) and steps (3) and steps (4) for a predefined number of loops or until the system obtain a predefined performance level.
This algorithm is optional SVM (OSVM) and it is suit-  In view of the different data characteristics, OSVM algorithm may run each section separately, as shown in Fig. 3.

Introduction of Ironmaking Process and Some
Faults of Blast Furnace The blast furnace (BF) is an important process unit in the metal industry. Blast furnace melting is reductive into pig iron ore that is continuous production process. In general, a typically furnace is 20-30 meters high and has a diameter of 10 meters. 12,13) Bigger blast furnace capacity is about 4 000 m 3 . Blast furnace is top-down divided into five parts that are furnace throat, furnace stack, furnace waist, furnace abdomen, furnace hearth. Blast furnace melting for raw material consists mainly of iron ore, fuel (coke) and flux (limestone) of three parts. Blast furnace production loaded raw material from top and oxygen are injected through tuyeres in the lower part of the furnace. Some blast furnaces use the other auxiliary fuel, such as pulverized coal (PC), heavy oil, natural gas etc. At high temperatures coke carbon with the oxygen in air blast generated carbon monoxide, which rise in the oven to remove the oxygen in the process of iron ore, and get iron. The molten pig iron and slag collect on the bottom or hearth of the furnace in turn and are tapped more or less regularly through the taphole via runners to torpedo cars or ladles, which are sent downstream for steel processing. Blast furnace gas is expelled from the top of blast furnace and it is the fuel of hot air stove, heating furnace, coke oven after dedusting.
Some parameters value can reflect whether blast furnace state is normal. If some parameters change greatly, abnormal phenomenon of blast furnace would appear. At this time, blast furnace needs an accurate diagnosis and workers need some guidance advice. Therefore, some special parameters may be selected as the criterions of fault diagnosis to detect whether the blast furnace system runs in a normal state. Blast furnace abnormality include heating, over-development of brim gas flow, hanging, cooling, chimney, lack of brim gas flow, low stock line, slip and etc other common disorders and abnormalities. Certainly, the fault of low stock line may be recognized by watching the location of the gauge rod.

Description of Datasets
According to the complexity of the BF process, 14 measured variables are selected from all measured variables. They are blast volume (m 3 /min), blast pressure (kPa), top pressure (kPa), differential pressure (kPa), permeability, the top temperature (includes four-point temperature), crossing temperature (including center and edge) (°C), feeding rate (batch/hour), [Si], physical heat (°C) and position of material (m). Blast volume and top pressure are set basic parameters. In the real production process of blast furnace, the thermocouple temperature sensors and pressure sensors can obtain the temperature and pressure, and calculate the number of material per hour on the approval that was expected to material speed. The ratio of air volume and differential pressure is permeability.
The higher temperature the blast furnace has, the more silicon content will be. If silicon content is above standard, heating will happen. If silicon content is below normal, cooling will happen. Right now, if permeability of the blast furnace is bad, hanging will be appeared. At this time, the blast furnace must be reduction pressure. However, if pressure drop suddenly, it would result in blast furnace slip. Therefore, in order to prevent the big blast fault to happen, blast furnace of initial stage of fault detection is very important. Selected faults conclude heating, cooling, hanging, slip.
In experiment, there are 2 600 labeled samples and 1 400 unlabelled samples that originate from the real-time production data of blast furnace. Where normal samples is 2 000 and fault samples is 600 of labeled samples set.
Because of the existence of a lot of normal data, experimental result becomes normal state of high precision. So selected estimate criterion is F1-measure to avoid this problem.

Parameters Analysis of OSVM
As discussed in section 4, four parameters of OSVM algorithm impact the performance of the proposed new algorithm. When putting this algorithm in practice one need to consider several important questions how the parameters values are chosen and what is the optimal parameters value. Our experiments evaluate different values of the four parameters. They are R1, R2, C and σ. The first parameter R1 is the ratio of pruned samples that it impacts the training time and performance. Using higher ratio of pruned samples would lead to a lower accurate of learning. Of course, if the ratio of pruned samples is smaller, the training time would become longer. The second one is the added ratio of unlabelled samples, namely R2. The value of parameter R2 is too small or too large that performance of OSVM is not all satisfactory. The variables C, σ are respectively the relative parameters of SVM. SVM will have a good performance when variables C, σ they must be optimal.
Generally, R1 and R2 are all random number in intervals [0, 1]. Here two variables R1 and R2 may be optimized by some usual method, such as genetic algorithm, particle swarm optimization, etc. This study applies recursive methods that 0.1 is starting point and each increase 0.1. The variable C is limited in and σ is limited in respectively. Variation of parameters will be introduced in the following section.

Simulation Experiment and Results Analysis
There are three experiments what are experiment with pruned negative samples, experiment with added unlabelled samples and experiment with standard SVM and optional SVM. All the experiments were carried out in turn in the following. All results reported below are the average from five runs. Table 2 shows the class code of the blast furnace state and the samples size of training samples and testing samples in the experiments. Normal sample sizes are more than the samples sizes of abnormal state. In order to experimental need, these data are normalized before all the experiment.
Negative class of training samples is randomly selected from the original generated sets according to a pruned ratio.
As can be seen from Table 3, training time and testing time has all changed. The smaller pruned ratio R1 is, the shorter total time is. But accurate of testing must not be ideal when whole time is the smallest. Therefore, total time and performance of algorithm must be considered at the same time. With its tiny variation, whole time has not changed much. A part of the meaningful results are displayed in Table 3. Table 4 compares different added ratio of unlabelled samples that produce different testing results. When the added ratio of unlabelled samples is 0.8, F1-measure is much better.
Considering the characteristics of the fault diagnosis of industrial processes, we choose to use a multi-class classification strategy that is based on two-class classification. One-against-all (OAA) scheme is adopted for this study. Because four kinds fault exist certain relevance, a fixed binary tree model can be established, as shown in Fig. 4. Therefore, in order to make a fair comparison between standard SVM and optional SVM algorithm, the same binary tree model is selected. Table 5 reports the fault diagnosis results between standard SVM and optional SVM. The proposed optional SVM is superior to the standard SVM in F1-measure.
The OSVM algorithm has flexibility and it may be suitable for different classification problems.

Conclusions
A novel algorithm is proposed to solve some imbalanced data classification problems by enhancing the standard SVM. The training sets are relatively more perfect than original one what makes it overall more appropriate fault diagnosis for blast furnace. Moreover, edited nearest neighbor rule is removed the noisy examples and the proposed algorithm is pruning the training set to spend less training time. Simulation results demonstrate clearly that optional SVM


extfieldsofstudy:
Computer Science
Medicine

text:
How to use Gagne's model of instructional design in teaching psychomotor skills.

Gagne's model of instructional design is based on the information processing model of the mental events that occur when adults are presented with various stimuli and focuses on the learning outcomes and how to arrange specific instructional events to achieve those outcomes. Applying Gagne's nine-step model is an excellent way to ensure an effective and systematic learning program as it gives structure to the lesson plans and a holistic view to the teaching. In this paper, we have chosen a routine practical procedure that junior doctors need to learn: insertion of a peritoneal (ascitic) drain and we use Gagne's “events of instruction” to design a lesson plan for this subject.

Introduction
Instructional events refer to actions of both teacher and learners during the teaching session. Selecting appropriate events and planning them in the right format and the right sequence is crucial in a successful lesson design. A lesson design is a plan showing the type of instructional events, their order and the kind of activity taking place in each event. In designing a lesson plan, there are 2 important factors: the objectives and the learners.
In this paper, we have chosen a routine practical procedure that junior doctors need to learn: insertion of a peritoneal (ascitic) drain. We use Gagne's "events of instruction" to design a lesson plan for this subject. Robert Gagne is considered to be one of the foremost contributors to the systematic approach to instructional design and his theory has provided a great number of valuable ideas for trainers and teachers. Gagne's model of instructional design is based on the information processing model of the mental events that occur when adults are presented with various stimuli and focuses on the learning outcomes and how to arrange specific instructional events to achieve those outcomes. Gagne's theories have been applied to the design of instruction in several domains, such as the military, flying, leadership, engineering and healthcare.

Gagne's model of instructional design
Essential to Gagne's ideas of instruction are what he calls "conditions of learning": internal conditions deal with what the learner knows prior to the instruction, external conditions deal with the

MEDICAL EDUCATION
stimuli that are presented to the learner, e.g. instructions provided by the teacher.
The first step in Gagne's theory is specifying the kind of outcomes to be achieved. He categorised these outcomes into five types: verbal information, intellectual skills, cognitive strategies, attitudes, and motor skills.
The second step is to organise appropriate instructional events. Gagne's "Events of Instruction" consist of the following: 1. Gaining attention 2. Informing the learner of the objective 3. Stimulating recall of prerequisite learning 4. Presenting the stimulus material 5. Providing learning guidance 6. Eliciting the performance 7. Providing feedback 8. Assessing the performance 9. Enhancing retention and transfer Inserting a peritoneal drain is a motor skill, which corresponds to affective and psychomotor skill outcomes. The ideal number of learners for this kind of session is 4 or 5. The following instructional events can be organised for a lesson to teach insertion of a peritoneal drain:

(I) Gaining attention
When students arrive at class, their attention can be directed toward many other things, so in order for any learning to take place, first their attention must be captured and their interest should be aroused. Here are some examples: 1) An abrupt stimulus change, such as gesturing or speaking loudly 2) Starting the lesson with a thought-provoking question or interesting fact.
3) Providing an interesting visual or sound stimulus. Depending on the audience, multimedia (like PowerPoint slides) can be used to combine photographs, pictures, and sound.
In our session, combining items 2 and 3 is a good starting point: a case scenario of a patient with ascites can be presented together with relevant investigations/images, asking the learners about the case. There is no doubt that curiosity motivates students to learn.

(II) Informing the learner of the objective
Early in each lesson students should encounter a list of learning objectives. This initiates the internal process of expectancy and helps motivate the learner to complete the lesson.
A direct statement can be used in our session: "upon completing this lesson you will be able to":  Consent the patient for insertion of a peritoneal drain  Identify the correct anatomical point  Identify the equipment required  Prepare, position and monitor the patient  Understand and perform the correct technique for insertion of a peritoneal drain under fully aseptic condition  Understand the principles of securing, dressing and connecting the drain  Understand the importance of appropriate analgesia post procedure  Complete the appropriate documentation in patient's medical notes

(III) Stimulating recall of prerequisite learning
Associating new information with prior knowledge and personal experience and getting the learners to think about what they already know can facilitate the learning process.
In our session, 20-30 minutes should be allocated for interactive discussion of the following: Asking questions about previous observations and experiences, indications of inserting a peritoneal drain, relevant anatomy, findings on examination and relevant tests before the procedure to confirm the diagnosis (e.g. abdominal ultrasound)

(IV) Presenting the stimulus material
This event is where the new content is actually presented to the learner. Content should be organised meaningfully, and explained and demonstrated using a variety of media.
In our session, different steps of the procedure should be explained: How to consent the patient, monitoring, equipment needed, positioning the patient, the technique of peritoneal drain insertion and how to secure the drain. Finally, tests needed after the procedure (biochemistry, cytology and microbiology tests on the fluid sample), and appropriate analgesia should be explained.

(V) Providing learning guidance
This event means showing what appropriate actions constitute correct performance, plus additional suggestions, including use of examples, case studies, graphical representations, and mnemonics to help learners encode information for long-term storage, or in simple terms, "make the stimulus as meaningful as possible".
As this is a practical procedure, if learners first observe the procedure they are in a better position to perform it themselves. The teacher shows the equipment and performs the procedure on a dummy, including the sterile technique, explaining it step by step. While performing the procedure, useful tips and hints can be provided.

(VI) Eliciting the performance (practice)
The action now turns to learners. In this event, the learner is required to practice the new skill or behaviour. Eliciting performance provides an opportunity for learners to confirm their correct understanding, and the repetition further increases the likelihood of retention.
In our session, each one of the learners should get familiar with the equipment, demonstrate the sterile technique and perform the procedure on the dummy under direct supervision.

(VII) Providing feedback
While observing each learner performing the procedure, individual and immediate feedback and guidance can be provided and any questions can be answered. In addition, feedback from other learners observing the performance is very helpful.

(VIII) Assessing the performance
At this point, the students demonstrate what they have learned without receiving additional coaching or hints. However, a single performance does not ensure that the new capability has been reliably stored and additional practice is needed.
In our session, this will constitute demonstration of the whole procedure by the learner without prompt or guidance. The main issue here is time and resources. If there is enough time, or if an additional session is organised, they can practice the procedure a few times and at the end demonstrate their performance. It is not possible to assess the desired outcome, insertion of a peritoneal drain on "a real patient" in the same session but during on-calls and on the wards, the learners get a chance of performing the procedure. Due to invasive nature of the procedure, they "must" be supervised and assisted initially and after practicing a few times, they will be able to perform it independently.

(IX) Enhancing retention and transfer
Once we are reasonably sure that the new capabilities are reliably stored, we can increase the likelihood that they will be retained over a long time period by providing practice and spaced reviews. The repetition of learned concepts is an effective mean of enhancing retention, although often disliked by students. Additionally, transfer of knowledge and skills to new problems and situations is a goal of most instruction, but classroom time constraint makes it more difficult to achieve.
To enhance retention, the learners should practice the procedure on a dummy a few times. More frequent practice broken by rest periods is more effective. The transfer of knowledge constitutes applying their skills in a clinical setting, while initially being supervised. The session can be closed by reviewing the key points, answering the questions and asking for learners' feedback.
In designing a session like this, several factors need to be considered, including the nature of objectives, setting, time, available resources, institutional constraints, content, number of learners, their characteristics and their preferences. The most effective way to achieve psychomotor objectives is to get the learners to perform and practice the activity after preparing them with some lectures or demonstrations. The session should cover the 3 areas that are necessary for teaching psychomotor skills: 1) Before practice: objectives, performance criteria and how it should be performed by an expert. 2) During practice: critical cues and how to use the information. 3) After practice: feedback, enhancing retention and transfer

Conclusions
Gagne's theories provide a great deal of valuable information to teachers. Applying Gagne's nine-step model is an excellent way to ensure an effective and systematic learning program as it gives structure to the lesson plans and a holistic view to the teaching.
We need to keep in mind that the exact form of these events is not something that can be specified in general for all lessons, but rather must be decided for each learning objective.
The performance most frequently required of students is to remember, while our intent is most often to help them understand, and by putting more structure into the objectives of the lesson plans we will be able to achieve this aim. As Gagne himself says, "organisation is the hallmark of effective instructional materials".


text:
Interoperation of an UHF RFID Reader and a TCP/IP Device via Wired and Wireless Links

A main application in radio frequency identification (RFID) sensor networks is the function that processes real-time tag information after gathering the required data from multiple RFID tags. The component technologies that contain an RFID reader, called the interrogator, which has a tag chip, processors, coupling antenna, and a power management system have advanced significantly over the last decade. This paper presents a system implementation for interoperation between an UHF RFID reader and a TCP/IP device that is used as a gateway. The proposed system consists of an UHF RFID tag, an UHF RFID reader, an RF end-device, an RF coordinator, and a TCP/IP I/F. The UHF RFID reader, operating at 915 MHz, is compatible with EPC Class-0/Gen1, Class-1/Gen1 and 2, and ISO18000-6B. In particular, the UHF RFID reader can be combined with the RF end-device/coordinator for a ZigBee (IEEE 802.15.4) interface, which is a low-power wireless standard. The TCP/IP device communicates with the RFID reader via wired links. On the other hand, it is connected to the ZigBee end-device via wireless links. The web based test results show that the developed system can remotely recognize information of multiple tags through the interoperation between the RFID reader and the TCP/IP device.

Introduction
The radio frequency identification (RFID) technology was originally developed by the US military for the purpose of missile tracking. An RFID is a radio frequency system that has a tag (or a transponder) consisting of an electronic microchip and a reader (or interrogator) for reading and writing data. The data exchange between the tag and the reader is achieved by using magnetic or electromagnetic fields. The production data stored in the microchip can be transferred over a distance of more than 10 m by the antenna. After decoding the tag data, the reader sends the result to the host computer. Because the RFID has a relatively long transmission range and a multi-tag recognition ability, it has the potential to become a core replacement technology for the conventional barcode systems used in the fields of safety, security, and logistics [1][2][3].
RFID technology is a non-contact method for information identification using radio frequency. Continuous growth of the Internet, the manufacture of low-cost tags, and standardization of electrical identification codes has led to several practical industrial applications of this technology. RFID technology uses several frequency ranges, such as low frequency (LF, 30 ~ 300 KHz), high frequency (HF, 3~30 MHz, typically 13.56 MHz), ultra-high frequency (UHF, 0.3 ~ 3 GHz), and microwave (>3 GHz) and is classified accordingly as LF RFID, HF RFID, UHF RFID, and microwave RFID, respectively. The advantage of using LF RFID systems is that these systems are cost effective, but they have short recognition ranges (<60 cm). On the other hand, the advantage of using HF RFID systems is their relatively big recognition range (~60 cm), but these systems are expensive. Among the above mentioned classes of RFID systems, UHF RFID systems are potentially suitable for many applications despite of the high cost of the tags. The UHF band offers longer reading distances (3.5 m ~ 10 m) than other RFID frequency bands because of a relatively compact high-gain antenna. A relatively high transmitter power is also permitted at an ultra-high frequency. Since modulated backscattering rather than inductive coupling is used for the signal processing, a tag antenna is simple and has a low manufacturing cost. The electromagnetic skin depth is also shorter at an ultra-high frequency than at lower operating frequencies. This enables the use of low-cost printed antennas and relatively thin metallization layers, which can save additional material costs [4,5]. Some useful information regarding the design of UHF RFID systems is available in the scientific literature. For instance, the best selection of key control, transmission, and inception parts is presented in [6]. Some design issues in UHF RFID IC and back-end circuits are discussed in [7,8]. The experimental performance of an UHF RFID system in real applications is discussed in [9,10]. Moreover, an FPGA-based system design for a UHF RFID reader is presented in [11].
This RFID technology already has many commercial applications, such as preventing automobile theft, gaining entrance to buildings, dispensing goods, and managing logistics. As RFID technology is deployed in transition from closed networks to standards-based open networks, interoperability with equipment made up of multiple devices is required. Users can minimize their total cost by setting up networks that provide this interoperability in a combined network that supports wired and wireless communication links. Rather than operating separately, RFID devices could be more effective by operating within an integrated network. As the volume of tag data that is created and transmitted, and the potential uses of the information will be growing, it can be expected that the role of RFID-related networks will become much more important. For example, although Cisco Systems is not building RFID tags or reader products, the company intends to work with RFID technology providers to help enable interoperable, end-to-end solutions [12].
In this study, we focus on a system co-implementation of a UHF RFID reader and a TCP/IP device via wired and/or wireless communication channels. This combined system can support functionality such as network availability and scalability. We have used a suitable Web-based test-bed and have performed the experiments using a set of prototypes. The objective of this study is to establish remote interoperation between the UHF RFID reader and the TCP/IP device via wired and/or wireless links. The wireless connection between the UHF RFID reader and the TCP/IP device is realized by using a ZigBee end-device. We also present in this paper the simulation results for a UHF antenna. Figure 1 shows the system configuration for interoperation between a UHF RFID reader and a TCP/IP device. The proposed system consists of three functional blocks: an RFID reader, a ZigBee end-device, and a TCP/IP interface device. A UHF RFID reader can communicate with a TCP/IP device by two methods. One is through a direct connection with the TCP/IP device via UART, and the other is through an indirect connection with the TCP/IP device via a ZigBee end-device.

System Design
ZigBee protocols define a type of sensor network for mainly residential and commercial applications such as heating, air conditioning and light control. Whereas Bluetooth is geared towards user mobility and eliminating cabling between short-distanced devices, ZigBee is more oriented towards remote control and automation. ZigBee networks take advantage of low data rates (~up to 250 kbit/s), low power consumption (~longer battery lifetimes of up to 10 years), and low cost (~$1.5 per module) compared to Bluetooth networks. Since the proposed system has been specially adapted for useful features such as low cost, low power, and remote control, we used a ZigBee device for the wireless transmitting link in our proposed system.   Figure 2 shows the functional block diagram of a 900 MHz RFID reader. The RFID reader consists of three functional blocks: a data processing block, a transmitting block, and a receiving block. The transmitting block sends request commands to an RF-tag in the recognition field. The receiving block receives data from RF-tags through an antenna. The data processing block deals with the tag information.  The transmitting block contains a signal generator, a modulator, a power amplifier, and a tuning circuit. The signal generator generates the carrier signals for the RFID system. In this study, we generate a 915 MHz signal from a 10 MHz crystal using an SI4113-BM frequency synthesizer. In order to design the modulator and the power amplifier, we use an RFMD RF2173 chip because this device has an output power of +36 dBm and a gain of 32 dB. Further, we control the output power level by using an EXAR MP7524A D/A converter via a microcontroller.

UHF RFID Reader
The receiving block consists of a detector, an amplifier, a filter, and a comparator, as shown in Figure 2. The detector splits the tag signal received from the antenna into two parts by using a directional coupler, an Alpha DC08-73 chip. The amplifier LT6200CS6 having a gain bandwidth of 165 MHz enhances the tag signal from the directional coupler. The amplified signal is supplied to the comparator, LMV7219M7, through an active RC filter, LT1568. The data processing block is operated by a microcontroller PIC18F452 with a built-in 10-bit A/D converter. Figure 3 shows the prototype of the UHF RFID reader, and Table 1 presents the design details.  Figure 4 shows the functional block diagram of a ZigBee end-device, which contains an RF data modem, a microcontroller, and a power management system. We used a CC2420 RF transceiver, manufactured by Chipcon, for ZigBee communication. The chip is a 2.4 GHz IEEE802.15.4 compliant RF transceiver designed for low-power communication. Further, we used an ATmega-128L RISC processor for the microcontroller's operation. The data communication between the RF modem and the microcontroller is carried out through SPI. Figure 5 shows the fabricated ZigBee end-device.

TCP/IP Device
A TCP/IP device can be used as a gateway for the remote management of a UHF RFID reader. The TCP/IP device can be directly connected with the RFID reader via UART or indirectly via a ZigBee end-device as shown in Figure 1. The role of TCP/IP device is basically to provide the function that can remotely handle tag data between a RFID reader and a terminal located on far away. When the TCP/IP device and the RFID reader are connected together with a wired type connection, the TCP/IP device transmits the achieved tag data to a near or far terminal directly, but in the case of a wireless connection, the tag data from the reader can be achieved by wireless type communication by the ZigBee coordinator in the TCP/IP device. Since the ZigBee coordinator is compatible with IEEE802.15.4, it can communicate with another ZigBee end device on the RFID reader side, therefore remote management of the tag information under various circumstances can be carried out by the interoperation between the RFID reader and the TCP/IP device. In this paper, we designed a TCP/IP device based on the ADM8668 reference board manufactured by Infineon. Figure 6 shows the functional block diagram of the designed TCP/IP device. Figure 7 shows the fabricated TCP/IP device.  Figure 8 shows the as-fabricated UHF antenna. The antenna has two layers: the top layer is made of copper, and the bottom layer of aluminum. The junction between the top layer and the bottom layer is connected to a 50 Ω film resistor. The antenna is enclosed by ABS plastic housing and is 13 × 13 cm 2 in size. The antenna is designed to ensure a circular polarization with a center frequency of 915 MHz, an upper frequency of 950 MHz, and a lower frequency of 840 MHz. The circular polarization antenna can transmit and receive signals from more than two channels with one frequency. Further, the antenna offers good transparency to obstacles, is robust against multipath interference, and has a low polarization loss.  Figure 9 shows the simulation results of the fabricated UHF antenna. Figure 9(a) shows the simulation results of the S 11 parameter. The narrowband reflection property between 800 MHz and 900 MHz is observed in the simulation. Figure 9(b) shows the results in the form of a radiation pattern. The maximum gain of the designed antenna is calculated as 9.5 dBi. (a) (b) Figure 10 shows a series of the prototypes fabricated using commercial parts. The image on the right shows the TCP/IP device and the RF coordinator. The image in the middle shows the UHF RFID reader and the ZigBee end-device, and the image on the left is of the antenna tag. The RFID reader and the TCP/IP device are connected with an IEEE802.15.4 ZigBee wireless channel. On the other hand, the reader and the RF end-device are linked with a wired UART. The TCP/IP device and the RF coordinator are also connected with a wired UART.  Figure 11 shows the test setup for the recognition of the UHF tag and the management of the recognized tag information. The test is divided into three parts in order to verify the results of these apparatus: tag recognition by the UHF RFID reader, transmission of the tag information by the ZigBee end-device and the RF coordinator, and remote management by the TCP/IP device. Figure 11. Test setup for interoperation between RFID reader and TCP/IP device. Figure 12-14 shows the GUI window for the remote RFID recognition under a Web-based test circumstance. The procedure of the interoperation between the RFID reader and the TCP/IP device is carried out sequentially step by step. The first step is to confirm the information of the TCP/IP device which is connected with the service network. The next step is also to confirm the information of the Antenna UHF RFID reader + Zigbee end-device RF coordinator + TCP/IP device RFID reader. The last process is to recognize information of tags registered from the RFID reader. Of course the RFID reader can newly register unregistered tags. Figure 12 shows the GUI screen for recognizing the TCP/IP device (RG; resident gateway). The information such as IP address, serial number and operation status, has shown in this window. Figure 12. Web GUI window for recognition of the TCP/IP device. Figure 13 shows the GUI screen image for recognizing the RFID reader. This screen supports to user basic information such as reader ID, firmware version, and reader serial number. Moreover, user can also know technical information such as operating frequency, RF power, and modulation depth.  Figure 14 shows the recognized tag information on the GUI screen image. These tags can be registered or removed by using register and delete buttons.

Conclusions
In this paper, we introduced a system implementation of a UHF RFID reader and a TCP/IP device. The UHF RFID reader operating at 915 MHz was compatible with EPC Class-0/Gen1, Class-1/Gen1 and 2, and ISO18000-6B. The TCP/IP device was prepared to retransmit the tag data for remote management. The ZigBee end-device and the RF coordinator could support a wireless link between the RFID reader and the TCP/IP device. To verify the operation of the designed prototypes, we used a Web-based test case. The fabricated prototypes can be used in application fields such as remote inventory management and ubiquitous sensor networks (USNs).