As one promising branch of biometrics, palmprint recognition has received significant attention and made extraordinary progress in the past decades. The crucial step of palmprint recognition is to extract the discriminative features for the subsequent identification or verification task. However, neither the traditional hand-crafted descriptors nor the deep convolutional neural network (CNN) with the original softmax loss shows satisfactory generalization ability under open-set settings. In this paper, we proposed an end-to-end method for open-set palmprint recognition by applying CNN with a novel loss function, namely, centralized large margin cosine loss (C-LMCL). The modified loss function compels the feature vectors from different classes to uniformly and separately distribute in the hyper feature space. At the same time, it makes intra-class feature vectors compactly gather to their corresponding class centers. Consequently, such trained model has the ability to generalize across unseen subjects and different datasets. Finally, a lot of experiments are conducted on two public palmprint datasets- Tongji and PolyU datasets. In particular, all the evaluations are made under open-set protocols that are more complex and challenging compared to the previous close-set scenarios. The experimental results on the Tongji and PolyU datasets indicate the superiority of our algorithm over the state-of-the-art performance. It effectively confirmed the bright prospects of employing palmprint information in biometric authentication.

Universal personal computing (UPC) supports nomadic computing on the Internet by allowing mobile users to access their personalized computing resources anywhere on the Internet using any available terminal. Since UPC must deal with heterogeneous systems in a mobile environment, independence of computing platforms and support of portable objects are imperative, which leads to the choice of Java. The system is modeled as distributed objects with CORBA as the common bus that facilitates distribution, interfacing, and integration of the objects. The combination of Java and CORBA constitutes an ideal environment for UPC. © 1998 John Wiley & Sons, Ltd.

In this paper, an automatic protocol test case generator that generates both test sequence and test data is presented. The underlying model is an extended finite state machine (EFSM) and Estelle is used as the formal description language. First, test sequences are generated using a combined control and data flow testing method. A set of path conditions associated with each test sequence is obtained using symbolic execution techniques. By solving the path conditions as a group of constraints, test data are then automatically generated. A prototype system implementing the above procedures has been built. The system has been used to generate test cases for real protocols such as TP0, FDDI and LAPB, and has been integrated in the UBC protocol testing environment.>

The authors present an approach that combines control flow and data flow analysis techniques to generate unified test sequences (UTSs) for testing protocols modeled as an extended finite-state machine (EFSM). The UTS has the same fault detection capability as the characterizing sequence (UIO, DS, or W) methods for the control part, and at the same time covers all paths that connect variable definitions and uses (all du-paths) for data flow testing. A transition dependence graph is proposed to represent the dependence relations between transitions needed to construct the test sequences. The executable UTS is then selected using constraint satisfaction problem (CSP) solution techniques and transition loop analysis.>