Biometric solution for embedded device gained significant attention in the commercial and research sectors over recent years. Combining multiple biometrics may enhance the performance of personal verification system in accuracy and reliability. This paper presents a new multi-biometric verification solution aimed at implementing on an embedded system within a wide range of applications. The system combines the voiceprint and fingerprint biometrics and makes decision at score level. Fusion strategy is based on score normalization and support vector machine (SVM) classifier. We tested the performances of SVM using three kernel functions for system adaptation. Experimental result demonstrates that proposed multi-biometric verification approach achieves 1.0067% equal error rate (EER) that can be deployed in majority of embedded devices such as PDA and smart cell phone for user's identity verification.

It has been demonstrated that a robot can learn to interact purposefully with its environment through a developmental acquisition of sensory-motor coordination. Teleoperation can bootstrap the process by enabling the robot to observe its own sensory responses to actions that lead to specific outcomes within an environment. This paper reports the results of learning to navigate an indoor environment by a simple mobile platform using only LIDAR and odometry. Descriptors of sensory-motor coordination in the form of LIDAR scan images coupled to concurrent motor activity are extracted from five teleoperated trials of driving the robot through the environment. From these a topological action map of the environment is constructed. A hierarchical discrete event dynamical system is defined wherein higher levels encompass increasing time intervals, longer sensory and motor histories, more complex behaviors, and higher degrees of abstraction. An event that cannot be handled at the current level precipitates a jump to the next higher level for processing. The sensory-motor associations learned and encoded in the action-map enable autonomous navigation while avoiding localization errors. Test results are given that demonstrate the robustness of autonomous navigation through the learned environment in the face of both stationary and moving obstacles.