The embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems and an exhaustive analysis of the state of the art of all current performance with respect to architectures, design methodologies, test and applications could be very interesting. The Advanced Encryption Standard (AES), based on the well-known algorithm Rijndael, is designed to be easily implemented in hardware and software platforms. General purpose computing on graphics processing unit (GPGPU) is an alternative to recongurable accelerators based on FPGA devices. This paper presents a direct comparison between FPGA and GPU used as accelerators for the AES cipher. The results achieved on both platforms and their analysis has been compared to several others in order to establish which device is best at playing the role of hardware accelerator by each solution showing interesting considerations in terms of throughput, speedup factor, and resource usage. This analysis suggests that, while hardware design on FPGA remains the natural choice for consumer-product design, GPUs are nowadays the preferable choice for PC based accelerators, especially when the processing routines are highly parallelizable.
Mobile devices are widely replacing the standard personal computers thanks to their small size and userfriendly use. As a consequence, the amount of information, often confidential, exchanged through these devices is raising. This makes them potential targets of malicious network hackers. The use of simple passwords or PIN are not sufficient to provide a suitable security level for those applications requiring high protection levels on data and services. In this paper a biometric authentication system, as a running Android application, has been developed and implemented on a real mobile device. A system test on real users has been also carried out in order to evaluate the human-machine interaction quality, the recognition accuracy of the proposed technique, and the scheduling latency of the operating system and its degree of acceptance. Several measures, such as system usability, users satisfaction, and tolerable speed for identification, have been carried out in order to evaluate the performance of the proposed approach.
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