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1: IntroductionIn recent years, several systems for automatically routing and sealing pavement surface cracks have been developed. Examples include (1) the CMU-UT Field Prototype (1992), (2) the CalDavis Field Prototype (1993), and (3) the UT Automated Road Maintenance Machine (ARMM) (1996). Through trial and error and over 8 years of perseverance , the UT ARMM has achieved an optimal balance between human and machine functions for automated pavement crack sealing[3 ]. For automation of pavement crack sealing, complete autonomy can be achieved , but at a cost and speed that is unacceptable [2]. This was apparent with the first CMU-UT field prototype, which used laser range sensing and machine vision to autonomously identify and map cracks.Currently, the ARMM combines machine vision and operator identification of the cracks to be sealed in order to map their exact locations in the machine's workspace . Recent field trials of the full scale crack sealer have indicated that automated pavement crack sealing is now technically, economically , and financially feasible. However, there is a need for periodic productivity studies of the full scale crack sealer to evaluate its performance as it is further refined.First, this paper briefly describes five specific functions for teleoperation of the ARMM. Then the paper will mainly focus on describing the methodology for determining the productivity of the ARMM. A mathematical model for measuring the system productivity has been developed based on the different types (longitudinal, trans-' Correspond to voungsuk(a

Security is a major concern within companies to prevent access to information by unauthorized persons.  In this work, we are interested in access control through facial recognition. To realize this access control system based on facial recognition, we used an embedded system under Arduino which gives us the possibility to assemble the performances of programming and electronics, more precisely, we programmed electronic systems for the automatic opening of doors without the action of a human being. From a sample of 100 individuals composed of 40 women and 60 men, 75 of whom were registered and 25 non-registered, our access control system obtained the results of 70 true positives, 5 false negatives, 8 false positives and 17 true negatives that constitute our confusion matrix. However, from the set of tests performed we can conclude that multi-modality fusion can be leveraged to increase the performance of the verification system as the verification performance of multimodal systems (feature fusion or score fusion) can be applied to give even better results.

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