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EN
Embedded platforms with GPU acceleration, designed for performing machine learning on the edge, enabled the creation of inexpensive and pervasive computer vision systems. Smartphones are nowadays widely used for profiling and tracking in marketing, based on WiFi data or beacon-based positioning systems. We present the Arahub system, which aims at integrating world of computer vision systems with smartphone tracking for delivering data useful in interactive applications, such as interactive advertisements. In this paper we present the architecture of the Arahub system and provide insight about its particular elements. Our preliminary results, obtained from real-life test environments and scenarios, show that the Arahub system is able to accurately assign smartphones to their owners, based on visual and WiFi/Bluetooth positioning data. We show the commercial value of such system and its potential applications.
EN
In the investigation of large-scale systems the problem of the complexity of their reliability functions arises. This problem may be solved approximately by assuming that the number of system components tends to infinity and finding the limit reliability function. In this paper a four-element class of limit reliability functions for a regular homogeneous series-parallel system is presented. The number of series components of the system has the order of the logarithm of the number of its parallel components. The result is obtained under the assumption that the lifetimes of the particular components are independent and identically distributed random variables. The class presented is different from the known class of limit distributions of minimax statistics of independent random variables with the same distribution. Moreover, some examples of the systems considered and their limit reliability functions are given. The results can be useful in the investigation of the reliability of large systems.
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