Аналитическая модель беспроводных сетей технологии IEEE 802.11ah
FRUCT is the largest regional cooperation framework between academia and industry in form of open innovations. FRUCT conferences are attended by the representatives of 25 FRUCT member universities from Russia, Finland, Denmark, Italy, Ukraine, India, industrial experts from Qt community, EMC, EIT ICT Labs, Nokia Siemens Networks and a number of guests from other companies and universities.
A fridge plays an important role in the kitchen in comparison to other appliances because it helps to store food products at optimal conditions for a long period of time. The ordinary refrigerators perfectly allow preserving meals but they are not effective in case of food management. Providing a remote control for home appliances extends the everyday usage of these devices. In addition to the remote control device, some manufacturers use additional modules such as internal cameras and hands-free speaker for convenient control of an appliance. All these devices are able to communicate with each other to reach common goals. The home appliance producer Liebherr in cooperation with technology company Microsoft developed a solution for remote control of refrigeration with possibility of food recognition using Machine Learning algorithms. This option enables automatic compiling of the list of food stored in the fridge and food ordering in an online shop without manual actions. This opportunity enables not only a convenient usage of an appliance but also allows reduction of electricity consumption because user does not open fridge doors frequently as far as he knows a list of food in refrigerator. In this paper we describe SmartDevice technology from Liebherr that was developed for adding smart features to the brand products. In particular, we review main business processes of SmartDevice, discuss advantages and disadvantages of this solution for the end customers and identify future research for creating smart fridges.
Generalized error-locating codes are discussed. An algorithm for calculation of the upper bound of the probability of erroneous decoding for known code parameters and the input error probability is given. Based on this algorithm, an algorithm for selection of the code parameters for a specified design and input and output error probabilities is constructed. The lower bound of the probability of erroneous decoding is given. Examples of the dependence of the probability of erroneous decoding on the input error probability are given and the behavior of the obtained curves is explained.