Evgenii Krouk — Acting Director, Academic Supervisor
Andrey Abrameshin — Deputy Director
Sergey Tumkovskiy — Deputy Director for Academics
Sergey Aksenov — Deputy Director for Research
Address: 34 Tallinskaya Ulitsa, Moscow
MIEM HSE - Institute with 55 years of history, trains specialists for high-tech industries. Teaching staff MIEM includes 4 Academicians of RAS, 5 Corresponding Member of RAS, 34 winner of the State Prize of the Russian Federation. Close ties with leading industry institutions: RAS institutes, international companies such as National Instruments, InfoWatch, Zyxel, QNAP, Altium Limited, as well as laboratories equipped with the latest : 3D visualization; laser technologies; telecommunications; cybersecurity - allow to prepare for specialists at the highest level.
Vol. 136: Encyclopaedia of Mathematical Sciences. Bk. VII: Subseries: Invariant Theory and Algebraic Transformation Groups. Springer, 2017.
Karasev M., Novikova E., Vybornyi E.
Russian Journal of Mathematical Physics. 2017. Vol. 24. No. 4. P. 454-464.
Zavyalov V., Chernyaev S., Shein K. et al.
In bk.: 28th International Conference on Low Temperature Physics. M.: Faculty of Physics, MSU, 2017.
Research Laboratory of Space Research is carrying out the project entitled ‘Development of a new generation of fast learning neural network recognition tools for the wide class of chemicals (highly intelligent artificial nose) on the basis of solid-state gas-sensitive matrices’ within the governmental program ‘Research and development in priority areas of Russian scientific and technological sector for 2014-2020’, under ‘Environmental Management ‘ direction.The object of applied research and experimental development (ARED) performed within the project are the means and methods of selective detection and recognition of a wide class of chemicals on the basis of solid-state gas-sensitive matrices (SSGSM) using a fast learning neural network.The purpose of ARED is research and development of a complex of scientific, technical and software solutions designed to create a new generation of fast learning means of neural network recognition for a wide class of chemicals (highly intelligent artificial nose) on the basis of GSSSM.The project is carried out in close cooperation with the Industrial and Technological Center UralAlmazInvest. Industrial partner of the project - NPP MikropriborThe results obtained will be analyzed for further theoretical research on methods and algorithms for detection and identification of gas mixes, as well as development of the complex software implementation of wide-range neural network tools for chemical recognition.The actual results of the research project were presented at the exhibition VUZPROMEKSPO-2015 on 2-4 December 2015 in the format of the poster session and are published on the official site of the laboratory.