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ïðîñèì ïðèíÿòü ó÷àñòèå â îïðîñåçàïîëíèòü àíêåòó

Ïðîåêòèðîâàíèå ñàìîîáó÷àþùåéñÿ ìîäåëè ìîáèëüíîãî ðîáîòà íà îñíîâå âèçóàëüíîé îäîìåòðèè è âûñîêîïðîèçâîäèòåëüíûõ àðèôìåòè÷åñêèõ óñòðîéñòâ

ãðàíò ÁÐÔÔÈ - Ô22ÊÈ-012

Öåëüþ çàÿâëÿåìîãî ïðîåêòà ÿâëÿåòñÿ ðàçðàáîòêà àðèôìåòè÷åñêèõ óñòðîéñòâ äëÿ àïïàðàòíûõ íåéðîííûõ ñåòåé, èñïîëüçóåìûõ â àâòîíîìíî óïðàâëÿåìûõ ðîáîòèçèðîâàííûõ ñèñòåìàõ.

Äàëåå ïëàíèðóåòñÿ èíòåãðàöèÿ ðàçðàáîòàííûõ àðèôìåòè÷åñêèõ óñòðîéñòâ â íåéðîííûå ñåòè ñ èõ äàëüíåéøèì èñïîëüçîâàíèåì â ðîáîòèçèðîâàííûõ ñèñòåìàõ àâòîíîìíîé íàâèãàöèè.

Äëÿ äîñòèæåíèÿ ïîñòàâëåííîé öåëè â ðàáîòå ïëàíèðóåòñÿ ðåøèòü ñëåäóþùèå çàäà÷è:

1) ðàçðàáîòêà HDL-îïèñàíèé óñòðîéñòâ, ðåàëèçóþùèõ àðèôìåòè÷åñêèå îïåðàöèè;

2) ïðîâåäåíèå ýêñïåðèìåíòàëüíûõ èññëåäîâàíèé ïîëó÷åííûõ ñõåìíûõ ðåàëèçàöèé íà ïðîãðàììèðóåìûõ èíòåãðàëüíûõ ñõåìàõ.

Íàó÷íàÿ ãèïîòåçà çàêëþ÷àåòñÿ â ðàçðàáîòêå àïïàðàòíûõ àëãîðèòìîâ öèôðîâûõ óñòðîéñòâ, ðåàëèçóþùèõ àðèôìåòè÷åñêèå îïåðàöèè, íà îñíîâå èñïîëüçîâàíèÿ êîìïðîìèññà ìåæäó êîëè÷åñòâîì âõîäíûõ ïåðåìåííûõ ñèñòåì áóëåâûõ ôóíêöèé è ÷èñëîì óðîâíåé ñõåìíûõ ðåàëèçàöèé, îñíîâàííûõ íà ðàçëîæåíèÿõ áóëåâûõ ôóíêöèé, îïèñûâàþùèõ ýòè ñèñòåìû. Ïðåäïîëàãàåòñÿ, ÷òî èñïîëüçîâàíèå ñâîéñòâ ðàçëè÷íûõ êëàññîâ áóëåâûõ ôóíêöèé, à òàêæå èõ ðàçëè÷íûõ âèäîâ ðàçëîæåíèé ïîçâîëèò ñóùåñòâåííûì îáðàçîì óâåëè÷èòü ñêîðîñòü âûïîëíåíèÿ àðèôìåòè÷åñêèõ îïåðàöèé.

Ëèòåðàòóðà:

1. D. Gorodecky and V. Suprun, Polynomial Expansion of Symmetric Boolean Functions // Recent Progress in the Boolean Domain, Cambridge Scholars Publishing, UK, 2014, Section 4.4, pp. 247-262.

2. D. Gorodecky, A novel approach of polynomial expansions of symmetric Boolean functions // Problems and New Solutions in the Boolean Domain, Cambridge Scholars Publishing, UK, 2016, Section 1.4, pp. 96-115.

3. D. Gorodecky “Design of Multipliers Using Fourier Transformations” // Further Improvements in the Boolean Domain, Cambridge Scholars Publishing, UK, 2018, Section 3.4, pp. 240-252.

4. D. Gorodecky and T. Villa, “Efficient Hardware Operations for the Residue Number System by Boolean Minimization” // Advanced Boolean Techniques, Springer Nature Switzerland, 2019, Section 11, pp. 237-258.

5. D. Gorodecky, "Reed-Muller Representation in Arithmetic Operations" // Reed-Muller Workshop 2019, May 24, 2019, Fredericton, New Brunswick, Canada, pp.53-58.

6. D. Gorodecky and T. Villa, "Efficient Implementation of Modular Division by Input Bit Splitting" // Proceedings of the 26th IEEE Symposium on Computer Arithmetic, June 10-12, 2019, Kyoto, Japan, pp. 54-60.

7. D. Gorodecky, P. Bibilo, “Constant Multiplication Based on Boolean Minimization” // Proceedings of the 14th International Workshop on Boolean Problems, Bremen, Germany, Sept. 24-25, 2020. (http://www.informatik.uni-bremen.de/iwsbp/program.php)

8. M Fu, T Fan, Z Ding, SQ Salih, N Al-Ansari, ZM Yaseen, "Deep learning data-intelligence model based on adjusted forecasting window scale: application in daily streamflow simulation" // IEEE Access 8, 2020, pp. 32632-32651.

9. ZM Yaseen, M Fu, C Wang, WHMW Mohtar, RC Deo, A El-Shafie, "Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons" // Water Resources Management 32 (5), 2018, pp. 1883-1899.

10. M Fu, W Zhu, Z Le, D Manko, "Improved visible light communication positioning algorithm based on image sensor tilting at room corners" // IET Communications 12 (10),  2018, pp. 1201-1206.

11. M Fu, W Wang, Z Le, MS Khorram, "Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model" // Neural Computing and Applications 26 (8), 2015, pp. 1789-1797.