Каргин Анатолий Алексеевич д.т.н., профессор, зав. кафедрой компьютерных технологий, декан физико-технического факультета

DEPARTMENT OF COMPUTER SCIENCE Six computer laboratories: Artificial intelligence machines; SCADA SYSTEMS; Computer design; Software development tools; Computer network (Network Academia CISCO); System administration. Four degree programs: Programming; System administration; Computer design; Artificial intelligence system.

ACM ICPC World Finals 2011, Silver medal The successful participation of our students in the Competitions on programming has the old history … 7 place (5 university) of SEERC ACM ICPC – 2008, 4 place (3 university) of SEERC ACM ICPC – 2009, 7 place (4 university) of SEERC ACM ICPC – 2010, 4 place (4 university) of SEERC ACM ICPC – 2011, Prize-winners of the All-Ukrainian Contests on programming last years SEERC – The Semi- final in SouthEastern European Region Winners of the Southern Caucasus Cup – Georgia, 2011 Winners of the Open Students Contest of SFU on programming – Russia, 2011 Winners of the Open Championship of Kovrov city – Russia, 2011 Our students repeated prize-winners of the international competitions on programming. Our students train in IT departments of the companies Yandex, FaceBook, IBM.

The 2011 World Finals of the ACM INTERNATIONAL COLLEGIATE PROGRAMMING CONTEST (ICPC) May – Orlando, FL, USA The Donetsk national university team has won an absolute eighth place in the world on programming (Silver medal).

PROJECT COGNITIVE LEVEL MODEL OF HIERARCHICAL SYSTEM OF THE BRAIN INFORMATION PROCESSING: THE COMPUTING INTELLIGENCE APPROACH Project supported by Ministry of Education and Science, Youth and Sports of Ukraine, National Academy of Sciences of Ukraine R/N U003469

M AIN PROJECT IDEA (P ROJECT SUPPORTED BY M INISTRY OF E DUCATION AND S CIENCE, Y OUTH AND S PORTS OF U KRAINE, N ATIONAL A CADEMY OF S CIENCES OF U KRAINE ) Conceptual information processing model based on analysis and generalization of interaction processes at quantum, atomic, molecular, cellular, system (cellular ensemble) and cognitive levels. Principal basic unit of information processing (PBUIP): ferment at molecular level, neuron at cellular level, cognitive element at cognitive level. Homeostasis model (internal processes control) at cellular, system (cellular ensemble) and cognitive levels as a set of cooperating units (PBUIP).

M ATHEMATICAL AND COMPUTER MODELS OF INFORMATION PROCESSING (PBUIP) (P ROJECT SUPPORTED BY M INISTRY OF E DUCATION AND S CIENCE, Y OUTH AND S PORTS OF U KRAINE, N ATIONAL A CADEMY OF S CIENCES OF U KRAINE ) Transducer 1 sn1 1 sn1 2 sn1 n … Transducer g sng 1 sng 2 sng n … … Elementary sensors Sensory subsystem Motive subsystem Prediction subsystem Situational agent (j) … ce i,j,1 ce i,j,2 ce i,j,h Contextual connections … Elementary effectors Effectors' subsystem Effector 1 u1 1 u1 2 u1 n … Effector r ur 1 ur 2 ur n …

CONTROL SYSTEM DESIGN INFORMATION TECHNOLOGY (PROJECT SUPPORTED BY MINISTRY OF EDUCATION AND SCIENCE, YOUTH AND SPORTS OF UKRAINE, NATIONAL ACADEMY OF SCIENCES OF UKRAINE)

Cognitive&Uncertainty Group The research group is engaged in development and application of cognitive models on the basis of type-2 fuzzy sets for the decision of the following problems: 1. Clustering. 2. Parametrical and structural identification. 3. Management in human - machine systems. By group are developed and introduced: 1. The module of the structural analysis of time series of cognitive factors. 2. The module of supporting decision-making about readiness of the person-operator in complex systems. 3. Library package and toolbox supporting discrete interval type-2 fuzzy logic systems. Areas of application: 1. Training complexes for the human – operator. 2. Computer games. 3. A robotics. 4. Learning systems. 5. Systems of identification on the basis of processing of the biometric data.

Tymofii Sharii, PhD. Thesis: The Information Technology of Speech Data Processing Based on Fuzzy Cognitive Models (2011) Digital Signal Processing Signal Transforms: Fourier, Mellin, Fresnel Digital Filtering, Adaptive Filtering Linear Prediction Pitch estimation Modulation Spectrum Artificial Intelligence Fuzzy Logic Neural Networks Cognitive Models Genetic Algorithms Probabilistic Models Scientific Interests and Skills: Sound Synthesis Sound Effects Musical Synthesis Speech Synthesis Programming Skills: Languages: C++, C#, Matlab, Java Technologies:.NET, MFC, Win32API, UML

Automatic Word Recognition System (Project supported by Ministry of Education and Science, Youth and Sports of Ukraine, National Academy of Sciences of Ukraine) Speech Signal Parameterization The FCAS Model (Fuzzy Cognitive Accented Speech) Database of Phonetic Images Micrpohone Sound Files (wav, mp3) Acoustic parameters (feature vectors) Sound Signal Text Phoneme Models [ a ] [ i ] compact vocalic … Phonetic Images (Phonemes) … Features The model has been tested on Russian words but it can be applied to any other language. Experiments showed that the proposed technology can be used for a word recognition (with small vocabularies) and for a word search in speech signals. Key features of the proposed technology: it segments a speech signal automatically on basis of Mel- Frequency Cepstrum and Jacobsons differential features; it evaluates weights of speech segments depending on their perception by human (prosodic features are analyzed); it estimates current rate of speech; it post-processes acoustic parameters using Principal Component Analysis; it processes speech information at several levels: acoustic level, Jacobson feature level, phoneme level and word level.

The SoundAnalyzer Software Module © T.Sharii, Donetsk National University Sonogram Analysis of a sound signal in time-domain; automatic segmentation Statistics on speech segments; their accordance to feature classes

Analysis of a spectrum; pitch estimation Parameters view (spectrums, cepstrums, fundamental frequency, energy, etc.) The SoundAnalyzer Software Module © T.Sharii, Donetsk National University

Dynamics of calculated parameters Decision Making The SoundAnalyzer Software Module © T.Sharii, Donetsk National University

module «Term-to-Concept» Fuzzy terms Categorization Systems DB of E-documents text Synthesis Systems Search Systems Text Model «context» «Text-Term-Concept» SYSTEM Fuzzy phrases module «Text-to-Term» KB of terms Hybrid model of knowledge presentation Partition of the initial text on fragments (sentences, paragraphs). Creation of e-documents catalogue. Creation of the automated terminals in business processes. Development of intellectual search machines. converter The conceptual multilevel word processing model based on division of semantic layers and method of generalization of the semantic loading of the text for symbol- morpheme- term layers was developed. On the basis of data from cognitive psychology the hybrid fuzzy model of knowledge representation is developed. The model of text information interpretation on the basis of fuzzy conceptual model is developed. The developed method can be used in development of information retrieval systems, systems of categorizing and other systems, including subsystems of morphological, terminological and semantic analysis. The technology of construction of the text interpretation automated systems is described. Project of automated system of text interpretation :

ПРОГРАММНОЕ или СИТУАЦИОННОЕ УПРАВЛЕНИЕ ? (1)вперёд ПЕ1; (2)задержка ; (3)выкл_вперёд ПЕ1; (4)влево ПЕ1; (5)задержка ; (6)выкл_влево ПЕ1; (7)вперёд ПЕ1; (1)вперёд ПЕ1 ; (2)влево ПЕ1 1; (3)вперёд ПЕ1 ; (4)вправо ПЕ1 1; (5)вперёд ПЕ1 ; (6)влево ПЕ1 1; ; ;

ПРОБЛЕМЫ ПРОГРАММНОГО УПРАВЛЕНИЯ Методы программного управления не используют информации об окружении. Поэтому они применимы только в условиях высокой организации и упорядочения окружения.

ПРОБЛЕМЫ СИТУАЦИОННОГО УПРАВЛЕНИЯ Неоднородность сенсорной информации Неоднородность исполнительных механизмов Особенности динамической предметной области Открытость Сложность и размерность базы знаний

ПРОБЛЕМЫ СИТУАЦИОННОГО УПРАВЛЕНИЯ Система управления

СТРУКТУРНЫЯ СХЕМА НЕЧЁТКОЙ СИТУАЦИОННОЙ ОБРАБОТКИ ИНФОРМАЦИИ

ПРЕДОБРАБОТКА ИНФОРМАЦИИ ЭТАП СИНТЕЗА - гранулирование информации; неоднородные по размерам гранулы - отображение множества информационных гранул в структуру сенсорной памяти; - настройка характеристики информационной гранулы; нечёткий фактор уверенности активности гранулы ЭТАП ОБРАБОТКИ - вычисление параметров нечёткого фактора уверенности

НЕЧЁТКИЙ ФАКТОР УВЕРЕННОСТИ а)б) 1 ~ 1 ~ 1 ~ в)

ОБРАБОТКА ИНФОРМАЦИИ В СЕНСОРНОЙ ПАМЯТИ Формирование модели текущей ситуации на момент времени t СЭП СЭС

ОБРАБОТКА ИНФОРМАЦИИ В ПРОТОТИПНОЙ ПАМЯТИ Структура прототипа ситуации динамический и статический фрагменты Оценка близости прототипа ситуации и текущей ситуации Активизация прототипа управления

ОБРАБОТКА ИНФОРМАЦИИ В ЭФФЕКТОРНОЙ ПАМЯТИ Интеграция нечётких характеристик активностей

ПОСТОБРАБОТКА ИНФОРМАЦИИ Динамическая модель исполнительного механизма