3 research outputs found
IoT ΠΏΡΠΈΡΡΡΡΠΉ Π½Π° Π±Π°Π·Ρ ΠΌΡΠΊΡΠΎΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ΡΠ° STM32
Π Π±Π°ΠΊΠ°Π»Π°Π²ΡΡΡΠΊΡΠΉ Π΄ΠΈΠΏΠ»ΠΎΠΌΠ½ΡΠΉ ΡΠΎΠ±ΠΎΡΡ Π±ΡΠ² ΡΠ΅Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΉ IoT ΠΏΡΠΈΡΡΡΡΠΉ Π½Π° Π±Π°Π·Ρ
ΠΌΡΠΊΡΠΎΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ΡΠ° STM32 Π·Ρ ΡΠΏΡΡΠΆΠ΅Π½Π½ΡΠΌ Π· Π½ΠΈΠΌ ΠΌΠΎΠ±ΡΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π»Π΅ΡΠΎΠ½Ρ.
Π‘ΠΈΡΡΠ΅ΠΌΠ° Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡ Π·Π°ΠΏΡΠΎΡΡΠ²Π°ΡΠΈ Π΄Π°Π½Ρ Π²ΡΠ΄ ΠΏΠ»Π°ΡΠΈ, Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΡΡΠΈ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΡ bluetooth. ΠΡΠΎΠ³ΡΠ°ΠΌΠ½ΠΈΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡ ΡΠΊΠ»Π°Π΄Π°ΡΡΡΡΡ ΡΠ· Π΄Π²ΠΎΡ
ΡΠ°ΡΡΠΈΠ½,
ΠΌΡΠΊΡΠΎΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ΡΠ° ΠΎΡΠ½Π°ΡΠ΅Π½ΠΎΠ³ΠΎ Π΄Π°ΡΡΠΈΠΊΠ°ΠΌΠΈ Π²ΠΈΠΌΡΡΡΠ²Π°Π½Π½Ρ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠΈ, ΠΎΡΠ²ΡΡΠ»Π΅Π½ΠΎΡΡΡ
ΡΠ° ΡΠΈΡΠΊΡ ΡΠ° ΡΠΎΠ·ΡΠΎΠ±Π»Π΅Π½ΠΎΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΈ Π½Π° ΠΌΠΎΠ²Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΡΠ²Π°Π½Π½Ρ C.In the bachelor's thesis was implemented IoT device based on STM32
microcontroller with pairing with a mobile phone.
The system allows you to request data from the board using bluetooth
technology. The software product consists of two parts, a microcontroller equipped
with sensors for measuring temperature, light and pressure and a program developed
in the C programming language
A sensory system for robots using evolutionary artificial neural networks.
The thesis presents the research involved with developing an Intelligent Vision System for an animat that can analyse a visual scene in uncontrolled environments. Inspiration was drawn both from Biological Visual Systems and Artificial Image Recognition Systems. Several Biological Systems including the Insect, Toad and Human Visual Systems were studied alongside popular Pattern Recognition Systems such as fully connected Feedforward Networks, Modular Neural Networks and the Neocognitron. The developed system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network architecture has features of modularity enhanced by the presence of lateral inhibitory connections. It was implemented using Evolutionary Artificial Neural Networks (EANN). A novel method called FUSION was used to train the DNN, which is an amalgamation of several concepts of learning in Artificial Neural Networks such as Unsupervised Learning, Supervised Learning, Reinforcement Learning, Competitive Learning, Self-organisation and Fuzzy Logic. The DNN has unique feature detecting capabilities. When the DNN was tested using images that comprised of combination of features used in the training set, the DNN was successful in recognising individual features. The combinations of features were never used in the training set. This is a unique feature of the DNN trained using Fusion that cannot be matched by any other popular ANN architecture or training method. The system proved to be robust in dealing with New and Noisy Images. The unique features of the DNN make the network suitable for applications in robotics such as obstacle avoidance and terrain recognition, where the environment is unpredictable. The network can also be used in the field of Medical Imaging, Biometrics (Face and Finger Print Recognition) and Quality Inspection in the Food Processing Industry and applications in other uncontrolled environments
Cognitive Radio Systems
Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems