Volodymyr Kochan

Smart license plate recognition system based on image processing using neural network

We describe the smart vehicle screening system, which can be installed into a tollbooth for automated recognition of vehicle license plate information using a photograph of a vehicle. An automated system could then be implemented to control the payment of fees, parking areas, highways, bridges or tunnels, etc. There are considered an approach to identify vehicle through recognizing of it license plate using image fusion, neural networks and threshold techniques as well as some experimental results to recognize the license plate successfully

Sensor errors prediction using neural networks

The features of neural networks used for increasing the accuracy of physical quantity measurement are considered by prediction of sensor drift. The technique of data volume increasing for predicting neural network training is offered at the expense of various data types replacement for neural network training and at the expense of the separate approximating neural network use.

Intelligent distributed sensor network

The intelligent functions of sensor measurement instrumentation are formed on basis of the original calibration and prediction methods. Measuring module structure as basic distributed sensor network (DSN) component is considered and an intelligent DSN (IDSN) structure is proposed. Procedures and functions of various levels of information processing in IDSN are formed. Developed IDSN is tested on design and exploitation stages.

Intelligent nodes for distributed sensor network

Neural networks models and their training algorithms on a central computer with reference to a previously developed distributed sensor network are considered. The requirements for its intelligent node are formulated. Also the nodes structure is offered which realises such intelligent functions, as sensor and other measuring channel components drift prediction using remote reprogramming.

Error compensation in an intelligent sensing instrumentation system

Methods of improving the measurement accuracy by estimation and correction of the maximum error components, are analyzed. The functional structure of the measurement channel in an intelligent sensing instrumentation system is described along with the procedures of component error correction. An experimental setup, implementing such methods in a multi-processing neural network configuration, is presented.

Estimation of Computational Complexity of Sensor Accuracy Improvement Algorithm Based on Neural Networks

The estimation method of computational complexity of sensor data acquisition and processing algorithm based on neural networks is considered in this paper. An application of this method allows to propose a three-level structure of distributed sensor network with improved accuracy.

Mobile Ad Hoc wireless network for pre- and post-emergency situations in nuclear power plant

This paper describes the mobile Ad-Hoc (wireless) network (MANET) for emergency scenarios in nuclear power plant (NPP). Authors proposed the system with such properties as flexibility and a self-forming and self-healing network topology that dynamically adjusts to the moving configuration per each intermediate routing node. It is also proposed to integrate MANET and Bluetooth-like technologies to create an unmanned formation of autonomous quadcopters that provides both indoor and outdoor communications coverage inside and outside of the NPP.

Approach to neural-based identification of multisensor conversion characteristic

The neural network based method of individual conversion characteristic identification of multisensor using reduced number of its calibration/testing results is proposed in this paper. The proposed method is based on reconstruction of surface points of multisensor conversion characteristic by modular neural network. Each neural network module reconstructs separate point of the surface. The simulation results show high reconstruction accuracy of the first approximation phase of the method.

Instrumentation for gathering data [DAQ systems]

The main characteristic of the intelligent distributed data acquisition system we describe in this article is that it supports intelligent functions and provides the desired accuracy of measurement. It is also reliable and adaptable, utilising two NN-based methods of sensor drift prediction, which allows it to increase its inter-testing interval, and provides accuracy of sensor data processing. The system is designed on a distributed intelligence scheme, which operates in real time for most industrial sensors.

Features of intelligent distributed sensor network higher level development

The functions and software structure of the higher level of intelligent distributed sensor network (IDSN) are considered. The main purpose of software functions is reaching high accuracy of the data acquisition and processing using neural networks. The proposed client-server software architecture provides effective use of computing resources of the IDSN central computer.