The paper presents a system for monitoring and assessment the speech quality in the IP telephony infrastructures using modular probes. The probes are placed at key nodes in the network where aggregating packet loss data. The system dynamically measures speech quality and results are collected on a central server. For data analysis we applied four-state Markov model for modeling the impact of network impairments on speech quality, afterwards, the resilient back propagation (Rprop) algorithm was used to train a neural network. Information about the speech quality are displayed in the form of automatically generated graphs and tables. The proposed solution has been tested with selected codecs and further generalizes the already presented concepts of the speech quality estimation in the IP environment.
Although extensive research has been conducted, understanding the exact phenomena occurring during the operation of polymer electrolyte fuel cells (PEFCs) remains difficult. This research attempted to identify new reasons for the reduced performance of PEFC using an imaging technique. To begin with, H+ and OH− indicator sensors, which display red, blue, and green values (RGB) using digital microscopes, are developed and attached to each electrode of a membrane electrode assembly to enable quantitative analysis of ion generation. The proposed reaction in the fuel cell can be confirmed, and various reactions occurring in the electrode can be examined using this approach. In particular, H+ is generated at the anode and cathode of the anion exchange membrane fuel cell, which is found to be a major cause of performance deterioration. 相似文献