Evaluation of the firewall influence on the session initiation by the SIP multimedia protocol

Firewalls is one of the major components to provide network security. By using firewalls, you can solve such problems as preventing unauthorized access, and deleting, modifying and/or distributing information under protection. The process of information flows filtration by a firewall introduces additional time delays, thus possibly leading to disruption of stable operation of the protected automated system or to inaccessibility of the services provided by the system. Multimedia services are particularly sensitive to service time delays. The main purpose of the work presented in this paper is to evaluate the influence of the firewall on the time delays in data transmission process in the automated system with multimedia data transmission protocols. The evaluation is provided by the queuing theory methods while a session is initiated between two users by the Session Initiation Protocol (SIP) with firewall message filtration. A firewall is a local or functional distributing tool that provides control over the incoming and/or outgoing information in the automated system (AS), and ensures the protection of the AS by filtering the information, i.e., providing analysis of the information by the criteria set and making a decision on its distribution.

Detection and Classification of Haze Affected Images Using CNN Approach

Detection and classifications of the haze affected image is important for the real time multimedia data transmission and reception in remote mode in order to improve the quality of the received image or video sequences. In this paper, Convolutional Neural Networks (CNN) classification approach is used with Shearlet Transform for the detection and segmentation of haze affected images.The image to be tested for haze pattern detection is preprocessed and then it is decomposed with shearlet transform. The features are computed from the shearlet transform decomposed coefficients and then these computed features are classified by the deep learning CNN for identifying the haze affected images. This proposed haze classification method is tested on both indoor and outdoor environmental images.