Control and Monitoring System for Intelligent Network Devices

Abstract

Nowadays, the informatics technologies have applied in almost every fields such as banking, retail, transportation, public safety/law enforcement, and education. Whilst, all information of process will be need to validate and verify as the requirement. Therefore, the monitoring becomes the important technique to keep the required standard of

products or services. There are many organizations using the intelligent devices to monitor for their business profit as well as the security protection on their properties. The intelligent motion detect has become important technique in many monitoring process such as transportation, security, or checking point. Almost every devices have applied into their products to recognize the correct performance of product. These detections may be mixed together between several signals and

difference information of the images, voices, and multi-transmission technique. However, this is very difficult methodology to identify these integrated signals during their online transceiver process. Whilst, this becomes important and necessary to

identify between client and server. There are not many publications in this problem. Radial basis function neural networks can be employed to recognize these integrate signals as classification action. With objective of this monitoring can be merged the remote sensing with identification system in function to maintain

their safety, and also for authorize tracking on their customer demographics. It can be also to identify the intruder or attacker. They would be following the plan to defraud their organization. This becomes important problem, and there are not

many researches to publish for this solving. There are not many research have been done on this personal detection and authorize details tracking. Traditional statistical classification procedures such as discriminant analysis are built on the

Bayesian decision theory [1]. Neural network is one of popular technique and can be emerged for conventional classification as an important tool. The neural classification has established with its advantage in the following theoretical aspects as selfadaptive

methods, which can be adjusted themselves to the data without any explicit specification of functional. Since any classification procedure seeks a functional relationship between the group membership and the attributes of the object,

which can provide the basis for establishing classification rule and performing statistical analysis [2]. Classification is one of the most frequently encountered decision making tasks of human activity as monitoring.

On the other hand, the effectiveness of neural network classification has been tested empirically. Neural networks have been successfully applied to a variety of real-world classification tasks in industry, business and science [3]. Applications include

bankruptcy prediction [4], [5], [6], handwriting recognition [7], [8], speech recognition [9], product inspection [10], fault detection [11], medical diagnosis [12], [13], and retail market & consumer [14]. This paper is to present the technique to classify the integrated signal during real-time online process as monitoring to recognize the requested location and identify on their characteristics of signal.