In the automatic-discovery process of smart home network devices, serious jams occur due to randomly and independently choosing delay time to send service response message. In order to solve this problem, taking Universal Plug and Play (UPnP) service discovery protocol as an example, considering different demands of reliability and real-time performance, a random service system model based on UPnP service discovery was proposed. A profit-loss function including system response index and waiting index was designed. Finally, the relation between the best length of buffer queue and the profit-loss coefficient was obtained. Through the comparison of arrival time, departure time, waiting time and travel time with different buffer queue lengths, the necessity of designing profit-loss function and the feasibility of this proposed model are verified.

In view of maintenance difficulties and high cost in large-scale development of Wireless Local Access Network (WLAN), the Control and Provisioning of Wireless Access Points (CAPWAP) protocol that applied to communication between Access Controller (AC) and Wireless Terminator Point (WTP) was researched and implemented. In Linux environment, main features were realized, such as state machine management, and WTP centralized configuration. A platform of WLAN centralized management system based on local Medium Access Control (MAC) framework was built up. Wireshark capture tool, Chariot and Iperf were used to test the platform. The capture test results verify the feasibility of the framework, and the results of throughput and User Datagram Protocol (UDP) test also show that network performance is efficient and stable.

To achieve automatic analysis for different types of ElectroCardioGraph (ECG), a sequential screening method for maximum value was brought to detect R wave, while Support Vector Machine (SVM) was used to identify arrhythmia heart beats finally. The localization algorithm based on mathematical morphology combined with characteristics of ECG defined R-wave screening interval to avoid threshold selection in traditional algorithm. After R-peaks being positioned, various types of arrhythmia heart beats were extracted with R wave crest as its center and classified by selecting Radial Basis Function (RBF) or SVM. The results of the simulation experiment on the MIT-BIH database files indicate that this algorithm acquired high relevance ratio at 99.36% for ECG with different types of heart beats. After learning, the SVM can effectively identify as many as 4 types, such as atrial premature beat, premature ventricular beat, bundle branch block and normal heart beat, the overall recognition rate is 99.75%.