Application of artificial neural networks to predict multiple quality of dry-cured ham based on protein degradation.

This study investigated protein degradation and quality changes during the processing of dry-cured ham, and then established the multiple quality prediction model based on protein degradation. From the raw material to the curing period, proteolysis index of external samples were higher than that of internal samples, however, the difference gradually decreased from the drying period to the maturing period. Protein degradation can be used as indicators for controlling quality of the hams. With protein degradation index as input variables, the back propagation-artificial neural networks (BP-ANN) models were optimized, with training function of trainlm, transfer function of logsig in input-hidden layer and tansig in hidden-output layer, and 20 hidden layer neurons. Furthermore, the relative errors of predictive data and experimental data of 12 samples were approximately 0 with the BP-ANN model. Results indicated that the BP-ANN has great potential in predicting multiple quality of dry-cured ham based on protein degradation.

An adaptive mechanism to guarantee the bandwidth fairness of TCP flows.

End-to-end TCP (transmission control protocol) congestion control can cause unfairness among multiple TCP connections with different RTT (Round Trip Time). The throughput of TCP connection is inversely proportional to its RTT. To resolve this problem, researchers have proposed many methods. The existing proposals for RTT-aware conditioner work well when congestion level is low. However, they over-protect long RTT flows and starve short RTT flows when congestion level is high. Due to this reason, an improved method based on adaptive thought is proposed. According to the congestion level of networks, the mechanism can adaptively adjust the degree of the protection to long RTT flows. Extensive simulation experiments showed that the proposed mechanism can guarantee the bandwidth fairness of TCP flows effectively and outperforms the existing methods.

On service differentiation in mobile Ad Hoc networks.

A network model is proposed to support service differentiation for mobile Ad Hoc networks by combining a fully distributed admission control approach and the DIFS based differentiation mechanism of IEEE802.11. It can provide different kinds of QoS (Quality of Service) for various applications. Admission controllers determine a committed bandwidth based on the reserved bandwidth of flows and the source utilization of networks. Packets are marked when entering into networks by markers according to the committed rate. By the mark in the packet header, intermediate nodes handle the received packets in different manners to provide applications with the QoS corresponding to the pre-negotiated profile. Extensive simulation experiments showed that the proposed mechanism can provide QoS guarantee to assured service traffic and increase the channel utilization of networks.