A Forward Error-Tolerant Reassembly Method for Receiving Network Data
نویسندگان
چکیده
Reassembly is an important operation for receiving network data. A fragment with errors is discarded by the conventional reassembly, which renders the information content of the whole fragment lost. The paper claims that the underlying associated relationships among the network data may bring the gain of Forward Error Correction (FEC). It describes the reassembly model based on multiple long-data and proposes a Forward ErrorTolerant Reassembly method based on the Associated Relationships among the network data (FETRAR). FETRAR is able to work alone without the explicit cooperation of the sender entity or other network entities. Simulation and test results illustrate that FETRAR outperforms the conventional reassembly method (short for CR) dramatically. FETRAR constructs much more complete long-data than CR in the one-way communication. It also remarkably deceases the average repeat times of fragments under ARQ with full use of the valid information in erroneous fragments.
منابع مشابه
Global Solar Radiation Prediction for Makurdi, Nigeria Using Feed Forward Backward Propagation Neural Network
The optimum design of solar energy systems strongly depends on the accuracy of solar radiation data. However, the availability of accurate solar radiation data is undermined by the high cost of measuring equipment or non-functional ones. This study developed a feed-forward backpropagation artificial neural network model for prediction of global solar radiation in Makurdi, Nigeria (7.7322 N lo...
متن کاملA generalized ABFT technique using a fault tolerant neural network
In this paper we first show that standard BP algorithm cannot yeild to a uniform information distribution over the neural network architecture. A measure of sensitivity is defined to evaluate fault tolerance of neural network and then we show that the sensitivity of a link is closely related to the amount of information passes through it. Based on this assumption, we prove that the distribu...
متن کاملApplication of Wavelet Neural Network in Forward Kinematics Solution of 6-RSU Co-axial Parallel Mechanism Based on Final Prediction Error
Application of artificial neural network (ANN) in forward kinematic solution (FKS) of a novel co-axial parallel mechanism with six degrees of freedom (6-DOF) is addressed in Current work. The mechanism is known as six revolute-spherical-universal (RSU) and constructed by 6-RSU co-axial kinematic chains in parallel form. First, applying geometrical analysis and vectorial principles the kinematic...
متن کاملAn Unsupervised Learning Method for an Attacker Agent in Robot Soccer Competitions Based on the Kohonen Neural Network
RoboCup competition as a great test-bed, has turned to a worldwide popular domains in recent years. The main object of such competitions is to deal with complex behavior of systems whichconsist of multiple autonomous agents. The rich experience of human soccer player can be used as a valuable reference for a robot soccer player. However, because of the differences between real and simulated soc...
متن کاملPrediction of the Liquid Vapor Pressure Using the Artificial Neural Network-Group Contribution Method
In this paper, vapor pressure for pure compounds is estimated using the Artificial Neural Networks and a simple Group Contribution Method (ANN–GCM). For model comprehensiveness, materials were chosen from various families. Most of materials are from 12 families. Vapor pressure data of 100 compounds is used to train, validate and test the ANN-GCM model. Va...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- JNW
دوره 9 شماره
صفحات -
تاریخ انتشار 2014