The network coding can spread a single original error over the whole network. The simulation shows that the propagated error mostly all the time pollute just 100% of the received packets at the sink if Hamming distance is adopted. If subspace codes are adopted, usually the propagated error will not pollute 100% of the received packets in the sense of subspace distance. However, it also usually pollutes 90% of received packets which is a high error ratio. Even if the rank code and the subspace code are adopted, these existing schemes based on traditional block codes can correct corrupted errors no more than C/2 because of the limitation of the block coding where C is the max flow min cut. It is an agent to find a dense error correction method in random network coding. List decoding of subspace codes can correct $$ \frac{C}{k}\hbox{-} 1 $$ errors where k is the size of information. When $$ \frac{C}{k} $$ is big, many errors in the sense of subspace distance can be corrected. However, the solution of list decoding is not unique. John Wright proposed a dense error correction technique based on L1 minimization, which can recover nearly 100% of the corrupted observations. In our proposal, the original packets are coded with John Wright's coding matrix, and then, the coded message is coded again with subspace codes. In the sink, the decoding procedures about list decoding of subspace codes and John Wright's scheme are performed. At last, the unique solution is achieved even though there are dense propagated errors in random network coding.
A new method is put forward after analyzing the problem of traditional subjective trust valuation model,and the improved trust valuation model is applied to web service environment.Make the web service entity within environment can automatically renew with the increase of bargain number.Finally,simulation experiments show that the improved trust valuation model can better reflect the entity trust degree in web service.
One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than C/2 corrupted errors where C is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than C/2 corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve John and Yi's model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments.
Traditional principal component analysis (PCA) which is based on linear transformation is an effective method that is used to reduce the dimension of seismic attributes. However, when there exit nonlinear attributes in original data, the principal component which is extracted using the PCA method may not reflect these nonlinear relationships. While kernel principal component analysis (KPCA) based on original data can extract nonlinear relationships. In this paper the KPCA is introduced and used in seismic attribute dimension reduction. The research result shows that the KPCA has excellent performance of feature extraction.
As the carrier of knowledge and the basis of teaching, teaching materials affect the teaching quality of schools. However, the selection of teaching materials in colleges and universities is in a dilemma because of the variety of teaching materials and the uneven quality of teaching materials. Blockchain has gradually drawn attention from various fields, due to its secure storage, non-forgery and non-tamper features. The combination of blockchain and teaching material evaluation can provide fair and transparent grading and evaluation for teaching materials, which is of great reference value for colleges and universities to select teaching materials. This paper proposes a reputation incentive mechanism for teaching material evaluation blockchain based on game theory, analyzes the behavior of users in the scoring stage of teaching material evaluation, and gives the Nash equilibrium solution. Through the numerical simulation experiment, the effectiveness of the incentive mechanism and the reliability of the teaching materials' evaluation results are proved.
To achieve a digital access to measurement of relative attitude between two separate objects, a separable digital protractor based on IMU is designed. The protractor consists of a pair of arms, with an IMU and a Bluetooth applied on each of them. Data of the two IMUs are transmitted to a processor on one arm and are used for IMU pose estimation. Since the IMU is rigidly tied to the arm, each estimated IMU pose is just that of the attached arm. By comparing the poses of the arms, we can achieve measurement of their relative attitude. Quaternion and Kalman Filter are used for pose representation and estimation respectively. Experiments are conducted on the accuracy and feasibility. Results show the angle measurement error is less than 2o, which is within the tolerance of most applications. With the help of wireless technology, the proposed protractor can bring lots of convenience in angle measurement for separate objects.
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