Earticle

Reliability is very important in wireless network since large number of wireless standards are widely used in our daily life. The network flow ratio and workload will increase significantly as well. It is observed that network manager is not able to ensure the reliability of the network even if the network connection is smooth. This paper proposes a network reliability evaluation model using factorization approach for the small and medium-sized wireless communication network. The factorization approach is a decomposition of an object into a product of other objects or factors, which when multiplied together give the original for example a number a polynomial or a matrix. With the model, the solution algorithms are proposed to work out the corresponding defined objectives. Experiments show that, the proposed model outperforms the ergodic method which uses large number of loops to obtain the network reliability. From the experiment, when Pc = 0.9 and Pm = 0.1 as well as Pc = 0.9 and Pm = 0.01, it could be find that there are six transmission lines which are with the maximum reliability.

Mobile crowd-sensing (MCS) is a promising sensing paradigm that leverages mobile users with smart devices to perform large-scale sensing tasks in order to provide services to specific applications in various domains. However, MCS sensing tasks may not always be successfully completed or timely completed for various reasons, such as accidentally leaving the tasks incomplete by the users, asynchronous transmission, or connection errors. This results in missing sensing data at specific locations and times, which can degrade the performance of the applications and lead to serious casualties. Therefore, in this paper, we propose a missing data inference approach, called missing data approximation with probabilistic tensor factorization (MDI-PTF), to approximate the missing values as closely as possible to the actual values while taking asynchronous data transmission time and different sensing locations of the mobile users into account. The proposed method first normalizes the data to limit the range of the possible values. Next, a probabilistic model of tensor factorization is formulated, and finally, the data are approximated using the gradient descent method. The performance of the proposed algorithm is verified by conducting simulations under various situations using different datasets.

This paper addresses the factorization method to estimate the projective structure of a scene from feature (points) correspondences over images with occlusions. We propose both a column and a row space approaches to estimate the depth parameter using the subspace constraints. The projective depth parameters are estimated by maximizing projection onto the subspace based either on the Joint Projection matrix (JPM) or on the the Joint Structure matrix (JSM). We perform the maximization over significant observation and employ Tardif's Camera Basis Constraints (CBC) method for the matrix factorization, thus the missing data problem can be overcome. The depth estimation and the matrix factorization alternate until convergence is reached. Result of Experiments on both real and synthetic image sequences has confirmed the effectiveness of our proposed method.

고속아다마르변환은 Cooley-Tukey 알고리즘에 의해서 발표되어졌고, 그것은 메트릭스 분할 또는 계승의 기술에 의한 것이다. 본 보문은 단순한 기생메트릭스를 크로넥커 적에 의해 앞단과 연결시켜가면서 고속아다마르 변환을 보였다. 이것은 기존에 발표된 방법에 비해 쉽게 기생메트릭스를 구할 수 있는 것을 확인했고 수학적으로 완전함을 증명했다.

The development of the FHT (fast Hadamard transform) was presented and based on the derivation by Cooley-Tukey algorithm. Alternately, it can be derived by matrix partitioning or matrix factorization techniques. This paper proposes a simple sparse matrix technique by Kronecker product of successive lower Hadamard matrix. The following shows how the Kronecker product can be mathematically defined and efficiently implemented using a matrix factorization methods.

This paper presents a simple factorization of the Hadamard matrix which is used to develop a fast algorithm for the Hadamard transform. This matrix decomposition is of the kronecker products of identity matrices and successively lower order Hadamard matrices. This following shows how the Kronecker product can be mathematically defined and efficiently implemented using a factorization matrix methods.