The invention discloses a multi-target shuffled frog-leaping algorithm based on multilevel message feedback The multi-target shuffled frog-leaping algorithm comprises the steps of optimizing a standard shuffled frog-leaping optimizing layer, a frog evolving and learning layer and an external file information exchanging layer, wherein the standard shuffled frog-leaping optimizing layer is used for obtaining new positions of the frog and comparing advantages and disadvantages of old positions with those of the new positions; entering the frog evolving and learning layer if the new positions are inferior to the old positions; obtaining Pareto domination solutions in the optimizing process and storing the Pareto domination solutions in an external file; extracting a certain number of non-domination solutions to conduct information exchange from the external file according to a preset strategy after every arriving ends so as to improve the qualities of the solutions in the file and provide a globally optimal solution for later frog optimizing and frog evolving and learning The multi-target shuffled frog-leaping algorithm based on the multilevel message feedback has the advantages of being visual, simple, clear, universal and the like, making good use of complete information of the positions of the frog in the arriving process, and powerfully strengthening the frog ability of jumping out of the locally optimal solution

Multi-target shuffled frog-leaping algorithm based on multilevel message feedback

The invention discloses a resource optimization method for maximizing the total throughput of network function virtualization. A network function virtualization architecture comprises a logic layer, avirtual layer, a physical layer and a management arrangement system, the logic layer comprises logic nodes and controllers, and the physical layer comprises a physical node; the logic nodes and controllers send information signals to a base station via an orthogonal channel through the corresponding physical node; the base station calculates the number of effective load bits, the number of used channels, transmitting power, a receiving signal to noise ratio, throughput and total transmitting energy of each logic node and each controller; and when the third number, the number of effective loadbits, the receiving signal to noise ratio, the first total transmitting energy and the second total transmitting energy meet corresponding requirements, and the logic nodes and the controllers meet the ultra-reliability requirements, the management arrangement system adaptively allocates the transmitting power and the number of used channels based on a hybrid optimization algorithm to maximize the total throughput.

Resource optimization method for maximizing total throughput of network function virtualization

The invention relates to a software and hardware division technology in system software and hardware collaborative design and provides an improved software and hardware division method applied to software and hardware collaborative design. The method is improved mainly for solving the problems that when a shuffled frog-leaping algorithm is applied to software and hardware division, convergence speed is low, and the algorithm is possibly trapped into local optimum, and therefore a software and hardware division algorithm with better performance than an original frog-leaping algorithm is proposed. According to the technical scheme, the software and hardware division method based on the improved shuffled frog-leaping algorithm comprises the steps that (1) a frog population is initialized; (2) G equidistant center coordinates are determined according to a frog group number G and a task node number N; (3) iterative updating is started; (4) the distance from the position coordinate of each frog to each center coordinate is calculated in sequence according to a made order; and (5) the position coordinate of the optimal frog is output to serve as an optimal software and hardware division scheme. The method is mainly applied to a software and hardware division occasion in software and hardware collaborative design.

Software and hardware division method based on improved shuffled frog-leaping algorithm

The embodiments of the present invention disclose an audio signal reconstruction method and device. The method includes the following steps that: compressed data corresponding to at least two audio signals are acquired; inverse quantization is performed on the compressed data corresponding to at least two audio signals so as to obtain measurement data corresponding to the at least two audio signals; a measurement matrix is obtained, sparse transformation coefficients corresponding to the at least two audio signals are jointly reconstructed according to the measurement data and measurement matrix corresponding to the at least two audio signals; and inverse sparse transformation is performed on the sparse transformation coefficients corresponding to the at least two audio signals, so that the at least two audio signals can be obtained. With the audio signal reconstruction method and device of the embodiments of the present invention adopted, the quality of the audio signals can be improved.

Audio signal reconstruction method and device

The invention discloses a software and hardware classifying method based on an improved PBIL algorithm. The method comprises following steps: 1, initializing a probability vector; 2, carrying out fitness estimation to Nth individual in the Ith generation population of the PBIL algorithm; 3, if the average fitness of the Ith generation population is less than the average fitness of an I-1th generation population, adding up 1 by a Threshold, or wherein the Threshold is equal to 0; 4, judging whether the Threshold is equal to Limit; 5, selecting front M individuals with good fitness from N individuals; 6, updating learning factors; 7, updating the probability vector of an I +1th generation; 8, if the number of iterations of the PBIL algorithm is less than a maximum evolution algebra, turning to 2, or turning to 9; 9, ending the software and hardware classifying method of the improved PBIL algorithm. According to the method of the invention, the system cost of the simulation algorithm in aspect of executing time is reduced; the system design quality is greatly improved; the embedded system is finished efficiently; and the method has relatively good performance expression and stability in solving the software and hardware classifying problem.

Software and hardware classifying method based on improved PBIL (Population-based Increased Learning) algorithm

The invention provides a shuffled frog-leaping based division method of software and hardware. The method comprises the steps of determining the maximum iterations of the algorithm at the initial; calculating the area and time cost of the hardware in the corresponding software and hardware division scheme corresponding to each frog; descending and sequencing the whole frog group according to the adaptive fitness of individual fog; updating the fog groups in each group at a plurality of times to enable the adaptive fitness of the worst frog in the group to be prior to that of the previous frog as well as meeting the system limitation condition; updating the optimal frog in the group by the random updating method in case of failing to update; shuffling the whole frog groups; finding the global optimal solution as the optimal software and hardware division scheme after the iteration is done. According to the method, the sub-task of one embedded composite system is divided through a processor, and meanwhile, the searching and the optimizing capacity of the system are improved; the optimal software and hardware division scheme can be found, and as a result, the task performing efficiency of the system can be increased.

Shuffled frog-leaping based division method of software and hardware

The invention discloses a spatial audio encoding-decoding method based on sparse expression, comprising steps of constantly collecting audio signal matrixes of M*N*t at different moments, wherein an audio signal matrix of every moment is an M*N dimension audio signal matrix consisting of audio signal values of the audio channels of the moment, wherein M and N are integers bigger than 0 and t is a number of the sampling moment, making an equation of X=M*N, performing encoding on the X*t audio signal matrix to obtain the audio signal matrix, the dimension of which is smaller than X*t, transmitting the audio signal matrix having the dimension smaller than the X*t to a decoding terminal, and utilizing compressed sensing to perform decoding reconstruction on the audio signal matrix having the dimension smaller than the X*t by the decoding terminal to recover the original audio signal The spatial audio encoding-decoding method based on sparse expression can simplify the microphone array needed for signal transmission, can collect the signals having strong correlation, can effectively solve problem that the signal correlation is poor, the sparseness result is not ideal and data transmission needed is substantial, and can effectively finish the mission of spatial audio encoding

Spatial audio encoding-decoding method based on sparse expression

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Shuffled frog-leaping based division method of software and hardware

Spatial audio encoding-decoding method based on sparse expression