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Compressed Sensing (CS) attempts to acquire and reconstruct a sparse signal from a sampling much below the Nyquist rate. In this paper, we proposed novel CS algorithms for reconstructing under-sampled and compressed electrocardiogram (ECG) signal. In the proposed CS-ECG scheme, the ECG signal was first sub-sampled randomly and mapped onto a two-dimensional (2D) space by using Cut and Align (CAB), for the purpose of promoting sparsity. A nonlinear optimization model was then used to reconstruct the 2D signal. In the compression scheme, the ECG signal was mapped into the frequency domain, and the compression was achieved by a series of multiplying and accumulating between the original ECG and a Gaussian random matrix. For the reconstruction, two matching pursuits (MP) methods and two blocks sparse Bayesian learning (BSBL) methods were implemented and evaluated by the percentage rootmean-square difference (PRD). Based on the test with real ECG data, it was found that the proposed CS scheme was capable of faithfully reconstructing ECG signals with only 30% acquisition.INDEX TERMS Compressed sensing (CS), compression, electrocardiogram (ECG), reconstruction, subsampling.

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Abstract：To research the non-uniform temperature field of power battery for electric vehicle during charge/discharge operation, using the method combining numerical simulation and experiment, based on the characteristic of temperature-rise in battery caused by its internal resistance, coupling thermal effect of anode and cathode, the time-varying heat source model is established to obtain more accurate and dynamic changing distribution of battery temperature field. A lithium-ion power battery for vehicle is taken as a sample, three-dimensional temperature field and temperature rise calculation of battery cell and module are analyzed, and the corresponding experiments are taken. Results show that, the temperature rise of discharging significantly greater than that of charging at the same charge/discharge rate, and the maximum temperature difference of cell increases with the rising of rate; The temperature rise of the battery is a nonlinear process that first increases then becomes constant varying with the time, and is higher with the increase of discharge rates; The temperature field of battery module is not a simple superposition with the temperature field of battery cells, and the thermal consistency of battery module is not as good as that of battery cell under the same charge/discharge rate.Key words：lithium-ion power battery；time-varying heat source model；temperature field 0 前言 1

BackgroundMulti-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets.ResultsThis paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions.ConclusionsThe proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.