To solve the problems that ant colony algorithm often gets sub optimal solution, the turning angle is too large and the convergence effect is poor when planning the global path of the robot, the traditional ant colony algorithm is improved. The grid map is applied to construct the working surrounding, the path heuristic function is designed to enhance the searching ability and convergence effect of the algorithm. Considering the influence of turning Angle on travel time, the heuristic function of turning Angle is introduced to ensure the ant colony to have more guidance in the planning and make the path smoother. Pheromone updating rules and path evaluation mechanism are established to guide ants to approach the path with the best comprehensive index. The algorithm is simulated on the MATLAB platform, the simulation indicates that the optimized algorithm has good convergence effect and strong optimization ability.
In electrical impedance tomography (EIT), different currents are applied to electrodes on the surface of an object and the resulting voltages are measured. The image of impedance (resitivity, conductivity) distribution or changes in the object is reconstructed based on these boundary measurements. EIT is a functional imaging technique, which may reveal the physiological and pathological information by human body's impedance properties. The advantages such as the non-invasive modality and the relative low cost make EIT become a research hot in medical imaging. However, the image reconstruction in EIT is a high ill-posed, non-linear, inverse problem, and it becomes a key and difficult point in EIT. In dynamic EIT, the image of impedance changes in the object is reconstructed. On the other hand, the image of impedance distribution in the object is reconstructed in static EIT, which is more difficult than in dynamic EIT. We study the reconstruction of static EIT in this paper, due to the static EIT has better clinical application prospect than the dynamic EIT.
Image reconstruction in electrical impedance tomography (EIT) is a non-linear inverse problem. The linear model is always used in most of the reconstruction algorithm in dynamic EIT, which causes large errors of image reconstruction. In this paper, we proposed a new image reconstruction method in dynamic EIT. In the method the artificial neural network based on the error back propagation algorithm (BP ANN) is used to express the non-linear relation between the impedance change position inside the measured object and the voltage change value measured on the surface of the object. Thus, the location ofthe impedance change can be decided by the measured voltage change on the surface, and then the impedance change image will be reconstructed with linear approximated method. The reconstructed error will be decreased largely, because the impedance change position can be detected precisely by our proposed method. The experimental results indicate that the precision of the reconstructed image with our method is greatly higher than that with the back projection method.
Image reconstruction in electrical impedance tomography (EIT) is a highly ill-posed, non-linear inverse problem. A new regularization method based on the spatial filtering theory to reconstruct the impedance distribution in EIT is proposed in this paper. The new regularized reconstruction for EIT is independent on the estimation of impedance distribution, so it achieves a lower implementation complexity than the maximum a posteriori (MAP) regularization method. The regularization level in our new method varies spatially so as to be suited to the correlation character of the object's impedance distribution. The computer simulation results indicate that the regularization method based on the spatial filtering theory performs better than Tikhonov regularization method in solving the ill-posed problem of dynamic EIT.
Video communication aiming at public switched telephone network (PSTN) applied with voice-band modem is attractive because of its low-cost facilities and the wide coverage of PSTN around the world, The key technique of video transmission over PSTN with voice-band modem is very low bit-rate video coding. Video coding based on discrete wavelet transform has become a hot research topic. But while in very low bit-rate video coding applications, the peak signal to noise ratio (PSNR) and the visual quality of image reconstructions are not very satisfactory by using the general orthogonal or biorthogonal wavelet which does not match well with human visual system characteristics. In this paper, a new kind of compact biorthogonal wavelet based on the modulation transfer function for human visual system model is used in very low bit-rate video coding scheme, in which a new improved Goh's 3D wavelet transform and motion compression technique are applied. The experimental results indicate that the new coding scheme using the constructed compact biorthogonal wavelet has a good performance in average PSNR, compression ratios and visual quality of image reconstruction when compared to the other motion-compensated 2D and 3D coding schemes based on the general biorthogonal wavelet transform.
As an important analysis tool, wavelet transform has made a great development in image compression coding, since Daubechies constructed a kind of compact support orthogonal wavelet and Mallat presented a fast pyramid algorithm for wavelet decomposition and reconstruction. In order to raise the compression ratio and improve the visual quality of reconstruction, it becomes very important to find a wavelet basis that fits the human visual system (HVS). Marr wavelet, as it is known, is a kind of wavelet, so it is not suitable for implementation of image compression coding. In this paper, a new method is provided to construct a kind of compactly supported biorthogonal wavelet based on human visual system, we employ the genetic algorithm to construct compactly supported biorthogonal wavelet that can approximate the modulation transform function for HVS. The novel constructed wavelet is applied to image compression coding in our experiments. The experimental results indicate that the visual quality of reconstruction with the new kind of wavelet is equivalent to other compactly biorthogonal wavelets in the condition of the same bit rate. It has good performance of reconstruction, especially used in texture image compression coding.
Electrical impedance tomography (EIT) is a functional imaging technique, which has potential application prospect in clinical diagnosis. It is well known that image reconstruction in EIT is a highly ill-posed, non-linear inverse problem. So far, various reconstruction methods have been used in EIT, among which the Newton-Raphson method is regarded as the most effective one, but it suffers the mathematical difficulty and the low resolution of the reconstruction which is far from the requirement of clinical application. In this paper, a quite different image reconstruction method for EIT is presented to solve the above problems. In the new method, a neural network such as BP is used to solve the non-linear inverse problem between the impedance variations inside body and the voltage changes measured at its surface with no need of computation of potential fields. The training sets of neural network are chosen from the solution of the forward problem of EIT in which finite element method (FEM) is used. After the non- linear relation function has been decided, the static image reconstruction can be accomplished by iteratively solving the forward problem with FEM until the voltage difference between measurement and calculation or impedance change is small enough. The provided method avoids calculating Jacobian matrix and solving ill-conditioned equations. Furthermore the resolution of the reconstructed images based on the new method is much higher than other methods with the same numbers of electrode and electrical current pattern. The computer simulation results demonstrate that the new reconstruction algorithm can be converged very quickly with a priori knowledge.
Phase-stepping interferometry has been extensively applied in optical metrology. But the phases calculated by the phase-stepping algorithm are wrapped into the range [-it, it] because of the arctangent function. The procedure of recovering the real phase from thd range [-it, it] is known as phase-unwrapping. The phase unwrapping may be a trivial problem with the cases of noisy, low modulation, corrupted regions etc. in the interferograms. The conventional algorithm always failed in those cases. Many algorithms were developed to solve the problem. A new phase unwrapping algorithm is proposed by our group. It is very suitable for the flat measurement. The new algorithm first fits the measured flat data to an ideal flat by a group of new formulas derived by ourselves. Then do operation of plus or minus 2it for the wrapped phases calculated by phase-stepping algorithm according to the ideal flat. Since the measured flat data do not affect each other in the phase unwrapping procedure, the conventional phase unwrapping problem is avoided. A flat metal plane is measured with many corrupted regions in a Linnik interference microscope. The experimental results indicate that our new algorithm is robust and fast.
A new phase measurement algorithm without phase-unwrapping problem is presented. It is mainly applied in the interferograms of few and straight fringes with noise and corrupted regions. Existing phase-unwrapping algorithms for those interferograms are trivial and time-consuming. The new algorithm first searches N 'seeding pixels' in the interferograms according to the good modulation. The interferograms are then segmented into N parts by the 'seeding pixels'. The adjacent 'seeding pixels' phase difference is limited in the range. Because the phase measurement is relative, assuming one of the 'seeding pixels' phase is constant, other 'seeding pixels' phases can be easily obtained by the phase-stepping algorithm. Moreover, other pixels phases in the interferograms can be solved by the simple operation with the 'seeding pixels' phases by the phase-stepping algorithm. The procedure of calculating phase is straightforward. The operation of plus or minus 2(pi) is unnecessary. So the phase unwrapping problem is avoided. A smooth flat mirror is measured in a Linik interference microscope. The interferograms are corrupted with many dirty spots or regions. The experimental result confirm that our new algorithm is fast and robust.
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