No abstract

Electromagnetic tomography (EMT) is used to create tomographic images of the electrical properties of conducting material based on electromagnetic measurements from coils evenly distributed around the imaging region. EMT is widely used in industrial and biomedical fields for which it offers the advantages of being non-contact, fast, and non-radiative. Most EMT measurement systems are implemented with commercial instruments, such as impedance analyzers and lock-in amplifiers, which are bulky and inconvenient for portable detection devices. In order to improve the portability and extensibility, a purpose-built flexible and modularized EMT system is presented in this paper. The hardware system consists of six parts: the sensor array, signal conditioning module, lower computer module, data acquisition module, excitation signal module, and the upper computer. The complexity of the EMT system is reduced by a modularized design. The sensitivity matrix is calculated by the perturbation method. The split Bregman algorithm is applied to solve the L1 norm regularization problem. The effectiveness and advantages of the proposed method are verified by numerical simulations. The average signal to noise ratio of the EMT system is 48 dB. Experimental results verified that the reconstructed images can show the number and positions of the imaging objects, demonstrating the feasibility and effectiveness of the novel imaging system design.

Electromagnetic tomography (EMT) is a versatile tomographic imaging technique for reconstruction of conductivity and/or permeability distribution due to the advantages of non-contact, non-intrusive, lowcost, simple structure and fast imaging. However, the ill-posed and ill-conditioned features of EMT make it difficult to obtain high quality reconstructed images. To improve the spatial resolution of the high conductivity medium imaging, the L1-L1 framework objective function is presented, which uses L1 norm as both data fidelity term and regularization term to weaken the influence of the data outliers and impose the sparsity feature of the measured objects. An improved Split Bregman method is proposed to solve the complicated optimization problem efficiently, which splits it into several simple sub-tasks.Each subtask can be solved by adopting the proper method. Besides, an acceleration strategy is introduced to improve the convergence rate. Numerical simulations are used to verify the effectiveness and competitive performance of the proposed improved method. The experiments are carried out by the designed modularized EMT system to further verify the effectiveness of the proposed method. The reconstructed images can precisely show the number and positions of the measured objects.