Guizhi Xu

Analysis of Wireless Energy Transmission for Implantable Device Based on Coupled Magnetic Resonance

WiTricity is a new technology for transmitting energy wirelessly via resonant coupling in the non-radiative near-field. In this paper, design of energy transmission system for implantable devices base on WiTricity is performed, the influence of system structural parameters on the energy transmission efficiency is investigated, the relations between the energy transfer efficiency with the transmission distance are analyzed, and also the frequency values of resonant coils with different parameters have been figured out.

Optimization of Array Magnetic Coil Design for Functional Magnetic Stimulation Based on Improved Genetic Algorithm

The human brain can be stimulated noninvasively by strong pulses of magnetic field that induce a flow of current in the tissue, leading to the excitation of neurons. Hence, transcranial magnetic stimulation (TMS) has a wealth of applications in the research, diagnosis and therapy of the brain. The key-point in the stimulation process is to ensure that the effect of the eddy currents evoked in the brain are meeting therapeutical expectations. However, the structure and parameters of the magnetic coils (MC) have very poor relationship with the TMS ability to specifically stimulate the target tissue without activating the surrounding tissues. In this paper, a cone-shaped coil, which consists of two circular coils with a cross angle, is studied and discussed. Furthermore, an array-coil unit which consists of 7 circular coils is also analyzed using an improved adaptive genetic algorithm (IAGA) to search for the optimal parameters of the coils in order to obtain the desired deep and sharp distribution of magnetic fields in deep magnetic stimulation.

Tumor Detection in MR Images Using One-Class Immune Feature Weighted SVMs

Tumor detection using medical images plays a key role in medical practices. One challenge in tumor detection is how to handle the nonlinear distribution of the real data. Owing to its ability of learning the nonlinear distribution of the tumor data without using any prior knowledge, one-class support vector machines (SVMs) have been applied in tumor detection. The conventional one-class SVMs, however, assume that each feature of a sample has the same importance degree for the classification result, which is not necessarily true in real applications. In addition, the parameters of one-class SVM and its kernel function also affect the classification result. In this study, immune algorithm (IA) was introduced in searching for the optimal feature weights and the parameters simultaneously. One-class immune feature weighted SVM (IFWSVM) was proposed to detect tumors in MR images. Theoretical analysis and experimental results showed that one-class IFWSVM has better performance than conventional one-class SVM.

Complexity Analysis of Magnetic Stimulation at the Acupoint of Zusanli (St36) on EEG

Magnetic stimulation has become a very topical research area in brain studies because of its non-invasiveness, painlessness and security. It is well known that acupuncture has many varied physiologic effects on a number of functional systems of human subjects. In this paper, the EEG is recorded while the acupoint of Zusanli (St36) is stimulated by excitations of the same magnitude at different frequencies (0.5 Hz, 1 Hz, 3 Hz). Differences of EEG on the pre- and post-stimulation of the acupoint with magnetic field based on complexity analysis are reported. The data are analyzed with sample entropy (SampEn) and illustrated using brain information mapping (BIM). The results indicate that the SampEn of all the data can be used to distinguish the magnetically stimulated states from the normal state. It is also observed that while all frequencies can induce increases of SampEn over the whole brain, with the most obvious changes being found in the temporal lobe. This observation is consistent with the responses of human to acupuncture. The change of SampEn has significant statistical significances with a stimulation at 3 Hz, even though the effect is less obvious with other frequencies, when compared with the brain activities in normal state (p<0.05).

Enhanced acoustic emission detection induced by electromagnetic stimulation with external magnetic field

The application of external magnetic field in the electromagnetically induced acoustic emission technique (EMAE) can enhance the ability of detecting the defects and reduce the stimulation current flow in the coil, due to the even bigger deformations generated by the enhanced Lorentz Force at the defects. In this paper, the finite element model of EMAE including an external magnetic field has been implemented based on the concrete experiment models. The samples deformation has been analyzed when the exciting coil locates at different position. The results of experiments indicate that the defect itself can generate the modulated acoustic emission. That could be used to enhance detection ability of small cracks in the thin-walled metallic structures.

Complexity Analysis of EEG Under Magnetic Stimulation at Acupoints

A large number of studies reveal that repetitive TMS (rTMS) can have strong influences on cortical functions, even though the effect is very dependent on the stimulation parameters. At the same time, it is commonly acknowledged that acupuncture plays an important role in traditional Chinese medicine, and it is indeed receiving more and more attentions from the world. It is likely that a combination of acupuncture and magnetic stimulation which is non-invasive, painless and secure could give rise to treatments with substantial medical significance. This study discusses (a) the difference of EEG with pre- and post-stimulations at two different acupoints including Zusanli (St36) and Hegu (LI4) with three different frequencies (0.5 Hz, 1 Hz and 3 Hz) based on complexity analysis; (b) the character of the somatosensory evoked potentials(SEPs) in response to acupoint stimulation (Hegu) and mock point stimulation. The result indicates that different acupoints can induce different brain activity under the same stimulation correlated with the physical properties of the acupoints; and P150 can be observed after acupoint stimulation whereas mock point stimulation does not produce the same effect.

A Numerical Computation Forward Problem Model of Electrical Impedance Tomography Based on Generalized Finite Element Method

Electrical impedance tomography (EIT) is a low-cost non-invasive imaging modality. It has the potential to be of great value in clinical diagnosis. One of the major problems in EIT with complex geometry shape is its high demand in computation capability, power, and memory. A generalized finite element method (GFEM) is proposed to calculate the forward problem accurately. Compared with the traditional FEM, a smaller number of nodes and elements with the proposed method are required to achieve the same accuracy in our numerical computation model. The value of signal-to-noise ratio for two-order GFEM is 47 dB, and 10 dB for conventional FEM. The results demonstrate the efficiency of the GFEM in EIT simulation. In the forward solution, it is capable of achieving better accuracy using less computational time and memory with GFEM.

Design and FEM Analysis of a New Distributed Vernier Traveling Wave Induction Heater for Heating Moving Thin Strips

A new traveling wave induction heating system with distributed windings and vernier structure (DV-TWIH) is proposed. That system is specially designed to address the inhomogeneous eddy current density problem that dominates the pattern of thermal distribution on the surface of work. The typical traveling wave induction heating (TWIH) system is replaced by double-layer vernier combined ones, which enables the magnetic fluxes generated by each phase to interact and complement each other to compensate for local weak magnetic fluxes in order to generate more uniform and concentrated eddy current density and temperature distribution in the work piece. In order to study the performance of the proposed DV-TWIH system, an interpolative FEA modeling method is introduced in this paper. Calculation results of the proposed systems are compared to those of typical TWIH and the traveling wave induction heating system with distributed windings but without vernier structure (D-TWIH) devices. Analytical analysis shows the attractive performance of the proposed system.

Development of acupuncture-reading with EEG, MRI and PET

Acupuncture has been used as a therapy for several thousand years and is now being used as an alternative treatment for many medical conditions. It offers multiple applications. Its primary use includes alleviation of pain, reduction of inflammation and improvement of sleep disturbances. It is one of the first complementary and alternative medicine modalities to be incorporated in an integrative approach to care. However, the mechanism of therapeutic effects with acupuncture is still unclear and the theory underlying acupuncture is still controversial. Now biomedical engineering is applied to research the mechanism of therapeutic effects with acupuncture. In this article, the newest methods including electroencephalogram, Functional magnetic resonance imaging, Positron emission tomography, which was used to obtain information from the nervous system to determine how acupuncture works have been surveyed. Most of the methods show the acupuncture at acupoint induces specific patterns of brain activity, and these patterns may relate to the therapeutic effects of acupuncture.

Tissue Detection in MR Images Based on an Improved SVM

In the brain Magnetic Resonance (MR) images, the boundary of each encephalic tissue is highly irregular. It is difficult to accurately detect the encephalic tissues. Owing to its powerful capacity in solving non-linearity problems, Support Vector Machine (SVM) has been widely used in object detection. The conventional SVMs, however, assume that each feature of a sample has the same importance degree for the detection result, which is not a true representation of real applications. In addition, the parameters of the SVM and its kernel function also affect detection result. In this study, Immune Algorithm (IA) was introduced in searching for the optimal feature weights and the parameters simultaneously. An Immune Feature Weighted SVM (IFWSVM) method was used to detect encephalic tissues in MR images. Theoretical analysis and experimental results showed that the IFWSVM has better performance than the conventional methods.