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Diffuse Optical Tomography (DOT) is a relatively novel, nonionizing and noninvasive technique for breast tumor imaging. Due to the diffusive nature of light propagation, the inverse problem is highly ill-posed. In this study, we propose the modified Beer-Lambert’s law that reduces the number of unknown values to reduce the ill-posedness issue. An integrated mobile DOT system is proposed. The system comprises a front-end circuit and an integrated digital signal processor. The sources and the detectors are distributed hexagonal geometry for increasing the spatial resolution. The processor included the front-end controller, the iteration algorithm engine and wireless communication controller were implemented on FPGA. The functionalities of the proposed system were validated using a breast phantom. Extensive experimental results illustrated to show the performance of the proposed system. The depth of the tumor detected by the system is 20mm.

Diffuse Optical Tomography (DOT) is a recent novel and non-invasive technology for medical imagery using near infrared light. The principle is based on the assessments of spatial variation in terms of light absorption and scattering coefficients that can be used to detect foreign bodies and tumors inside the human tissues. A previous study demonstrates a successfully reconstructed three-dimensional(3-D) image for breast tumor detection in software. Thus, the work within this study has been implemented on an integrated mobile DOT system which comprises a light sensors array front-end circuit, an analog to digital converter, a Bluetooth wireless communication module to send the results to the GUI interface on a remote displaying device. The functionalities of the proposed system were validated using an experimental static human breast phantom. The experimental results show that the proposed system can detect a tumor of the size of 0.445cm2at a depth of 20mm from the surface of the skin.

Diffuse Optical Tomography (DOT) is a recent novel and non-invasive technology for medical imagery using near infrared light. The principle is based on the assessments of spatial variation in terms of light absorption and scattering coefficients that can be used to detect foreign bodies and tumors inside the human tissues. However, the current DOT technology is limited to a two-dimensional image reconstruction. In this paper, we propose an innovative three-dimensional nonlinear reconstruction algorithm to improve spatial and temporal resolutions. This algorithm has been implemented on an integrated mobile DOT system which comprises a light sensors array front-end circuit, an analog to digital converter, a Bluetooth wireless communication module to send the results to the GUI interface on a remote displaying device. The functionalities of the proposed system were verified using an experimental static human breast phantom. The experimental results show that the proposed system can detect a tumor of the size of 0.445cm2 at a depth of 2cm from the surface of the skin.

This paper presents a new concept of Space-Airborne Bi-Static Linear Array SAR (SA-BiLASAR) imaging using array antenna, which inherited the advantages of conventional linear array antenna 3-D SAR and Bi-Static SAR. Firstly, the geometrical model and the signal model are established. Secondly, a time domain back projection imaging algorithm is presented for SA-BiLASAR data processing. In the end, the feasibility of the SA-BiLASAR and the imaging algorithm are demonstrated by simulated results.

Due to its great potential value in theory and application, nonlinear control strategies of doubly fed induction generator (DFIG), especially passivity-based control (PBC) methods, have been a focus in the literature. A PBC strategy for DFIG with time-varying load torque to track flux and speed trajectories is proposed in this paper. The key point of this method is the identification of terms, known as workless forces, which appear in the dynamic equations of DFIG but have no effect on the energy balance equation of the closed loop. The speed of DFIG is estimated via an ANN estimator avoiding the use of mechanical sensors. PBC combined with ANN speed estimator not only reserves the advantages of PBC such as nonexistence of singularity, but also rejects the flux and speed tracking error caused by stator and rotor resistance variation. This algorithm simplifies the control structure and enhances the robustness of the control system. The feasibility and effectiveness is testified by the experimental results based on dSPACE.