Abstract: An apparatus for downhole multi-dimensional imaging includes an acquisition unit configured to acquire a formation resistivity signal, an ultrasonic echo signal and an orientation signal regularly; a sector calculation unit configured to calculate, based on said orientation signal, a sector where a currently acquired signal is from; and a multi-dimensional imaging unit, configured to calculate, based on the signals acquired by the acquisition unit, data of resistivity, distance from a drilling tool to a borehole wall and ultrasonic echo amplitude, and distribute said data into all sectors for feature recognition and extraction, thus obtaining key features characterizing a current formation being drilled, said key features being transmitted to ground for guiding drilling process. The structural complexity and the length of the downhole imaging measurement instrument can be reduced, and feature recognition can be directly performed on the imaging data underground.

Abstract: A system and method to screen for malignant gliomas, other brain tumors, and brain injuries use disturbance coefficient, differential impedances, and artificial neural networks. The system uses prescribed excitation signals with several system configurations to measure the differential impedances, calculate harmonic responses and nonlinearity of brain tissue, and estimate the disturbance coefficient that indicates the likelihood of malignant gliomas, other brain tumors, and brain injuries. The disturbance coefficient is a weighted sum of many parameters such as receiving differential impedances, transmission differential impedances, harmonic responses, frequency dispersion, and nonlinear responses using different system configurations and different excitation signals. The method includes arranging the transmitters, receivers, and transmission lines to maximize the sensitivity of detecting brain tissue condition.

Abstract: A system and method to screen for malignant gliomas, other brain tumors, and brain injuries use disturbance coefficient, differential impedances, and artificial neural networks. The system uses prescribed excitation signals with several system configurations to measure the differential impedances, calculate harmonic responses and nonlinearity of brain tissue, and estimate the disturbance coefficient that indicates the likelihood of malignant gliomas, other brain tumors, and brain injuries. The disturbance coefficient is a weighted sum of many parameters such as receiving differential impedances, transmission differential impedances, harmonic responses, frequency dispersion, and nonlinear responses using different system configurations and different excitation signals. The method includes arranging the transmitters, receivers, and transmission lines to maximize the sensitivity of detecting brain tissue condition.