An electrical Impedance based tool is designed and developed to aid physicians performing clinical exams focusing on cancer detection. Current research envisions improvement in sensor-based measurement technology to differentiate malignant and benign lesions in human subjects. The tool differentiates malignant anomalies from nonmalignant anomalies using Electrical Impedance Spectroscopy (EIS). This method exploits cancerous tissue behavior by using EIS technique to aid early detection of cancerous tissue. The correlation between tissue electrical properties and tissue pathologies is identified by offering an analysis technique based on the Cole model. Additional classification and decision-making algorithm is further developed for cancer detection. This research suggests that the sensitivity of tumor detection will increase when supplementary information from EIS and built-in intelligence are provided to the physician.

This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of the position and velocity, respectively. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. The proposed Kalman filter removes the effect of the gravitational force in the state-space model. As a result, the resulting error is eliminated and the resulting position is smoother and ripple-free.

The present study determines the effect of compression over bioimpedance of healthy soft tissue (in-vitro and in-vivo). Electrical impedance spectroscopy (EIS) is a promising tissue characterization and tumor detection technique that uses tissue impedance or admittance to characterize tissue and identify tissue properties as well as cell structure. Variation in EIS measurements while applying pressure suggests that compression tends to affect soft tissue bioimpedance. Moreover, the displacements in tissue caused by applied compression may provide useful information about the structure and state of the tissue. Thus combining the changes to the electrical properties of tissue resulted by applied compression, with the changes in tissue displacements caused by applied compression, and consequently measuring the effect that electrical and mechanical properties have on each other, can be useful to identify tissue structure. In this study, multifrequency bioimpedance measurements were performed on in-vitro and in-vivo soft tissue at different pressure levels. Increasing compression on the in-vitro tissue results in an increase in both extracellular resistance and membrane capacitance while it causes a reduction in the intracellular resistance. However, as the compression over the in-vivo samples increases, the intracellular and extracellular resistance increase and the membrane capacitance decreases. The in-vivo measurements on human body are also tested on contra-lateral tissue sites and similar tissue impedance variation trends are observed in the contra-lateral sites of human body. The evidence from these tests suggests the possibility of using this EIS-Pressure combined measurement method to improve tumor detection in soft tissue. Based upon the observations, the authors envision developing an advanced model based upon the Cole model, which is dependent on tissue displacements.

In this paper we propose a neuro-fuzzy (NF) control strategy to enhance desired suspension performance. The proposed method consists of two parts: a fuzzy control strategy to establish an efficient controller to improve ride comfort and road handling (RCH) and an inverse mapping model to estimate the current needed for a semi-active damper. The fuzzy logic rules are extracted based on Skyhook and Groundhook. The inverse mapping model is based on an artificial neural network and incorporated into the fuzzy controller to enhance RCH. To validate the effectiveness of the proposed NF controller, a quarter car model is adopted and numerical analysis is presented. To verify the performance of the NF controller (NFC), comparisons with existing semiactive techniques are made and two sets of results are reported. First, a sinusoidal road input is considered and time domain results are presented. Second, for the same sinusoidal input, frequency response of the developed controllers is obtained. It is shown that the developed NFC enhances RCH considerably and outperforms other existing controllers in terms of both ride comfort and handling

In this paper, the nonlinear mode coupling at 2:1 internal resonance has been studied both analytically and experimentally. A modified micro T-beam structure is proposed, and the equations of motion are developed using Lagrange’s energy method. A two-variable expansion perturbation method is used to describe the nonlinear behavior of the system. It is shown that in a microresonator with 2:1 internal resonance, the low-frequency mode is autoparametrically excited after the excitation amplitude reaches a certain threshold. The effect of damping on the performance of the system is also investigated.