Frequency and phase modulation performance of an injection-locked CW magnetron.

It is demonstrated that the output of a 2.45-GHz magnetron operated as a current-controlled oscillator through its pushing characteristic can lock to injection signals in times of the order of 100-500 ns depending on injection power, magnetron heater power, load impedance, and frequency offset of the injection frequency from the natural frequency of the magnetron. Accordingly, the magnetron can follow frequency and phase modulations of the injection signal, behaving as a narrow-band amplifier. The transmission of phase-shift-keyed data at 2 Mb/s has been achieved. Measurements of the frequency response and anode current after a switch of phase as a function of average anode current and heater power give new insight into the locking mechanisms and the noise characteristics of magnetrons

Students\u27 Attitudes Towards Statistics in Medical Research: A Comparison of Four Health Sciences Programs

Driven by a market that is imposing greater scrutiny on health care providers as well as by an explosive increase in health-related research, there is a growing need for an improved understanding of statistical design and analysis among today\u27s students and practitioners in the health sciences. Although most students in the health sciences are required to take an introductory statistics course prior to entering professional programs, little is known about the attitudes those students possess regarding the use of statistics in medical research

Predictive Diagnostic Analysis of Mammographic Breast Tissue Microenvironment

Improving computer-aided early detection techniques for breast cancer is paramount because current technology has high false positive rates. Existing methods have led to a substantial number of false diagnostics, which lead to stress, unnecessary biopsies, and an added financial burden to the health care system. In order to augment early detection methodology, one must understand the breast microenvironment. The CompuMAINE Lab has researched computational metrics on mammograms based on an image analysis technique called the Wavelet Transform Modulus Maxima (WTMM) method to identify the fractal and roughness signature from mammograms. The WTMM method was used to color code the mammograms based on the type of tissue present and assign the Hurst exponent (H) value to corresponding tissue: dense tissue with H greater than 0.55, fatty tissue with H less than 0.45, and disrupted tissue with H between 0.45 and 0.55, with the latter being a key trait in tumorous tissue. This analysis on the full breast was performed on 127 cases for the Medio Lateral Oblique (MLO) view. We are revisiting these data by analyzing the region behind the nipple for the MLO view and the region outside the nipple area. After performing the WTMM analysis on each breast, non-parametric statistical analysis methods were performed to determine the level of significance between normal, benign, and cancerous cases. Furthermore, we utilized logistic models to assess the predictability of these metrics for future datasets