Attitude Measurement

In many practical situations, it is important to determine and measure the attitude of a particular vehicle, such as a ship, an airplane, a piece of mechanical equipment such as a, crane lifter, or a spacecraft. For this reason, many attitude sensors have been developed with advanced computer and semiconductor technologies. This section first introduces the various attitude sensors with an explanation of their operating principles and then presents several methodologies for attitude measurement and determination, including ships and crane lifters, aircraft, and spacecraft applications

Eye-Movement-Control-Independent Brain Computer Interface Using Modulation of Steady-State Responses in Visual Evoked Potentials

Brain computer interfaces based on steady-state visually evoked potential (SSVEP-BCI) have been developed substantially in recent years, but these are not available to patients with severe paralysis or eye-impairment who lost the control ability of eye-movement. This paper proposes an eye-movement-independent SSVEP-BCI available in the eyes-closed state based on the modulation of SSVEP elicited by performing a mental task. Although performance of the proposed BCI depended on subjects, electrode locations, mental tasks employed, and flickering frequencies, the mean precision and recall, which were obtained from the confusion matrix, reached 72% to 95% using the support vector machine classifier across 18 normal subjects under the stimulus frequency of 10Hz or 14Hz. Results from simulated information transfer rate and its inter-individual difference suggest that it is adequate to set an inter-trial interval at 2s to 3s for better performance of the proposed BCI. It is consequently feasible to develop a practical eye-movement-control-independent BCI by optimizing the parameters such as the stimulation frequency and electrode sites each user

Orthogonality-Constrained CNMF-Based Noise Reduction with Reduced Degradation of Biological Sound

The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow progression of the disease. Hence, a device that enables the early diagnosis of both diseases is necessary. In our previous study, a sensor for monitoring biological sounds such as vascular and respiratory sounds was developed and a noise reduction method based on semi-supervised convolutive non-negative matrix factorization (SCNMF) was proposed for the noisy environments of users. However, SCNMF attenuated part of the biological sound in addition to the noise. Therefore, this paper proposes a novel noise reduction method that achieves less distortion by imposing orthogonality constraints on the SCNMF. The effectiveness of the proposed method was verified experimentally using the biological sounds of 21 subjects. The experimental results showed an average improvement of 1.4 dB in the signal-to-noise ratio and 2.1 dB in the signal-to-distortion ratio over the conventional method. These results demonstrate the capability of the proposed approach to measure biological sounds even in noisy environments