No abstract

Whitecap formation is an important factor in the exchange of momentum, heat, and gas on the ocean surface. The long-term measurement of whitecaps is necessary to deepen our understanding of the mechanisms of ocean surface motion. However, traditional detection methods are highly sensitive to illumination. Under various illumination conditions, significant light pollution may be introduced into images. The poor performance caused by using images degraded with light pollution is not conducive to automated long-term whitecap measurement. In this study, we propose a two-step method for the detection of whitecaps under various illumination conditions. An abnormal detection method based on previous whitecap detection methods for the accurate detection of whitecaps in light-polluted areas is proposed as the first step. Using the detection results, we propose a post-processing method based on optical flow trajectories at two sampling rates to separate actual whitecap components in images containing false positives. Experiments show that the method proposed in this study can more accurately detect whitecaps in images with light pollution when compared to existing methods.

Previous research has shown that prolonged hand grips, forceful grips, or grips with awkward postures are associated with many kinds of upper limb musculoskeletal disorders (MSDs). For many years, different preventive measures have been taken to reduce the risk of upper limb MSDs. All these preventive measures require the hand tool or the task to be carefully analyzed. This study presented an instrumented glove based on the Force Sensitive Resistors (FSRs) and bending sensors that can provide real time feedback on hand gripping pressure and bending. This paper introduced the system design of this instrumented glove. And a pilot study with a camera gripping testing was followed. The results showed that the instrumented glove can provide quantitative data for real time analysis for different hand gripping tasks. Thus, this instrumented glove can be used for ergonomic analysis of hand tools or tasks. It can be implemented to help minimize risk of repetitive strain injuries of upper limbs in the future.