In this paper, the pressure monitoring telemetry system has been designed and implemented for an accurate pressure measure-ment inside the gastrointestinal tract with minimizing pain and inconvenience. The system is composed of a miniaturized pres-sure measurement capsule and an external receiver. The per-formance of the telemetry capsule for monitoring pressure in the gastrointestinal tract is demonstrated by the results of animal in-vivo experiments.

In order to control the moving speed of an endoscopic capsule in the human intestine, electrical stimulation method is proposed in this paper. The miniaturized endoscopic capsule with the function of various electrical stimulations has been designed and implemented. An in-vivo animal experiment has been performed to show the ability of controlling the movement speed of the endoscopic capsule according to the level of electrical stimulation. In-vivo experiments were performed by inserting the implemented capsule into a pig's intestinal tract. From the experimental results, the activation of peristaltic movement and the relationship between the moving speed of capsule and the stimulation amplitude could be found. It is shown that the moving speed of capsule in the intestine can be controlled by adjustment of the stimulation level applied in the capsule electrodes. The results of the in-vivo experiment verify that the degree of contraction in the intestinal tract is closely related with the level of stimulating electrical voltage, suggesting that the moving speed of capsule in the human gastrointestinal tract can be controlled by externally adjusting the amplitude of stimulating pulse signal.

Faster R-CNN uses a region proposal network which consists of a single scale convolution filter and fully connected networks to localize detected regions. However, using a single scale filter is not enough to detect full regions of characters. In this letter, we propose a simple but effective way, i.e., utilizing variously sized convolution filters, to accurately detect Chinese characters of multiple scales in documents. We experimentally verified that our method improved IoU by 4% and detection rate by 3% than the previous single scale Faster R-CNN method.

A non-linear extension of generalized hyperplane approximation (GHA) method is introduced in this letter. Although GHA achieved a high-confidence result in motion parameter estimation by utilizing the supervised learning scheme in histogram of oriented gradient (HOG) feature space, it still has unstable convergence range because it approximates the non-linear function of regression from the feature space to the motion parameter space as a linear plane. To extend GHA into a non-linear regression for larger convergence range, we derive theoretical equations and verify this extension's effectiveness and efficiency over GHA by experimental results.