Diseases of the gastro-intestinal tract are becoming more prevalent. New techniques and devices, such as the wireless capsule endoscope and the telemetry capsule, that are able to measure the various signals of the digestive organs (temperature, pH, and pressure), have been developed for the observation of the digestive organs. In these capsule devices, there are no methods of moving and grasping them. In order to make a swift diagnosis and to give proper medication, it is necessary to control the moving speed of the capsule. This paper presents a wireless system for the control of movements of an electrical stimulus capsule. This includes an electrical stimulus capsule which can be swallowed and an external transmitting control system. A receiver, a receiving antenna (small multi-loop), a transmitter, and a transmitting antenna (monopole) were designed and fabricated taking into consideration the MPE, power consumption, system size, signal-to-noise ratio and the modulation method. The wireless system, which was designed and implemented for the control of movements of the electrical stimulus capsule, was verified by in-vitro experiments which were performed on the small intestines of a pig. As a result, we found that when the small intestines are contracted by electrical stimuli, the capsule can move to the opposite direction, which means that the capsule can go up or down in the small intestines.

Since retina blood vessels (RBV) are a major factor in ophthalmological diagnosis, it is essential to detect RBV from a fundus image. In this letter, we proposed the detection method of RBV using a morphological analysis and support vector machine classification. The proposed RBV detection method consists of three strategies: pre-processing, features extraction and classification. In pre-processing, noises were reduced and RBV were enhanced by anisotropic diffusion filtering and illumination equalization. Features were extracted by using the image intensity and morphology of RBV. And a support vector machine (SVM) classification algorithm was used to detect RBV. The proposed RBV detection method was simulated and validated by using the DRIVE database. The averages of accuracy and TPR are 0.94 and 0.78, respectively. Moreover, by comparison, we confirmed that the proposed RBV detection method detected RBV better than the recent RBV detections methods.