Improved Wireless Capsule Endoscope Localization with Phase Detection Algorithm

AbstractThis paper presents a phasor-distance based faulty phase detection and fault classification technique for parallel transmission lines. Detection and classification of faulty phase(s) have been carried out by deriving indices from the change in phasor values of current with a distance of one cycle. The derived indices have zero values during normal operating conditions whereas the index corresponding to the faulty phase exceeds the pre-defined threshold in case of occurrence of a fault. A separate ground detection algorithm has been utilized for the identification of involvement of ground in a faulty situation. The performance of the proposed technique has been evaluated for intra-circuit, inter-circuit and simultaneous faults with wide variations in system and fault conditions. The suggested technique has been evaluated for over 23,000 diversified simulated fault cases as well as 14 recorded real fault events. The performance of the proposed technique remains consistent under Current Transformer (CT) saturation as well as different amount and direction of power flow. Moreover, suitability to different power system network has also been studied. Also, faults having fault current less than pre-fault conditions have been detected accurately. The results obtained suggest that it is able to detect faulty phases as well as classify faults within quarter-cycle from the inception of fault with impeccable accuracy. Besides, as modern digital relays have been already equipped with phasor computation facility, phasor-based technique can be easily incorporated with relative ease. At last, a comparative evaluation suggests its superiority in terms of fault classification accuracy, fault detection time, diversify fault scenarios and computational requirement among other existing techniques.

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Error reduction through post processing for wireless capsule endoscope video

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-020-00503-9 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Pål Anders Floor ◽

Ivar Farup ◽

Marius Pedersen ◽

Øistein Hovde

Keyword(s):

Error Reduction ◽

Capsule Endoscope ◽

Post Processing ◽

Wireless Capsule

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Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽

10.3390/s21041081 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1081

Author(s):

Tamon Miyake ◽

Shintaro Yamamoto ◽

Satoshi Hosono ◽

Satoshi Funabashi ◽

Zhengxue Cheng ◽

...

Keyword(s):

Learning Algorithm ◽

Detection System ◽

Calibration Method ◽

Detection Algorithm ◽

Video Data ◽

Detection Accuracy ◽

Phase Detection ◽

Gait Phase ◽

Logistic Regression Algorithm ◽

Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

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