Design of a smart-device and FPGA based wireless capsule endoscopic system

Khan, Tareq; Shrestha, Ravi; Wahid, Khan A.; Babyn, Paul

doi:10.1016/j.sna.2014.10.033

Cited by 16 publications

(12 citation statements)

References 23 publications

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“…The proposed algorithm is fully implemented and tested on the wireless capsule endoscopy system developed in our lab [9]. Here, four identical white LEDs are used to suit the objective.…”

Section: Resultsmentioning

confidence: 99%

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Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function

Shrestha

Mohammed

Hasan

et al. 2016

IEEE Trans. Biomed. Circuits Syst.

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Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal (GI) diseases by capturing images of human small intestine. Accurate diagnosis of endoscopic images depends heavily on the quality of captured images. Along with image and frame rate, brightness of the image is an important parameter that influences the image quality which leads to the design of an efficient illumination system. Such design involves the choice and placement of proper light source and its ability to illuminate GI surface with proper brightness. Light emitting diodes (LEDs) are normally used as sources where modulated pulses are used to control LED's brightness. In practice, instances like under- and over-illumination are very common in WCE, where the former provides dark images and the later provides bright images with high power consumption. In this paper, we propose a low-power and efficient illumination system that is based on an automated brightness algorithm. The scheme is adaptive in nature, i.e., the brightness level is controlled automatically in real-time while the images are being captured. The captured images are segmented into four equal regions and the brightness level of each region is calculated. Then an adaptive sigmoid function is used to find the optimized brightness level and accordingly a new value of duty cycle of the modulated pulse is generated to capture future images. The algorithm is fully implemented in a capsule prototype and tested with endoscopic images. Commercial capsules like Pillcam and Mirocam were also used in the experiment. The results show that the proposed algorithm works well in controlling the brightness level accordingly to the environmental condition, and as a result, good quality images are captured with an average of 40% brightness level that saves power consumption of the capsule.

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“…The proposed algorithm is fully implemented and tested on the wireless capsule endoscopy system developed in our lab [9]. Here, four identical white LEDs are used to suit the objective.…”

Section: Resultsmentioning

confidence: 99%

“…Works found in literature show that around 25%-45% of total power is consumed by the illumination hardware in a WCE system [9], [25]. Reducing brightness level to save power consumption will result in darker images with fewer details.…”

Section: Illumination System In Wcementioning

confidence: 99%

Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function

Shrestha

Mohammed

Hasan

et al. 2016

IEEE Trans. Biomed. Circuits Syst.

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“…For example, Harwood, Dooley, Scott, & Joiner (2014) states that high internet use is something common on smart devices, this is a direct statement about the utilisation of smart devices for internet access which requires network connectivity. On the other hand, Khan, Shrestha, Wahid, & Babyn (2015) mentions direct wireless interfacing and full-duplex communication between devices, this statement is a bit more indirect but at the same time assumes that smart devices have network connectivity. One of the most explicit reference to connectivity was obtained from Cheng & Mitomo (2017) which explains that what makes these devices "smart" is their wireless communication capability, which enables them to connect to the internet.…”

Section: Connectivitymentioning

confidence: 99%

What is a smart device? - a conceptualisation within the paradigm of the internet of things

2018

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The Internet of Things (IoT) is an interconnected network of objects which range from simple sensors to smartphones and tablets; it is a relatively novel paradigm that has been rapidly gaining ground in the scenario of modern wireless telecommunications with an expected growth of 25 to 50 billion of connected devices for 2020 Due to the recent rise of this paradigm, authors across the literature use inconsistent terms to address the devices present in the IoT, such as mobile device, smart device, mobile technologies or mobile smart device. Based on the existing literature, this paper chooses the term smart device as a starting point towards the development of an appropriate definition for the devices present in the IoT. This investigation aims at exploring the concept and main features of smart devices as well as their role in the IoT. This paper follows a systematic approach for reviewing compendium of literature to explore the current research in this field. It has been identified smart devices as the primary objects interconnected in the network of IoT, having an essential role in this paradigm. The developed concept for defining smart device is based on three main features, namely context-awareness, autonomy and device connectivity. Other features such as mobility and userinteraction were highly mentioned in the literature, but were not considered because of the nature of the IoT as a network mainly oriented to device-to-device connectivity whether they are mobile or not and whether they interact with people or not. What emerges from this paper is a concept which can be used to homogenise the terminology used on further research in the Field of digitalisation and smart technologies.

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“…Some other studies on image encoders employing image compression techniques to overcome the problem of a transceiver with limited data transfer capability have been progressed in capsule endoscopes image [1,9,10]. However, since there is a limit to the image compression method, in order to transmit the high resolution image, the transmitter supporting the high data rate is needed.…”

Section: Introductionmentioning

confidence: 99%

Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication

et al. 2018

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A technology for low-power high data-rate digital capsule endoscopy with human body communication (CEHBC) is presented in this paper. To transfer the image data stably with low power consumption, the proposed system uses three major schemes: Frequency selective digital transmission (FSDT) modulation with HBC, the use of an algorithm to select electrode pairs, and the LineSync algorithm. The FSDT modulation supports high-data rate transmission and prevents the signal attenuation effect. The selection algorithm of the electrode pair finds the best receiving channel. The LineSync algorithm synchronizes the data and compensates for data polarity during the long data transmission section between the capsule endoscope and the receiver. Because all the major functional blocks of the CEHBC transmitter can be implemented as digital logics, they can be easily fabricated using the field programmable gate array (FPGA). Moreover, this CEHBC transmitter can achieve low power-consumption and can support a relatively high data rate in spite of using its clock a few tens of MHz slower. The proposed CEHBC-TXD is the digital portion of the CEHBC transmitter that provides low-power (3.7 mW) and high data-rate (6 Mbps) performance while it supports a high-resolution image (480 × 480 byte) at 3.13 fps.

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Design of a smart-device and FPGA based wireless capsule endoscopic system

Cited by 16 publications

References 23 publications

Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function

Automated Adaptive Brightness in Wireless Capsule Endoscopy Using Image Segmentation and Sigmoid Function

What is a smart device? - a conceptualisation within the paradigm of the internet of things

Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication

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