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This paper presents the design of a hardware-efficient, low-power image processing system for next-generation wireless endoscopy. The presented system is composed of a custom CMOS image sensor, a dedicated image compressor, a forward error correction (FEC) encoder protecting radio transmitted data against random and burst errors, a radio data transmitter, and a controller supervising all operations of the system. The most significant part of the system is the image compressor. It is based on an integer version of a discrete cosine transform and a novel, low complexity yet efficient, entropy encoder making use of an adaptive Golomb-Rice algorithm instead of Huffman tables. The novel hardware-efficient architecture designed for the presented system enables on-the-fly compression of the acquired image. Instant compression, together with elimination of the necessity of retransmitting erroneously received data by their prior FEC encoding, significantly reduces the size of the required memory in comparison to previous systems. The presented system was prototyped in a single, low-power, 65-nm field programmable gate arrays (FPGA) chip. Its power consumption is low and comparable to other application-specific-integrated-circuits-based systems, despite FPGA-based implementation.
This article was published in the following journal.
Name: IEEE journal of biomedical and health informatics
This paper presents a wireless capsule microsystem to detect and monitor pH, pressure, and temperature of the gastrointestinal (GI) tract in real-time. This research contributes to the integration of ...
A real-time image filtering technique is proposed which could result in faster implementation for fingerprint image enhancement. One major hurdle associated with fingerprint filtering techniques is th...
Considerable progress in wireless power transfer has been made in the realm of non-radiative transfer, which employs magnetic-field coupling in the near field. A combination of circuit resonance and i...
One of the significant challenges in Capsule Endoscopy (CE) is to precisely determine the pathologies location. The localization process is primarily estimated using the received signal strength from ...
Medical hardware and software device interoperability standards are not uniform. The result of this lack of standardization is that information available on clinical devices may not be readily or free...
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The purpose of this study is to conduct a cost-effectiveness analysis of wireless capsule endoscopy in the investigation of patients with overt obscure gastrointestinal bleeding. To inform...
This study aims to evaluate the effectiveness of 4D image acquisition and post-processing with Vios Works for the evaluation of 3D images acquired on GE Magnetic Resonance Imaging scanners...
To compare the small bowel cleanliness for wireless capsule endoscopy using two different Polyethylene Glycol administration schedules (before the wireless capsule endoscopy ingestion ver...
Small bowel wireless capsule endoscopy is the investigation modality of choice for suspected diseases of the small bowel. The procedure is safe and noninvasive, the main risk being capsule...
Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.
Improvement in the quality of an x-ray image by use of an intensifying screen, tube, or filter and by optimum exposure techniques. Digital processing methods are often employed.
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Instrumentation consisting of hardware and software that communicates with the BRAIN. The hardware component of the interface records brain signals, while the software component analyzes the signals and converts them into a command that controls a device or sends a feedback signal to the brain.
Communications networks connecting various hardware devices together within or between buildings by means of a continuous cable or voice data telephone system.