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Chronic periodontal is a very common infection that instigates the destruction of oral tissue, and for its treatment, it is necessary to minimize the infection and the defects regeneration. Periodontium consists of four types of tissues: (a) cementum, (b) periodontal ligament, (c) gingiva, and 4) alveolar bone. In separated cavities, regenerative process also allows various cell proliferations. Guided...
This paper proposes a computationally efficient embedded system, implemented on field programmable gate arrays (FPGA), for real-time brain imaging. An RS-232 core is employed to obtain the brain data from a functional near-infrared spectroscopy (fNIRS) imaging modality on sample basis. A 32-bit floating point core (IEEE754) is developed on FPGA to manipulate floating point data with precision. Recursive...
This paper presents a brain activity monitoring system developed on a field programmable gate array (FPGA). A high definition multimedia interface (HDMI) core is introduced to display an anatomical brain on an LED/LCD monitor in realtime. A desired 2D view of a 3D anatomical image, selected beforehand by the user by a proposed user interface software, is converted to the jpeg formatted image. The...
This paper presents an embedded system for real-time multi-channel brain activity detection by implementing the Kalman filter (KF) core on a field-programmable gate array (FPGA). The KF with a model driven approach is implemented on an FPGA, for the first time as per our knowledge. The model driven based brain activation model and its parameters' estimation methodology by KF is depicted from Aqil...
In this paper, a field-programmable gate array (FPGA) based multiprocessor architecture is proposed for real-time image processing. The system is developed based on a hardware-software co-design philosophy. A total of five soft-core MicroBlaze processors are configured in master-slave topology and furnished with BRAM cores. A frame grabbing and a serial port cores are developed for camera and PC interfacing,...
This paper proposes an online framework to dynamically model the impulse-response function (IRF) of a system having shared-band noises, to facilitate the output prediction in real-time. The online independent-component analysis is performed to un-mix the measured signal. The automatic recognition of the anticipated IRF, amongst the unmixed signals, is achieved by proposing a peak-detection-&-correlation...
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