One of the diabetes complications, diabetic retinopathy (DR), is characterised by the damage of retinal vessels, especially on the macular region. Located at the centre of retina and appeared as a cloudy dark spot in colour fundus image, macula is a fundamental area for high acumen of colour vision. Foveal avascular zone (FAZ) is located at the centre of macula and encircled by interconnected capillary beds. FAZ has a round or oval shape with an average diameter of 500-600 μm. In DR patients, the FAZ becomes larger due to the loss of perifoveal retinal vessels. In this study, a scheme for automated segmentation of FAZ in colour fundus images is proposed. The scheme consists of four stages: pre-processing, image enhancement, vessels segmentation and FAZ segmentation. Result shows that the average sensitivity, specificity and accuracy obtained are 80.86%, 99.17% and 97.49%. This indicates that the proposed scheme has successfully detected the FAZ.
In automation and standardization of quality of cane sugar in sugar factory, quantized identification process needs to be done. Identification of cane sugar was done based on image of cane sugar. In classification and identification based on image, colour models used could influence success rate of identification. This paper presents comparative study among RGB, HSV, HSI, YCbCr, and L*a*b colour models in automatic identification of cane sugar. System designed could identify 8 kinds of cane sugar based on their image with success rate of 85%. System was designed with Artificial Neural Network classifier with one hidden layer using Levenberg-Marquardt algorithm. Colour and textural features were extracted from 120 images of cane sugar for Artificial Neural Network inputs. HSV was the best colour model for identification, with highest result of 87.5%, followed by YCbCr, L*a*b, and RGB.
A building security management system based on single board computer is currently under development. In this paper, a part of the system which is an automatic door access system using contactless smart card for identification is discussed. Running test shows that the system is working as expected. The system can identify multiple users each with different card. More specific access such as access by day, hour, even minute can be implemented. Alarm is used as indicator if the door is unlocked or any forced intrusion is happening. The corresponding system is going to be integrated in a building security management system to provide safer work environment.
Orthogonal Frequency Division Multiplexing (OFDM) is a modulation technique which provides higher bit rate and efficient bandwidth. This paper presents an implementation of a 4/16/64 Order Quadrature Amplitude Modulation (QAM) Mapper-Demapper for 256 Sub channel OFDM Model on Xilinx SPARTAN 3E Field-Programmable Gate Array (FPGA) series, using schematic approach. This QAM-OFDM model is reconfigurable in term of its QAM order. The result shows that under the clock frequency around 262 MHz, the implementation works well, high precision is achieved at its serial output. A precision process conducted at 20 ns internal clock input period, with the 25 Mbps input bit rate requires 81.94 μs QAM processing-time. The implementation consumes about 80 % of the total FPGA slices (3736 slices).
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