The tongue reflects the abnormal condition and behavior of the internal organs of the body, such as problems of the heart, liver, pancreas, stomach, intestines, blood diseases and others, which lead to changes in some of the features and characteristics of the tongue. The most important of these is tongue color, which can be adopted as a biometric that can be used in Computerized Tongue Diagnostic Systems (CTDS). Quantitative diagnosis of the tongue requires some devices, including image acquisition devices such as cameras, light sources, filters, color checkers, image analysis and processing devices through the application of some algorithms or image processing and color correction software, as well as a computer. This study proposes a real-time imaging system to analyze tongue color and diagnose diseases using a webcam under specific conditions. The proposed system was designed in a Matlab GUI environment. After testing the system on a data set of more than 100 images, the preliminary results showed that the proposed system gives a disease diagnosis with an accuracy rate of no less than 86.667%. The proposed system contributed to the diagnosis of several diseases in real time, with an accuracy of 95.45%, with ease of use, implementation and low cost. This gives impetus to further studies to apply computerized diagnosis in medical applications, to enhance the medical reality, monitor patient health, and make an accurate diagnosis.
In this paper, the performance of coded systems is considered in the presence of Suzuki fading channels, which is a combination of both short-fading and long-fading channels. The problem in manipulating a Suzuki fading model is the complicated integration involved in the evaluation of the Suzuki probability density function (PDF). In this paper, we calculated noise PDF after the zero-forcing equalizer (ZFE) at the receiver end with several approaches. In addition, we used the derived PDF to calculate the log-likelihood ratios (LLRs) for turbo-coded systems, and results were compared to Gaussian distribution-based LLRs. The results showed a 2 dB improvement in performance compared to traditional LLRs at 10 - 6 of the bit error rate (BER) with no added complexity. Simulations were obtained utilizing the Matlab program, and results showed good improvement in the performance of the turbo-coded system with the proposed LLRs compared to Gaussian-based LLRs.
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