This study presents an alternative approach for the nondestructive assessment of fruit quality parameters with the use of a simplified optical fiber red–green–blue system (OF-RGB). The optical sensor system presented in this work is designed to rapidly measure the firmness, acidity, and soluble solid content of an intact Sala mango on the basis of color properties. The system consists of three light-emitting diodes with peak emission at 635 (red), 525 (green), and 470 nm (blue), as well as a single photodetector capable of sensing visible light. The measurements were conducted using the reflectance technique. The analyses were conducted by comparing the results obtained through the proposed system with those measured using two commercial spectrometers, namely, QE65000 and FieldSpec 3. The developed RGB system showed satisfactory accuracy in the measurement of acidity (R2=0.795) and firmness (R2=0.761), but a relatively lower accuracy in the measurement of soluble solid content (R2=0.593) of intact mangoes. The results obtained through OF-RGB are comparable with those measured by QE65000 and FieldSpec 3. This system is a promising new technology with rapid response, easy operation, and low cost with potential applications in the nondestructive assessment of quality attributes.
Many researches are conducted to improve Hopfield Neural Network (HNN) performance especially for speed
and memory capacity in different approaches. However, there is still a significant scope of developing HNN using
Optical Logic Gates. We propose here a new model of HNN based on all-optical XNOR logic gates for real time color
image recognition. Firstly, we improved HNN toward optimum learning and converging operations. We considered each
unipolar image as a set of small blocks of 3-pixels as vectors for HNN. This enables to save large number of images in
the net with best reaching into global minima, and because there are only eight fixed states of weights so that only single
iteration performed to construct a vector with stable state at minimum energy. HNN is useless in dealing with data not in
bipolar representation. Therefore, HNN failed to work with color images. In RGB bands each represents different values
of brightness, for d-bit RGB image it is simply consists of d-layers of unipolar. Each layer is as a single unipolar image
for HNN. In addition, the weight matrices with stability of unity at the diagonal perform clear converging in comparison
with no self-connecting architecture. Synchronously, each matrix-matrix multiplication operation would run optically in
the second part, since we propose an array of all-optical XOR gates, which uses Mach-Zehnder Interferometer (MZI) for
neurons setup and a controlling system to distribute timely signals with inverting to achieve XNOR function. The
primary operation and simulation of the proposal HNN is demonstrated.