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The paper presents a color sensor system that can process light reflected from a surface and produce a digital output representing the color of the surface. The end-user interface circuit requires only a 3-bit pseudo flash analog-to-digital converter (ADC) in place of the conventional/typical design comprising ADC, digital signal processor and memory. For scalability and compactness, the ADC was designed such that only two comparators were required regardless of the number of color/wavelength to be identified. The complete system design has been implemented in hardware (bread board) and fully characterized. The ADC achieved less than 0.1 LSB for both INL and DNL. The experimental results also demonstrate that the color sensor system is working as intended at 20 kHz while maintaining greater than 2.5 ENOB by the ADC. This work proved the design concept and the system will be realized with integrated circuit technology in future to improve its operating frequency.

This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.

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Food quality monitoring in the production process is essential. The control of food quality and freshness is of growing interest for both consumer and food industry. Near infrared (NIR) spectroscopy is popular as it does not need any sample preparation. However, NIR spectroscopy is costly and needs reliable calibration. A noncontact, non-destructive optical process is proposed in this work to monitor the quality of the food. It is shown that the reflected phase information can be used to detect the quality of the fruits. The color and the spectral reflectance change with storage. The changes in the spectral feature due to ripening or decay of apples are used to non-destructively monitor the quality of the fruit. A closed relationship between the reflected phase information and degradation is obtained. The developed model is simple, low cost, and does not need extensive calibration as compared to conventional technologies currently used like NIR besides being robust to skin color or appearances of the fruit. The phase-based reflectance spectroscopy could revolutionize the on/inline quality monitoring of the fruits.