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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.

This paper presents a gesture recognition system based on the pressure changes produced by wrist tendon movements for wearable devices. The data of the pressure variations are captured by means of flexible and ultrathin force resistive sensors. A learning algorithm, Support Vector Machine, helps the system to distinguish various hand gestures through developed programming on MATLAB after extracting the key features of data. In order to achieve rapid gesture recognition with a shorter computational time, higher precision and less space complexity, genetic optimization algorithm is used to find the optimal parameter c (cost factor) and g (kernel function parameters) in SVM algorithm. The SVM parameter optimization improves the classification accuracy and the performance of the classifier. Finally, developed wearable resistive-based wrist-worn gesture sensing system classifies the hand gesture with high accuracy (>70%) and the results are displayed on the GUIDE user interface.

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The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.INDEX TERMS Optimal node placement, reliable wireless networks, network deployment optimization, particle swarm optimization, gray wolf optimization, Harris Hawks optimization, sine cosine optimization, industrial wireless mesh networks.The associate editor coordinating the review of this manuscript and approving it for publication was Xiaolong Li .