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There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.

Several technological applications require well-designed control systems to induce a desired speed in direct current (DC) motors. Some controllers present saturation in the duty cycle, which generates variable switching frequency and subharmonics. The zero average dynamics and fixed point induction control (ZAD-FPIC) techniques have been shown to reduce these problems; however, little research has been done for DC motors, considering fixed switching frequency, quantization effects, and delays. Therefore, this paper presents the speed control of a DC motor by using a buck converter controlled with the ZAD-FPIC techniques. A fourth-order, non-linear mathematical model is used to describe the system dynamics, which combines electrical and electromechanical physical models. The dynamic response and non-linear system dynamics are studied for different scenarios where the control parameters are changed. Results show that the speed of the motor is successfully controlled when using ZAD-FPIC, with a non-saturated duty cycle presenting fixed switching frequency. Simulation and experimental tests show that the controlled system presents a good performance for different quantization levels, which makes it robust to the resolution for the measurement and type of sensor.

One of the biggest problems with distribution systems correspond to the load unbalance created by power demand of customers. This becomes a difficult task to solve with conventional methods. Therefore, this paper uses integer linear programming and Branch and Bound algorithm to balance the loads in the three phases of the distribution system, employing stored data of power demand. Results show that the method helps to decrease the unbalance factor in more than 10%, by selecting the phase where a load should be connected. The solution may be used as a planning tool in distribution systems applied to installations with systems for measuring power consumption in different time intervals. Furthermore, in conjunction with communications and processing technologies, the solution could be useful to implement with a smart grid.

This paper presents the dynamic analysis of a permanent magnet DC motor using a buck converter controlled by zero average dynamics (ZADs) and fixed-point inducting control (FPIC). Initially, the steady-state behavior of the closed-loop system was observed and then transient behavior analyzed while maintaining a fixed ZAD control parameter and changing the FPIC parameter. Other behaviors were studied when the value of the ZAD control parameter changed and the FPIC parameter was maintained at the initial value. Besides, bifurcation diagrams were built with one and two delay periods by changing the control parameter of the FPIC and maintaining fixed ZAD parameters while some disturbances were carried out in the electric source. The results show that the ZAD-FPIC controller allowed good regulation of the speed for different reference values. The ZAD-FPIC control technique is effective for controlling the buck converter with the motor, even with two delay periods. The robustness of the system was checked by changing the voltage of the source. It was shown that the system used a fixed switching frequency because the duty cycle was not saturated for certain ranges of the control parameters shown in the research. This technique can be used for higher order systems with experimental phenomena such as quantization effects, time delays, and variations in the input signal.