In the automotive industry, despite the robotic systems on the production lines, factories continue employing workers in several custom tasks getting for semi-automatic assembly operations. Specifically, the assembly of electrical harnesses of engines comprises a set of connections between electrical components. Despite the task is easy to perform, employees tend not to notice that a few components are not being connected properly due to physical fatigue provoked by repetitive tasks. This yields a low quality of the assembly production line and possible hazards. In this work, we propose a sound detection system based on machine/deep learning (ML/DL) approaches to identify click sounds produced when electrical harnesses are connected. The purpose of this system is to count the number of connections properly made and to feedback to the employees. We collect and release a public dataset of 25,000 click sounds of 25 ms length at 22 kHz during three months of assembly operations in an automotive production line located in Mexico. Then, we design an ML/DL-based methodology for click sound detection of assembled harnesses under real conditions of a noisy environment (noise level ranging from ¿16.67 dB to ¿12.87 dB) including other machinery sounds. Our best ML/DL model (i.e., a combination between five acoustic features and an optimized convolutional neural network) is able to detect click sounds in a real assembly production line with an accuracy of 94.55±0.83 %.

The Internet of Things plays role in all arenas. In this manuscript, we have to consider the growing flowers in a garden, vegetable, fruit, and other farming. We are considering the greenhouse which aims to introduce the productions of yields. Of course, the growth of plants, and farms are vital and need of everyone, keeping in view of this manuscript is aimed to discuss and study in line of IoT and agriculture. In this work, we propose a greenhouse automation system based on Arduino for the monitoring of temperature, humidity, and moisture of the soil. Arduino can obtain data on the environmental conditions of the greenhouse from various sensors and transfer the data to the ESP8266 module. Consequently, it's possible to change the state of greenhouse control devices like fans, lamp heater, and water pump in obedience to the necessary conditions of the crops. These parameters are modified by the type of plant to maximize their growth, the Aloe Vera plant was used in this project. For the architecture of the Internet of Things was used Blynk coming from the embedded board and the communication link with the Blynk Server was through the Wi-Fi protocol. Results indicate that the system allows the control and monitoring in real-time of the greenhouse correctly. As a future improvement, it is intended with the data obtained, to search for the best optimal conditions for plant growth through artificial intelligence