Yuwei Dai

Coffee is one of the most widely consumed beverages globally, with Americans averaging 3.1 cups per day. However, before coffee beans can be brewed into a drinkable form, they undergo several critical stages, including harvesting, processing, roasting, grinding, and extraction. During the processing and roasting phases, a significant challenge arises: stones that are similar in size and shape to coffee beans can inadvertently mix into the batch. These stones are difficult to detect using conventional methods, and their presence can have severe consequences. When stones are ground alongside coffee beans, they can cause significant damage to the grinder’s burrs. Commercial coffee grinders typically employ conical or flat burrs, which consist of two circular discs or an inner blade and a disc. These burrs undergo specialized heat treatment and surface processing to ensure durability and precision, making them highly expensive components. Replacing damaged burrs is not only costly but also requires meticulous calibration of the parallelism between the inner blade and the disc to maintain grinding quality. The introduction of stones into the grinding process can lead to equipment damage, resulting in operational downtime and financial losses. To address this issue, this paper proposes a novel method based on Empirical Mode Decomposition (EMD) for detecting stones in coffee beans. The approach analyzes the acoustic wave patterns generated when stones impact or rotate within the grinder.

Technological advancements are propelling medical technology towards automation through the application and widespread use of automatic control, sensing, and Internet of Things (IoT) technologies. Currently, IoT technology has been extensively applied in medical devices, aiming to ensure patient safety through more real-time detection and more effective management. In the monitoring of intravenous infusion, accurately sensing the infusion conditions in real time is particularly important. This article introduces a low-cost smart infusion device based on IoT technology, which controls the infusion rate with a peristaltic pump and monitors the volume of fluid delivered. It uses an improved, self-calibrating weighing sensor to achieve the real-time closed-loop control of the flow rate, ensuring patient safety. Additionally, the Blynk dashboard can be used for monitoring and controlling the flow rate and infusion volume.

In the present day, IoT technology is widely applied in the field of medical devices to facilitate real-time monitoring and management by medical staff, thereby better-ensuring patient safety. In IoT intravenous infusion monitoring sensors, it is particularly important to ensure that air bubbles are not infused into the patient’s body. The most common method for bubble detection during intravenous infusions is the use of infrared or laser sensors, which can usually meet design requirements at a relatively low cost. Another method is the use of ultrasonic detection of bubbles, which achieves high accuracy but has not been widely promoted in the market due to higher costs. This proposed work introduces a new type of sensor that detects bubbles by monitoring changes in capacitance between two electrodes installed at the surface of the infusion pipe. If this sensor is deployed on the ESP32 platform, which is widely used in embedded IoT devices, it can achieve 35 μL bubble detection precision with an average power consumption of 5.18 mW and a mass production cost of $0.022. Although the precision of this sensor is significantly lower than the low-cost IR bubble sensor, it still satisfies the design requirement of the IV infusion IoT sensor.