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"Arduino (Microcontroller)"
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A method to deliver patterned electrical impulses to Schwann cells cultured on an artificial axon
Information from the brain travels back and forth along peripheral nerves in the form of electrical impulses generated by neurons and these impulses have repetitive patterns. Schwann cells in peripheral nerves receive molecular signals from axons to coordinate the process of myelination. There is evidence, however, that non-molecular signals play an important role in myelination in the form of patterned electrical impulses generated by neuronal activity. The role of patterned electrical impulses has been investigated in the literature using co-cultures of neurons and myelinating cells. The co-culturing method, however, prevents the uncoupling of the direct effect of patterned electrical impulses on myelinating cells from the indirect effect mediated by neurons. To uncouple these effects and focus on the direct response of Schwann cells, we developed an in vitro model where an electroconductive carbon fiber acts as an artificial axon. The fiber provides only the biophysical characteristics of an axon but does not contribute any molecular signaling. In our \"suspended wire model\", the carbon fiber is suspended in a liquid media supported by a 3D printed scaffold. Patterned electrical impulses are generated by an Arduino 101 microcontroller. In this study, we describe the technology needed to set-up and eventually replicate this model. We also report on our initial in vitro tests where we were able to document the adherence and ensheath of human Schwann cells to the carbon fiber in the presence of patterned electrical impulses (hSCs were purchased from ScienCell Research Laboratories, Carlsbad, CA, USA; ScienCell fulfills the ethic requirements, including donor's consent). This technology will likely make feasible to investigate the response of Schwann cells to patterned electrical impulses in the future.
Journal Article
Design and Implementation of a Pressure Monitoring System Based on IoT for Water Supply Networks
Increasing the efficiency of water supply networks is essential in arid and semi-arid regions to ensure the supply of drinking water to the inhabitants. The cost of renovating these systems is high. However, customized management models can facilitate the maintenance and rehabilitation of hydraulic infrastructures by optimizing the use of resources. The implementation of current Internet of Things (IoT) monitoring systems allows decisions to be based on objective data. In water supply systems, IoT helps to monitor the key elements to improve system efficiency. To implement IoT in a water distribution system requires sensors that are suitable for measuring the main hydraulic variables, a communication system that is adaptable to the water service companies and a friendly system for data analysis and visualization. A smart pressure monitoring and alert system was developed using low-cost hardware and open-source software. An Arduino family microcontroller transfers pressure gauge signals using Sigfox communication, a low-power wide-area network (LPWAN). The IoT ThingSpeak platform is used for data analysis and visualization. Additionally, the system can send alarms via SMS/email in real time using the If This, Then That (IFTTT) web service when anomalous pressure data are detected. The pressure monitoring system was successfully implemented in a real water distribution network in Spain. It was able to detect both breakdowns and leaks in real time.
Journal Article
An open-source behavior controller for associative learning and memory (B-CALM)
Associative learning and memory, i.e., learning and remembering the associations between environmental stimuli, self-generated actions, and outcomes such as rewards or punishments, are critical for the well-being of animals. Hence, the neural mechanisms underlying these processes are extensively studied using behavioral tasks in laboratory animals. Traditionally, these tasks have been controlled using commercial hardware and software, which limits scalability and accessibility due to their cost. More recently, due to the revolution in microcontrollers or microcomputers, several general-purpose and open-source solutions have been advanced for controlling neuroscientific behavioral tasks. While these solutions have great strength due to their flexibility and general-purpose nature, for the same reasons, they suffer from some disadvantages including the need for considerable programming expertise, limited online visualization, or slower than optimal response latencies for any specific task. Here, to mitigate these concerns, we present an open-source behavior controller for associative learning and memory (B-CALM). B-CALM provides an integrated suite that can control a host of associative learning and memory behaviors. As proof of principle for its applicability, we show data from head-fixed mice learning Pavlovian conditioning, operant conditioning, discrimination learning, as well as a timing task and a choice task. These can be run directly from a user-friendly graphical user interface (GUI) written in MATLAB that controls many independently running Arduino Mega microcontrollers in parallel (one per behavior box). In sum, B-CALM will enable researchers to execute a wide variety of associative learning and memory tasks in a scalable, accurate, and user-friendly manner.
Journal Article
The Real-Time Estimation of Respiratory Flow and Mask Leakage in a PAPR Using a Single Differential-Pressure Sensor and Microcontroller-Based Smartphone Interface in the Development of a Public-Oriented Powered Air-Purifying Respirator as an Alternative to Lockdown Measures
In this study, a prototype system was developed as a potential alternative to lockdown measures against the spread of airborne infectious diseases such as COVID-19. The system integrates real-time estimation functions for respiratory flow and mask leakage into a low-cost powered air-purifying respirator (PAPR) designed for the general public. Using only a single differential-pressure sensor (SDP810) and a controller (Arduino UNO R4 WiFi), the respiratory flow (Q3e) is estimated from the differential pressure (ΔP) and battery voltage (Vb), and both the wearing status and leak status are transmitted to and displayed on a smartphone application. For evaluation, a testbench called the Respiratory Airflow Testbench was constructed by connecting a cylinder–piston drive to a mannequin head to simulate realistic wearing conditions. The estimated respiratory flow Q3e, calculated solely from ΔP and Vb, showed high agreement with the measured flow Q3m obtained from a reference flow sensor, confirming the effectiveness of the estimation algorithm. Furthermore, an automatic leak detection method based on the time-integrated value of Q3e was implemented, enabling the detection of improper wearing. This system thus achieves respiratory flow estimation and leakage detection based only on ΔP and Vb. In the future, it is expected to be extended to applications such as pressure control synchronized with breathing activity and health monitoring based on respiratory and coughing analysis. This platform also has the potential to serve as the foundation of a PAPR Wearing Status Network Management System, which will contribute to societal-level infection control through the networked sharing of wearing status information.
Journal Article
IoT based saline level monitoring system
In the process of medication, it is a common practice to treat patients with saline for dehydration and other medical ailments to improve the health condition of the patients. When fed with saline continuous observation of nurses is mandatory in monitoring the level of the saline. There are many cases where patients are being harmed due to the staff inattentiveness, as their absence does not notice the completion of saline level in the container. This arise the problem of back flow of blood immediately after the completion of saline in container. Hence to protect the patient from getting harmed an IoT based saline level monitoring system has been developed. The proposed model incorporates a sensor which continuously detects the saline drops. Whenever the sensor does not detect the drops for a certain interval it alerts the staff of the hospital with the buzzer, helping to monitor the safety of the patients.
Journal Article
Automation of the Oil Extraction Process Performed by Means of A Screw Press
The continuous development of the oil-manufacturing industries causes the necessity of improving extraction technologies. In this case, the specific interest is focused on the control systems of screw presses. Among a great variety of such machines, the small household presses are in significant demand among consumers. Various seeds and kernels require different technological conditions to be provided in order to maximize the qualitative and quantitative characteristics of the extracted oil. Therefore, the main objective of this research is developing and testing the control system allowing for automation of the oil extraction process. Particularly, the temperature parameters of the pressing chamber, extracted oil, and electric motor are to be monitored and limited. In addition, the consumer should be able to predefine the mass of the oil to be extracted. Considering the small household screw press LiangTai LTP200, the general algorithm (block diagram) of the control system operation is proposed and the corresponding experimental prototype is developed. The latter is based on the Arduino Mega microcontroller and is equipped with three temperature sensors, two coolers (fans), one heater, and one mass sensor. The proposed control system allows for continuous monitoring and limiting of the pressing chamber, oil, and electric motor temperatures, as well as the mass of the extracted oil. The experimental data show that the pressing chamber preheating process lasts for about 3 min (170…190 s) and its maximal temperature does not exceed 44°C. The temperature of the extracted oil does not rise over 61°C. The motor temperature changes within the range of 69...71°C. The oil extraction productivity is as follows: 1.2 kg/h (sunflower seeds), 1.06 kg/h (walnut kernels), 0.9 kg/h (almond kernels), and 0.78 kg/h (peanut kernels). The obtained results can used in further investigations focused on analyzing the influence of these parameters on the quantitative and qualitative characteristics of the extracted oil.
Journal Article
Walk Assistance For Outwardly Challenged People
The blind’s competency is to navigate to a particular place and to commence their daily activities is of decisive importance for their prosperity. It is estimated about one billion people are blind out of 285 million people visually impaired of all ages, according to the statistics of World Health Organization. This work includes an affordable and more efficient navigation aid for the blind which provides artificial vision by providing knowledge about an environmental scenario of static and dynamic characteristics of objects around them. This system induces a smart cane with ultrasonic sensors placed to intimate the intermediaries to their acknowledgment through Bluetooth. ZIGBEE used in this project so as to met the sensor and control device communication standards for navigation for the blind with the smart cane via Google maps their destination.
Journal Article
Fuzzy Logic Control Solar-Powered Portable Cooling Box
There are still some areas in the country that are having difficulties receiving uninterrupted electricity for medical aid like vaccines or insulin that require maintenance at certain temperatures. A combination of renewable energy for electricity and portable cooling boxes for medical aid can be a tremendous solution to this problem. In this research, the temperature inside the portable cooling box is kept at 2–16 °C by Peltier units running on solar power. The temperature sensor indicates the inside box’s temperature and the current sensor indicates how much power is being generated. Depending on the variation in solar radiation, the entire system is powered by direct solar or batteries. All data is processed by the Arduino using a fuzzy inference system (FIS). At the end of the process, the prototype project achieved its goal and thus played an important role in providing healthcare resources to marginalized stakeholders.
Journal Article
A novel smart irrigation framework with timing allocation using solenoid valves and Arduino microcontroller
Irrigation in agriculture is the most common way of providing water to agricultural land or fields at normal stretches through channels and embedded platforms with the internet of things (IoT), to upgrade rural development. In this paper, the arrangement of the various types of irrigation systems and embedded platforms for agriculture was studied. The embedded platform can be designed in a suitable framework that can assist the irrigation system in growing more water-required crops. In this work, three relay switches, two solenoid valves, and one water pump source were connected to Arduino ESP32. The free version of Sinric Google Cloud was utilized significantly to control three devices namely, two solenoid valves using two relay switches and a water pump source using one relay switch. The experiment was executed in a prototype manner with timing allocation by considering two agricultural fields where water was supplied either in one field at a time and showed more prominent results to save time, replacement of manual valves, man intervention, power, and suitable quantity of water for more water-required crops namely, arecanut and coconut.
Journal Article