Uno r3

Manufactured by Arduino

Sourced in Italy, United States

The Arduino Uno R3 is a microcontroller board based on the ATmega328P. It features 14 digital input/output pins, 6 analog inputs, a USB connection, a power jack, an ICSP header, and a reset button. The board can be powered via the USB connection or an external power supply. It has a operating voltage of 5V.

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Lab products found in correlation

Matlab, by MathWorks (2 mentions) Photomultiplier tube, by Hamamatsu Photonics (2 mentions) Xlumplfln w, by Olympus (2 mentions) Prairieview, by Bruker (2 mentions) Sen 3301, by Nihon Kohden (1 mentions) Ni pcie 6343 x, by National Instruments (1 mentions) Bno055, by Bosch (1 mentions) Sfh 2700 fa, by Osram (1 mentions) Power meter, by Thorlabs (1 mentions) Labview, by National Instruments (1 mentions) Su8020, by Hitachi (1 mentions) Prusaslicer, by Prusa Research (1 mentions) Prusa mini, by Prusa Research (1 mentions) Fusion 360, by Autodesk (1 mentions) Pt1 m, by Thorlabs (1 mentions)

47 protocols using uno r3

Controlled Rotational Exploration in Mice

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To ensure even sampling of the entire rotation space, we carefully controlled the rotation of the chamber. Three stabilizing bearings (608-2RS) were mounted on optical posts (TR-series, Thorlabs) and placed around the cage to eliminate any X-Y translation. A DC motor (ZGA37RG, Greartisan) was coupled to a rubber wheel and used to spin the chamber using the outer surface. The speed of the DC motor was controlled with a microcontroller (UNO R3, Arduino) drive the rotation of the chamber (3–7 rpm).
The chamber was rotated for 90–120 s followed by a 5 s rest period, which was then repeated 2–6 times for each recording. Mice were monitored via video to ensure that they followed along with the chamber rotation. The total time spent rotating on the floating air chamber never exceeded 15 min, in order to limit potential discomfort.
For experiments in light and darkness, the lights in the experimental box were switched using a microcontroller (UNO R3, Arduino). The initial repeat was always performed in a light on trial, so that the mouse could register visual landmarks in its environment. Afterward, the box lights were turned on or off in between repeats, during the rest period.
The chamber was thoroughly cleaned and disinfected between experiments in order to remove any odor traces.

Sit K.K, & Goard M.J. (2023). Coregistration of heading to visual cues in retrosplenial cortex. Nature Communications, 14, 1992.

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Automated Electrochromic Film Control

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Data acquisition and electronics control were achieved with a combination of Python 2.7 and an Arduino Uno R3 microcontroller. A Python script captured individual frames from a Logitech HD Webcam C310 camera for all behavior experiments.
Electrochromic film (Justin Cary, CaryShop) was switched on by a Python script communicating via USB with an Arduino Uno R3 microcontroller, using a DC 12V relay module (SainSmart) and a 12-60V inverter.

Stednitz S.J., McDermott E.M., Ncube D., Tallafuss A., Eisen J.S, & Washbourne P. (2018). Forebrain control of behaviorally-driven social orienting in zebrafish. Current biology : CB, 28(15), 2445-2451.e3.

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Automated Photomultiplier Tube Monitoring System

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The PMT outputs an analog signal from 0 V to +5 V, proportional to the light intensity reaching the detection window. For the signal digital conversion to be realized without losing resolution, an external 16-bit ADC (ADS1015) was connected to an Arduino Uno R3 (Figure 4b). After the digital conversion, a 2.4 GHz RF transceiver (NRF24L01 Transceiver, Nordic Semiconductor, Trondheim, Norway) was connected to the microcontroller, and was continuously transmitting data with a 1 Hz sampling frequency (the microcontroller read the PMT signal once every second). A MATLAB interface was also developed in which it is possible to visualize the signal in real-time. A power source was designed on a PCB, which was projected to be connected to a battery. The system requirements are 10 V for the UV LEDs, +5 V, −5 V and 0.5 to 1.1V (gain adjustment) for the Photomultiplier, and +10 V to Arduino Uno R3.

Simões J, & Dong T. (2018). Continuous and Real-Time Detection of Drinking-Water Pathogens with a Low-Cost Fluorescent Optofluidic Sensor. Sensors (Basel, Switzerland), 18(7), 2210.

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Arduino-Based Signal Acquisition and Triggering

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In this design, an Arduino UNO R3 (Somerville, MA, USA) is used as the signal acquisition and processing device. The built-in ADC of the Arduino UNO R3 is used to set the threshold of the trigger source. The judgment is made by writing a program to compare the size of the input signal and the threshold. Then, the corresponding instructions are set and executed to the host computer through serial port transmission.

Wu X., Yang Z., Dong Y., Teng L., Li D., Han H., Zhu S., Sun X., Zeng Z., Zeng X, & Zheng Q. (2024). A Self-Powered, Skin Adhesive, and Flexible Human-Machine Interface Based on Triboelectric Nanogenerator. Nanomaterials (Basel, Switzerland), 14(16).

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Closed-Loop Smart Metasurface System

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The simulation results of the unit cell and metasurface are obtained using commercial software, namely, CST Microwave Studio, with a time-domain solver. The sample is fabricated using the print-circuit board (PCB) technology. The real-time monitoring and processing of spatial variation is executed by the MCU (Arduino UNO R3), which is connected to an FPGA (for voltage control) and a gyroscope sensor (ADXL335 3-Axis). The detailed operating process of this closed-loop system is provided in Supplementary Note S5. The control components, namely, the gyroscope, MCU, and FPGA, are fixed onto the back of the smart metasurface (see Supplementary Notes S6 and S7 for details). The maximum power consumption is ~10 W, including 3.6 W for diodes (each PIN diode requires ~1 mA at 2 V), 1 W for sensors, and 5 W for the FPGA.

Ma Q., Bai G.D., Jing H.B., Yang C., Li L, & Cui T.J. (2019). Smart metasurface with self-adaptively reprogrammable functions. Light, Science & Applications, 8, 98.

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Arduino-based Communication Gateway for Sensor Systems

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The Uno R3 (Arduino^®, Turin, Italy) was chosen as the communication gateway between the device and PC. This is a popular platform in the low-price segment of the market that has many compatible parts. The variety of Input/Output-ports and the fact that the power input can be provided via a cable or battery make this platform a prototyping-friendly solution. The Uno R3 connects to a PC via the standard USB port, which enables the system to be used in conjunction with a variety of operating systems, such as Windows or Linux. A Windows serial port is used as the communication protocol. The set of commands that can be used to control the device are called by software that runs on the PC. The maximal sampling rate of this protocol is about 200 Hz, which is acceptable for this system as it is twice the maximal sampling rate of the sensor. The data flow is illustrated by Figure 2.

Zhao Y., Görne L., Yuen I.M., Cao D., Sullman M., Auger D., Lv C., Wang H., Matthias R., Skrypchuk L, & Mouzakitis A. (2017). An Orientation Sensor-Based Head Tracking System for Driver Behaviour Monitoring. Sensors (Basel, Switzerland), 17(11), 2692.

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Foraging Behavior in Water-Restricted Mice

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One week after surgery, mice started a water restriction schedule to maintain 85 to 90% of free-drinking bodyweight for 5 days. The experimenter petted the mice 5 min per day for 3 days in a row and then started task training. All behavioral tasks were conducted during the dark period of the light/dark cycle.
The foraging task shuttle box had two chambers (10 cm by 10 cm by 15 cm) connected by a narrow corridor (45 cm by 5 cm by 15 cm; Fig. 1A). A water port (1.2-mm O.D. steel tube, 3 cm above the floor) was attached to the end of one chamber, defined as the reward zone, with the other as the waiting zone. The position of the mouse in the shuttle box was tracked online with a custom MATLAB (2016b, MathWorks) program through an overhead camera (XiangHaoDa, XHD-890B). The experimental procedure control and behavioral event acquisition were implemented with a custom MATLAB program and an integrated circuit board (Arduino UNO R3).

Gao Z., Wang H., Lu C., Lu T., Froudist-Walsh S., Chen M., Wang X.J., Hu J, & Sun W. (2021). The neural basis of delayed gratification. Science Advances, 7(49), eabg6611.

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Perfusion Culture of Cells on Inserts

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The dynamic culture of cells on inserts was carried out using a peristaltic pump (Model 114 ST, Watson Marlow, Falmouth, UK) in a cell culture incubator under standard conditions (37 °C, 5% CO₂). The mounted stepper motor was operated with an Arduino UNO R3 microcontroller with the use of the AccelStepper library v1.61. The circuit consisted of four series-connected chambers and a bubble trap, shown in Figure S3b. Sterilization was performed as described above. The pump was operated at 10 RPM, generating a constant fluid flow of 1.42 mL∙min⁻¹, which results in an average surface shear stress of 0.012 dyn∙cm⁻² (12 × 10⁻⁴ Pa). The entire culture medium of 12 mL was exchanged every four days.

Lindner M., Laporte A., Block S., Elomaa L, & Weinhart M. (2021). Physiological Shear Stress Enhances Differentiation, Mucus-Formation and Structural 3D Organization of Intestinal Epithelial Cells In Vitro. Cells, 10(8), 2062.

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Light-Controlled Neuronal Activation Protocols

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Light illumination was delivered through an electronic stimulator (SEN-3301, Nihon Kohden) connected to a light source (470 nm, 3.1 mW/mm² at maximum, Niji, Blue Box Optics). The light intensity was controlled by our original Python programs⁴⁸ with a microcontroller (Arduino Uno R3). In Fig. 2a, we set the delay at 0 s, the interval at 10 s, the duration at 5 s, and the train at three times, and the intensity was automatically adjusted to 1, 2, 5, 10, 20, 50, and 100%. In Fig. 3a–c, we set the delay at 200 ms, the interval at 0 s, the duration at 200 ms, and the train at 1, and the intensity was adjusted to 50, 5, and 2%. In Fig. 4a, we set the delay at 0 ms, the interval at 15 s, the duration at 5 s, and the train at 3 times, and the intensity was automatically adjusted to 50, 5, and 2%. In Fig. 2f, we set the delay at 0 ms, the interval at 0 s, the duration at 30 s, the train at 1, and the intensity at 100%. In Fig. 5a, we set the delay at 0 ms, the interval at 0 s, the duration at 600 s, the train at 1, and the intensity at 1%.

Mukai Y., Li Y., Nakamura A., Fukatsu N., Iijima D., Abe M., Sakimura K., Itoi K, & Yamanaka A. (2023). Cre-dependent ACR2-expressing reporter mouse strain for efficient long-lasting inhibition of neuronal activity. Scientific Reports, 13, 3966.

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Arduino-Based Heartbeat Monitoring System

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The hardware shown in Figure 1 that has been used to build the proposed system is as follows:

Arduino Uno R3 (ESP32).

Heartbeat sensor.

Breadboard and jumper wires.

Abdel Hameed M., Hassaballah M., Hosney M.E, & Alqahtani A. (2022). An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders. Computational Intelligence and Neuroscience, 2022, 2247675.

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