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dfr0144 capacitive touch sensor adafruit  (Adafruit Industries)

 
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    Adafruit Industries dfr0144 capacitive touch sensor adafruit
    Dfr0144 Capacitive Touch Sensor Adafruit, supplied by Adafruit Industries, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/capacitance+touch+sensor/5+at42qt1070+breakout+capacitive+pad+sensor+standalone+touch/pm42248694-52-312-316
    Average 86 stars, based on 1 article reviews
    dfr0144 capacitive touch sensor adafruit - by Bioz Stars, 2026-10
    86/100 stars

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    Related Articles

    other:

    Article Title: Dopamine dynamics during stimulus-reward learning in mice can be explained by performance rather than learning.
    Article Snippet: A capacitance-touch sensor (MPR121, AdaFruit.com) attached to the spout was used to detect licks.

    Article Title: Dopamine dynamics during stimulus-reward learning in mice can be explained by performance rather than learning
    Article Snippet: A capacitance-touch sensor (MPR121, AdaFruit.com) attached to the spout was used to detect licks.



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    Image Search Results


    (a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and MPR121 capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.

    Journal: bioRxiv

    Article Title: SqueakPose Studio: An end-to-end platform for pose estimation and real-time edge-AI deployment

    doi: 10.64898/2026.01.24.700912

    Figure Lengend Snippet: (a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and MPR121 capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.

    Article Snippet: Capacitive sensing for fluid intake was handled via Adafruit MPR121 12-key capacitive touch sensors, communicating with the RP2040 over I2C.

    Techniques: Control