Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Welcome to your new project. Imagine what you can build here.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

"This is what makes time travel possible." —Doc Brown In order to travel through time, the vehicle integrated with the flux capacitor needed to be traveling at 88 mph (140.8 km/h), and required 1.21 gigawatts of power (1,210,000,000 watts)

The semgdaq board is a wearable 6 channel data acquisition unit for capturing surface electromyographic (sEMG) signals from human arm muscles using SJ2-3593D jack connectors while conditioning, digitizing, processing and feature extracting them then transmitting the feature data as vectors to an external AI accelerated board through an SM12B-SRSS IDC connector using 12C and UART communication protocals where AI models are run for various applications including robotic control, muscle signals medical assessment and gesture recognition. The feature vectors are comprised of onset detection, slope sign changes, autoregression coefficients and Short Time Fourier Transform magnitude spectrum data for each segment or window of the signals in real time. This vectors can be used as the basis for further feature extraction on more computationally resourceful hardware where machine learning algorthms can be employed for descision making in the applications mentioned earlier. The board leverages INA125P instrumentation amplifiers together with filter stages utilizing LM324QT op-amps for conditioning and an STM32G4A1VET6 microcontroller for the digitization, processing, feature extraction and data transmission. Since AI models can only be as good as the data, the design of such a DAQ is necessary to ensure clean, reliable and real-time data for AI applications requiring sEMG feature data. The board also has USB-FS and JTAG to cater for debugging and external flash memory to extend its data storage and processing capability. The power (5V) is fed through a screw terminal and is regulated by two LDK320AM LDO regulators to offer 5V, 3.3V and 1.8V to meet the requirements of various components on the board.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

LCD timer with Arduino that uses an encoder to set the time. The code works but it's limited, feel free to upgrade Feel free to review the board.

como puedo corregir este codigo para que funcione en flux.io import time import random import matplotlib.pyplot as plt import io import base64 from fluxio import Flow flow = Flow() @flow.task def linear_search(arr, target): for i in range(len(arr)): if arr[i] == target: return i return -1 @flow.task def binary_search(arr, target): low = 0 high = len(arr) - 1 while low <= high: mid = (low + high) // 2 if arr[mid] == target: return mid elif arr[mid] < target: low = mid + 1 else: high = mid - 1 return -1 @flow.task def measure_time(func, arr, target): start_time = time.time() func(arr, target) end_time = time.time() return end_time - start_time @flow.task def generate_data_and_measure(): sizes = [100, 1000, 5000, 10000, 50000, 100000] linear_times = [] binary_times = [] for size in sizes: arr = random.sample(range(size * 2), size) target = random.choice(arr) sorted_arr = sorted(arr) linear_time = measure_time(linear_search, arr, target) binary_time = measure_time(binary_search, sorted_arr, target) linear_times.append(linear_time) binary_times.append(binary_time) return sizes, linear_times, binary_times @flow.task def plot_results(sizes, linear_times, binary_times): plt.plot(sizes, linear_times, label='Búsqueda Lineal') plt.plot(sizes, binary_times, label='Búsqueda Binaria') plt.xlabel('Tamaño del Array') plt.ylabel('Tiempo de Ejecución (segundos)') plt.title('Análisis de Complejidad Temporal') plt.legend() plt.grid(True) buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) img_str = base64.b64encode(buf.read()).decode('utf-8') plt.close() return img_str @flow.task def main(): sizes, linear_times, binary_times = generate_data_and_measure() img_str = plot_results(sizes, linear_times, binary_times) return img_str if __name__ == "__main__": flow.run(main)

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Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Nice — you can do a clean pulse + latch using a single quad Schmitt-NAND chip: 74HC132 (or 74LVC132 for 3.3 V systems). The HC132 contains four 2-input NAND gates with Schmitt inputs so you can both clean a noisy SYN480R DATA line and build an SR latch (NAND SR is active-LOW) inside one package. Only a few passives and a driver transistor are needed. Below is a ready-to-build recipe (parts, wiring, explanation, tuning tips, and an ASCII schematic) — no extra logic ICs required. Parts (per latch) 1 × 74HC132 (quad 2-input NAND with Schmitt inputs). If your system is 3.3 V use 74LVC132 / 74HC132 rated for 3.3 V. Rin = 47 kΩ (input series) Cfilter = 10 nF (input RC to ground) — tweak for debounce/clean time Rpulldown = 100 kΩ (pull-down at input node, optional) Rpullup = 100 kΩ (pull-up for active-LOW R input so reset is idle HIGH) Rbase = 10 kΩ, Q = 2N2222 (NPN) or small N-MOSFET (2N7002) to drive your load Diode for relay flyback (1N4001) if you drive a coil Optional small cap 0.1 µF decoupling at VCC of IC Concept / how it works (short) Use Gate1 (G1) of 74HC132 as a Schmitt inverter by tying its two inputs together and feeding a small RC filter from SYN480R.DATA. This removes HF noise and provides a clean logic transition. Because it's a NAND with tied inputs its function becomes an inverter with Schmitt behavior. Use G2 & G3 as the cross-coupled NAND pair forming an SR latch (active-LOW inputs S̄ and R̄). A low on S̄ sets Q = HIGH. A low on R̄ resets Q = LOW. Wire the cleaned/inverted output of G1 to S̄. A valid received pulse (DATA high) produces a clean LOW on S̄ (because G1 inverts), setting the latch reliably even if the pulse is brief. R̄ is your reset input (pushbutton, HT12D VT, MCU line, etc.) — idle pulled HIGH. Q drives an NPN/MOSFET to switch your load (relay, LED, etc.). Recommended wiring (pin mapping, assume one chip; use datasheet pin numbers) I’ll refer to the 4 gates as G1, G2, G3, G4. Use G4 optionally for additional conditioning or to build a toggler later. SYN480R.DATA --- Rin (47k) ---+--- Node A ---||--- Cfilter (10nF) --- GND | Rpulldown (100k) --- GND (optional, keeps node low) Node A -> both inputs of G1 (tie inputs A and B of Gate1 together) G1 output -> S̄ (S_bar) (input1 of Gate2) Gate2 (G2): inputs = S̄ and Q̄ -> output = Q Gate3 (G3): inputs = R̄ and Q -> output = Q̄ R̄ --- Rpullup (100k) --- VCC (reset is idle HIGH; pull low to reset) (optional) R̄ can be wired to a reset pushbutton to GND or to an MCU pin Q -> Rbase (10k) -> base of 2N2222 (emitter GND; collector to one side of relay coil) Other side of relay coil -> +V (appropriate coil voltage) Diode across coil If you prefer MOSFET low side switching: Q -> gate resistor 100Ω -> gate of 2N7002 2N7002 source -> GND ; drain -> relay coil low side

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

NEO-M9N GPS Breakout with on-board chip antenna is a high quality GPS board with equally impressive configuration options. The NEO-M9N module is a 92-channel u-blox M9 engine GNSS receiver, meaning it can receive signals from the GPS, GLONASS, Galileo, and BeiDou constellations witn ~1.5 meter accuracy. This breakout supports concurrent reception of four GNSS. This maximizes position accuracy in challenging conditions, increasing precision and decreases lock time; and thanks to the onboard rechargeable battery, you'll have backup power enabling the GPS to get a hot lock within seconds! Additionally, this u-blox receiver supports I2C (u-blox calls this Display Data Channel) which made it perfect for the Qwiic compatibility so we don't have to use up our precious UART ports. Utilizing our handy Qwiic system, no soldering is required to connect it to the rest of your system. However, we still have broken out 0.1"-spaced pins in case you prefer to use a breadboard.

MDD10A is the dual channel version of MD10C which is designed to drive 2 brushed DC motors with high current up to 10A continuously. Just like MD10C, the MDD10A also supports locked-antiphase and sign-magnitude PWM signal. It is also using full solid state components which result in faster response time and eliminate the wear and tear of the mechanical relay.

A generic fixed capacitor for rapid developing circuit topology. Save precious design time by seamlessly add more information to this part (value, footprint, etc.) as it becomes available. Standard capacitor values: 1.0pF 10pF 100pF 1000pF 0.01uF 0.1uF 1.0uF 10uF 100uF 1000uF 10,000uF 1.1pF 11pF 110pF 1100pF 1.2pF 12pF 120pF 1200pF 1.3pF 13pF 130pF 1300pF 1.5pF 15pF 150pF 1500pF 0.015uF 0.15uF 1.5uF 15uF 150uF 1500uF 1.6pF 16pF 160pF 1600 pF 1.8pF 18pF 180pF 1800pF 2.0pF 20pF 200pF 2000pF 2.2pF 22pF 20pF 2200pF 0.022uF 0.22uF 2.2uF 22uF 220uF 2200uF 2.4pF 24pF 240pF 2400pF 2.7pF 27pF 270pF 2700pF 3.0pF 30pF 300pF 3000pF 3.3pF 33pF 330pF 3300pF 0.033uF 0.33uF 3.3uF 33uF 330uF 3300uF 3.6pF 36pF 360pF 3600pF 3.9pF 39pF 390pF 3900pF 4.3pF 43pF 430pF 4300pF 4.7pF 47pF 470pF 4700pF 0.047uF 0.47uF 4.7uF 47uF 470uF 4700uF 5.1pF 51pF 510pF 5100pF 5.6pF 56pF 560pF 5600pF 6.2pF 62pF 620pF 6200pF 6.8pF 68pF 680pF 6800pF 0.068uF 0.68uF 6.8uF 68uF 680uF 6800uF 7.5pF 75pF 750pF 7500pF 8.2pF 82pF 820pF 8200pF 9.1pF 91pF 910pF 9100pF #generics #CommonPartsLibrary

This is a simple power factor correction project based on UCC28180DR. It is used in power supplies to correct the power factor, making the load more "resistive" and efficient. #PFC #AC #DC #TI

This is a boost converter board based on LM3481 with 5V input and 12V 1.8A output voltage #dcdc #boost #ti #power #referenceDesign #powermanagement #texas-instruments #template #reference-design

This is a reference design of high efficiency synchronous step-down dc-dc converters optimized for battery powered portable applications. #power #dcdc #ti #buck #referenceDesign #powermanagement #texas-instruments #template #reference-design

This is a module boost DC-DC Regulator with 12V 0.3A(1.2A) output and 5V input voltage for LEDs #ti #ledpower #led #boost #referenceDesign #powermanagement #texas-instruments #template #reference-design

This is a boost converter board based on LM3481 with 5V input and 12V 1.8A output voltage #dcdc #boost #ti #power #referenceDesign #powermanagement #texas-instruments #template #reference-design

This is a power module based on a TPS61085 voltage booster IC with an output voltage of 12V 3A(max) #boost #power #dcdc #ti #referenceDesign #powermanagement #texas-instruments #template

This is a power module based on a TPS61085 voltage booster IC with an output voltage of 12V 3A(max) #boost #power #dcdc #ti #referenceDesign #powermanagement #texas-instruments #template

Time-to-Digital converter plug-on module (shield) for Raspberry Pi B+. The board utilizes a TDC7200 (TI) with a time resolution of ~50ps. The Start/Stop inputs to the TDC can be configured with JP2 from either external signals or coming from the RPi's GPIO5 pin in case a timing signal is present at this pin from another plug-on board on the same RPi. Start and Stop signals can be configured from different signals (coming from the input connectors) or the same source (setting "common" on JP3) either supplied from the Start input connector or GPIO5. Read-out of the TDC chip is done through the RPi's SPI bus.

This is a board for energy-efficient projects, based on STM32 with a BQ25570 controller and a temperature humidity sensor. There is also a display and a supercapacitor in the role of power supply #MPPT #solar #STM32 #ti

This is a power module based on a TPS61085 voltage booster IC with an output voltage of 12V 3A(max) #boost #power #dcdc #ti #referenceDesign #powermanagement #texas-instruments #template

This is a reference design of high efficiency synchronous step-down dc-dc converters optimized for battery powered portable applications. #power #dcdc #ti #buck #referenceDesign #powermanagement #texas-instruments #template #reference-design

FlowState Headband EVT1 — 4-Channel EEG Calibration Device Closed-loop EEG neurofeedback headband for theta/beta baseline calibration. 4-layer mixed-signal PCB, 40x30mm. Core ICs: - ADS1299-4PAG (TI) — 4-channel 24-bit EEG analog front end, SPI interface, 250 SPS - nRF5340 (Nordic) — Dual-core BLE 5.3 SoC, 128 MHz app core + 64 MHz network core Key requirements: - Separate analog and digital power domains (dual LDO: LP5907 for AVDD, AP2112 for DVDD) - Split analog/digital ground planes with single-point connection - 6 electrode inputs (4 active + 1 reference + 1 DRL) with individual TVS ESD protection on each - LIS2DH12 accelerometer (I2C) for motion artifact detection - MCP73831 USB-C battery charging (300-500 mAh LiPo) - 2.4 GHz chip antenna or PCB trace antenna at board edge with 10mm keepout - Conformal coating for sweat/moisture protection Reference designs: - Analog front-end: TI ADS1299 EVM (SBAS499) - Digital/BLE: Nordic nRF5340 DK reference schematic Critical constraint: Microvolt-level EEG signals — analog input routing and power supply filtering are the highest-priority layout concerns.

Wearable health device prototype schematic with ECG sensing via AD8232, motion sensing via BNO085 or BMI270 IMU breakout, on-board processing using TI TM4C123G LaunchPad or MSPM0G3507 LaunchPad, optional ESP8266 Wi-Fi, LiPo battery charging with MCP73831, regulated 3.3 V power from TPS63031 buck-boost or AP2112 LDO prototype option, and user interfaces including electrode connector, JST battery connector, programming header, and optional on/off switch.