This design implements a USB security token powered by an STM32 microcontroller. The device is engineered for compactness and efficient PCB integration while ensuring robust security features. Key elements of the design include: - **Microcontroller Core:** A STM32F103T8U6 serves as the primary processing unit, handling USB communication and security protocols. - **USB Interface:** A USB-A plug provides connectivity to the host. Dedicated net portals ensure proper routing of the VBUS, D+, D–, and ground signals. - **Power Regulation:** A low-dropout regulator supplies a stable 3.3V operating voltage, ensuring low noise and proper current supply to the microcontroller and peripherals. - **Signal Conditioning and EMI Filtering:** An EMI filter is used to maintain signal integrity and reduce interference while preserving the security token’s functionality. - **Synchronous Elements:** A ceramic resonator is incorporated to provide a precise clock source for USB data transfer and microcontroller operations. - **Additional Components:** Surface-mount resistors, capacitors, and LED indicators are deployed to ensure proper conditioning, decoupling, and status feedback. Their compact 0402 packages facilitate a highly integrated design. - **Connectivity and Net Portals:** Custom net portals are used throughout the schematic to streamline connectivity and PCB layout, keeping the design modular and easy to modify. This USB security token is designed with industry-standard components and robust connectivity to ensure secure, reliable operation in portable security applications. #USBToken #STM32 #PCBDesign #SecurityTechnology #PortableSecurity #Microcontrollers #USBInterface #PowerRegulation #EMIProtection #CompactDesign

A low-pass filter is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency.

sEMG-DAQ 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 transmitting them as sEMG data to an external AI accelerated board through an SM12B-SRSS IDC connector where AI models are run for various applications including robotic control, muscle signals medical assessment and gesture recognition. The board leverages an INA125P instrumentation amplifier together with filter stages utilizing LM324QT op-amps for conditioning and an STM32G4A1VET6 microcontroller for the digitization, processing and data transmission of the signals. 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 data. The board also has USB-FS and JTAG to cater for debugging. 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.

This project is a Signal Generator, designed to output waveforms such as sine, square and triangular waves. It leverages the capabilities of operational amplifiers (op-amps) like the LMV321 in combination with resistors and capacitors to shape the output signals. The design benefits from the op-amps' negative feedback to stabilize and regulate the signal output. #project #LMV321

The electronic speed controller (ESC) is an essential part of an electric propulsion system’s hardware. It acts like the brain of the system by telling the motor how fast to go based on data signals it receives from the throttle controller. For smaller applications like drones and RC vehicles, this controller has the name ‘ESC’, whereas for larger manufacturing applications it may be called an electronic control unit, inverter, or motor controller. #drone #motorController #PCA9685 #controller #motor #esc #referenceDesign #project #EFM8BB21F16G #MPU9250 #template

(AL8805W5) The PicoBuck LED Driver is an economical and easy to use driver that will allow you to control and blend three different LEDs on three different channels. By default, each channel is driven at 330mA; that current can be reduced by either presenting an analog voltage or a PWM signal to the board. Version 12 of the board adds a solderable jumper that can be closed to increase the maximum current to 660mA. The new voltage regulator also increased the voltage rating on the various components on the board, allowing it to be used up to the full 36V rating of the AL8805 part.

The Texas Instruments MSP430FR604x and MSP430FR603x family comprises highly integrated ultrasonic sensing and measurement system-on-chips (SoCs) designed specifically for water and heat metering applications. The featured components, including MSP430FR6047, MSP430FR60471, MSP430FR6045, MSP430FR6037, MSP430FR60371, and MSP430FR6035, deliver best-in-class ultrasonic water flow measurement with ultra-low power consumption. These microcontrollers excel with an active mode current consumption of approximately 120 µA/MHz and a standby mode power draw as low as 450 nA with a real-time clock (RTC) enabled. Key functionalities include a high-precision differential time-of-flight (dTOF) accuracy of less than 25 ps, integrated analog front-end, programmable pulse generation (PPG), and an analog comparator. They also interface directly with standard ultrasonic sensors up to 2.5 MHz and feature up to 256KB FRAM, robust RAM options, and integrated LCD drivers for up to 264 segments. The embedded low-energy accelerator (LEA) enhances digital signal processing capabilities, making these components ideal for battery-powered metering solutions. Peripherals include multiple enhanced serial communication interfaces, high-performance ADCs, DMA controllers, and a suite of timers and encryption modules. These features combine to offer a powerful solution for high-accuracy, low-cost, and ultra-low-power metering applications.

A simple 3DO in-game reset mod. MCU sniffs controller data and fires a reset signal to the console when L+R+X+P buttons are pressed.

This design is three parts, one a bandpass filter to allow only a certain frequency to come through the device, and the other two to filter and jam out different frequencies that would be coming near the device. The bandpass is set at 450MHz as is the common frequency for phones, the top jammer is set up for blocking out typical radio frequencies while the bottom is set to block frequencies above that of a phone signal. Vin=20 volts

The SparkFun ZOE-M8Q GPS Breakout is a high accuracy, miniaturized, GPS board that is perfect for applications that don't possess a lot of space. The on-board ZOE-M8Q is a 72-channel GNSS receiver, meaning it can receive signals from the GPS, GLONASS, BeiDou, and Galileo constellations. This increases precision and decreases lock time and thanks to the onboard rechargable 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.

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

This ADM3054BRWZ-RL7-based reference design is a CAN bus transceiver circuit, providing reliable data communication over the CAN network. The design features a range of capacitors and resistors to ensure signal integrity, and a NUP2105L for voltage protection. It's ideal for applications requiring reliable data communication in an automotive or industrial environment. #referenceDesign #project #CANbus #interface #transceiverCircuit #ADM3054 #ADM3054BRWZ-RL7 #referenceDesign #canbus #texas-instruments #template #reference-design #reference-design

Design a circuit that detects the presence of the infrared signal. The circuit should light a red LED when infrared signal is detected otherwise it should light a green LED. You may use NPN transistors, IR LED, Photodiode, Red LED, Green LED and a rectifier diode.

The Ariel AI Chip, a state-of-the-art integrated circuit designed for high-performance computing applications, incorporates an innovative architecture that leverages radical transistor technology to optimize AI and machine learning tasks. At the heart of this chip lies a quad-core CPU operating at a clock speed of 2GHz, distinguished by its part number CPU-RT-4C-2G. The chip's power management is efficiently handled by a DC power supply, specified as DCPS-5V, ensuring a stable 5V input. Key to its operation are two NPN transistors, identified by part numbers NPN-TRANS-001 and NPN-TRANS-002, which, along with a pair of 1kΩ resistors (RES-1K and RES-1K-002) and a 10µF capacitor (CAP-10UF), form the critical signal processing and conditioning circuitry. This assembly is designed for seamless integration into advanced computing systems, particularly those focused on Flux AI environments, where its performance and efficiency can be fully leveraged. The Ariel AI Chip sets a new benchmark in AI computing, offering unparalleled processing power and efficiency for cutting-edge applications.

This project is a reference design utilizing Texas Instruments' PGA300ARHHR, a precision analog and digital IC, for signal processing. The circuit also includes a Diodes Incorporated's FZT603QTA Transistor, passive components like resistors and capacitors, and connectors from JST Sales America Inc. #project #referenceDesign #industrialsensing #texas-instruments #template #reference-design

The BLM02AX121SN1# is a chip ferrite bead manufactured by Murata, designed to function as a resistor at noise frequencies, thereby minimizing resonance and maintaining signal integrity. This surface-mount device (SMD) features a compact size of 0.4mm x 0.2mm, making it ideal for noise suppression in small electronic equipment, such as PA modules for cellular phones. The component operates effectively across a wide frequency range (30MHz to several hundred MHz) without requiring a ground connection, making it suitable for circuits without stable ground lines. The BLM02AX121SN1# offers a rated current of 250mA at 125°C, a maximum DC resistance of 0.50Ω, and an impedance of 1200Ω at 100MHz with a tolerance of ±25%. It is available in two packaging options: bulk (bag) with a standard packing quantity of 1000 units and 180mm paper tape with 20000 units. The device is compliant with RoHS and REACH standards, ensuring its suitability for consumer and certain medical and industrial applications.

The Ariel AI Chip, a pioneering component in the realm of artificial intelligence hardware, integrates a suite of electronic elements tailored for high-performance computing applications. At the heart of this assembly lies a CPU with a Radical Transistor architecture, featuring a quad-core setup clocked at 2GHz, identified by the part number CPU-RT-4C-2G. Power management is facilitated through a DC Power Supply, marked DCPS-5V, ensuring a stable 5V supply to the intricate circuitry. The chip's switching capabilities are bolstered by two NPN transistors, NPN-TRANS-001 and NPN-TRANS-002, which play a crucial role in signal modulation. Essential to the chip's operation are the passive components: two 1kΩ resistors (RES-1K and RES-1K-002) and a 10µF capacitor (CAP-10UF), which together with the transistors, form a robust network ensuring reliable performance under varying load conditions. Designed for integration into advanced AI systems, this chip stands out for its innovative use of standard components in a configuration that emphasizes efficiency, reliability, and high-speed data processing capabilities.

The Ariel AI chip prototype, designed for integration with Flux AI for advanced simulation and testing, incorporates a suite of electronic components optimized for high-performance computing applications. At the heart of this system lies a CPU with a radical transistor architecture, featuring a 4-core configuration and a clock speed of 2GHz, identified by part number CPU-RT-4C-2G. Power management is facilitated through a DC Power Supply, specified as DCPS-5V, ensuring a stable 5V supply to the system. The circuit's dynamic performance is modulated by two NPN transistors, NPN-TRANS-001 and NPN-TRANS-002, which, along with precision resistors RES-1K and RES-1K-002 (both 1kΩ), and a 10μF capacitor (CAP-10UF), form the critical signal processing path leading to the CPU. This configuration is designed to provide an efficient, reliable processing environment for AI computations, with an emphasis on minimizing latency and maximizing throughput. The Ariel AI chip's architecture, combining traditional components with an innovative CPU design, offers a versatile platform for developing advanced AI applications, reflecting a significant step forward in computational technology.

The Ariel AI Chip, an innovative component designed for high-performance computing applications, integrates a sophisticated array of electronic parts to deliver unparalleled processing capabilities. At the heart of this system is a CPU with a radical transistor architecture, featuring a core count of 4 and a clock speed of 2GHz, identified by its part number CPU-RT-4C-2G. Power management within the chip is efficiently handled by a DC Power Supply, rated at 5V, with the part number DCPS-5V, ensuring stable and reliable operation. The chip's signal processing and amplification needs are addressed through the inclusion of two NPN transistors, with part numbers NPN-TRANS-001 and a similar variant, providing the necessary gain and switching capabilities for complex computational tasks. Signal conditioning is further enhanced by a pair of 1kΩ resistors, RES-1K and RES-1K-002, and a 10µF capacitor, CAP-10UF, which work together to filter and stabilize the power supply and signal pathways, ensuring clean and noise-free operation. This integration of components within the Ariel AI Chip offers electrical engineers a robust platform for developing advanced AI systems, combining high processing power with efficient power management and signal integrity, suitable for a wide range of applications in the field of artificial intelligence.

Wireless Stethoscope Hardware Design: ERC/DRC Compliance, Signal Integrity, and Verified Footprints

make this for me now # Device Summary & Specification Sheet ## 1. Overview A rugged, Arduino-Uno-and-Raspberry-Pi-style single-board micro-PC featuring: - Smartphone-class CPU (Snapdragon 990) - USB-C Power Delivery + 4×AA alkaline backup + ambient-light harvester - On-board Arduino-Uno-compatible ATmega328P - External NVMe SSD via USB3 bridge & optional Thunderbolt 3 eGPU support - 5× USB 3.0 ports, HDMI in/out, Gigabit Ethernet & SFP fiber, Wi-Fi, Bluetooth, LoRa - 0.96″ OLED status display, 3.5 mm audio jack with codec --- ## 2. Key Specifications | Category | Specification | |--------------------|-------------------------------------------------------------------------------| | CPU | Snapdragon 990, octa-core up to 2.84 GHz | | Memory | 6 GB LPDDR4x DRAM | | Storage Interface | PCIe Gen3 ×4 → M.2 NVMe + USB 3.1 Gen1 bridge | | MCU | ATmega328P (Arduino-Uno-compatible) | | Power Input | USB-C PD up to 20 V/5 A; 4×AA alkaline backup; ambient-light photodiode boost | | Power Rails | 12 V, 5 V, 3.3 V, 1.8 V, 1.2 V via buck/buck-boost regulators | | USB Hub | 5× USB 3.0 downstream ports | | Display | 0.96″ 128×64 OLED via I²C/SPI | | Networking | 1 × Gigabit RJ45; 1 × SFP fiber; Wi-Fi 802.11ac + Bluetooth; LoRa SX1276 | | Video I/O | HDMI 2.0 input (RX) & output (TX) | | Audio | 3.5 mm jack + TLV320AIC3101 codec; Bluetooth audio | | Form Factor | Raspberry Pi–style header + Arduino-Uno shield headers; 4× standoff mounts | --- ## 3. Complete Parts List | Part | Function | Qty | |------------------------------------------------------------------------------------------------|-----------------------------------------------|-----| | [Snapdragon 990](https://www.flux.ai/search?type=components&q=Snapdragon%20990) | Main application CPU | 1 | | [LPDDR4x DRAM](https://www.flux.ai/search?type=components&q=LPDDR4x%20DRAM) | System memory | 1 | | [eMMC 64GB](https://www.flux.ai/search?type=components&q=eMMC%2064GB) | On-board storage | 1 | | [M.2 NVMe Connector](https://www.flux.ai/search?type=components&q=M.2%20NVMe%20Connector) | External SSD interface | 1 | | [JMS583](https://www.flux.ai/search?type=components&q=JMS583) | PCIe→USB 3.1 bridge for NVMe | 1 | | [Titan Ridge](https://www.flux.ai/search?type=components&q=Titan%20Ridge) | Thunderbolt 3/eGPU controller | 1 | | [STUSB4500](https://www.flux.ai/search?type=components&q=STUSB4500) | USB-C Power-Delivery controller | 1 | | [LTC4412](https://www.flux.ai/search?type=components&q=LTC4412) | Ideal-diode OR-ing | 1 | | [LTC3108](https://www.flux.ai/search?type=components&q=LTC3108) | Ambient-light (solar) energy harvester | 1 | | [Battery Holder 4×AA](https://www.flux.ai/search?type=components&q=Battery%20Holder%204xAA) | Alkaline backup power | 1 | | [TPS53318](https://www.flux.ai/search?type=components&q=TPS53318) | 6 V→5 V synchronous buck regulator | 1 | | [MCP1700-3302E/TO](https://www.flux.ai/search?type=components&q=MCP1700-3302E/TO) | 6 V→3.3 V LDO | 1 | | [TPS63060](https://www.flux.ai/search?type=components&q=TPS63060) | Buck-boost for 12 V rail (eGPU power) | 1 | | [ATmega328P](https://www.flux.ai/search?type=components&q=ATmega328P) | Arduino-Uno microcontroller | 1 | | [ESP32-WROOM-32](https://www.flux.ai/search?type=components&q=ESP32-WROOM-32) | Wi-Fi + Bluetooth co-processor | 1 | | [SX1276](https://www.flux.ai/search?type=components&q=SX1276) | LoRa transceiver | 1 | | [TUSB8041](https://www.flux.ai/search?type=components&q=TUSB8041) | 5-port USB 3.0 hub IC | 1 | | [Ethernet PHY](https://www.flux.ai/search?type=components&q=Ethernet%20PHY) | Gigabit Ethernet physical transceiver | 1 | | [SFP Cage](https://www.flux.ai/search?type=components&q=SFP%20Cage) | Fiber-optic SFP module connector | 1 | | [TDA19978](https://www.flux.ai/search?type=components&q=TDA19978) | HDMI 2.0 receiver (input) | 1 | | [TFP410](https://www.flux.ai/search?type=components&q=TFP410) | HDMI 2.0 transmitter (output) | 1 | | [TLV320AIC3101](https://www.flux.ai/search?type=components&q=TLV320AIC3101) | Audio codec for 3.5 mm jack & Bluetooth audio | 1 | | [Audio Jack 3.5mm](https://www.flux.ai/search?type=components&q=Audio%20Jack%203.5mm) | Headphone/mic port | 1 | | [SSD1306 OLED](https://www.flux.ai/search?type=components&q=SSD1306%20OLED) | Status display | 1 | --- ## 4. High-Level Design & Implementation Steps 1. **Schematic Creation** - Section off blocks: CPU + memory, power-supply, NVMe/USB bridge, MCU, wireless, USB hub, video, audio, I/O headers. - Place all parts from the list and wire per respective datasheet reference circuits. 2. **Power System** - Implement USB-C PD with STUSB4500 → buck/buck-boost & LDO rails. - Add LTC4412 to OR-ing battery pack into 6 V bus. - Include LTC3108 harvest input to 6 V bus (no recharge to AA). - Add decoupling & bulk caps per regulator datasheets; PTC fuses & ESD diodes on inputs. 3. **SoC & High-Speed Interfaces** - Break out Snapdragon 990 BGA with DDR4 routing (point-to-point). - Route PCIe Gen3 lanes to M.2 slot via JMS583 and to Titan Ridge for TB/eGPU. - Follow high-speed layout guidelines: impedance, length matching, EMI filters. 4. **MCU & Expansion Headers** - Place ATmega328P with UNO-style 2.54 mm headers; add ISP 6-pin, I²C pull-ups, reset circuit. 5. **Connectivity Blocks** - USB hub: TUSB8041 + ESD + chokes → five USB-A 3.0 ports. - Ethernet: RJ45 + magnetics + PHY; SFP cage + PHY/SFP interface. - Wireless: ESP32 module pad + SX1276 + antenna matching; program pads. 6. **Video & Audio** - HDMI RX (TDA19978) & TX (TFP410) blocks with TMDS ESD & termination. - Audio codec + 3.5 mm jack; route differential audio lines; add ESD. 7. **Display & Indicators** - SSD1306 OLED on I²C; add pull-ups & backlight power if needed. - LEDs for power, charge, status. 8. **PCB Layout & Mechanical** - Follow Raspberry Pi 2×20 header footprint & UNO shield hole pattern. - Add reinforcement for rugged use; standoffs at corners. - Define 4-layer stackup: • Top: signal • L2: GND • L3: PWR • Bottom: signal 9. **DRC/ERC Reviews** - Run ERC for decoupling, pull-ups, power nets. - Run DRC for high-speed clearance, differential pairs, via aspect ratios. 10. **Fabrication & Testing** - Export Gerbers, IPC-2581, BOM. - Prototype assembly; verify power sequencing, rails, interfaces. - Functional tests: USB-C PD negotiation, NVMe throughput, eGPU over TB3, wireless comm, audio, OLED. --- This spec sheet and part list should guide your Flux schematic and PCB layout workflow through to fabrication and validation. Let me know if you’d like to deep-dive into any specific block or review datasheet details next!

SOT-23-5 is a small 5-pin Surface Mount Device (SMD) package with a body size of 3mm x 1.4mm and a lead pitch of 0.95mm. The package has a low profile and is commonly used for small signal transistors and diodes. #part #template

This project is a distance detecting sensor circuit build around GP2Y0D805Z0F IC from SHARP/Socle Technology. It includes decoupling capacitors, feedback resistors, and a LED for signal indication, with power being supplied via the J1 connector. #referenceDesign #industrialsensing #sharp #template #reference-design

The Ariel AI chip prototype is an advanced electronic component designed for integration into the Flux AI environment, facilitating simulation and testing of AI applications. This component features a collection of carefully selected parts including a DC power supply (DCPS-5V), NPN transistors (NPN-TRANS-001 and NPN-TRANS-002), resistors (RES-1K and RES-1K-002), a capacitor (CAP-10UF), and a cutting-edge CPU (CPU-RT-4C-2G) with a 4-core architecture, operating at a clock speed of 2GHz. The CPU's innovative radical transistor architecture is specifically tailored for high-performance computing tasks associated with AI and machine learning applications. This configuration ensures efficient power management, signal processing, and data flow within the chip, making it an ideal choice for developers and engineers looking to push the boundaries of AI technology. The inclusion of standard components like NPN transistors, resistors, and capacitors, alongside the specialized CPU, allows for a versatile and robust design, suitable for a wide range of AI applications.

A simple walkie-talkie circuit running off an Arduino Nano. Utilizes a BJT to amplify the microphone voltage and transmits the radio signal through a nRF24L01 series Transceiver.

This project is a distance detecting sensor circuit build around GP2Y0D805Z0F IC from SHARP/Socle Technology. It includes decoupling capacitors, feedback resistors, and a LED for signal indication, with power being supplied via the J1 connector. #referenceDesign #industrialsensing #sharp #template #reference-design

This ADM3054BRWZ-RL7-based reference design is a CAN bus transceiver circuit, providing reliable data communication over the CAN network. The design features a range of capacitors and resistors to ensure signal integrity, and a NUP2105L for voltage protection. It's ideal for applications requiring reliable data communication in an automotive or industrial environment. #referenceDesign #project #CANbus #interface #transceiverCircuit #ADM3054 #ADM3054BRWZ-RL7 #referenceDesign #canbus #texas-instruments #template #reference-design #reference-design

A circuit that uses a high/low signal to route a separate PWM signal to one of two outputs.

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.

The Ariel AI chip prototype is an advanced electronic component designed to enhance the capabilities of Flux AI systems through a sophisticated arrangement of transistors, resistors, capacitors, and a cutting-edge CPU. Key components include two NPN transistors (part numbers NPN-TRANS-001 and NPN-TRANS-002), which are essential for signal amplification, alongside precision resistors (RES-1K and RES-1K-002) each with a resistance of 1kΩ, and a capacitor (CAP-10UF) with a capacitance of 10μF, crucial for filtering and stabilizing the voltage supply. At the heart of the design is a revolutionary CPU (part number CPU-RT-4C-2G) featuring a quad-core setup with a clock speed of 2GHz, based on a radical transistor architecture, designed to deliver unparalleled computational performance for AI tasks. This component set is powered by a 5V DC power supply (DCPS-5V), ensuring a stable and efficient operation. The Ariel AI chip is engineered for high-speed, reliable performance in demanding AI applications, representing a significant advancement in electronic component design for artificial intelligence systems.

This ISO1042BDWR-based reference design is a CAN bus transceiver circuit, providing reliable data communication over the CAN network. The design features a range of capacitors and resistors to ensure signal integrity, and a NUP2105L for voltage protection. It's ideal for applications requiring reliable data communication in an automotive or industrial environment. #referenceDesign #project #CANbus #interface #transceiverCircuit #ISO1042 #ISO1042BDWR

Attach multiple fans from the output of a board that turns on and off one fan. The original fan output is used as a signal to turn a MOSFET on and off to turn on 5 fans from the same original logic. An external power supply powers all the fans.

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

This PCB is for a quad copter drone. The drone will use a 4S lipo battery as a power supply and each of the motors will use 30Amp ESC's. The microcontroller that handles the RF signals is a Raspberry Pi Pico.

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.

This is a reference design of a PCB utilizing the TMF8801-1BM time-of-flight (ToF) sensor from ams-OSRAM. It comprises electronic components such as resistors, capacitors, voltage regulators, and GPIO connectors. The logic signals are managed via Mosfets BSS138 while the Sensor IC is powered & controlled by a 3.3V AP2112K Voltage Regulator. #industrialSensing #referenceDesign #lzer #I2C #osramusa #template #reference-design

Pneumatic Shield for Arduino Nano. You can control 2 pumps and 2 valves with PWM signals. Also, there is manual control with switches. #Arduino #Nano #Shield #project

A low-pass filter is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency.

USB Type - C Multimeter. Designed for 5V-1A to 20V-5A PD charging. Features a generic OLED to display stats. Passthrough for voltage negotiation signals.

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.

This project involves designing a portable device that can inject a 1kHz sine wave signal into a 100V audio line to detect short circuits. The device should be compact (around the size of a smartphone) and include LED indicators to show the status: a green LED for normal operation and a red LED for short-circuit detection. The device will be powered either by a switching power supply or rechargeable batteries.

A ground reference point from which a signal is measured.

A 1 Hz clock signal generator. Simulation testing not done.

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.