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

Learn how to design PCBs faster with generative AI in this 20 minute hands-on tutorial. You’ll learn how to use Flux Copilot, an AI-powered hardware design assistant, to research parts, review your design, and even connect components. https://youtu.be/FL7e0OXTLic

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

Learn how to design PCBs faster with generative AI in this 20 minute hands-on tutorial. You’ll learn how to use Flux Copilot, an AI-powered hardware design assistant, to research parts, review your design, and even connect components. https://youtu.be/FL7e0OXTLic

Portable Audio DSP project utilizing multiple ICs, capacitors, resistors, and LEDs for advanced audio processing and control. Designed for embedded audio applications with ESP32, ADC, DAC, and interface components. #audioDevices #DSP #ADC #audio #DAC

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.