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A Practical Guide to AI, Python, and Hardware Projects Welcome to your BeagleY-AI journey! This compact, powerful, and affordable single-board computer is perfect for developers and hobbyists. With its dedicated 4 TOPS AI co-processor and a 1.4 GHz Quad-core Cortex-A53 CPU, the BeagleY-AI is equipped to handle both AI applications and real-time I/O tasks. Powered by the Texas Instruments AM67A processor, it offers DSPs, a 3D graphics unit, and video accelerators. Inside this handbook, you‘ll find over 50 hands-on projects that cover a wide range of topics—from basic circuits with LEDs and sensors to an AI-driven project. Each project is written in Python 3 and includes detailed explanations and full program listings to guide you. Whether you‘re a beginner or more advanced, you can follow these projects as they are or modify them to fit your own creative ideas. Here’s a glimpse of some exciting projects included in this handbook: Morse Code Exerciser with LED or BuzzerType a message and watch it come to life as an LED or buzzer translates your text into Morse code. Ultrasonic Distance MeasurementUse an ultrasonic sensor to measure distances and display the result in real time. Environmental Data Display & VisualizationCollect temperature, pressure, and humidity readings from the BME280 sensor, and display or plot them on a graphical interface. SPI – Voltmeter with ADCLearn how to measure voltage using an external ADC and display the results on your BeagleY-AI. GPS Coordinates DisplayTrack your location with a GPS module and view geographic coordinates on your screen. BeagleY-AI and Raspberry Pi 4 CommunicationDiscover how to make your BeagleY-AI and Raspberry Pi communicate over a serial link and exchange data. AI-Driven Object Detection with TensorFlow LiteSet up and run an object detection model using TensorFlow Lite on the BeagleY-AI platform, with complete hardware and software details provided.

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A Practical Guide to AI, Python, and Hardware Projects Welcome to your BeagleY-AI journey! This compact, powerful, and affordable single-board computer is perfect for developers and hobbyists. With its dedicated 4 TOPS AI co-processor and a 1.4 GHz Quad-core Cortex-A53 CPU, the BeagleY-AI is equipped to handle both AI applications and real-time I/O tasks. Powered by the Texas Instruments AM67A processor, it offers DSPs, a 3D graphics unit, and video accelerators. Inside this handbook, you‘ll find over 50 hands-on projects that cover a wide range of topics—from basic circuits with LEDs and sensors to an AI-driven project. Each project is written in Python 3 and includes detailed explanations and full program listings to guide you. Whether you‘re a beginner or more advanced, you can follow these projects as they are or modify them to fit your own creative ideas. Here’s a glimpse of some exciting projects included in this handbook: Morse Code Exerciser with LED or BuzzerType a message and watch it come to life as an LED or buzzer translates your text into Morse code. Ultrasonic Distance MeasurementUse an ultrasonic sensor to measure distances and display the result in real time. Environmental Data Display & VisualizationCollect temperature, pressure, and humidity readings from the BME280 sensor, and display or plot them on a graphical interface. SPI – Voltmeter with ADCLearn how to measure voltage using an external ADC and display the results on your BeagleY-AI. GPS Coordinates DisplayTrack your location with a GPS module and view geographic coordinates on your screen. BeagleY-AI and Raspberry Pi 4 CommunicationDiscover how to make your BeagleY-AI and Raspberry Pi communicate over a serial link and exchange data. AI-Driven Object Detection with TensorFlow LiteSet up and run an object detection model using TensorFlow Lite on the BeagleY-AI platform, with complete hardware and software details provided.

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Easy and Affordable Digital Signal Processing The aim of this book is to teach the basic principles of Digital Signal Processing (DSP) and to introduce it from a practical point of view using the bare minimum of mathematics. Only the basic level of discrete-time systems theory is given, sufficient to implement DSP applications in real time. The practical implementations are described in real time using the highly popular ESP32 DevKitC microcontroller development board. With the low cost and extremely popular ESP32 microcontroller, you should be able to design elementary DSP projects with sampling frequencies within the audio range. All programming is done using the popular Arduino IDE in conjunction with the C language compiler. After laying a solid foundation of DSP theory and pertinent discussions on the main DSP software tools on the market, the book presents the following audio-based sound and DSP projects: Using an I²S-based digital microphone to capture audio sound Using an I²S-based class-D audio amplifier and speaker Playing MP3 music stored on an SD card through an I²S-based amplifier and speaker Playing MP3 music files stored in ESP32 flash memory through an I²S-based amplifier and speaker Mono and stereo Internet radio with I²S-based amplifiers and speakers Text-to-speech output with an I²S-based amplifier and speaker Using the volume control in I²S-based amplifier and speaker systems A speaking event counter with an I²S-based amplifier and speaker An adjustable sinewave generator with I²S-based amplifier and speaker Using the Pmod I²S2 24-bit fast ADC/DAC module Digital low-pass and band-pass real-time FIR filter design with external and internal A/D and D/A conversion Digital low-pass and band-pass real-time IIR filter design with external and internal A/D and D/A conversion Fast Fourier Transforms (FFT)

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PÚCA DSP is an open-source, Arduino-compatible ESP32 development board for audio and digital signal processing (DSP) applications with expansive audio-processing features. It provides audio inputs, audio outputs, a low-noise microphone array, an integrated test-speaker option, additional memory, battery-charge management, and ESD protection all on a small, breadboard-friendly PCB. Synthesizers, Installations, Voice UI, and More PÚCA DSP can be used for a wide range of DSP applications, including but not limited to those in the fields of music, art, creative technology, and adaptive technology. Music-related examples include digital-music synthesis, mobile recording, Bluetooth speakers, wireless line-level directional microphones, and the design of smart musical instruments. Art-related examples include acoustic sensor networks, sound-art installations, and Internet-radio applications. Examples related to creative and adaptive technology include voice user interface (VUI) design and Web audio for the Internet of Sounds. Compact, Integrated Design PÚCA DSP was designed for portability. When used with an external 3.7 V rechargeable battery, it can be deployed almost anywhere or integrated into just about any device, instrument, or installation. Its design emerged from months of experimentation with various ESP32 development boards, DAC breakout boards, ADC breakout boards, Microphone breakout boards, and audio-connector breakout boards, and – despite its diminutive size – it manages to provide all of that functionality in a single board. And it dos so without compromising signal quality. Specifications Processor & Memory Espressif ESP32 Pico D4 Processor 32-bit dual core 80 MHz / 160 MHz / 240 MHz 4 MB SPI Flash with 8 MB additional PSRAM (Original Edition) Wireless 2.4 GHz Wi-Fi 802.11b/g/n Bluetooth BLE 4.2 3D Antenna Audio Wolfson WM8978 Stereo Audio Codec Audio Line In on 3.5 mm stereo onnector Audio Headphone / Line Out on 3.5 mm stereo connector Stereo Aux Line In, Audio Mono Out routed to GPIO Header 2x Knowles SPM0687LR5H-1 MEMS Microphones ESD protection on all audio inputs and outputs Support for 8, 11.025, 12, 16, 22.05, 24, 32, 44.1 and 48 kHz sample rates 1 W Speaker Driver, routed to GPIO Header DAC SNR 98 dB, THD -84 dB (‘A’ weighted @ 48 kHz) ADC SNR 95 dB, THD -84 dB (‘A’ weighted @ 48 kHz) Line input impedance: 1 MOhm Line output impedance: 33 Ohm Form Factor and Connectivity Breadboard friendly 70 x 24 mm 11x GPIO pins broken out to 2.54 mm pitch header, with access to both ESP32 ADC channels, JTAG and capacitive touch pins USB 2.0 over USB Type C connector Power 3.7/4.2 V Lithium Polymer Rechargeable Battery, USB or external 5 V DC power source ESP32 and Audio Codec can be placed into low power modes under software control Battery voltage level detection ESD protection on USB data bus Downloads GitHub Datasheet Links Crowd Supply Campaign (includes FAQs) Hardware Overview Programming the Board The Audio Codec

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Easy and Affordable Digital Signal ProcessingThe aim of this book is to teach the basic principles of Digital Signal Processing (DSP) and to introduce it from a practical point of view using the bare minimum of mathematics. Only the basic level of discrete-time systems theory is given, sufficient to implement DSP applications in real time. The practical implementations are described in real time using the highly popular ESP32 DevKitC microcontroller development board. With the low cost and extremely popular ESP32 microcontroller, you should be able to design elementary DSP projects with sampling frequencies within the audio range. All programming is done using the popular Arduino IDE in conjunction with the C language compiler.After laying a solid foundation of DSP theory and pertinent discussions on the main DSP software tools on the market, the book presents the following audio-based sound and DSP projects: Using an I²S-based digital microphone to capture audio sound Using an I²S-based class-D audio amplifier and speaker Playing MP3 music stored on an SD card through an I²S-based amplifier and speaker Playing MP3 music files stored in ESP32 flash memory through an I²S-based amplifier and speaker Mono and stereo Internet radio with I²S-based amplifiers and speakers Text-to-speech output with an I²S-based amplifier and speaker Using the volume control in I²S-based amplifier and speaker systems A speaking event counter with an I²S-based amplifier and speaker An adjustable sinewave generator with I²S-based amplifier and speaker Using the Pmod I²S2 24-bit fast ADC/DAC module Digital low-pass and band-pass real-time FIR filter design with external and internal A/D and D/A conversion Digital low-pass and band-pass real-time IIR filter design with external and internal A/D and D/A conversion Fast Fourier Transforms (FFT)

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The Elektor Audio DSP FX Processor combines an ESP32 microcontroller and an ADAU1701 Audio DSP from Analog Devices. Besides a user-programmable DSP core, the ADAU1701 has high-quality analog-to-digital and digital-to-analog converters built-in and features an I²S port. This makes it suitable as a high-quality audio interface for the ESP32. Programs for the ESP32 can be created with Arduino, Platform IO, CMake or by using the Espressif IDF in another way. Programs for the ADAU7101 audio DSPs are created with the free visual programming tool SigmaStudio by dragging and dropping pre-defined algorithm blocks on a canvas. Applications Bluetooth/Wi-Fi audio sink (e.g. loudspeaker) & source Guitar effect pedal (stomp box) Music synthesizer Sound/function generator Programmable cross-over filter for loudspeakers Advanced audio effects processor (reverb, chorus, pitch shifting, etc.) Internet-connected audio device DSP experimentation platform Wireless MIDI MIDI to CV converter and many more... Specifications ADAU1701 28-/56-bit, 50-MIPS digital audio processor supporting sampling rates of up to 192 kHz ESP32 32-bit dual-core microcontroller with Wi-Fi 802.11b/g/n and Bluetooth 4.2 BR/EDR and BLE 2x 24-bit audio inputs (2 V RMS, 20 kΩ) 4x 24-bit audio outputs (0.9 V RMS, 600 Ω) 4x Control potentiometer MIDI in- and output I²C expansion port Multi-mode operation Power supply: 5 V DC USB or 7.5-12 V DC (barrel jack, center pin is GND) Current consumption (average): 200 mA Included 1x ESP32 Audio DSP FX Processor board (assembled) 1x ESP32-PICO-KIT 2x Jumpers 2x 18-pin headers (female) 4x 10 KB potentiometers Downloads Documentation GitHub

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This collection features the best of Elektor Magazine's articles on embedded systems and artificial intelligence. From hands-on programming guides to innovative AI experiments, these pieces offer valuable insights and practical knowledge for engineers, developers, and enthusiasts exploring the evolving intersection of hardware design, software innovation, and intelligent technology. Contents Programming PICs from the Ground UpAssembler routine to output a sine wave Object-Oriented ProgrammingA Short Primer Using C++ Programming an FPGA Tracking Down Microcontroller Buffer Overflows with 0xDEADBEEF Too Quick to Code and Too Slow to Test? Understanding the Neurons in Neural NetworksEmbedded Neurons MAUI Programming for PC, Tablet, and SmartphoneThe New Framework in Theory and Practice USB Killer DetectorBetter Safe Than Sorry Understanding the Neurons in Neural NetworksArtificial Neurons A Bare-Metal Programming Guide Part 1: For STM32 and Other Controllers Part 2: Accurate Timing, the UART, and Debugging Part 3: CMSIS Headers, Automatic Testing, and a Web Server Introduction to TinyMLBig Is Not Always Better Microprocessors for Embedded SystemsPeculiar Parts, the Series FPGAs for BeginnersThe Path From MCU to FPGA Programming AI in Electronics DevelopmentAn Update After Only One Year AI in the Electronics LabGoogle Bard and Flux Copilot Put to the Test ESP32 and ChatGPTOn the Way to a Self-Programming System… Audio DSP FX Processor Board Part 1: Features and Design Part 2: Creating Applications Rust + EmbeddedA Development Power Duo A Smart Object CounterImage Recognition Made Easy with Edge Impulse Universal Garden LoggerA Step Towards AI Gardening A VHDL ClockMade with ChatGPT TensorFlow Lite on Small MicrocontrollersA (Very) Beginner’s Point of View Mosquito DetectionUsing Open Datasets and Arduino Nicla Vision Artificial Intelligence Timeline Intro to AI AlgorithmsPrompt: Which Algorithms Implement Each AI Tool? Bringing AI to the Edgewith ESP32-P4 The Growing Role of Edge AIA Trend Shaping the Future

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Elektor GREEN and GOLD members can download their digital edition here. Not a member yet? Click here. The AlertAlfred AI Security SystemPowered by a Raspberry Pi 5 and the Hailo 8L Module AI in Electronics DevelopmentAn Update After Only One Year Intro to AI AlgorithmsPrompt: Which Algorithms Implement Each AI Tool? Single-Board Computers for Artificial Intelligence ProjectsBackground and Overview From Sensor Data to Machine Learning ModelsGesture Detection with an Accelerometer and Edge Impulse Build a Leaky Integrate-and-Fire Spiking NeuronArtificial Intelligence Without Software ChatGPT for Electronic DesignDoes GPT-4o Do It Any Better? Bringing AI to the Edge with ESP32-P4 Exploring Speech Functions on Raspberry Pi ZeroWhen Overclocking Gives Freedom of Speech The Growing Role of Edge AIA Trend Shaping the Future Unlocking the Power of Edge AIA Conversation with François de Rochebouët of STMicroelectronics A VHDL Clock Made with ChatGPT AI’s Real ImpactSayash Kapoor on “AI Snake Oil” and More The Latest Stuff From BeagleBoardBeagleY-AI, BeagleV-Fire, BeagleMod, BeaglePlay and BeagleConnect Freedom Mosquito Detection Using Open Datasets and Arduino Nicla Vision AI Today and Tomorrow: Insights from Espressif, Arduino, and SparkFun Artificial Intelligence Timeline BeagleY-AIThe Latest SBC for AI Applications AI in FocusPerspectives from the Elektor Community Machine Vision with OpenMVCreate a Soda Can Detector A Conversation with the Digital MindChatGPT vs Gemini Skilling Me Softly with This Bot?Is the AI Revolution in the Electronic Field Failing Due to a Lack of Social Precision?

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A Beginner's Guide to AI and Edge Computing Artificial Intelligence (AI) is now part of our daily lives. With companies developing low-cost AI-powered hardware into their products, it is now becoming a reality to purchase AI accelerator hardware at comparatively very low costs. One such hardware accelerator is the Hailo module which is fully compatible with the Raspberry Pi 5. The Raspberry Pi AI Kit is a cleverly designed hardware as it bundles an M.2-based Hailo-8L accelerator with the Raspberry Pi M.2 HAT+ to offer high speed inferencing on the Raspberry Pi 5. Using the Raspberry Pi AI Kit, you can build complex AI-based vision applications, running in real-time, such as object detection, pose estimation, instance segmentation, home automation, security, robotics, and many more neural network-based applications. This book is an introduction to the Raspberry Pi AI Kit, and it is aimed to provide some help to readers who are new to the kit and wanting to run some simple AI-based visual models on their Raspberry Pi 5 computers. The book is not meant to cover the detailed process of model creation and compilation, which is done on an Ubuntu computer with massive disk space and 32 GB memory. Examples of pre-trained and custom object detection are given in the book. Two fully tested and working projects are given in the book. The first project explains how a person can be detected and how an LED can be activated after the detection, and how the detection can be acknowledged by pressing an external button. The second project illustrates how a person can be detected, and how this information can be passed to a smart phone over a Wi-Fi link, as well as how the detection can be acknowledged by sending a message from the smartphone to your Raspberry Pi 5.

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The reComputer J3010 is a compact and powerful edge AI device powered by the NVIDIA Jetson Orin Nano SoM, delivering an impressive 20 TOPS AI performance – up to 40 times faster than the Jetson Nano. Pre-installed with Jetpack 5.1.1, it features a 128 GB SSD, 4x USB 3.2 ports, HDMI, Gigabit Ethernet, and a versatile carrier board with M.2 Key E for WiFi, M.2 Key M for SSD, RTC, CAN, and a 40-pin GPIO header. Applications AI Video Analytics Machine Vision Robotics Specifications Jetson Orin Nano System-on-Module AI Performance reComputer J3010, Orin Nano 4 GB (20 TOPS) GPU 512-core NVIDIA Ampere architecture GPU with 16 Tensor Cores (Orin Nano 4 GB) CPU 6-core Arm Cortex-A78AE v8.2 64-bit CPU 1.5 MB L2 + 4 MB L3 Memory 4 GB 64-bit LPDDR5 34 GB/s (Orin Nano 4 GB) Video Encoder 1080p30 supported by 1-2 CPU cores Video Decoder 1x 4K60 (H.265) | 2x 4K30 (H.265) | 5x 1080p60 (H.265) | 11x 1080p30 (H.265) Carrier Board Storage M.2 Key M PCIe (M.2 NVMe 2280 SSD 128 GB included) Networking Ethernet 1x RJ-45 Gigabit Ethernet (10/100/1000M) M.2 Key E 1x M.2 Key E (pre-installed 1x Wi-Fi/Bluetooth combo module) I/O USB 4x USB 3.2 Type-A (10 Gbps)1x USB 2.0 Type-C (Device Mode) CSI Camera 2x CSI (2-lane 15-pin) Display 1x HDMI 2.1 Fan 1x 4-pin Fan Connector (5 V PWM) CAN 1x CAN Multifunctional Port 1x 40-Pin Expansion header 1x 12-Pin Control and UART header RTC RTC 2-pin, supports CR1220 (not included) Power Supply 9-19 V DC Mechanical Dimensions 130 x 120 x 58.5 mm (with Case) Installation Desktop, wall-mounting Operating Temperature −10°C~60°C Included 1x reComputer J3010 (system installed) 1x Power adapter (12 V / 5 A) Downloads reComputer J301x Datasheet NVIDIA Jetson Devices and carrier boards comparisions reComputer J401 schematic design file reComputer J3010 3D file

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The reComputer J1020 v2 is a compact edge AI device powered by the NVIDIA Jetson Nano 4 GB production module, delivering 0.5 TFLOPs of AI performance. It features a robust aluminum case with a passive heatsink and comes pre-installed with JetPack 4.6.1. The device includes 16 GB of onboard eMMC storage and offers 2x SCI, 4x USB 3.0, M.2 Key M, HDMI, and DP. Applications Computer Vision Machine Learning Autonomous Mobile Robot (AMR) Specifications Jetson Nano 4 GB System-on-Module AI Performance Jetson Nano 4 GB (0.5 TOPS) GPU NVIDIA Maxwel architecture with 128 NVIDIA CUDA cores CPU Quad-core ARM Cortex-A57 MPCore processor Memory 4 GB 64-bit LPDDR4 25.6 GB/s Video Encoder 1x 4K30 | 2x 1080p60 | 4x 1080p30 | 4x 720p60 | 9x 720p30 (H.265 & H.264) Video Decoder 1x 4K60 | 2x 4K30 | 4x 1080p60 | 8x 1080p30 | 9x 720p60 (H.265 & H.264) Carrier Board Storage 1x M.2 Key M PCIe Networking Ethernet 1x RJ-45 Gigabit Ethernet (10/100/1000M) I/O USB 4x USB 3.0 Type-A1x Micro-USB port for device mode CSI Camera 2x CSI (2-lane 15-pin) Display 1x HDMI Type A; 1x DP Fan 1x 4-pin Fan Connector (5 V PWM) CAN 1x CAN Multifunctional Port 1x 40-Pin Expansion header 1x 12-Pin Control and UART header Power Supply DC 12 V/2 A Mechanical Dimensions 130 x 120 x 50 mm (with Case) Installation Desktop, wall-mounting Operating Temperature −10°C~60°C Included reComputer J1020 v2 (system installed) 12 V/2 A power adapter (with 5 interchangeable adapter plugs) Downloads reComputer J1020 v2 datasheet reComputer J1020 v2 3D file Seeed NVIDIA Jetson Product Catalog NVIDIA Jetson Device and Carrier Boards Comparison

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OV7740 is a AI Camera powered by Kendryte K210, an edge computing system-on-chip(SoC) with a dual-core 64bit RISC-V CPU and state-of-art neural network processor. Features Dual-Core 64-bit RISC-V RV64IMAFDC (RV64GC) CPU / 400Mhz(Normal) Dual Independent Double Precision FPU 8MiB 64bit width On-Chip SRAM Neural Network Processor(KPU) / 0.8Tops Field-Programmable IO Array (FPIOA) AES, SHA256 Accelerator Direct Memory Access Controller (DMAC) Micropython Support Firmware encryption support On-board Hardware: Flash: 16M Camera :OV7740 2x Buttons Status Indicator LED External storage: TF card/Micro SD Interface: HY2.0/compatible GROVE Applications Face recognition/detection Object detection/classification Obtain the size and coordinates of the target in real-time Obtain the type of detected target in real-time Shape recognition Video recorder Included 1x UNIT-V(include 20cm 4P cable and USB-C cable)

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