#Jetson Nano Projects

[tannatechbizllp]

Unlocking the Power of NVIDIA Jetson Nano Developer Kit for AI and Robotics | Tanna TechBiz

Leverage the NVIDIA Jetson Nano Developer Kit to build intelligent AI and robotics projects with speed, efficiency, and powerful performance.

[govindhtech]

What Is NanoVLM? Key Features, Components And Architecture

The NanoVLM initiative develops VLMs for NVIDIA Jetson devices, specifically the Orin Nano. These models aim to improve interaction performance by increasing processing speed and decreasing memory usage. Documentation includes supported VLM families, benchmarks, and setup parameters such Jetson device and Jetpack compatibility. Video sequence processing, live streaming analysis, and multimodal chat via online user interfaces or command-line interfaces are also covered.

What's nanoVLM?

NanoVLM is the fastest and easiest repository for training and optimising micro VLMs.

Hugging Face streamlined this teaching method. We want to democratise vision-language model creation via a simple PyTorch framework. Inspired by Andrej Karratha's nanoGPT, NanoVLM prioritises readability, modularity, and transparency without compromising practicality. About 750 lines of code define and train nanoVLM, plus parameter loading and reporting boilerplate.

Architecture and Components

NanoVLM is a modular multimodal architecture with a modality projection mechanism, lightweight language decoder, and vision encoder. The vision encoder uses transformer-based SigLIP-B/16 for dependable photo feature extraction.

Visual backbone translates photos into language model-friendly embeddings.

Textual side uses SmolLM2, an efficient and clear causal decoder-style converter.

Vision-language fusion is controlled by a simple projection layer that aligns picture embeddings into the language model's input space.

Transparent, readable, and easy to change, the integration is suitable for rapid prototyping and instruction.

The effective code structure includes the VLM (~100 lines), Language Decoder (~250 lines), Modality Projection (~50 lines), Vision Backbone (~150 lines), and a basic training loop (~200 lines).

Sizing and Performance

HuggingFaceTB/SmolLM2-135M and SigLIP-B/16-224-85M backbones create 222M nanoVLMs. Version nanoVLM-222M is available.

NanoVLM is compact and easy to use but offers competitive results. The 222M model trained for 6 hours on a single H100 GPU with 1.7M samples from the_cauldron dataset had 35.3% accuracy on the MMStar benchmark. SmolVLM-256M-like performance was achieved with fewer parameters and computing.

NanoVLM is efficient enough for educational institutions or developers using a single workstation.

Key Features and Philosophy

NanoVLM is a simple yet effective VLM introduction.

It enables users test micro VLMs' capabilities by changing settings and parameters.

Transparency helps consumers understand logic and data flow with minimally abstracted and well-defined components. This is ideal for repeatability research and education.

Its modularity and forward compatibility allow users to replace visual encoders, decoders, and projection mechanisms. This provides a framework for multiple investigations.

Get Started and Use

Cloning the repository and establishing the environment lets users start. Despite pip, uv is recommended for package management. Dependencies include torch, numpy, torchvision, pillow, datasets, huggingface-hub, transformers, and wandb.

NanoVLM includes easy methods for loading and storing Hugging Face Hub models. VisionLanguageModel.from_pretrained() can load pretrained weights from Hub repositories like “lusxvr/nanoVLM-222M”.

Pushing trained models to the Hub creates a model card (README.md) and saves weights (model.safetensors) and configuration (config.json). Repositories can be private but are usually public.

Model can load and store models locally.VisionLanguageModel.from_pretrained() and save_pretrained() with local paths.

To test a trained model, generate.py is provided. An example shows how to use an image and “What is this?” to get cat descriptions.

In the Models section of the NVIDIA Jetson AI Lab, “NanoVLM” is included, however the content focusses on using NanoLLM to optimise VLMs like Llava, VILA, and Obsidian for Jetson devices. This means Jetson and other platforms can benefit from nanoVLM's small VLM optimisation techniques.

Training

Train nanoVLM with the train.py script, which uses models/config.py. Logging with WANDB is common in training.

VRAM specs

VRAM needs must be understood throughout training.

A single NVIDIA H100 GPU evaluating the default 222M model shows batch size increases peak VRAM use.

870.53 MB of VRAM is allocated after model loading.

Maximum VRAM used during training is 4.5 GB for batch size 1 and 65 GB for batch size 256.

Before OOM, 512-batch training peaked at 80 GB.

Results indicate that training with a batch size of up to 16 requires at least ~4.5 GB of VRAM, whereas training with a batch size of up to 16 requires roughly 8 GB.

Variations in sequence length or model architecture affect VRAM needs.

To test VRAM requirements on a system and setup, measure_vram.py is provided.

Contributions and Community

NanoVLM welcomes contributions.

Contributions with dependencies like transformers are encouraged, but pure PyTorch implementation is preferred. Deep speed, trainer, and accelerate won't work. Open an issue to discuss new feature ideas. Bug fixes can be submitted using pull requests.

Future research includes data packing, multi-GPU training, multi-image support, image-splitting, and VLMEvalKit integration. Integration into the Hugging Face ecosystem allows use with Transformers, Datasets, and Inference Endpoints.

In summary

NanoVLM is a Hugging Face project that provides a simple, readable, and flexible PyTorch framework for building and testing small VLMs. It is designed for efficient use and education, with training, creation, and Hugging Face ecosystem integration paths.

[makers-muse]

What Are the Must-Have Tools for a Future-Ready STEM Lab in Agartala? 

Introduction: Why Every STEM Lab in Agartala Needs the Right Tools 

A STEM Lab in Agartala is more than just a classroom—it’s a hands-on innovation center where students explore robotics, coding, AI, and engineering. To make learning engaging and future-ready, schools must equip their STEM Lab in Agartala with the right tools and technologies. 

In this guide, we’ll explore the must-have tools that every future-ready STEM Lab in Agartala should have. 

1. Robotics Kits – Powering Hands-On Learning 

A top-quality STEM Lab in Agartala must include robotics kits to teach students about automation, AI, and engineering. Some of the best robotics kits include: 

 LEGO Mindstorms EV3 – Ideal for beginners, offering block-based coding.    Arduino & Raspberry Pi Kits – Great for advanced robotics and IoT projects.    VEX Robotics Kits – Used for competitions and real-world problem-solving. 

These kits help students develop logical thinking and problem-solving skills while preparing them for careers in automation and robotics. 

2. 3D Printers – Bringing Creativity to Life 

A STEM Lab in Agartala should have 3D printers to help students design and prototype real-world objects. Some essential options include: 

Creality Ender 3 – Affordable and beginner-friendly for schools.  Ultimaker 2+ – High-quality prints for advanced projects.  ️ Anycubic Photon – Best for precise, resin-based 3D printing. 

With 3D printing, students can turn their ideas into reality, fostering creativity and innovation. 

3. Coding & AI Learning Kits – Preparing for the Future 

To make a STEM Lab in Agartala future-ready, it must include coding and AI tools for teaching programming skills. Some of the best choices are: 

Scratch & Blockly – Block-based coding for beginners.    Python & Java Programming Platforms – Industry-standard coding languages.  Google AIY & NVIDIA Jetson Nano – AI and machine learning kits for advanced learning. 

These tools help students learn AI, data science, and machine learning, making them ready for future tech careers. 

4. Virtual Reality (VR) & Augmented Reality (AR) – Immersive Learning 

A cutting-edge STEM Lab in Agartala should include VR and AR tools to create immersive educational experiences. The best options are: 

VR and AR tools make learning more engaging and interactive, helping students visualize complex concepts easily. 

5. IoT & Smart Sensors – Learning About the Connected World 

An IoT-enabled STEM Lab in Agartala prepares students for the future of smart technology and automation. Essential IoT tools include: 

Arduino IoT Cloud – Teaches real-world IoT applications.    ESP8266 & ESP32 Microcontrollers – Used for smart device projects.    Smart Sensors (Temperature, Humidity, Motion) – For creating real-time monitoring systems. 

With IoT tools, students can build smart home projects, automated weather stations, and AI-driven devices. 

6. Electronics & Circuit Design Kits – Understanding Engineering Basics 

A future-ready STEM Lab in Agartala must include electronics kits for hands-on engineering projects. The best options are: 

 LittleBits Electronics Kit – Easy-to-use snap circuits for beginners.    Snap Circuits Pro – Teaches circuit design in a fun way.    Breadboards & Multimeters – Essential for real-world electronics projects. 

Electronics kits enhance problem-solving skills and prepare students for engineering careers. 

7. STEM Software & Simulations – Enhancing Digital Learning 

A well-equipped STEM Lab in Agartala should also have digital tools and software for coding, engineering, and simulations. Some must-have software include: 

Tinkercad – Online 3D design and electronics simulation.    MATLAB & Simulink – Used for data analysis and AI applications.  AutoCAD & SolidWorks – Industry-grade design software. 

These digital tools help students practice real-world STEM applications in a virtual environment. 

Conclusion: Build a Future-Ready STEM Lab in Agartala with the Right Tools 

A high-quality STEM Lab in Agartala must include robotics kits, 3D printers, AI and coding tools, IoT kits, VR devices, and circuit design tools to prepare students for technology-driven careers. 

By investing in these essential tools, schools in Agartala can create an engaging, innovative, and future-ready learning environment. 

Want to set up a STEM Lab in Agartala? Contact us today to Upgrade the best solutions for your school!  

[airdrop2000]

OpenPose vs. MediaPipe: In-Depth Comparison for Human Pose Estimation

Developing programs that comprehend their environments is a complex task. Developers must choose and design applicable machine learning models and algorithms, build prototypes and demos, balance resource usage with solution quality, and ultimately optimize performance. Frameworks and libraries address these challenges by providing tools to streamline the development process. This article will examine the differences between OpenPose vs MediaPipe, two prominent frameworks for human pose estimation, and their respective functions. We'll go through their features, limitations, and use cases to help you decide which framework is best suited for your project.

Understanding OpenPose: Features, Working Mechanism, and Limitations

At Saiwa , OpenPose is a real-time multi-person human pose detection library developed by researchers at Carnegie Mellon University. It has made significant strides in accurately identifying human body, foot, hand, and facial key points in single images. This capability is crucial for applications in various fields, including action recognition, security, sports analytics, and more. OpenPose stands out as a cutting-edge approach for real-time human posture estimation, with its open-sourced code base well-documented and available on GitHub. The implementation uses Caffe, a deep learning framework, to construct its neural networks.

Key Features of OpenPose

OpenPose boasts several noteworthy features, including:

- 3D Single-Person Keypoint Detection in Real-Time: Enables precise tracking of individual movements.

- 2D Multi-Person Keypoint Detections in Real-Time: Allows simultaneous tracking of multiple people.

- Single-Person Tracking: Enhances recognition and smooth visuals by maintaining continuity in tracking.

- Calibration Toolkit: Provides tools for estimating extrinsic, intrinsic, and distortion camera parameters.

How OpenPose Works: A Technical Overview

OpenPose employs various methods to analyze human positions, which opens the door to numerous practical applications. Initially, the framework extracts features from an image using the first few layers. These features are then fed into two parallel convolutional network branches.

- First Branch: Predicts 18 confidence maps corresponding to unique parts of the human skeleton.

- Second Branch: Predicts 38 Part Affinity Fields (PAFs) that indicate the relationship between parts.

Further steps involve cleaning up the estimates provided by these branches. Confidence maps are used to create bipartite graphs between pairs of components, and PAF values help remove weaker linkages from these graphs.

Limitations of OpenPose

Despite its capabilities, OpenPose has some limitations:

- Low-Resolution Outputs: Limits the detail level in keypoint estimates, making OpenPose less suitable for applications requiring high precision, such as elite sports and medical evaluations.

- High Computational Cost: Each inference costs 160 billion floating-point operations (GFLOPs), making OpenPose highly inefficient in terms of resource usage.

Exploring MediaPipe: Features, Working Mechanism, and Advantages

MediaPipe is a cross-platform pipeline framework developed by Google for creating custom machine-learning solutions. Initially designed to analyze YouTube videos and audio in real-time, MediaPipe has been open-sourced and is now in the alpha stage. It supports Android, iOS, and embedded devices like the Raspberry Pi and Jetson Nano.

Key Features of MediaPipe

MediaPipe is divided into three primary parts:

1. A Framework for Inference from Sensory Input: Facilitates real-time processing of various data types.

2. Tools for Performance Evaluation: Helps in assessing and optimizing system performance.

3. A Library of Reusable Inference and Processing Components: Provides building blocks for developing vision pipelines.

How MediaPipe Works: A Technical Overview

MediaPipe allows developers to prototype a vision pipeline incrementally. The pipeline is described as a directed graph of components, where each component, known as a "Calculator," is a node. Data "Streams" connect these calculators, representing time series of data "Packets." The calculators and streams collectively define a data-flow graph, with each input stream maintaining its queue to enable the receiving node to consume packets at its rate. Calculators can be added or removed to improve the process gradually. Developers can also create custom calculators, and MediaPipe provides sample code and demos for Python and JavaScript.

MediaPipe Calculators: Core Components and Functionality

Calculators in MediaPipe are specific C++ computing units assigned to tasks. Data packets, such as video frames or audio segments, enter and exit through calculator ports. The framework integrates Open, Process, and Close procedures for each graph run. For example, the ImageTransform calculator receives an image as input and outputs a transformed version, while the ImageToTensor calculator accepts an image and produces a tensor.

MediaPipe vs. OpenPose: A Comprehensive Comparison

When comparing MediaPipe and OpenPose, several factors must be considered, including performance, compatibility, and application suitability.

Performance: Efficiency and Real-Time Capabilities

MediaPipe offers end-to-end acceleration for ML inference and video processing, utilizing standard hardware like GPU, CPU, or TPU. It supports real-time performance and can handle complex, dynamic behavior and streaming processing. OpenPose, while powerful, is less efficient in terms of computational cost and may not perform as well in resource-constrained environments.

Compatibility: Cross-Platform Support and Integration

MediaPipe supports a wide range of platforms, including Android, iOS, desktop, edge, cloud, web, and IoT, making it a versatile choice for various applications. Its integration with Google's ecosystem, particularly on Android, enhances its compatibility. OpenPose, though also cross-platform, may appeal more to developers seeking strong GPU acceleration capabilities.

Application Suitability: Use Cases and Industry Applications

- Real-Time Human Pose Estimation: Both frameworks excel in this area, but MediaPipe's efficiency and versatility make it a better choice for applications requiring real-time performance.

- Fitness Tracking and Sports Analytics: MediaPipe offers accurate and efficient tracking, making it ideal for fitness and sports applications. OpenPose's lower resolution outputs might not provide the precision needed for detailed movement analysis.

- Augmented Reality (AR): MediaPipe's ability to handle complex, dynamic behavior and its support for various platforms make it suitable for AR applications.

- Human-Computer Interaction: MediaPipe's versatility and efficiency in processing streaming time-series data make it a strong contender for applications in human-computer interaction and gesture recognition.

Conclusion: Making an Informed Choice Between MediaPipe and OpenPose

Choosing between MediaPipe and OpenPose depends on the specific needs of your project. Both frameworks offer unique advantages, but MediaPipe stands out for its efficiency, versatility, and wide platform support. OpenPose, with its strong GPU acceleration capabilities, remains a popular choice for projects that can accommodate its computational demands.

By assessing your project's requirements, including the intended deployment environment, hardware preferences, and desired level of customization, you can make an informed decision on which framework to use. Both MediaPipe and OpenPose represent significant advancements in human pose estimation technology, empowering a wide range of applications and experiences in computer vision.

[make2explore]

Check out this project of🕺Pose Estimation and Emotion Analysis powered by #Nvidia Jetson Nano 😍👏👍

#MachineLearning #ArtificialIntelligence #ComputerVision #DIY #Tech #electronics

Know More - https://tinyurl.com/27fkr3ws

[uctronics-blog]

IMX219 cameras for Nvidia Jetson Nano with autofocus support featuring extension cable that are made possible for spy cams, we also have no-infrared cams dedicated to special uses.

Buy them right here: http://bit.ly/Uctronics_Nvidia_Cams

Read the blog here: http://bit.ly/uctronics_jetson_nano

[mostlysignssomeportents]

Mini retro computers

Some people work while they're stressed and locked indoors. I wrote most of a book during the covid crisis: https://twitter.com/search?q=from%3Adoctorow%20%23dailywords&src=typed_query&f=live I was feeling pretty pleased with myself on that score, but then I found out what Oriol Ferrer Mesià did with his time. His "Modern Retro Computer Terminals" project are a series of tiny computers built around low-cost processors like the Raspberry Pi and Nvidia Jetson Nano, run off a 3D printer and assembled. https://uri.cat/projects/modern-retro-terminal/

The project includes an "UltraWide 8.8″ LCD Terminal" built around a skinny 4:1 LCD and powered by an OpenGL-capable Nvidia Jetson Nano. It is so wide it didn't fit in Mesià's 3D printer bed, so it had to be assembled from multiple pieces.

I'm also very fond of the "16:9 5″ LCD Terminal v2" - mostly because safety orange is my favorite color. I get serious Chumby vibes off this one. They're extraordinary pieces, and Mesià shows off some smart parametric designs. I hope he considers releasing some of those design files.

[astronautabby]

#Sponsored Say hi to my new #AI! As an advocate for #stemeducation, I’m a huge fan of NVIDIA’s new Jetson Nano 2GB Developer Kit which provides hands-on practice with robotics and AI for young people at an accessible price point! Pre-order Now by clicking the #linkinbio! Big thanks @NVIDIAEmbedded for sending me their new #jetsonnano product! I had a ton of fun unboxing this. Which projects do you think I should make first with the Jetson Nano? #ad #AI #robotics #STEMlearning #womeninstem #nvidia #nasa #stemgirls #distancelearning https://www.instagram.com/p/CGYeuIjFWXb/?igshid=1ct1o8cqjx0kz

[govindhtech]

ROSCon 2024: Accelerating Innovation In AI-Driven Robot Arms

NVIDIA Isaac accelerated libraries and AI models are being incorporated into the platforms of robotics firms.

NVIDIA and its robotics ecosystem partners announced generative AI tools, simulation, and perceptual workflows for Robot Operating System (ROS) developers at ROSCon in Odense, one of Denmark’s oldest cities and a center of automation.

New workflows and generative AI nodes for ROS developers deploying to the NVIDIA Jetson platform for edge AI and robotics were among the revelations. Robots can sense and comprehend their environment, interact with people in a natural way, and make adaptive decisions on their own with generative AI.

Generative AI Comes to ROS Community

ReMEmbR, which is based on ROS 2, improves robotic thinking and action using generative AI. Large language model (LLM), vision language models (VLMs), and retrieval-augmented generation are combined to enhance robot navigation and interaction with their surroundings by enabling the construction and querying of long-term semantic memories.

The WhisperTRT ROS 2 node powers the speech recognition feature. In order to provide low-latency inference on NVIDIA Jetson and enable responsive human-robot interaction, this node optimizes OpenAI’s Whisper model using NVIDIA TensorRT.

The NVIDIA Riva ASR-TTS service is used in the ROS 2 robots with voice control project to enable robots to comprehend and react to spoken commands. Using its Nebula-SPOT robot and the NVIDIA Nova Carter robot in NVIDIA Isaac Sim, the NASA Jet Propulsion Laboratory independently demonstrated ROSA, an AI-powered agent for ROS.

Canonical is using the NVIDIA Jetson Orin Nano system-on-module to demonstrate NanoOWL, a zero-shot object detection model, at ROSCon. Without depending on preset categories, it enables robots to recognize a wide variety of things in real time.

ROS 2 Nodes for Generative AI, which introduces NVIDIA Jetson-optimized LLMs and VLMs to improve robot capabilities, are available for developers to begin using right now.

Enhancing ROS Workflows With a ‘Sim-First’ Approach

Before being deployed, AI-enabled robots must be securely tested and validated through simulation. By simply connecting them to their ROS packages, ROS developers may test robots in a virtual environment with NVIDIA Isaac Sim, a robotics simulation platform based on OpenUSD. The end-to-end workflow for robot simulation and testing is demonstrated in a recently released Beginner’s Guide to ROS 2 Workflows With Isaac Sim.

As part of the NVIDIA Inception program for startups, Foxglove showcased an integration that uses Foxglove’s own extension, based on Isaac Sim, to assist developers in visualizing and debugging simulation data in real time.

New Capabilities for Isaac ROS 3.2

Image credit to NVIDIA

NVIDIA Isaac ROS is a collection of accelerated computing packages and AI models for robotics development that is based on the open-source ROS 2 software platform. The forthcoming 3.2 update improves environment mapping, robot perception, and manipulation.

New standard workflows that combine FoundationPose and cuMotion to speed up the creation of robotics pick-and-place and object-following pipelines are among the main enhancements to NVIDIA Isaac Manipulator.

Another is the NVIDIA Isaac Perceptor, which enhances the environmental awareness and performance of autonomous mobile robots (AMR) in dynamic environments like warehouses. It has a new visual SLAM reference procedure, improved multi-camera detection, and 3D reconstruction.

Partners Adopting NVIDIA Isaac 

AI models and NVIDIA Isaac accelerated libraries are being included into robotics firms’ platforms.

To facilitate the creation of AI-powered cobot applications, Universal Robots, a Teradyne Robotics business, introduced a new AI Accelerator toolbox.

Isaac ROS is being used by Miso Robotics to accelerate its Flippy Fry Station, a robotic french fry maker driven by AI, and to propel improvements in food service automation efficiency and precision.

Using the Isaac Perceptor, Wheel.me is collaborating with RGo Robotics and NVIDIA to develop a production-ready AMR.

Isaac Perceptor is being used by Main Street Autonomy to expedite sensor calibration. For Isaac Perceptor, Orbbec unveiled their Perceptor Developer Kit, an unconventional AMR solution.

For better AMR navigation, LIPS Corporation has released a multi-camera perception devkit.

For ROS developers, Canonical highlighted a fully certified Ubuntu environment that provides long-term support right out of the box.

Connecting With Partners at ROSCon

Connecting With Partners at ROSCon Canonical, Ekumen, Foxglove, Intrinsic, Open Navigation, Siemens, and Teradyne Robotics are among the ROS community members and partners who will be in Denmark to provide workshops, presentations, booth demos, and sessions. Highlights consist of:

“Nav2 User Gathering” Observational meeting with Open Navigation LLC’s Steve Macenski.

“ROS in Large-Scale Factory Automation” with Carsten Braunroth from Siemens AG and Michael Gentner from BMW AG

“Incorporating AI into Workflows for Robot Manipulation” Birds of a Feather meeting with NVIDIA’s Kalyan Vadrevu

“Speeding Up Robot Learning in Simulation at Scale” Birds of a Feather session with Macenski of Open Navigation and Markus Wuensch from NVIDIA on “On Use of Nav2 Docking”

Furthermore, on Tuesday, October 22, in Odense, Denmark, Teradyne Robotics and NVIDIA will jointly organize a luncheon and evening reception.

ROSCon is organized by the Open Source Robotics Foundation (OSRF). Open Robotics, the umbrella group encompassing OSRF and all of its projects, has the support of NVIDIA.

Read more on Govindhtech.com

[makers-muse]

What Are the Essential Tools and Equipment for a STEM Lab in Rajasthan? 

Introduction: Building a Future-Ready STEM Lab in Rajasthan 

With Rajasthan embracing technology-driven education, setting up a STEM lab in Rajasthan has become essential for schools. A well-equipped STEM lab in Rajasthan provides hands-on learning experiences that prepare students for careers in engineering, robotics, AI, and more. But what tools and equipment are needed to build a high-quality STEM lab in Rajasthan? 

Here’s a complete guide to the essential tools and equipment for a cutting-edge STEM lab in Rajasthan. 

1. Robotics Kits & Coding Tools for a STEM Lab in Rajasthan 

Robotics and coding are integral parts of STEM education. Schools need: 

Arduino & Raspberry Pi Kits – For learning programming, electronics, and automation 

LEGO Mindstorms & VEX Robotics Kits – To build and program robots 

Scratch & Python Coding Platforms – For beginner-friendly coding exercises 

Drones & AI Modules – To introduce students to artificial intelligence and automation 

These tools help students develop logical thinking and computational skills, making them ready for future careers in technology. A STEM lab in Rajasthan equipped with robotics fosters innovation and creativity. 

2. 3D Printers & Prototyping Equipment for a STEM Lab in Rajasthan 

Innovation thrives when students can create prototypes of their ideas. A STEM lab in Rajasthan should include: 

3D Printers (like Creality or Ultimaker) – For designing and printing functional models 

Laser Cutters & CNC Machines – To teach students about precision manufacturing 

3D Modeling Software (Tinkercad, Fusion 360) – To design real-world engineering projects 

By incorporating prototyping tools, students in STEM labs in Rajasthan gain exposure to product development, engineering, and entrepreneurship. 

3. Science & Electronics Experiment Kits in a STEM Lab in Rajasthan 

Hands-on experiments make learning science interactive and engaging. Schools should equip their STEM lab in Rajasthan with: 

Physics Kits (Newton’s Laws, Optics, and Electromagnetism Experiments) 

Chemistry Kits (Safe Lab Chemicals, Beakers, and Reaction Experiments) 

Biology Kits (Microscopes, DNA Extraction, and Ecosystem Models) 

Circuit Boards & Soldering Kits – To learn about electrical engineering and IoT 

With these kits, students in STEM labs in Rajasthan can explore scientific concepts practically, strengthening their understanding and problem-solving skills. 

4. AI & Machine Learning Tools for a STEM Lab in Rajasthan 

With the rise of AI and data science, it’s crucial to introduce students to basic AI concepts. Essential tools for a STEM lab in Rajasthan include: 

AI Development Boards (Jetson Nano, Google Coral) – For experimenting with AI projects 

Machine Learning Platforms (Google Colab, TensorFlow, Teachable Machine) – For building AI models 

Speech & Image Recognition Kits – To introduce students to computer vision and natural language processing 

AI tools allow students in STEM labs in Rajasthan to work on cutting-edge projects, boosting their career opportunities in AI and automation. 

5. IoT & Smart Technology Kits for a STEM Lab in Rajasthan 

IoT is transforming industries, and students must learn how smart devices work. Schools should include in their STEM lab in Rajasthan: 

IoT Development Kits (ESP8266, NodeMCU, Arduino IoT Cloud) 

Sensors (Temperature, Motion, Humidity, RFID) – To build smart home and automation projects 

Wireless Modules (Bluetooth, Wi-Fi, LoRaWAN) – To introduce connected device technology 

With IoT tools, students in STEM labs in Rajasthan can develop real-world smart solutions, preparing them for the future of technology. 

6. Renewable Energy & Environmental Science Kits in a STEM Lab in Rajasthan 

Sustainability is a key focus in Rajasthan, and students should learn about renewable energy sources. A STEM lab in Rajasthan should include: 

Solar Panel Kits – To teach about solar energy and power generation 

Wind Turbine Models – For understanding wind energy 

Water Purification & Conservation Experiments – To promote sustainability projects 

These tools help students in STEM labs in Rajasthan develop eco-friendly solutions for environmental challenges. 

7. Virtual & Augmented Reality (VR/AR) Systems in a STEM Lab in Rajasthan 

Immersive learning through VR and AR makes STEM education more engaging. Schools should invest in: 

VR Headsets (Oculus Quest, HTC Vive) – To explore virtual science labs and simulations 

AR Learning Apps (Google Expeditions, Merge Cube) – For interactive learning experiences 

3D Anatomy & Space Exploration Software – To make subjects like biology and astronomy exciting 

By integrating VR and AR, students in STEM labs in Rajasthan experience interactive, hands-on education, improving conceptual understanding. 

Start Building a STEM Lab in Rajasthan Today! 

Setting up a STEM lab in Rajasthan is an investment in the future. With the right tools, students can: 

Develop critical problem-solving skills 

Engage in hands-on, innovative learning 

Prepare for future careers in science and technology 

Want to equip your school with a high-tech STEM lab in Rajasthan? Contact us today to explore funding options and expert guidance! 

[tannatechbiz]

Buy Best NVIDIA Jetson Nano

Get started today with the Jetson Nano Developer Kit by TANNA TechBiz!!

NVIDIA Jetson Nano Developer Kit is a powerful workstation that lets you run multiple neural networks in parallel for applications like, segmentation, image classification, object detection and speech processing.

Jetson Nano is small but, supported by Nvidia JetPack, which includes, Linux OS, NVIDIA CUDA, cuDNN, board support package and TensorRT software libraries for multimedia processing, deep learning, computer vision, GPU computing, and much more. Do you know? Software is yet available using a flash SD card image, making it rapid and easy to get started.

To get you encouraged, let’s build an actual hardware project with an NVIDIA Jetson Nano TANNA TechBiz. You can Buy NVIDIA Jetson Nano developer kit at a lower cost. It conveys & computes performance to run modern Artificial Intelligence workloads at unprecedented size. It is extremely power-efficient, consuming as little as 5 watts.

Do you know? Heavy applications can be executed using this Nvidia Jetson Nano. It also has an SD Card image boot like Raspberry pi, but with GPU (Graphical Processing Unit). Jetson Nano is used for various ML (Machine Learning) & DL (Deep Learning) research in various fields for Speech processing, Image processing, and sensor based analysis.

Jetson Nano delivers 472 GFLOPS for running modern AI (Artificial Intelligence) algorithms fast, a 128-core integrated NVIDIA GPU, with a quad-core 64-bit ARM CPU, as well as 4 GB LPDDR4 memory. It runs multiple neural networks in parallel and processes several high-resolution sensors concurrently.

NVIDIA Jetson Nano makes it easy for developers to connect a diverse set of the latest sensors to allow a range of AI applications. This SDK is used across the NVIDIA Jetson family of products and is fully compatible with NVIDIA's Artificial Intelligence frameworks for deploying and training deploying AI software.

[arducam-blog]

Arducam Autofocus Camera for Raspberry Pi 2019

The sensor we are using: 5MP OV5647

You can also use the 8MP IMX219 sensor.

Find all the cool cams for Raspberry Pi here: http://bit.ly/Pi-Cams

Get them now from one of our distributors here: http://bit.ly/Buy-Arducam

[wolfliving]

The Jetson Nano

*Let’s see if anybody needs this Maker AI gizmo.

*It’s a press release.

https://nvidianews.nvidia.com/news/nvidia-announces-jetson-nano-99-tiny-yet-mighty-nvidia-cuda-x-ai-computer-that-runs-all-ai-models?ncid=so-twi-gj-78738&linkId=100000005468164

GPU Technology Conference—NVIDIA today announced the Jetson Nano™, an AI computer that makes it possible to create millions of intelligent systems.

The small but powerful CUDA-X™ AI computer delivers 472 GFLOPS of compute performance for running modern AI workloads and is highly power-efficient, consuming as little as 5 watts.

Unveiled at the GPU Technology Conference by NVIDIA founder and CEO Jensen Huang, Jetson Nano comes in two versions — the $99 devkit for developers, makers and enthusiasts and the $129 production-ready module for companies looking to create mass-market edge systems.

Jetson Nano supports high-resolution sensors, can process many sensors in parallel and can run multiple modern neural networks on each sensor stream. It also supports many popular AI frameworks, making it easy for developers to integrate their preferred models and frameworks into the product.

Jetson Nano joins the Jetson™ family lineup, which also includes the powerful Jetson AGX Xavier™ for fully autonomous machines and Jetson TX2 for AI at the edge. Ideal for enterprises, startups and researchers, the Jetson platform now extends its reach with Jetson Nano to 30 million makers, developers, inventors and students globally.

“Jetson Nano makes AI more accessible to everyone — and is supported by the same underlying architecture and software that powers our nation’s supercomputers,” said Deepu Talla, vice president and general manager of Autonomous Machines at NVIDIA. “Bringing AI to the maker movement opens up a whole new world of innovation, inspiring people to create the next big thing.”

Jetson Nano Developer Kit The power of AI is largely out of reach for the maker community and in education because typical technologies do not pack enough computing power and lack an AI software platform.

At $99, the Jetson Nano Developer Kit brings the power of modern AI to a low-cost platform, enabling a new wave of innovation from makers, inventors, developers and students. They can build AI projects that weren’t previously possible and take existing projects to the next level — mobile robots and drones, digital assistants, automated appliances and more.

The kit comes with out-of-the-box support for full desktop Linux, compatibility with many popular peripherals and accessories, and ready-to-use projects and tutorials that help makers get started with AI fast. NVIDIA also manages the Jetson developer forum, where people can get answers to technical questions.

“The Jetson Nano Developer Kit is exciting because it brings advanced AI to the DIY movement in a really easy-to-use way,” said Chris Anderson of DIY Robocars, DIY Drones and the Linux Foundation’s Dronecode project. “We’re planning to introduce this technology to our maker communities because it’s a powerful, fun and affordable platform that’s a great way to teach deep learning and robotics to a broader audience.”

Jetson Nano Module In the past, companies have been constrained by the challenges of size, power, cost and AI compute density. The Jetson Nano module brings to life a new world of embedded applications, including network video recorders, home robots and intelligent gateways with full analytics capabilities. It is designed to reduce overall development time and bring products to market faster by reducing the time spent in hardware design, test and verification of a complex, robust, power-efficient AI system.

The design comes complete with power management, clocking, memory and fully accessible input/outputs. Because the AI workloads are entirely software defined, companies can update performance and capabilities even after the system has been deployed.

“Cisco Collaboration is on a mission to connect everyone, everywhere for rich and immersive meetings,” said Sandeep Mehra, vice president and general manager for Webex Devices at Cisco. “Our work with NVIDIA and use of the Jetson family lineup is key to our success. We’re able to drive new experiences that enable people to work better, thanks to the Jetson platform’s advanced AI at the edge capabilities.”

To help customers easily move AI and machine learning workloads to the edge, NVIDIA worked with Amazon Web Services to qualify AWS Internet of Things Greengrass to run optimally with Jetson-powered devices such as Jetson Nano.

“Our customers span very diverse industries, including energy management, industrial, logistics, and smart buildings and homes,” said Dirk Didascalou, vice president of IoT, Amazon Web Services, Inc. “Players in all of these industries are building intelligence and computer vision into their applications to take action at the edge in near real time. AWS IoT Greengrass allows our customers to perform local inference on Jetson-powered devices and send pertinent data back to the cloud to improve model training.”

One Software Stack Across the Entire Jetson Family NVIDIA CUDA-X is a collection of over 40 acceleration libraries that enable modern computing applications to benefit from NVIDIA’s GPU-accelerated computing platform. JetPack SDK™ is built on CUDA-X and is a complete AI software stack with accelerated libraries for deep learning, computer vision, computer graphics and multimedia processing that supports the entire Jetson family.

The JetPack includes the latest versions of CUDA, cuDNN, TensorRT™ and a full desktop Linux OS. Jetson is compatible with the NVIDIA AI platform, a decade-long, multibillion-dollar investment that NVIDIA has made to advance the science of AI computing.

Reference Platforms to Prototype Quickly NVIDIA has also created a reference platform to jumpstart the building of AI applications by minimizing the time spent on initial hardware assembly. NVIDIA JetBot™ is a small mobile robot that can be built with off-the-shelf components and open sourced on GitHub.

Jetson Nano System Specs and Software Key features of Jetson Nano include:

GPU: 128-core NVIDIA Maxwell™ architecture-based GPU

CPU: Quad-core ARM® A57

Video: 4K @ 30 fps (H.264/H.265) / 4K @ 60 fps (H.264/H.265) encode and decode

Camera: MIPI CSI-2 DPHY lanes, 12x (Module) and 1x (Developer Kit)

Memory: 4 GB 64-bit LPDDR4; 25.6 gigabytes/second

Connectivity: Gigabit Ethernet

OS Support: Linux for Tegra®

Module Size: 70mm x 45mm

Developer Kit Size: 100mm x 80mm

Availability The NVIDIA Jetson Nano Developer Kit is available now for $99. The Jetson Nano module is $129 (in quantities of 1,000 or more) and will begin shipping in June. Both will be sold through NVIDIA’s main global distributors. Developer kits can also be purchased from maker channels, Seeed Studio and SparkFun.

About NVIDIA NVIDIA‘s (NASDAQ: NVDA) invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. More information at http://nvidianews.nvidia.com/.

Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, performance and abilities of Jetson Nano, Jetson Nano Developer Kit, Jetson Nano module and JetPack SDK; Jetson Nano running all AI models and ability to create millions of intelligent systems; Jetson Nano supporting many frameworks making it easy for developers to integrate their models and frameworks into the product; Jetson products extending its reach to users globally; Jetson Nano making AI more accessible to everyone and bringing AI to the maker movement opening up a new world of innovation and inspiring the next big thing; Jetson Nano Developer Kit bringing the power of modern AI, enabling a new wave of innovation; enabling AI projects that were not possible before and taking existing projects to the next level; excitement over the Jetson Nano Developer Kit bringing AI to the DIY movement and the reasons for the planned introduction of the technology to maker communities; the Jetson Nano module opening up a new world of embedded applications, its ability to bring products to market faster and companies’ ability to update performance and capabilities after the system has been deployed; Cisco’s mission, work with NVIDIA and Jetson being the key to its success and its ability to drive new experiences that enable people to work better due to Jetson; the benefits of Jetson and AWS Internet of Things Greengrass working together and it enabling customers to perform inference on Jetson devices and help improve model training; players in industries building intelligence and computer visions into their applications; and the availability of the Jetson Nano Developer Kit and Jetson Nano module are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2019 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, CUDA, Jetson, Jetson AGX Xavier, Jetson Nano, NVIDIA JetBot, NVIDIA JetPack and TensorRT are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

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Jetson Nano: The Ultimate AI Development Kit for Robotics and More

Jetson Nano is powered by NVIDIA's CUDA-X AI software stack, which includes libraries and tools for machine learning, computer vision, and more. It's capable of processing data from multiple sensors and cameras in real-time, making it an ideal platform for robotics and other applications that require fast, efficient processing.

The Jetson Nano works by leveraging the power of NVIDIA's CUDA cores, which are designed to handle complex calculations quickly and efficiently. It also features a dedicated hardware video encoder and decoder, enabling it to process and stream high-quality video in real-time.

But the Jetson Nano isn't just for robotics. It has many applications in various industries, such as autonomous vehicles, drones, smart homes, industrial automation, and healthcare. With the Jetson Nano, you can harness the power of NVIDIA's cutting-edge technology to create intelligent machines that can see, hear, and learn.

Compared to Raspberry Pi, another popular single-board computer, Jetson Nano has a more powerful GPU, making it better suited for AI and robotics projects. However, Raspberry Pi may be a better choice for simpler projects that don't require as much processing power.

Setting up Jetson Nano is easy. You can download the software image from NVIDIA's website and flash it onto a microSD card. Then, insert the microSD card into the Jetson Nano and power it on. You'll be up and running in no time.