Harnessing the OKdo Nano C100 Developer Kit for Leukemia Classification

Deval Shah

20 Jan, 2024

Harnessing the OKdo Nano C100 Developer Kit for Leukemia Classification

Optimizing Medical AI: Revolutionizing Diagnostics with the OKdo Nano C100 Developer Kit

artificial intelligence

- Medical Devices

- microchips

- sensors

The OKdo Nano C100 Developer Kit presents an innovative approach to medical diagnostics, particularly in leveraging AI for leukemia detection. Its affordability and compatibility with the NVIDIA ecosystem democratizes access to AI technologies. The kit is powered by the NVIDIA® Jetson Nano Module, equipping it to handle complex AI workloads efficiently. This makes it an excellent choice for processing-intensive tasks, especially in medical AI applications.1

In this article, we will explore the technical specifications of the OKdo Nano C100 Developer Kit and its potential in medical AI applications, focusing on leukemia classification. We will highlight how its cost-effectiveness and robust processing capabilities make it a game-changer in medical diagnostics, offering a power-efficient solution for advanced healthcare technology.

The OKdo Nano C100 Developer Kit: An Overview

The NVIDIA® Jetson Nano module powers the OKdo Nano C100 Developer Kit. This module is central to the kit's capabilities, featuring a 128-core NVIDIA® Maxwell GPU and a Quad-core ARM A57 CPU, supported by 4 GB of 64-bit LPDDR4 memory. These components deliver robust processing power, crucial for handling sophisticated AI algorithms and workloads.

The OKdo Nano C100 boasts extensive input/output capabilities regarding connectivity and integration. It includes a range of ports and connectors, such as USB 3.0 Type A, USB 2.0 Micro-B, HDMI/DisplayPort, M.2 Key E, Gigabit Ethernet, GPIOs, I2C, I2S, SPI, UART, 2x MIPI-CSI camera connector, a Micro SD card slot, and a fan connector. This array of I/O options significantly enhances its versatility, allowing for the integration of various sensors and devices, which is essential in diverse AI applications, including medical imaging and diagnostics.3

Another standout feature of the OKdo Nano C100 is its power efficiency. The kit can be powered by a micro-USB 5V 2A or a DC power adapter 5V 4A, consuming as little as 5 watts. This efficient power usage is particularly beneficial for sustained operations in data-intensive tasks.

Furthermore, the Developer Kit is supported by NVIDIA® JetPack, a comprehensive suite with a board support package (BSP), Linux OS, and essential software libraries like NVIDIA® CUDA, cuDNN, and TensorRT. These libraries are integral for deep learning, computer vision, GPU computing, and multimedia processing. The support provided by NVIDIA® JetPack simplifies the development process, reducing complexity and enhancing the kit's capability to handle advanced AI workloads, such as those required for leukemia classification.4

For more detailed information about the technical specifications and capabilities of the OKdo Nano C100 Developer Kit, please visit their official website.

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Building a Leukemia Classification System with the OKdo Nano C100

Building a leukemia classification system using the OKdo Nano C100 Developer Kit involves several key steps, focusing on integrating AI frameworks and models specific to medical imaging.

We will discuss how to start with the OKdo Nano C100 developer kit and integrate it with AI frameworks to build a system. Here's a step-by-step guide:

Initial Setup: Begin by unboxing the OKdo C100 Nano Developer Kit. Connect it to an HDMI monitor, a USB keyboard, and a mouse. An Ethernet connection is required for OS updates. Optionally, you can fit an M.2 E key wireless module for WiFi support. Connect a quality 5V/4A power supply, but do not power on.5
Camera Integration: The Nano C100 has two MIPI-CSI camera ports, supporting cameras based on imx219 and imx477 sensors. For the leukemia classification project, attach one or two compatible camera modules, such as the Raspberry Pi V2 camera, by sliding the ribbon cable into the connector with the blue marking facing away from the heat sink.5
Software Installation: The NVIDIA Jetson Nano module in the C100 comes with 16GB onboard eMMC storage. For development, it’s recommended to flash the system image to a microSD card (32GB or larger, Speed Class 10, A1 rated) and boot from there. Use BalenaEtcher to flash the official OKdo Nano C100 Developer Kit OS image, available on the OKdo Software & Downloads hub.5
4. First Boot and Configuration: You'll follow a setup sequence, including accepting the NVIDIA end-user license agreement (EULA) upon initial boot. Set up the system language, keyboard layout, time zone, username, password, and computer name. Choose the maximum software partition size and configure the module in MAX power mode.5
Leveraging NVIDIA JetPack: The kit is supported by NVIDIA JetPack, which includes a board support package (BSP), Linux OS, NVIDIA CUDA, cuDNN, and TensorRT software libraries. This suite is vital for deep learning, computer vision, GPU computing, multimedia processing, and more. It simplifies the development process for AI applications.
AI Framework Integration: Integrate AI frameworks and models suitable for medical imaging. Leverage NVIDIA JetPack’s support for CUDA and deep learning libraries to build and train models for image classification, object detection, and segmentation.
Utilize Dual MIPI-CSI Camera Connectors: The dual MIPI-CSI camera connectors and the kit's support for AI video processing are crucial for medical imaging applications. They allow for integrating high-quality imaging sensors essential for accurate leukemia classification.

The OKdo Nano C100's support for AI video processing is critical to the leukemia classification problem. This feature enables the Developer Kit to handle real-time video data, a capability essential for medical applications where dynamic imaging (like blood flow or cell movement) may be involved. The system can identify and analyze moving leukemia cells by processing video data, providing a more comprehensive understanding of the disease. This real-time processing capability, combined with the high-performance GPU and CPU of the OKdo Nano C100, makes it a powerful tool for advanced medical imaging and analysis tasks.3

Educational Implications: A Tool for University Students

The OKdo Nano C100 Developer Kit is well-suited for educational purposes, especially for university students studying AI, machine learning, and medical imaging. With its NVIDIA Jetson Nano module, this kit offers an accessible yet powerful platform for developing and testing AI applications. It supports full desktop Linux and is compatible with peripherals that support 4K, including Ethernet, USB, and HDMI, making it a versatile tool for learning and experimentation.

The kit's 128-core NVIDIA Maxwell GPU allows students to engage in various AI applications such as image classification, object detection, segmentation, and speech processing. This feature-rich environment is ideal for hands-on learning in AI and machine learning courses. It also uses NVIDIA’s Deepstream SDK, offering a comprehensive toolkit for AI-based video and image understanding, which is crucial for advanced studies in these fields.

In terms of ease of use, the OKdo Nano C100 is designed for simplicity and accessibility. Its platform is built for easy use, running on as little as 5 watts, which is advantageous in an educational setting where resources and technical support might be limited. The Jetson developer kits are primarily intended for developing and testing software in a pre-production environment. They are suitable for educational projects emphasizing real-world applications and practical learning.

Overall, the OKdo Nano C100 Developer Kit is a practical, accessible, and powerful tool that can significantly enhance the learning experience of university students in AI and related fields.

Future Outlook in Healthcare AI with OKdo Nano C100

With its advanced AI capabilities, the OKdo Nano C100 Developer Kit is poised to impact future medical AI developments significantly. Its robust features, including a 128-core NVIDIA Maxwell GPU, Quad-core ARM A57 CPU, and extensive I/O capabilities, make it an ideal tool for complex AI workloads.

In healthcare, the potential impact of the OKdo Nano C100 on quality and diagnostics is substantial. Its ability to run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing can revolutionize medical imaging and diagnostics. For instance, its capability to process and analyze complex medical images in leukemia classification can lead to more accurate and faster diagnoses, improving patient outcomes. Additionally, its suitability for various AI applications means it can adapt to various healthcare needs, from drug discovery to patient monitoring, enhancing overall healthcare quality and efficiency.

Conclusion

The OKdo Nano C100 Developer Kit is a significant technological advancement, particularly in on-premise AI systems. Its high computing capabilities, efficient power usage, and comprehensive software support make it an ideal platform for developing sophisticated AI models. This is especially relevant in medical diagnostics, where its application in leukemia classification demonstrates its potential to transform healthcare practices.

By enabling more accurate and quicker diagnoses, the OKdo Nano C100 advances the technology behind healthcare AI and directly contributes to improved patient care and outcomes. As AI continues to evolve, tools like the OKdo Nano C100 will undoubtedly play a pivotal role in shaping the future of healthcare and medical diagnostics.

References

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"artificial intelligence"

AI helps us to squeeze every possible bit of performance out of our machines

Arnaud Hubaux, Senior Technical Program Manager explains about optimization of ASML machines as part of a series from the founding partners of the AI Innovation Center at High Tech Campus Eindhoven.

Victor Donker

fromHigh Tech Campus Eindhoven

06 Apr, 2021

artificial intelligence

- machine learning

- microchips

- photonic chips

In 2019 the chipmaking industry churned out more than 634 billion computer chips, generating a global turnover of 412 billion euro’s. Although the companies that design and produce the chips, like Intel, Samsung and NXP, are known the world over, the same cannot be said about the company that makes the incredibly complex machines on which these chips are manufactured. We’re talking of course about ASML, the multinational Brainport-based company that with a sense of irony describes itself as ‘the most important tech company you’ve never heard of’.

Yet ASML’s lithography machines are the backbone of the chipmaking world. Their incredibly accurate lasers, used by large chip manufacturers, print chip patterns onto silicon wafers with resolutions up to 13 nanometers (a nanometer is a millionth of a millimeter). Although ASML is primarily a hardware company, software has become increasingly important to them over the years. This includes artificial intelligence (AI) and machine learning (ML), which the company uses to improve machine performance.

As a senior program manager Arnaud Hubaux drives the adoption of AI at ASML. Hubaux introduced machine learning solutions to detect defects during production and optimise the behaviour of their lithography machines. “We use AI as a complementary technology to our product portfolio, which helps our customers get the most out of their chip manufacturing processes,” he says.

Firstly, can you tell us why ASML decided to co-invest in the AI Innovation Center?

Hubaux: “Many people don’t realise that ASML’s chipmaking machines rely extensively on software to produce and maintain nanometer precision. That includes machine learning and artificial intelligence solutions. The AI Innovation Center is a gateway for us to talk about the challenges of what AI means in that context. Also, with the start of this Center I’m excited that students fresh out of university can immediately lea

Image: The device senses and wirelessly transmits signals without bulky chips or batteries. Courtesy of the researchers

Engineers fabricate a chip-free, wireless electronic "skin"

The device senses and wirelessly transmits signals related to pulse, sweat, and ultraviolet exposure, without bulky chips or batteries.

Jennifer Chu

21 Aug, 2022

artificial skin sensors

- Biomedical Devices

- wearable

This article was first published on MIT News.

Wearable sensors are ubiquitous thanks to wireless technology that enables a person’s glucose concentrations, blood pressure, heart rate, and activity levels to be transmitted seamlessly from sensor to smartphone for further analysis.

Most wireless sensors today communicate via embedded Bluetooth chips that are themselves powered by small batteries. But these conventional chips and power sources will likely be too bulky for next-generation sensors, which are taking on smaller, thinner, more flexible forms.

Now MIT engineers have devised a new kind of wearable sensor that communicates wirelessly without requiring onboard chips or batteries. Their design, detailed today in the journal Science, opens a path toward chip-free wireless sensors.

The team’s sensor design is a form of electronic skin, or “e-skin” — a flexible, semiconducting film that conforms to the skin like electronic Scotch tape. The heart of the sensor is an ultrathin, high-quality film of gallium nitride, a material that is known for its piezoelectric properties, meaning that it can both produce an electrical signal in response to mechanical strain and mechanically vibrate in response to an electrical impulse.

The researchers found they could harness gallium nitride’s two-way piezoelectric properties and use the material simultaneously for both sensing and wireless communication.

In their new study, the team produced pure, single-crystalline samples of gallium nitride, which they paired with a conducting layer of gold to boost any incoming or outgoing electrical signal. They showed that the device was sensitive enough to vibrate in response to a person’s heartbeat, as well as the salt in their sweat, and that the material’s vibrations generated an electrical signal that could be read by a nearby receiver. In this way, the device was able to wirelessly transmit sensing information, without the