Embedded Computing Design

Wireless technology has come a long way since its inception in the late 1920s to the development of Bluetooth in the early 1990s. There are various pros and cons regarding the scalability and efficiency of such wireless systems.

Despite the advancements in AI processors, the image signal processing capacity of the system is not enough to balance the requirements of self-driving vehicles. The continuous improvement in ISP hardware technology can bridge this gap in the industry.

In Qi charging technology, inductive charging methods are used where devices need to be nearby each other. The technology is not entirely wireless, as the phone must continue to stay on the Qi charging pad in order to charge it completely. 

One of the key aspects of Artificial Intelligence (AI) and Machine Learning (ML) applications is image processing and computer vision. With the rise in complexity and demand for AI and ML applications, it is important to develop efficient, cutting-edge tools for image processing and analytics of visual data. 

With the increased demand for customizations to secure intelligent solutions, the flexibility of changing the features without affecting the target launch date is very crucial. Thus inculcating scalability and flexibility parameters play a major role in reducing the time to market.

As the field of Artificial Intelligence gains traction, the devices are becoming more computational and power-consuming. Subsequently, the processing load on the edge devices significantly grows with the performance and the complexity of the system architecture. Hence, higher resolution images and more sophisticated algorithms are inculcated in the system which further needs to be optimized with the increasing demand for AI processing to achieve high TOPS performance.

Edge AI and sensor fusion are two disciplines of application development for engineers due to their complexity and multidimensionality. It is more convenient to have a complete solution that integrates a wide range of sensors with edge devices supporting multiple use cases.

One of the key aspects of Artificial Intelligence (AI) and Machine Learning (ML) applications is image processing and computer vision. With the rise in complexity and demand for AI and ML applications, it is important to develop efficient, cutting-edge tools for image processing and analytics of visual data.

With the massive demand for AI applications, the underlying hardware needs to be compatible with advanced software tools for optimal product development life-cycle. AndeSight V1.5 is one of the newest IDEs that enables users to efficiently develop embedded use cases for AI and much more.

With the rise of electric vehicles, there has been a demand in the industry to attain more efficient, environment-friendly technologies for EV batteries. A revolutionary platform, Sinanode meets the market requirements and provides a solution in a cost-effective way. 

Accelerating AI inference models has become an essential task as we progress towards creating more sophisticated and efficient AI applications. Flexibile and complete AI assistance are critical components for success in the fast-growing AI sector. 

In recent years, Deep Neural Networks have made significant advances in bringing the accuracy of computer vision algorithms to the next level. OpenVINO toolkit is one such example that enables the optimization of DNN models while providing high performance.

Emerging IoT technology contributes to the growth of OEMs in various sectors as it helps collect massive amounts of data from multiple sources. Although it is complex to collect, process, and analyze this load of data, the blend of AI with the help of IoT has the power to revamp the operation of industries, businesses and economies.

Working with AI models can be highly demanding in terms of computational resources — with more processing power comes greater power requirements. To solve this problem, Ceremorphic developed the ThreadArch multi-thread processor technology.

With every passing day, technical demands evolve, and so does the technology we use. Edge services and cloud computing have both improved significantly in the past few years with a wide range of functionalities. Combining both of these technologies offers great advantages. 

TP-Link recently announced a range of smart, secure home devices under the Tapo brand that includes HomeKit-enabled smart lights, plugs, smart switches, and a few more IoT-enabled consumer appliances.

IoT networks are prone to cyberattacks if not secured effectively. The QuarkLink IoT security management platform secures and enhances Macronix’s ArmorFlash Ecosystem with its most resilient and scalable IoT security approach.

The microcontroller development board is the core component of an embedded or IoT application, thus it becomes significantly important to choose the board before designing the application. As there were many launches of development boards in 2021, it may be a difficult decision to choose the one suitable for your target application. 

Amazon Web Services recently announced its IoT TwinMaker that optimizes the development of digital twins, making the complex process much faster and easier. AWS IoT allows developers to construct and use digital twins of real-world systems to monitor and optimize operations more quickly and easily.

Machine learning (ML) requires industry-standard performance benchmarks to aid in the creation and competitive assessment of the numerous ML-related software and hardware solutions.