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Solar Panel defect detection using AI techniques 
Solar energy is a source of clean energy, naturally harnessing the power of the sun. When solar panels are deployed to generate electricity, greenhouse gases are not emitted into the atmosphere.
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Is solid-state LiDAR the key to autonomous driving?
The technology for autonomous vehicles has been around for a while, and major automakers and tech companies worldwide have invested billions of dollars in making it a reality.
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AI Model optimisation using OpenVINO
In this article we explore the advantages of making use of the native APIs and runtime engine of OpenVINO to maximize the performance and efficiency of DNN model inference.
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Real-time Driver Monitoring Systems on Edge AI Device | Technical Paper
As road accident cases are increasing due to the inattention of the driver, automated driver monitoring systems (DMS) have gained an increase in acceptance.
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We have achieved TISAX Assessment Level 3 certification
We are excited to announce that we have achieved TISAX (Trusted Information Security Assessment Exchange) assessment Level-03 permanent label.
A guide to a Design Engineer’s approach to Failure Mode & Effects Analysis
In the automotive industry, safety plays an immensely crucial role. All crucial systems of the automobile need to pass stringent functional safety requirements.
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Horizontal scaling of Video processing applications
Video is a common data input in the field of Computer Vision & Image processing. For example, in applications such as infrastructure maintenance and defect detection, video is captured from a camera source mounted on a drone or a locomotive.
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Perceiving the World in-’Depth’ with 3D LiDAR 
Simultaneous Localization and Mapping (SLAM) is a technology used in robotics and autonomous systems to create maps of unknown environments while simultaneously tracking the location of the robot within that environment.
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3D LiDAR SLAM – Scan Matching Explained  
Simultaneous Localization and Mapping (SLAM) is a popular technique in robotics that involves building a map of an unknown environment while simultaneously localizing the robot within that environment.
3D LiDAR SLAM – Graph SLAM Explained
Graph-based SLAM (also known as Graph SLAM) uses a graph to represent the environment and the robot’s pose estimates. It is widely used in many robotics applications like autonomous vehicles, mobile robots and unmanned aerial vehicles.


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