Embedded AI for Predictive Maintenance in Industries Embedded AI helps detect industrial machine failures by analysing real-time sensor data such as vibration, temperature and motor current. Using machine learning and TinyML, embedded systems can identify abnormal machine behaviour and support early fault detection.Technologies like ESP32, …
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Developing a Linux-Based Smart Surveillance System Using Raspberry Pi A Linux-based smart surveillance system using Raspberry Pi combines embedded Linux, Python and OpenCV to build an affordable security monitoring solution. It enables real-time video capture, motion detection, recording and remote monitoring through a network connection. …
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Stack vs Heap Memory in Embedded Systems: Key Differences Stack and heap memory are two important areas of RAM used by microcontrollers to manage program data during execution. Stack memory automatically manages function calls, local variables and return addresses, while heap memory supports dynamic memory …
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Predictive Analytics in Smart Manufacturing Using IoT Sensors Predictive analytics in smart manufacturing uses IoT sensors, industrial data, and machine learning algorithms to predict equipment failures, monitor production performance, and improve manufacturing efficiency. Sensors collect real-time data such as temperature, vibration, pressure, humidity, and motor …
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Edge Computing vs Cloud Computing in IoT: Key Differences LiDAR and camera-based object detection are two major technologies used in autonomous systems and self-driving vehicles. LiDAR uses laser pulses to measure distances and generate 3D point clouds, enabling accurate object localization and environmental mapping. Camera-based …
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Edge Computing vs Cloud Computing in IoT: Key Differences Edge computing and cloud computing are two important technologies used in IoT systems. Edge computing processes sensor data closer to its source, reducing latency and bandwidth usage while supporting real-time decisions. Cloud computing uses centralized remote …
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Sensor Fusion in Embedded Systems Techniques & Algorithms Sensor fusion in embedded systems combines data from multiple sensors to improve measurement accuracy, reliability, and real-time decision-making. Common techniques include Kalman Filters, Extended Kalman Filters, and Complementary Filters. It is widely used in robotics, autonomous vehicles, …
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OpenMV for Embedded Computer Vision: Features, Working Principles and Real-World Applications OpenMV is an embedded machine vision platform that enables real-time image processing directly on compact hardware. It supports MicroPython programming, color detection, object tracking, shape recognition and selected machine learning applications. OpenMV is widely …
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Python Memory Management: Garbage Collection Explained Every Python program uses memory to store variables, objects, functions, and data structures. As applications grow, managing this memory efficiently becomes important. If unused objects remain in memory, applications may consume more resources than necessary and experience performance issues. …
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Dijkstra’s Algorithm Demystified: A Complete Guide to Shortest Path Algorithms Imagine you are travelling from one city to another. There may be several possible routes, but which route has the minimum distance? This is a common problem in computer science, and Dijkstra’s Algorithm provides an …
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