Category: Edge Computing

Field Data Logger

A field data logger is an electronic device used to automatically record measurements such as temperature, humidity, or pressure in outdoor or remote environments. It collects data over time without the need for constant human supervision, storing the information for later analysis. Field data loggers are often used in scientific research, agriculture, and environmental monitoring.

Geo-Fencing System

A geo-fencing system is a technology that uses GPS, RFID, Wi-Fi, or mobile data to create a virtual boundary around a specific real-world location. When a device enters or leaves this area, the system can trigger actions like sending alerts, enabling features, or restricting access. Geo-fencing is commonly used for location-based services, security, and automation…

TinyML Deployment Strategies

TinyML deployment strategies refer to the methods and best practices used to run machine learning models on very small, resource-constrained devices such as microcontrollers and sensors. These strategies focus on making models small enough to fit limited memory and efficient enough to run on minimal processing power. They also involve optimising power consumption and ensuring…

Edge Inference Optimization

Edge inference optimisation refers to making artificial intelligence models run more efficiently on devices like smartphones, cameras, or sensors, rather than relying on distant servers. This process involves reducing the size of models, speeding up their response times, and lowering power consumption so they can work well on hardware with limited resources. The goal is…

TinyML Optimization

TinyML optimisation is the process of making machine learning models smaller, faster, and more efficient so they can run on tiny, low-power devices like sensors or microcontrollers. It involves techniques to reduce memory use, improve speed, and lower energy consumption without losing too much accuracy. This lets smart features work on devices that do not…

Edge AI Deployment

Edge AI deployment means running artificial intelligence models directly on devices like smartphones, cameras or sensors, instead of sending data to remote servers for processing. This approach allows decisions to be made quickly on the device, which can be important for tasks that need fast response times or for situations where there is limited internet…

TinyML Frameworks

TinyML frameworks are specialised software tools that help developers run machine learning models on very small and low-power devices, like sensors or microcontrollers. These frameworks are designed to use minimal memory and processing power, making them suitable for devices that cannot handle large or complex software. They enable features such as speech recognition, image detection,…

Edge AI Optimization

Edge AI optimisation refers to improving artificial intelligence models so they can run efficiently on devices like smartphones, cameras, or sensors, which are located close to where data is collected. This process involves making AI models smaller, faster, and less demanding on battery or hardware, without sacrificing too much accuracy. The goal is to allow…