AcademiX supports engineering institutions with laboratory planning and technology selection for defined teaching, project and research requirements. Configurations may include instruments, embedded platforms, automation components, software, interfaces and accessories.
Laboratory planning should consider instruments, software, accessories, interfaces, safety and experiment workflows together. AcademiX configurations are developed around the institution’s intended teaching and research use.
Bench environments for circuit measurement, signal analysis, power electronics, embedded debugging and data acquisition.
Measurement and experimentation platforms for communication systems, antennas, RF circuits and software-defined radio.
Platforms for microcontrollers, embedded Linux, edge computing, sensor integration, industrial communication and secure device connectivity.
Control and connectivity benches for PLC programming, HMI development, remote I/O, industrial networks and operational data acquisition.
Integrated workcells for motion, perception, manipulation, machine vision, edge inference and safe experiment design.
Tool-supported workflows for requirements, static analysis, unit testing, formal verification, secure communication and embedded trust.
Measurement setups for material, sensor, transistor, memory and device research requiring controlled electrical stimulus and repeatable data capture.
Identify curriculum topics, project themes, research questions and expected user groups.
Consider existing instruments, computers, utilities, space, network access and safety constraints.
Select hardware, software, interfaces, accessories and measurement or control sequence.
Confirm compatibility, facility requirements, installation assumptions and procurement scope.
Record the agreed configuration, connectivity, accessories and intended experiment framework.
A compact setup for defined experiments, demonstrations or a limited number of student groups.
A shared environment for student projects, multidisciplinary work and guided prototyping.
A configuration built around specific measurement, validation, simulation or development requirements.
A multi-domain space combining selected instrumentation, computing, automation and software workflows.
Generate a signal, power a circuit, capture responses, apply protocol decoding and document results.
Calibrate the measurement path, capture S-parameters and compare response across frequency.
Acquire sensor data, process it locally, expose selected values and review communication behaviour.
Read field signals, execute control logic, operate an HMI and log process values for review.
Detect an object, calculate coordinates, command motion and validate safety and repeatability assumptions.
Create requirements, link them to implementation artefacts, execute analysis or tests and review evidence.
Electronics measurement, RF, SDR, antennas, embedded systems, signal analysis and software assurance.
Power electronics, DAQ, sensors, control systems, PLC, HMI, industrial networking and data logging.
Embedded Linux, edge computing, IoT, secure communication, data pipelines, vision and verification tools.
Robotics, machine vision, sensors, motion, automation, workcell integration and measurement.
Embedded validation, networked systems, HIL-oriented architectures, software assurance and cybersecurity.
Electrical characterisation, sensor measurement, device testing, data acquisition and automated experiments.
No. The lab scope is defined around the institution's intended experiments, project work, research needs, existing infrastructure and applicable safety requirements.
Yes. A department can begin with a focused bench or one laboratory area and expand later when required.
Yes. Existing equipment can be reviewed to identify compatibility, reuse opportunities and gaps before a new configuration is proposed.
Depending on the selected solution, the scope may include application software, probes, fixtures, cables, adapters, sensors, interfaces and other required accessories.
Possibly, but the overlap should be defined carefully. Teaching requires repeatable, accessible experiments, while research may require higher flexibility, sensitivity or specialised interfaces.
Useful starting information includes the department, intended users, priority experiments or research areas, current equipment, available infrastructure and expected implementation period.