Electronics, Embedded Systems & Instrumentation

Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.

1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Measurement-chain and noise analysis

   A measurement chain is analysed stage by stage: sensor sensitivity and nonlinearity, amplifier gain and noise, filter response, converter resolution and sampling. Noise sources include thermal, shot and flicker noise, interference and quantisation. We help build an error budget, compare predicted noise with measured spectra and identify the dominant contributions, which guides design effort to where it matters.

2. Sensor calibration and regression

   Calibration relates sensor output to reference values, using regression with attention to the model form, hysteresis, temperature dependence and drift. Reference standards should be several times more accurate than the sensor. We compare linear and higher-order fits, check residual patterns and report uncertainty of the calibration, including the prediction interval for new measurements, not only the fit error on the calibration points.

3. Signal conditioning and data acquisition

   Choices of gain, anti-alias filter, sampling rate and resolution determine what can be measured. Violating the sampling theorem produces aliasing that cannot be removed afterwards. Grounding, shielding and cable choices influence interference. We review acquisition settings for the signals of interest and suggest tests, such as shorted-input and known-signal measurements, to verify performance.

4. Algorithm performance on embedded targets

   Embedded algorithms for filtering, estimation or classification must be evaluated for accuracy, latency, memory and energy on the target hardware. Fixed-point arithmetic introduces quantisation effects that differ from floating-point simulations. Timing should be measured, including worst-case execution time, not just averages. We help design benchmarks and report results with the hardware and compiler settings used.

5. Documenting uncertainty and traceability

   Instrumentation papers should present uncertainty following recognised guidance, combining type A and type B contributions, and state calibration traceability. Specifications such as accuracy and resolution are defined precisely, since they are often confused. Clear statements of test conditions, temperature range and warm-up time allow readers to judge whether the performance applies to their application.

6. Reporting instrumentation research for reviewers

   Reviewers of instrumentation papers look first for evidence that the stated accuracy was tested independently of the calibration data. We recommend a separate validation set, preferably measured on a different day, and a table that lists each uncertainty contribution with its distribution and value. Raw data and the processing script should be available. Where a design is open hardware, schematics and firmware version information make replication feasible. Clear limits on temperature, humidity and input range prevent readers from applying the results outside the conditions that were verified.

7. Power and timing budgets for embedded sensing

   Battery-powered instruments are limited by energy as much as accuracy. A power budget lists sleep, sampling and transmission currents and duty cycles, so battery life can be estimated and compared with measurement. Timing budgets show how long acquisition, processing and communication take, revealing whether sampling jitter or processing delay affects accuracy. Reporting both budgets together shows readers the trade-off between precision, speed and energy that the design represents and lets them judge the result for their own constraints.

Common pitfalls this service helps avoid

• Confusing resolution with accuracy. A device can display many digits yet be inaccurate.
• Aliasing from inadequate filtering. Out-of-band signals fold into the band of interest and cannot be removed later.
• Evaluating only on a workstation. Fixed-point effects and timing limits appear only on target hardware.
• Calibration errors reported as total uncertainty. Fit error on calibration data understates uncertainty for future measurements.

What good reporting looks like

A report on instrumentation describes the sensor and chain, the error budget, calibration procedure and reference standards, acquisition settings, measured performance with uncertainty, algorithm benchmarks on target hardware and limitations. It does not claim metrological accreditation or compliance with product standards, which require accredited laboratories.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Suppose a researcher builds a low-cost strain measurement system. An error budget shows that amplifier offset drift dominates over the sensor noise. A calibration against a reference load cell at three temperatures reveals a temperature dependence that a single linear fit misses. After including temperature compensation, the prediction interval narrows. The report states accuracy for the tested temperature range only.

Questions to settle before you begin

• What is my error budget and which term dominates?
• What reference standard will I calibrate against?
• Is my sampling rate and filtering adequate?
• How will I benchmark on the target device?
• Over what conditions does my stated accuracy apply?

Key terms

Error budget

A tabulation of contributions to total measurement uncertainty.

Aliasing

Distortion when frequencies above half the sampling rate fold into the band.

Resolution

The smallest change in input that produces a detectable change in output.

Traceability

A chain of calibrations linking a measurement to a reference standard.

Worst-case execution time

The longest time a code section can take to run.

Working together on Electrical, Electronics & Communication Engineering projects

Electrical, electronics and communication research engagements begin with the system, signals and performance metrics, plus the tools and data you already use. We ask for block diagrams or equations, measurement setups, datasets and the target journals or standards for context. Analyses are documented with parameter values and software versions so that results can be reproduced. The service provides research analysis and modelling; it does not provide product certification, regulatory compliance testing or safety sign-off, which need accredited laboratories and qualified professionals.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Measurement & Quality Analytics

  Analysis of measurement systems and quality data: repeatability, reproducibility, calibration uncertainty and process capability, so that conclusions rest on trustworthy measurements.

• Signal Processing & Sensor Analytics

  Processing and interpretation of sensor and instrumentation signals: filtering, spectral and time-frequency analysis and feature extraction for condition assessment and experiments.

• Applied Physics & Instrumentation

  Analysis and modelling support for applied-physics experiments and instrumentation: optics, acoustics, thermal physics and measurement systems, with uncertainty analysis.

• Signal Processing & Communication Systems

  Modelling and analysis of communication links and signal-processing algorithms: modulation, channel models, detection and performance metrics such as error rate.