Sher Muhammad Nizamani
Doctoral researcher and mechatronics engineer working on ADXL355 MEMS accelerometers, Raspberry Pi data acquisition, Python signal processing, time-series analysis, machine-learning regression and experimental pavement engineering.
MEMS sensors, signal processing and engineering machine learning
This portfolio presents my work in physical measurement, sensor data acquisition, Python time-series processing, comparison with laboratory reference measurements and evaluation of machine-learning regression models for engineering applications.
MEMS Accelerometers and Data Acquisition
Experimental measurement workflows combining ADXL355 accelerometers, Raspberry Pi data acquisition, LVDTs, force measurements and temperature measurements.
Python Time-Series and Frequency Analysis
Python workflows for data cleaning, signal synchronization, target-frequency filtering and extraction of physically meaningful time- and frequency-domain features.
Machine-Learning Regression for Engineering Data
Evaluation of linear and nonlinear regression, tree-based and boosting models, and support-vector regression for experimental engineering datasets.
Sensor Validation and Model Evaluation
Comparison of measured and predicted values using R², MAE, RMSE and MAPE, together with frequency-response and signal-quality assessment.
Asphalt dynamic modulus, phase angle and smart sensing research
My doctoral project, SN4SI — Sensors Network for Smart Self-Sustaining Interactive Infrastructures, is part of CIRCOPAV WP1 — Smart and Connected Assets. The research brings together experimental asphalt testing, MEMS sensor integration, Raspberry Pi data acquisition, signal processing, viscoelastic characterization and machine-learning model evaluation.
ADXL355 Sensor Integration and Data Acquisition
Development and implementation of a Raspberry Pi-based acquisition workflow for ADXL355 accelerometer measurements and comparison with laboratory reference measurements.
Python Signal Processing
Data cleaning, digital filtering, FFT, PSD, sinusoidal curve fitting, frequency extraction, amplitude estimation, phase-angle determination and automated processing.
Machine Learning for Dynamic Modulus and Phase Angle
Machine-learning workflows are evaluated for predicting dynamic/complex modulus and phase angle, and for exploring sensor-calibration approaches.
Accelerometer Calibration and Validation
Comparison with reference measurements using synchronization, amplitude and phase comparison, frequency-response evaluation, repeatability and signal-quality assessment.
Controlled Cyclic Laboratory Testing
Cyclic laboratory testing with MTS force measurements, LVDT displacement, temperature measurements and MEMS accelerometers under different temperature and loading-frequency conditions.
Asphalt Viscoelastic Characterization
Temperature- and frequency-dependent analysis of asphalt response, including dynamic/complex modulus and phase angle, alongside conventional viscoelastic modelling and machine-learning model evaluation.
Physical measurement → signal interpretation → model evaluation
Asphalt material testing is the application domain for the sensing, data-acquisition, signal-processing, validation and machine-learning work presented here.
Python signal processing and machine learning for experimental engineering data
Force, displacement, acceleration, temperature and loading-frequency measurements are processed into interpretable engineering features for sensor validation and regression-model evaluation.
From experimental time series to amplitude, frequency and phase features
The workflow produces figures and tables for comparing measured and predicted values.
ADXL355 sensor calibration and asphalt scuffing-test experiments
These laboratory videos document accelerometer measurement validation and embedded sensing during wheel loading. Muted looping autoplay is enabled where browser settings allow it, with controls available for pause and unmute.
ADXL355 accelerometer and MTS measurement validation
Experimental measurement showing the instrumented test setup and acceleration signals used for comparison with reference laboratory measurements.
Embedded accelerometer sensing under wheel loading
Scuffing-test experiment with accelerometer integration inside the asphalt slab to explore sensing under wheel-loading conditions.
Accelerometer installation for asphalt specimen and slab testing
Different installation positions and conditions were evaluated before selecting surface-mounted measurement for specimen testing, while embedded placement was also explored for slab-based scuffing tests.
Accelerometer positioning trials
Different installation conditions and positions were investigated to identify a suitable accelerometer location.
Surface-mounted specimen sensing
The experimental development led to a surface-mounted accelerometer configuration for specimen measurements.
Asphalt scuffing-test setup
Scuffing-test machine setup showing the specimen, control interface and acquisition workstation during laboratory testing.
CIRCOPAV presentations, RILEM activities and doctoral training
Selected activities from the CIRCOPAV doctoral programme, including SN4SI project presentations, technical committee meetings, advanced training and international scientific events in pavement engineering.
RILEM TC 308-PAR Annual Meeting
Participated in technical discussions on performance-based asphalt recycling, recycled mixtures, binder availability, recycling agents and performance evaluation.
CIRCOPAV Mid-Term Meeting
Presented the progress of my doctoral project within WP1 — Smart and Connected Assets, followed by technical discussion and feedback.
CIRCOPAV Training Week 3
Presented doctoral research progress during the Doctoral Candidate sessions and participated in technical training, expert lectures and industrial visits.
4th International Winter School
Participated in international training covering digital twins, smart pavement materials, road instrumentation, decarbonisation and digital road-management strategies.
CIRCOPAV Training Week — Parma
Delivered a 10-minute presentation on a result from my doctoral research as part of scientific communication training.
CIRCOPAV SN4SI Project Presentation at ISPACAD 2026
Presented the SN4SI project during the dedicated CIRCOPAV session, covering research objectives, methodology, progress, current results and future research directions.
2nd RILEM International Symposium on Bituminous Materials
Attended ISBM2026 to follow current scientific and technical developments in bituminous materials and pavement engineering.
Sensor systems, asphalt testing and international research activities
Selected images of MEMS sensors, laboratory instrumentation, Raspberry Pi data acquisition, asphalt specimen testing, doctoral presentations and CIRCOPAV activities.
Raspberry Pi sensor data acquisitionInstrumented dynamic testingPython-based data processingSurface-mounted MEMS accelerometerScuffing-test machine setupISPACAD 2026 · GranadaCIRCOPAV Training · ParmaISBM2026 · PadovaContact Sher Muhammad Nizamani for research, engineering and R&D opportunities
Download my engineering and research CV or send a professional enquiry about sensors, signal processing, machine learning, experimental testing or pavement engineering.
Sher Muhammad Nizamani
Mechatronics Engineer · Sensor Systems · Signal Processing · Applied Machine Learning · Doctoral Researcher
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