Signal Processing
Navigating the complex world of radio waves for effective, reliable communication
Signal processing reveals all kinds of valuable information hidden in data from a variety of sources – from audio signals, video and images to sensor systems.
It might involve collecting information from the sensors in a fitness tracker and reporting back on sleep patterns, for example, or converting the information from ultrasound, radar and cameras on an autonomous vehicle into data needed to control navigation.
In a factory setting, signal processing is typically used to collect and analyse the data from sensors attached to production line machines to detect when the equipment needs remedial maintenance work.
Signal processing techniques also remove unwanted disturbances, outliers and artifacts from ‘noisy’ real-word data, resulting in cleaner and more reliable datasets – essential for accurate modelling, predictions and other advanced data analysis tasks.
Real-world challenges
Key skills
- Expertise in real-time system design
Specialising in the design of systems, such as radar, that require non-stop operation over extensive periods.
- Reliability engineering
Ensuring high system reliability for critical applications, essential in fields such as autonomous vehicle navigation and industrial monitoring.
- Expenditure optimisation
Delivering complex signal processing solutions within strict budgetary limits, optimising expenditure without sacrificing performance.
- Intelligence at the edge
The system understands its surroundings, allowing autonomous operation where communication to central processing is limited or unavailable.
- Python code implementation
Employing Python for data processing pipelines, providing practical code examples for the software development community.
- Signal interpretation
Translating complex raw data from varied sources into actionable insights, crucial for making informed decisions.
- Noise filtering techniques
Sophisticated signal processing methods to minimise unwanted disturbances and artefacts, resulting in cleaner and more dependable datasets.
- Predictive maintenance
Using signal analysis to forecast when equipment requires maintenance, significantly reducing downtime and operational expenses by detecting issues before they cause failure.
- Low size, weight and power (SWAP) operation
Obtaining optimal processing performance per watt when power budgets are highly constrained.
When someone says ‘signal processing’, many people instantly think of complex mathematics or hard to program computer systems and then run a mile. It is better to think of signal processing as being the enabling technology that allows us to interpret, change and generate information. Signal processing allows us to understand the output of sensors and receivers, to extract and derive meaning from what would otherwise just be a mess of data to give us useful outputs. It drives our modern world.
Dr Peter Debenham
Senior Consultant
What sets us apart when it comes to signal processing?
Our signal processing expertise enables us to transform complex data into actionable insights with precision and clarity. Our areas of expertise include:
- Clean datasets
- Continuous operation
- Data modelling
- Data processing pipeline
- Image processing
- Modelling and predictions
- Noise reduction
- Non-expert operation
- Predictive maintenance
- Python code programming
- Radar systems
- Real-time data capture
- Reliability
- Low SWAP
- Sensor systems
- Signal extraction
- Video analysis
Contact Us
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Surveillance Radar for Comprehensive Threat Detection
Advancing a perimeter surveillance solution with long-range detection and low false-alarm rates, using state-of-the-art Passive Electronically Scanned Array technology for robust and maintenance-free operation in a range of demanding environments.
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Distributed Real Time Spectrum Monitoring
Developing an innovative distributed spectrum monitoring system, using low-cost software-defined radio platforms, to provide superior interference detection and larger coverage for high-value sites.
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Intelligent Mobility
Advancing intelligent mobility by integrating cutting-edge electronic-scanning radar technology to ensure the safe and efficient operation of autonomous vehicles in complex real-world environments.
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Telehealth Innovation – Connecting Patients to their Carers
Revolutionising telehealth with the MonitorMe phone, seamlessly connecting patients to healthcare providers for vital signs monitoring.
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Drone Inspection of Solar Farms
Enhancing solar farm efficiency through advanced drone inspection and automated analysis of diagnostic data.
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Microwave Camera for Detecting Hidden Objects
Developing a groundbreaking microwave camera technology, enabling real-time detection of concealed items in a non-intrusive manner, improving airport security with minimal passenger disruption.
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Sensors for Automatic Passenger Counting
Innovative approach combining ultrasonic and infrared sensors above train doors, significantly enhancing passenger counting accuracy for public transport safety and efficiency.
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Ensuring the Future Safety of Windfarms
Developing a cutting-edge solution using deep learning to effectively differentiate between radar tracks from aircraft and wind turbines, enhancing the safety and efficiency of air traffic control.
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Related Technical Papers
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Sensing Auditory Evoked Potentials with Non-Invasive Electrodes and Low-Cost Headphones
This paper presents a sensor for measuring auditory brainstem responses to help diagnose hearing problems away from specialist clinical settings using non-invasive electrodes and commercially available headphones. The challenge of reliably measuring low level electronic signals in the presence of significant noise is addressed via a precision analog processing circuit which includes a novel impedance measurement approach to verify good electrode contact. Results are presented showing that the new sensor was able to reliably sense auditory brainstem responses using noninvasive electrodes, even at lower stimuli levels.
GPU Computing
Power limits restrict CPU speeds, but GPUs offer a solution for faster computing. Initially designed for graphics, GPUs now handle general computing, thanks to advancements by NVIDIA, AMD, and Intel. With hundreds of cores, GPUs significantly outperform CPUs in parallel processing tasks. Modern supercomputers, like Titan, utilize thousands of GPU cores for immense speed. NVIDIA’s CUDA platform simplifies GPU programming, making it accessible for parallel tasks. While GPUs excel in parallelizable problems, they can be limited by data transfer rates and algorithm design. NVIDIA’s Tesla GPUs provide high performance in both single and double precision calculations. Additionally, embedded GPUs like the NVIDIA Jetson TX2 deliver powerful, low-power computing for specialized applications. Overall, GPUs offer superior speed and efficiency for parallel tasks compared to CPUs.