Digital signal processing
We make sensor instrumentation and signal processing algorithms for extracting information from raw sensor data.
We have a particular specialism for real-time signal analyses and hardware acceleration for fast signal processing in portable and power constrained situations.
Our technologies have many applications in non-destructive testing and biomedical engineering, and our researchers are always exploring innovative approaches that will revolutionise the technology we use every day.
We are experts in this field and cover all aspects related to digital signal processing in a range of environments.
Some specific areas we focus on are:
Image recognition and machine learning
Image recognition replies on innovative techniques in pattern recognition and machine learning and is vital in many applications from security to robotics and precision agriculture. We’re driving forward recent advances in deep learning and GPUs to make vision-based solutions more intelligent, robust and deploying them in real-time applications.
Low-power sensor nodes
Very low power consumption sensor nodes are at the heart of the emerging Internet of Things. These sensors need to process data and we make ultra-low power consumption signal processing (just nano-Watts!) for embedding in sensor nodes to enhance their functionality and battery life.
Brain monitoring used to be confined to lab environments. Wearable EEG means this is no longer the case: we can monitor the brain easily, in a range of situations. We are using wearable EEG to explore the new applications of brain interfacing that are now possible.
Real-time signal processing
Real-time signal processing based on fast digital signal processors started in the 1970s and is now one of the fastest growing areas in the field of digital technology. We create integrated DSP instruments for designing, downloading and running very high performance audio-bandwidth filters in real-time.
Check out some of our research highlights below:
- Signal Wizard
- Non-destructive testing of materials
- Analysis of cardiovascular and autonomic function
- Neurotechnology (EEG and transcranial stimulation)
- Low power sensor nodes