Sensing Beyond Barriers: Theory, Algorithms and Applications

Data capture via imaging and sensing has become a common aspect of our existence and helps extend human vision and perception. Whether it is a microscope used for cell counting or the latest version of autonomous vehicle which aims to see through the fog; the sensing apparatus is expensive and limited in functionality. For example, the cameras of a self-driving car may white out due to exposure to excessive light when coming out of a tunnel.

In many of applications, hardware (that captures data) and algorithms (which recover meaningful information from data) are treated decoupled entities; first capture data, extract information later. Hence, there is a limit to what can be recovered from the data based on the limitations of the hardware. Can we go beyond such limitations?

The purpose of this research is to achieve a synergistic balance between hardware and algorithms by means of a co-design, so that popularly held limits in data capture and imaging can be broken, thus making the invisible, visible.

Questions that we seek to answer include: Can we do bio-imaging with low-cost sensors (e.g. Microsoft Kinect)? Can we capture information beyond the usual dynamic range? Can we non-invasively classify blood cells by inferring cell geometry? Can we remove reflections in photographs? Can we see through diffusive media? These questions require us to go beyond the conventional barriers (e.g. dynamic range, spatio-temporal resolution, how fast the data is captured etc).

The work in this proposal relies a co-design approach where carefully optimized capture process yields computationally encoded measurements from which the information is decoded using recovery algorithms. This approach is used to modify hardware and develop new algorithms to recover information. Application areas span from bio-imaging (cell-classification, fluorescence lifetime imaging, terahertz spectroscopy), consumer imaging (autonomous vehicles) to conceptualization of new sensing hardware.

Three specific barriers are considered:

(1) Dynamic Range Barrier.

We propose the use of recording measurements that are non-linearly mapped by modulo operations. This is a fundamentally new way of sensing or digitising information and is largely unexplored. Our initial work shows that a simple correction to the Nyquist rate linked with Shannon's sampling theory allows for recovery of a bandlimited signal from modulo information. Remarkably, the sampling bound is independent of the the threshold. In this proposal we study a larger class of signals including sum-of-sinusoids, sparse signals and smooth signal and their link with application areas such as direction-of-arrival estimation and beamforming.

(2) Resolution Barrier.

Recovering spikes from low-pass filtered measurements is a classical problem and is known as super-resolution. However, in many practical cases of interest, the pulse or filter may be distorted due to physical properties of propagation and transmission. Such cases can not be handled well by existing signal models. Inspired by problems in spectroscopy, ground penetrating radar, photoacoustic imaging and ultra-wide band arrays, on which we base our experiments, in this work we take a step towards recovering spikes from time-varying pulses and prepare algorithms for non-ideal super-resolution. Furthermore, when the pulse or filter is smooth and not necessarily bandlimited, optimial bandwith selction for sparse-deconvolution is an open problem that is addressed in this work.

(3) Bandwidth Barrier.

We define the notion of bandwidth in context of Special Affine Fourier transforms which generalises a number of well known transformations. This allows us to prepare a unifying approach for studying sampling theory which is applicable to a wider class of signal models.


Our algorithms are validated on experimentally acquired data with the help of inter-disciplinary and multi-university collaborations

Through multi-disciplinary and multi-institutional collaborations, the work outlined in this proposal seeks to create tangible impact on real-world applications and poses new theoretical problems. On the academic front, we hope to continue with our collaborative style and develop strong and long-lasting collaborations around original ideas. This will help us build knowledge and contribute to the scientific discipline of expertise. Beyond scholastic impact, young researchers who will join the project and work closely on the implementation of ideas will have the opportunity to be trained and exchange ideas with world-class collaborators.

Practical motivation has always been a key aspect of my research and theoretical ideas are frequently transformed into patents. On this front, a lead role on the proposal with long-term support from FLF scheme will potentially help us with commercialization of ideas.

Our proposal impacts academic communities (science and engineering), health technologies, consumer Technologies, military and defense sectors as well as UK and international policy makers. Our work also leads to societal engagement in form of knowledge transfer at public scale.

Our work is at the core of science and engineering communities. Our specialized results will be published in leading venues such as IEEE transactions. For inter-disciplinary results, we plan to submit our work in corresponding top-tier journals. We also plan to participate in in workshops and organize tutorials to facilitate dissemination of new knowledge. As in the past, and keeping the interdisciplinary nature of this proposal in mind, we will publish in venues such as IEEE Magazines, IEEE Spectrum etc.

Working on topics like terahertz imaging, photoacoustic tomography and time-of-flight imaging has a direct link with advancements in life sciences and health technologies. Knowledge transfer in this area will bring us closer to the goal of improving quality of life. For instance, with terahertz imaging, our goal is to image layers of tissue so that we can detect subsurface abnormalities. This is particularly targeted to applications such detection of skin cancer. With photoacoustic tomography, our ultimate goal is to achieve non-invasive classification of cell types; red blood cells (RBCs), white blood cells (WBCs) and circulating tumor cells (CTCs).

Working on topic of 3D imaging and high dynamic range sensing, our work has a direct link with autonomous vehicle navigation. Such vehicles use 3D sensors for capturing a richer representation of a scene. 3D sensors are at the heart of our collaboration with University of Siegen and Heriot Watt University. Such connections would be useful to make our algorithms practicable for real-world applications. In a similar spirit, capturing high dynamic range images prevent accidents in autonomous vehicles. For instance, a vehicle driving out of a tunnel may lose vision because of sudden increase of exposure from dark to light, causing the imaging sensor to saturate. In this case, our sensing beyond dynamic range barrier could lead to new, commercially viable solutions.

Our work will directly benefit military applications. Antenna arrays are frequently used in military applications and dynamic range is big issue in this context. Close by signals tend to saturate the antenna (when compared to far away signal, due to inverse square law of dissipation). Again, our modulo sensing method has the capacity to deal with dynamic range issues creating disruptive solutions for military applications.

Potential benefits of our methods in context of healthcare solutions could help us reform policies. We hope to do so by proposing low-cost, portable solutions to both terahertz and photoacoustic tomography applications.