On Optimal Sampling Trajectories for Mobile Sensing

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The vibrational response of solid materials and structural components is substantially governed by their mechanical and geometrical properties. Low-frequency vibrations and modal frequencies are sensitive to global geometrical deviations, while high-freque ...

Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in unde ...

Finite rate of innovation (FRI) is a powerful reconstruction framework enabling the recovery of sparse Dirac streams from uniform low-pass filtered samples. An extension of this framework, called generalised FRI (genFRI), has been recently proposed for han ...

Traditional sampling involves encoding a signal through (time, value)-pairs. In contrast, time encoding machines (TEMs) characterize a signal by recording time points which depend on the integral of the signal over time. We study multi-channel TEMs where c ...

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is of ...

While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes the form of an inverse problem of an unknown signal, which is crucially dependent on specifi ...

We study the design of sampling trajectories for stable sampling and the reconstruction of bandlimited spatial fields using mobile sensors. The spectrum is assumed to be a symmetric convex set. As a performance metric we use the path density of the set of ...

PurposeIn this work, the limits of image reconstruction in k-space are explored when non-bijective gradient fields are used for spatial encoding. TheoryThe image space analogy between parallel imaging and imaging with non-bijective encoding fields is parti ...

Foundations of signal processing are heavily based on Shannon's sampling theorem for acquisition, representation and reconstruction. This theorem states that signals should not contain frequency components higher than the Nyquist rate, which is half of the ...

New advances in the field of image sensors (especially in CMOS technology) tend to question the conventional methods used to acquire the image. Compressive Sensing (CS) plays a major role in this, especially to unclog the Analog to Digital Converters which ...

In our daily lives, our mobile phones sense our movements and interactions via a rich set of embedded sensors such as a GPS, Bluetooth, accelerometers, and microphones. This enables us to use mobile phones as agents for collecting spatio-temporal data. The ...

Jaggies (staircasing effects) along slanted lines or curved edges are omnipresent in digital imaging. They are so widespread in digital display devices that very often they are associated with the modern "computerized" world (and sometimes even intentional ...

Spatial sampling is traditionally studied in a static setting where static sensors scattered around space take measurements of the spatial field at their locations. In this paper we study the emerging paradigm of sampling and reconstructing spatial fields ...

Consider the task of sampling and reconstructing a bandlimited spatial field in

\Re^2

using moving sensors that take measurements along their path. It is inexpensive to increase the sampling rate along the paths of the sensors but more expensive to incre ...

We address the resolution of inverse problems where visual data must be recovered from incomplete information optically acquired in the spatial domain. The optical acquisition models that are involved share a common mathematical structure consisting of a l ...

We consider the problem of super-resolution from unregistered aliased images with unknown spatial scaling factors and shifts. Due to the limitation of pixel size in the image sensor, the sampling rate for each image is lower than the Nyquist rate of the sc ...

We study the source coding problem in sensor networks deployed to monitor the evolution of spatio-temporal temperature distributions. The sensors sample the temperature field, quantize the samples and transmit the encoded samples through digital channels t ...

Sound waves propagate through space and time by transference of energy between the particles in the medium, which vibrate according to the oscillation patterns of the waves. These vibrations can be captured by a microphone and translated into a digital sig ...

We address the problem of identifying continuous-time auto regressive (CAR) models from sampled data. The exponential nature of CAR autocorrelation functions is taken into account by means of exponential B-splines modelling, allowing one to associate the a ...

In this paper we consider the problem of sampling far below the Nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. This signal model is key for applications such as Ultra Wide Band (UWB) communica ...

On Optimal Sampling Trajectories for Mobile Sensing

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