Search

Fluorescence lifetime imaging is a valuable technique for probing characteristics of wide ranging samples and sensing of the molecular environment. However, the desire to measure faster and reduce effects such as photo bleaching in optical photon-count measurements for lifetime estimation lead to inevitable effects of convolution with the instrument response functions and noise, causing a degradation of the lifetime accuracy and precision. To tackle the problem, this paper presents a robust and computationally efficient framework for recovering fluorophore sample decay from the histogram of photon-count arrivals modelled as a decaying single-exponential function. In the proposed approach, the temporal histogram data is first decomposed into multiple bins via an adaptive multi-bin signal representation. Then, at each level of the multi-resolution temporal space, decay information including both the amplitude and the lifetime of a single-exponential function is rapidly decoded based on a novel statistical estimator. Ultimately, a game-theoretic model consisting of two players in an “amplitude-lifetime” game is constructed to be able to robustly recover optimal fluorescence decay signal from a set of fused multi-bin estimates. In addition to theoretical demonstrations, the efficiency of the proposed framework is experimentally shown on both synthesised and real data in different imaging circumstances. On a challenging low photon-count regime, our approach achieves about 28% improvement in bias than the best competing method. On real images, the proposed method processes data on average around 63 times faster than the gold standard least squares fit. Implementation codes are available to researchers. [ABSTRACT FROM AUTHOR]

It is shown that when signals are separated by means of the generalized Wiener filter, the degree of separability can be deduced from the signal structure.

Signals collected for modal analysis are often filtered in order to reduce sensor noise, out‐of‐band oscillations, or for dealing with closely‐spaced modes. This filtering introduces filtering artefacts into the data due to the non‐ideal filter response and is well understood for impulse excitation. However, for ambient vibration data filtering, artefacts become superimposed, preventing their visual identification, and will corrupt the correlation function of the data used in time‐domain operational modal analysis (OMA) techniques such as covariance‐driven stochastic subspace identification and the random decrement technique. This corruption leads to inaccurate, misleading, biased or spurious frequency and damping estimates, with the inaccuracy increasing for systems with higher damping or lower signal‐to‐noise ratios. Counter‐intuitively, the error in damping estimates is as large for modes with natural frequencies far from the filter cutoff frequency as for modes which are close to the cutoff frequency. In this paper, an alternative to filtering for time‐domain OMA, trimming of the correlation of noise from unfiltered correlation functions, is introduced and tested using 10,000 numerically generated ambient vibration data sets. It has been shown that this technique reduces the mean absolute error in the frequency estimates by over 200% and the mean absolute error in the damping estimates by over 400%. Additionally, a new technique which incorporates fitting of filtering artefacts as part of the modal analysis is introduced for where filtering of ambient vibration data is unavoidable and is demonstrated using real‐world acceleration data collected from a two‐way spanning concrete slab subject to footfall excitation. [ABSTRACT FROM AUTHOR]

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm. [ABSTRACT FROM AUTHOR]

The number of nodes in sensor networks is continually increasing, and maintaining accurate track estimates inside their common surveillance region is a critical necessity. Modern sensor platforms are likely to carry a range of different sensor modalities, all providing data at differing rates, and with varying degrees of uncertainty. These factors complicate the fusion problem as multiple observation models are required, along with a dynamic prediction model. However, the problem is exacerbated when sensors are not registered correctly with respect to each other, i.e., if they are subject to a static or dynamic bias. In this case, measurements from different sensors may correspond to the same target, but do not correlate with each other when in the same Frame of Reference (FoR), which decreases track accuracy. This paper presents a method to jointly estimate the state of multiple targets in a surveillance region, and to correctly register a radar and an Infrared Search and Track (IRST) system onto the same FoR to perform sensor fusion. Previous work using this type of parent-offspring process has been successful when calibrating a pair of cameras, but has never been attempted on a heterogeneous sensor network, or in a maritime environment. This article presents results on both simulated scenarios and a segment of real data that show a significant increase in track quality in comparison to using incorrectly calibrated sensors or single-radar only. [ABSTRACT FROM AUTHOR]

Mobile Cloud Computing or Fog computing refers to offloading computationally intensive algorithms from a mobile device to the cloud or an intermediate cloud in order to save resources e.g., time and energy in the mobile device. This paper proposes new solutions for situations when the cloud or fog is not available. First, the sensor network is modelled using a network of queues, then a linear programming technique is used to make scheduling decisions. Various centralized and distributed algorithms are then proposed, which improves overall system performance. Extensive simulations show slightly higher energy usage in comparison to the baseline non-offloading case, however, the job completion rate is significantly improved, the efficiency score metric shows the extra energy usage is justified. The algorithms have been simulated in various environments including high and low bandwidth, partial connectivity, and different rate of information exchanges to study the pros and cons of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Objective: Snapshot imaging has several advantages in automated gel electrophoresis compared with the finish-line method in capillary electrophoresis; this comes at the expense of resolution. A novel signal processing algorithm is proposed enabling a multisnapshot imaging (MSI) modality whose objective is to substantially improve resolution. MSI takes multiple-captures in time as macromolecules are electrophoresed. Peaks from latter snapshots have high resolution, but low signal-to-noise ratio (SNR), while earlier snapshots have low resolution, but high SNR. Methods: Signals at different capture-times are related by a scale-in-separation, shift-in-separation, and amplitude gain. The proposed method realigns the multiple captures using least-squares and fuses them. The algorithm accounts for the partial waveforms observed as the chromatic peaks exit the sensor's field-of-view. Results: MSI improves resolution by approximately $\text{10}\%$ on average per minute of additional electrophoresis. Conclusions: Comprehensive analysis of the resolution quantified on several data sets demonstrates the effectiveness of MSI. Significance: MSI can double the resolution compared with traditional snap-shot imaging over a typical set of captures. [ABSTRACT FROM AUTHOR]

This paper presents an approximate minimum mean squared error (MMSE) adaptive beam-pattern design (ABD) method for MIMO active sensing systems. The proposed approximate MMSE ABD method leverages the physics of the MIMO arrays to provide a linear-Gaussian approximation that is specific to MIMO active sensing systems, and yields a computationally simple optimization problem. Computational complexity analysis confirms this theoretical reduction in the number of floating-point operations required, most notably that evaluation of the proposed approximate optimization cost function grows polynomially with the number of targets being tracked, whereas for evaluation of the exact cost the growth is exponential. Additionally, numerical results indicate that, even for a simple scenario with a single target being tracked, the proposed approximate MMSE ABD method does indeed reduce the mean squared error of target parameter estimation compared to the nonadaptive case, with a reduction in computation time of four orders of magnitude compared to exact MMSE ABD. [ABSTRACT FROM AUTHOR]

Minimizing the expected mean squared error is one of the fundamental metrics applied to adaptive waveform design for active sensing. Previously, only cost functions corresponding to a lower bound on the expected mean squared error have been expressed for optimization. In this paper, we express an exact cost function to optimize for minimum mean squared error adaptive waveform design (MMSE-AWD). This is expressed in a general form which can be applied to nonlinear systems. Additionally, we provide a general example for how this method of MMSE-AWD can be applied to a system that estimates the state using a particle filter (PF). We make the case that there is a compelling reason to choose to use the PF (as opposed to alternatives such as the unscented Kalman filter and extended Kalman filter), as our MMSE-AWD implementation can reuse the particles and particle weightings from the PF, simplifying the overall computation. Finally, we provide a numerical example, based on a simplified multiple-input-multiple-output radar system, which demonstrates that our MMSE-AWD method outperforms a simple nonadaptive radar, whose beam-pattern has a uniform angular spread, and also an existing approximate MMSE-AWD method. [ABSTRACT FROM AUTHOR]

Measuring markers of stress such as pH and redox potential are important when studying toxicology in in vitro models because they are markers of oxidative stress, apoptosis and viability. While surface enhanced Raman spectroscopy is ideally suited to the measurement of redox potential and pH in live cells, the time-intensive nature and perceived difficulty in signal analysis and interpretation can be a barrier to its broad uptake by the biological community. In this paper we detail the development of signal processing and analysis algorithms that allow SERS spectra to be automatically processed so that the output of the processing is a pH or redox potential value. By automating signal processing we were able to carry out a comparative evaluation of the toxicology of silver and zinc oxide nanoparticles and correlate our findings with qPCR analysis. The combination of these two analytical techniques sheds light on the differences in toxicology between these two materials from the perspective of oxidative stress. [ABSTRACT FROM AUTHOR]

Presents a study which considered single channel blind deconvolution using nonstationary signal processing. Characteristics of blind deconvolution; Description of the system models; Effect of observation noise.