Complementarity of PALM and SOFI for super-resolution live cell imaging of focal adhesions

Live cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenging task for super-resolution microscopy. We have addressed this important issue by combining photo-activated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed cell focal adhesion images, we investigated the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework was used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualized the dynamics of focal adhesions, and revealed local mean velocities around 190 nm per minute. The complementarity of PALM and SOFI was assessed in detail with a methodology that integrates a quantitative resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as the fluorophore density and the photo-activation and photo-switching rates

Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

Motion estimation using higher-order statistics

The objective of this paper is to introduce a fourth-order cost function of the displaced frame difference (DFD) capable of estimating motion even for small regions or blocks. Using higher than second-order statistics is appropriate in case the image sequence is severely corrupted by additive Gaussian noise. Some results are presented and compared to those obtained from the mean kurtosis and the mean square error of the DFD.Peer Reviewe

Bispectral reconstruction of speckle-degraded images

The bispectrum of a signal has useful properties such as being zero for a Gaussian random process, retaining both phase and magnitude information of the Fourier transform of a signal, and being insensitive to linear motion. It has found applications in a wide variety of fields. The use of these properties for reducing speckle in coherent imaging systems was investigated. It was found that the bispectrum could be used to restore speckle-degraded images. Coherent speckle noise is modeled as a multiplicative noise process. By using a logarithmic transformation, this speckle noise is converted to a signal independent, additive process which is close to Gaussian when an integrating aperture is used. Bispectral reconstruction of speckle-degraded images is performed on such logarithmically transformed images when we have independent multiple snapshots

Reconstruction of ultrasound RF echoes modelled as stable random variables

International audienceThis paper introduces a new technique for reconstruction of biomedical ultrasound images from simulated compressive measurements, based on modeling data with stable distributions. The proposed algorithm exploits two types of prior information: on one hand, our proposed approach is based on the observation that ultrasound RF echoes are best characterized statistically by alpha-stable distributions. On the other hand, through knowledge of the acquisition process, the support of the RF echoes in the Fourier domain can be easily inferred. Together, these two facts form the basis of an ℓp minimization approach that employs the iteratively reweighted least squares (IRLS) algorithm, but in which the parameter p is judiciously chosen, by relating it to the characteristic exponent of the underlying alpha-stable distributed data. We demonstrate, through Monte Carlo simulations, that the optimal value of the parameter p is just below that of the characteristic exponent α, which we estimate from the data. Our reconstruction results show that the proposed algorithm outperforms previously proposed reconstruction techniques, both visually and in terms of two objective evaluation measures