Fast Change Detection

Automated detection of changes in a sequence of images captured under fixed background and steady light condition is an often required operation having widespread application. Possible application areas can be as diverse as from military to atmospheric science, from medicine to video surveillance, etc. There are many approaches to the problem of detecting changes; the more reliable one tries to make these more complex and computationally expensive these become, requiring sophisticated algorithms and specialised hardware. However, often one needs to use simple and computationally cheap procedures to be used with cots hardware when the problem scenario has static features with transient change in features associated to some small part of the image or field of view. A super-pixel-based change detection algorithm has been descried here that is basically a modification of the image differencing technique. The procedure has been seen to detect even a small transient change in intensity at a frame rate of as high as fifty frames per second using cots hardware.Defence Science Journal, 2011, 61(1), pp.51-56, DOI:http://dx.doi.org/10.14429/dsj.61.47

Scattering based hyperspectral imaging of plasmonic nanoplate clusters towards biomedical applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/122443/1/jbio201500177-sup-0001-supinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/122443/2/jbio201500177.pd

Two‐Photon Fluorescence Imaging Super‐Enhanced by Multishell Nanophotonic Particles, with Application to Subcellular pH

A novel nanophotonic method for enhancing the two‐photon fluorescence signal of a fluorophore is presented. It utilizes the second harmonic (SH) of the exciting light generated by noble metal nanospheres in whose near‐field the dye molecules are placed, to further enhance the dye's fluorescence signal in addition to the usual metal‐enhanced fluorescence phenomenon. This method enables demonstration, for the first time, of two‐photon fluorescence enhancement inside a biological system, namely live cells. A multishell hydrogel nanoparticle containing a silver core, a protective citrate capping, which serves also as an excitation quenching inhibitor spacer, a pH indicator dye shell, and a polyacrylamide cladding are employed. Utilizing this technique, an enhancement of up to 20 times in the two‐photon fluorescence of the indicator dye is observed. Although a significant portion of the enhanced fluorescence signal is due to one‐photon processes accompanying the SH generation of the exciting light, this method preserves all the advantages of infrared‐excited, two‐photon microscopy: enhanced penetration depth, localized excitation, low photobleaching, low autofluorescence, and low cellular damage. The two‐photon fluorescence signal of a fluorophore is enhanced by utilizing the second harmonic of the exciting light generated by noble metal nanospheres in whose near‐field dye molecules are placed. A multishell hydrogel nanoparticle containing a silver core, protective citrate capping, pH indicator dye, and polyacrylamide cladding is utilized for pH sensing and fluorescence imaging in live cells.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92437/1/2213_ftp.pd

Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning

Objectives: Distinguishing between radiation necrosis(RN) and metastatic progression is extremely challenging due to their similarity in conventional imaging. This is crucial from a therapeutic point of view as this determines the outcome of the treatment. This study aims to establish an automated technique to differentiate RN from brain metastasis progression using radiomics with machine learning. Methods: 86 patients with brain metastasis after they underwent stereotactic radiosurgery as primary treatment were selected. Discrete wavelets transform, Laplacian-of-Gaussian, Gradient, and Square were applied to magnetic resonance post-contrast T1-weighted images to extract radiomics features. After feature selection, dataset was randomly split into train/test (80%/20%) datasets. Random forest classification(RFC), logistic regression, and support vector classification(SVC) were trained and subsequently validated using test set. The classification performance was measured by area under the curve(AUC) value of receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: The best performance was achieved using RFC with a Gradient filter (AUC=0.910, std=0.047), (accuracy 0.8, std=0.071), (sensitivity=0.796 std=0.055), (specificity =0.922, std=0.059). For SVC the best result obtains using wavelet_HHH with a high AUC of 0.890 with std=0.89, accuracy of 0.777 with std=0.062, sensitivity=0.701, std=0.084, and specificity=0.85 with std=0.112. Logistic regression using wavelet_HHH provides a poor result with AUC=0.882 & std=0.051, accuracy of 0.753 & std=0.08, sensitivity=0.717 & std=0.208, and specificity=0.816 with std=0.123. Conclusion: This type of machine-learning approach can help accurately distinguish RN from recurrence in magnetic resonance imaging, without the need for biopsy. This has the potential to improve the therapeutic outcome.Comment: 10 pages, 4 Figures, 2 Tables. American Journal of Clinical Oncology, August 202

Permanent Lattice Compression of Lead-Halide Perovskite for Persistently Enhanced Optoelectronic Properties

Under mild mechanical pressure, halide perovskites show enhanced optoelectronic properties. However, these improvements are reversible upon decompression, and permanent enhancements have yet to be ..

Holographic Microscopy with Acoustic Modulation for Detection of Nano-sized Particles and Pathogens in Solution

We present a method for the detection of nanoparticles in solution using an acoustically actuated holographic microscope. This type of microscopy can be used for high-throughput biosensing applications, e.g., detection of viruses in a liquid

Ultrasonic assisted machining

A commercially available DMG MORI ULTRASONIC 65 monoBLOCK machining centre was installed in WMG in 2013 and has been primarily used to machine aerospace grade materials such as carbon fibre reinforced plastic (CFRP) and titanium alloy Ti 6Al-4V (individually and stacked) and 2000 / 6000 series aluminium alloys. Rather than discuss a single set of experimental work in detail, this paper discusses some of the issues that have been encountered when applying the technique of ultrasonic assisted machining (UAM) and some of the effects that have been observed using examples from the research conducted so far to illustrate some of the more important findings

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications

Ultrasonic assisted machining

A commercially available DMG MORI ULTRASONIC 65 monoBLOCK machining centre was installed in WMG in 2013 and has been primarily used to machine aerospace grade materials such as carbon fibre reinforced plastic (CFRP) and titanium alloy Ti 6Al-4V (individually and stacked) and 2000 / 6000 series aluminium alloys. Rather than discuss a single set of experimental work in detail, this paper discusses some of the issues that have been encountered when applying the technique of ultrasonic assisted machining (UAM) and some of the effects that have been observed using examples from the research conducted so far to illustrate some of the more important findings