Unravelling intermittent features in single particle trajectories by a local convex hull method

We propose a new model-free method to detect change points between distinct phases in a single random trajectory of an intermittent stochastic process. The local convex hull (LCH) is constructed for each trajectory point, while its geometric properties (e.g., the diameter or the volume) are used as discriminators between phases. The efficiency of the LCH method is validated for six models of intermittent motion, including Brownian motion with different diffusivities or drifts, fractional Brownian motion with different Hurst exponents, and surface-mediated diffusion. We discuss potential applications of the method for detection of active and passive phases in the intracellular transport, temporal trapping or binding of diffusing molecules, alternating bulk and surface diffusion, run and tumble (or search) phases in the motion of bacteria and foraging animals, and instantaneous firing rates in neurons

Challenges in anomaly and change point detection

This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced. On the other, a selection of important surveys and books, as well as two selected active research topics in the field, are presented

Compound sequential change-point detection in parallel data streams

We consider sequential change-point detection in parallel data streams, where each stream has its own change point. Once a change is detected in a data stream, this stream is deactivated permanently. The goal is to maximize the normal operation of the pre-change streams, while controlling the proportion of post-change streams among the active streams at all time points. Taking a Bayesian formulation, we develop a compound decision framework for this problem. A procedure is proposed that is uniformly optimal among all sequential procedures which control the expected proportion of post-change streams at all time points. We also investigate the asymptotic behavior of the proposed method when the number of data streams grows large. Numerical examples are provided to illustrate the use and performance of the proposed method

Raman structural studies of the nickel electrode

Raman spectroscopy is sensitive to empirically controlled nickel electrode structural variations, and has unique potential for structural characterization of these materials. How the structure relates to electrochemical properties is examined so that the latter can be more completely understood, controlled, and optimized. Electrodes were impregnated and cycled, and cyclic voltammetry is being used for electrochemical characterization. Structural variation was observed which has escaped detection using other methods. Structural changes are induced by: (1) cobalt doping, (2) the state of change or discharge, (3) the preparation conditions and type of buffer used, and (4) the formation process. Charged active mass has an NiOOH-type structure, agreeing with X-ray diffraction results. Discharged active mass, however, is not isostructural with beta-Ni(OH)2. Chemically prepared alpha phases are not isostructural either. A disordered structural model, containing point defects, is proposed for the cycled materials. This model explains K(+) incorporation. Band assignments were made and spectra interpreted for beta-Ni(OH)2, electrochemical NiOOH and chemically precipitated NiOOH

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

Change detection and deformation monitoring is an active area of research within the field of engineering surveying as well as overlapping areas such as structural and civil engineering. The application of Terrestrial Laser Scanning (TLS) techniques for change detection and deformation monitoring of concrete structures has increased over the years as illustrated in the past studies. This paper presents a review of literature on TLS application in the monitoring of structures and discusses registration and georeferencing of TLS point cloud data as a critical issue in the process chain of accurate deformation analysis. Past TLS research work has shown some trends in addressing issues such as accurate registration and georeferencing of the scans and the need of a stable reference frame, TLS error modelling and reduction, point cloud processing techniques for deformation analysis, scanner calibration issues and assessing the potential of TLS in detecting sub-centimetre and millimetre deformations. However, several issues are still open to investigation as far as TLS is concerned in change detection and deformation monitoring studies such as rigorous and efficient workflow methodology of point cloud processing for change detection and deformation analysis, incorporation of measurement geometry in deformation measurements of high-rise structures, design of data acquisition and quality assessment for precise measurements and modelling the environmental effects on the performance of laser scanning. Even though some studies have attempted to address these issues, some gaps exist as information is still limited. Some methods reviewed in the case studies have been applied in landslide monitoring and they seem promising to be applied in engineering surveying to monitor structures. Hence the proposal of a three-stage process model for deformation analysis is presented. Furthermore, with technological advancements new TLS instruments with better accuracy are being developed necessitating more research for precise measurements in the monitoring of structures

Engineering molecularly-active nanoplasmonic surfaces for DNA detection via colorimetry and Raman scattering

We report a novel nanophotonic biosensor surface capable of both colorimetric detection and Raman-scattered detection of DNA infection markers at extreme sensitivities. Combining direct-write lithography, dip-pen nanolithography based DNA patterning, and molecular self-assembly, we create molecularly-active plasmonic nanostructures onto which metallic nanoparticles are located via DNA-hybridization. Arraying these structures enables optical surfaces that change state when contacted by specific DNA sequences; shifting the surface color while simultaneously generating strong Raman-scattering signals. Patterning the DNA markers onto the plasmonic surface as micro-scale symbols results in easily identifiable color shifts, making this technique applicable to multiplexed lab-on-a-chip and point-of-care diagnostic applications

Lipid coated liquid crystal droplets for the on-chip detection of antimicrobial peptides

We describe a novel biosensor based on phospholipid-coated nematic liquid crystal (LC) droplets and demonstrate the detection of Smp43, a model antimicrobial peptide (AMP) from the venom of North African scorpion Scorpio maurus palmatus. Mono-disperse lipid-coated LC droplets of diameter 16.7 ± 0.2 μm were generated using PDMS microfluidic devices with a flow-focusing configuration and were the target for AMPs. The droplets were trapped in a bespoke microfluidic trap structure and were simultaneously treated with Smp43 at gradient concentrations in six different chambers. The disruption of the lipid monolayer by the Smp43 was detected (<6 μM) at concentrations well within its biologically active range, indicated by a dramatic change in the appearance of the droplets associated with the transition from a typical radial configuration to a bipolar configuration, which is readily observed by polarizing microscopy. This suggests the system has feasibility as a drug-discovery screening tool. Further, compared to previously reported LC droplet biosensors, this LC droplet biosensor with a lipid coating is more biologically relevant and its ease of use in detecting membrane-related biological processes and interactions has the potential for development as a reliable, low-cost and disposable point of care diagnostic tool

Online Graph-Based Change Point Detection in Multiband Image Sequences

The automatic detection of changes or anomalies between multispectral and hyperspectral images collected at different time instants is an active and challenging research topic. To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes. In this paper, we introduce a novel online framework for detecting changes in multitemporal remote sensing images. Acting on neighboring spectra as adjacent vertices in a graph, this algorithm focuses on anomalies concurrently activating groups of vertices corresponding to compact, well-connected and spectrally homogeneous image regions. It fully benefits from recent advances in graph signal processing to exploit the characteristics of the data that lie on irregular supports. Moreover, the graph is estimated directly from the images using superpixel decomposition algorithms. The learning algorithm is scalable in the sense that it is efficient and spatially distributed. Experiments illustrate the detection and localization performance of the method