Hyperspectral anomaly detection using spectral–spatial features based on the human visual system

ABSTRACT

Anomaly detection (AD) is one of the most attracting topics within the recent 10 years in hyperspectral imagery (HSI). The goal of the AD is to label the pixels with significant spectral or spatial differences to their neighbours, as targets. In this paper, we propose a method that uses both spectral and spatial information of HSI based on human visual system (HVS). By inspiring the retina and the visual cortex functionality, the multiscale multiresolution analysis is applied to some principal components of hyperspectral data, to extract features from different spatial levels of the image. Then the global and local relations between features are considered based on inspiring the visual attention mechanism and inferotemporal (IT) part of the visual cortex. The effects of the attention mechanism are implemented using the logarithmic function which well highlights, small variations in pixels’ grey levels in global features. Also, the maximum operation is used over the local features for imitating the function of IT. Finally, the information theory concept is used for generating the final detection map by weighting the global and local detection maps to obtain the final anomaly map. The result of the proposed method is compared with some state-of-the-art methods such as SSRAD, FLD, PCA, RX, KPCA, and AED for two well-known real hyperspectral data which are San Diego airport and Pavia city, and a synthetic hyperspectral data. The results demonstrate that the proposed method effectively improves the AD capabilities, such as enhancement of the detection rate, reducing the false alarm rate and the computation complexity.     

Belief revision in structured probabilistic argumentation

In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.     

Cell and biomolecular mechanics in silico

Recent developments in computational cell and biomolecular mechanics have provided valuable insights into the mechanical properties of cells, subcellular components and biomolecules, while simultaneously complementing new experimental techniques used for deciphering the structure-function paradigm in living cells. These computational approaches have direct implications in understanding the state of human health and the progress of disease and can therefore aid immensely in the diagnosis and treatment of diseases. We provide an overview of the computational approaches that are currently used in understanding various aspects of cell and bimolecular mechanics. Our emphasis is on state-of-the-art techniques and the progress made in addressing key challenges in biomechanics.     

Simulation for manufacturing system design and operation: Literature review and analysis

This paper provides a comprehensive review of discrete event simulation publications published between 2002 and 2013 with a particular focus on applications in manufacturing. The literature is classified into three general classes of manufacturing system design, manufacturing system operation, and simulation language/package development. The paper further categorizes the literature into 11 subclasses based on the application area. The current review contributes to the literature in three significant ways: (1) it provides a wide coverage by reviewing 290 papers; (2) it provides a detailed analysis of different aspects of the literature to identify research trends through innovative data mining approaches as well as insights derived from the review process; and (3) it updates and extends the existing classification schemes through identification and inclusion of recently emerged application areas and exclusion of obsolete categories. The results of the literature analysis are then used to make suggestions for future research.     

A miniaturized transient hot-wire device for measuring thermal conductivity of non-conductive fluids

This paper presents the design, fabrication and characterization of a novel transient hot-wire device for measurement of thermal conductivity of non-conductive fluids. The key features of the cell are small sample amount, simple fabrication, accurate measurement and transparency, which allows the study of both thermal and optical properties of the fluids. A 20 μm-diameter platinum hot wire is symmetrically suspended along the central axis of the capsulated cell to avoid the effect from the walls. A four-point resistance measurement method was used to measure the transient resistance with high accuracy. The accuracy of the thermal conductivity measurement was validated with reference values and very good agreement was achieved.     

Application of Complex Networks Theory in Urban Traffic Network Researches

Networks and Spatial Economics - Complex network theory is a multidisciplinary research direction of complexity science which has experienced a rapid surge of interest over the last two decades....     

Supercomputing of reducing sequenced bases in de novo sequencing of the human genome

The Journal of Supercomputing - DNA sequencing is one of the important sub-disciplines of bioinformatics, which has various applications in medicine, history, demography, and archaeology. De...     

Nanotechnology for Neuroscience: Promising Approaches for Diagnostics,Therapeutics and Brain Activity Mapping

Unlocking the secrets of the brain is a task fraught with complexity and challenge – not least due to the intricacy of the circuits involved. With advancements in the scale and precision of scientific technologies, we are increasingly equipped to explore how these components interact to produce a vast range of outputs that constitute function and disease. Here, an insight is offered into key areas in which the marriage of neuroscience and nanotechnology has revolutionized the industry. The evolution of ever more sophisticated nanomaterials culminates in network‐operant functionalized agents. In turn, these materials contribute to novel diagnostic and therapeutic strategies, including drug delivery, neuroprotection, neural regeneration, neuroimaging and neurosurgery. Further, the entrance of nanotechnology into future research arenas including optogenetics, molecular/ion sensing and monitoring, and piezoelectric effects is discussed. Finally, considerations in nanoneurotoxicity, the main barrier to clinical translation, are reviewed, and direction for future perspectives is provided.