Comparison of Basis-Vector Selection Methods for Target and Background Subspaces as Applied to Subpixel Target Detection

This paper focuses on comparing three basis-vector selection techniques as applied to target detection in hyperspectral imagery. The basis-vector selection methods tested were the singular value decomposition (SVD), pixel purity index (PPI), and a newly developed approach called the maximum distance (MaxD) method. Target spaces were created using an illumination invariant technique, while the background space was generated from AVIRIS hyperspectral imagery. All three selection techniques were applied (in various combinations) to target as well as background spaces so as to generate dimensionally-reduced subspaces. Both target and background subspaces were described by linear subspace models (i.e., structured models). Generated basis vectors were then implemented in a generalized likelihood ratio (GLR) detector. False alarm rates (FAR) were tabulated along with a new summary metric called the average false alarm rate (AFAR). Some additional summary metrics are also introduced. Impact of the number of basis vectors in the target and background subspaces on detector performance was also investigated. For the given AVIRIS data set, the MaxD method as applied to the background subspace outperformed the other two methods tested (SVD and PPI)

Array-Based Statistical Analysis of the MK-3 Authenticated Encryption Scheme

Authenticated encryption (AE) schemes are symmetric key cryptographic methods that support confidentiality, integrity and source authentication. There are many AE algorithms in existence today, in part thanks to the CAESAR competition for authenticated encryption, which is in its final stage. In our previous work we introduced a novel AE algorithm MK-3 (not part of the CAESAR competition), which is based on the duplex sponge construction and it is using novel large 16×16 AES-like S-boxes. Unlike most AE schemes, MK-3 scheme provides additional customization features for users who desire unique solutions. This makes it well suited for government and military applications. In this paper, we develop a new array- based statistical analysis approach to evaluate randomness of cryptographic primitives and show its effectiveness in the analysis of MK-3. One of the strengths of this method is that it focuses on the randomness of cryptographic primitive function rather than only on the randomness of the outpu

Customization Modes for the Harris MK-3 Authenticated Encryption Algorithm

MK-3 is a new proprietary authenticated encryption algorithm based on the duplex sponge construction. To provide security autonomy capability, such that different users can have sovereign variants of the encryption algorithm, MK-3 is designed to be customizable. Two levels of customization are supported, Factory Customization and Field Customization. Customization is done by modifying functions and function parameters in the algorithm to yield differing cipher functions while preserving the algorithm’s security. This paper describes the MK-3 algorithm’s customization options and discusses results of testing designed to verify security autonomy among the customized variants

Non-Gaussian Linear Mixing Models for Hyperspectral Images

Modeling of hyperspectral data with non-Gaussian distributions is gaining popularity in recent years. Such modeling mostly concentrates on attempts to describe a distribution, or its tails, of all image spectra. In this paper, we recognize that the presence of major materials in the image scene is likely to exhibit nonrandomness and only the remaining variability due to noise, or other factors, would exhibit random behavior. Hence, we assume a linear mixing model with a structured background, and we investigate various distributional models for the error term in that model. We propose one model based on the multivariate t-distribution and another one based on independent components following an exponential power distribution. The former model does not perform well in the context of the two images investigated in this paper, one AVIRIS and one HyMap image. On the other hand, the latter model works reasonably well with the AVIRIS image and very well with the HyMap image. This paper provides the tools that researchers can use for verifying a given model to be used with a given image

Geometric basis-vector selection meth ods and subpixel target detection as applied to hyperspectral imagery

Abstract-In this paper, we compare three basis-vector selection methods as applied to subpixel target detection. This is a continuation of previous research in which a similar comparison was performed based on an AVIRIS image. Our goal is to find out to what extent our previous observations apply more broadly to other images, more specifically, a HYDICE image used in this paper. Our target detection approach is based on generating a radiance target region using a physical model to generate radiance spectra as observed under a wide range of atmospheric, illumination, and viewing conditions. The advantage of this approach is that the resulting target detection is invariant to those changing conditions. For the purpose of target detection, we use a structured model to describe each image spectra as a linear combination of the target and background basis-vectors, and then we apply a matched subspace detector. Finally, we find ROC curves to describe the relationship between the detection rate (DR) and the false alarm rate (FAR). Due to a large number of cases considered, we use summary metrics to represent our results. The obtained results are quite different from those obtained in [1] for the AVIRIS image. The best method for generating the background basis vectors in the AVIRIS image was the MaxD method, while the SVD method proved to be best for the HYDICE image used in this paper. Further research is needed to find out the reasons for these differences. It is not surprising that different methods are optimal for different types of data. However, it would be useful to be able to recognize the optimal method without assuming knowledge of the targets in the image

Lysophosphatidic acid down-regulates human RIPK4 mRNA in keratinocyte- derived cell lines

The tight control of proliferating keratinocytes is vital to the successful function of the skin. Differentiation of dividing cells is necessary to form a skin barrier. The same dividing cells are necessary to heal wounds and when malignant form tumors. RIPK4, a serine-threonine kinase, plays critical roles in these processes. Its loss of function was associated with pathological keratinocyte proliferation and development of squamous cell carcinoma (SCC) in humans and mice. The current study extends previous findings in the importance of RIPK4 in keratinocyte proliferation. A serum-derived phospholipid, lysophosphatidic acid (LPA), was identified as an important biologic inhibitor of RIPK4. LPA functions by inhibiting the transcription of RIPK4 mRNA. LPA treatment led to increased keratinocyte proliferation, and this was compromised in cells with reduced RIPK4 expression. The current study may help to explain the mechanism by which RIPK4 was downregulated during SCC progression and provide insights on RIPK4 functions. It may also allow for targeting of RIPK4 through a natural component of serum