Industrial baking of sponge cakes requires various quality indicators to be measured during production such as moisture content and sponge hardness. Existing techniques for measuring these properties require randomly selected sponges to be removed from the production line before samples are manually cut out of each sponge in a destructive way for testing. These samples are subsequently processed manually using dedicated analysers to measure moisture and texture properties in a lengthy process, which can take a skilled operator around 20 min to complete per sponge. In this study, the authors present a new, single sensor hyperspectral imaging approach, which has the potential to measure both sponge moisture content and hardness simultaneously. In the last decade, hyperspectral imaging systems have reduced in cost and size and, as a result, they are becoming widely used in a number of industries and research areas. Recently, there has been an increased use of this technology in the food industry and in food science applications and research. The application of this technology in the cake production environment, empowered by sophisticated signal and image processing techniques and prediction algorithms as presented in this study has the potential to provide on‐line, real‐time, stand‐off cake quality monitoring.

The Line Hough Transform (LHT) is a robust and accurate line detection algorithm, useful for applications such as lane detection in Advanced Driver Assistance Systems. For real-time implementation, the LHT is demanding in terms of computation and memory, and hence Field Programmable Gate Arrays (FPGAs) are often deployed. However, many small FPGAs are incapable of implementing the LHT due to the large memory requirement of the Hough Parameter Space (HPS). This paper presents a memory-efficient architecture of the LHT named the Angular Regions - Line Hough Transform (AR-LHT). We present a suitable FPGA implementation of the AR-LHT and provide a performance and resource analysis after targeting a Xilinx xc7z010-1 device. Results demonstrate that, for an image of 1024×1024 pixels, approximately 48% less memory is used than the Standard LHT. The FPGA architecture is capable of processing a single image in 9.03ms.

Systems biologists and cancer researchers require interactive visualization tools that enable them to more easily navigate and discover patterns at different levels of the biological hierarchy of signaling pathways. Furthermore, biologists are often interested in understanding and exploring the causal biochemical links between processes. When exploring the literature of particular biological pathways or specific proteins within those pathways, biologists find it useful to know the contexts in which biochemical links are active and, importantly, to be aware of potential conflicts when different experiments introduce alternative interpretations of the function of a pathway or biochemical reaction. We introduce BioLinker, a interactive visualization system that helps users to perform bottom-up exploration of complex protein interaction networks. Five interconnected views provide the user with a range of ways to explore pathway data, including views that show potential conflicts within pathway databases and publications and that highlight contextual information about individual proteins. Additionally, we discuss system details to show how our system manages the large amount of protein interactions extracted from the literature of biological pathways.

BACKGROUND: Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity. RESULTS: Here, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy. CONCLUSIONS: Our taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts.

Accurate sea-ice segmentation from satellite synthetic aperture radar (SAR) images plays an important role for understanding the interactions between sea-ice, ocean and atmosphere in the Arctic. Processing sea-ice SAR images are challenging due to poor spatial resolution and severe speckle noise. In this paper, we present a multi-stage method for the sea-ice SAR image segmentation, which includes edge-preserved filtering for preprocessing, k-means clustering for segmentation and conditional morphology filtering for post-processing. As such, the effect of noise has been suppressed and the under-segmented regions are successfully corrected.

A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data with a very large number of objects and dimensions from higher dimensions onto a lower-dimensional space. In visual analytics tasks, the density of the multidimensional clusters can strongly affect how these clusters are perceived. However, this feature can be lost when that dataset is projected into a 2D space, adversely affecting the effectiveness of visual analytics tasks. Thus, it makes sense to preserve, as far as possible, information about the density during the dimensionality reduction. This paper is a study of motion-enhanced cluster perception where the clusters are shown in 2D scatterplots and cluster density is mapped to the motion of the individual constituent points. We consider different types of density-based motion, where the magnitude of the motion is directly related to the density of the clusters. We conducted a series of user studies with large datasets to investigate how motion is a powerful perceptual cue well-suited for grouping or segmenting types during perceptual tasks. We found that the use of motion enabled users to be easily able to distinguish between clusters with different densities. The amount of visual change per unit time was different for the different motions, and we describe the ranges and thresholds for each of them. Specifically, we looked at two projection techniques that output 2D scatterplots for a range of data analysis tasks. We focus on high-dimensional, real-world datasets that might require analyses involving cluster identification, similarity seeking, and cluster ranking tasks. Our results indicate that incorporating density-based motion into visualization analytics systems effectively enables the exploration and analysis of multidimensional datasets.

We investigated ways to automatically analyze movement and verbal behavior of teams of soldiers engaged in simulated military missions. Analysis of location data revealed that soldiers' locations were consistent with 3 stationary patterns and 2 movement patterns. Analysis of their dialogue detected statistical regularities. An automated text classifier was developed that employed these regularities to code dialogue utterances. These analyses demonstrate the feasibility of interpreting collaborative activity automatically and constructing models of team context for teams of soldiers engaged in trained activities.

The hit-or-miss transform (HMT) is a well-known morphological transform capable of identifying features in digital images. When image features contain noise, texture, or some other distortion, the HMT may fail. Various researchers have extended the HMT in different ways to make it more robust to noise. The most successful, and most recent extensions of the HMT for noise robustness, use rank-order operators in place of standard morphological erosions and dilations. A major issue with the proposed methods is that no technique is provided for calculating the parameters that are introduced to generalize the HMT, and, in most cases, these parameters are determined empirically. We present here, a new conceptual interpretation of the HMT which uses a percentage occupancy (PO) function to implement the erosion and dilation operators in a single pass of the image. Further, we present a novel design tool, derived from this PO function that can be used to determine the only parameter for our routine and for other generalizations of the HMT proposed in the literature. We demonstrate the power of our technique using a set of very noisy images and draw a comparison between our method and the most recent extensions of the HMT.

Anubis is a simple state monitoring service that supports coordinated action among distributed management agents. It uses a temporal consistency model that addresses symmetric and asymmetric network partitions. We have used Anubis to support distributed management of adaptive applications in grid and utility computing environments and our experience has shown that the abstraction and properties provided by the service simplify the task of programming distributed management behavior. We support this claim by examining three common use cases that our developers encountered, namely: resource management, lifecycle coordination, and compositional failure management.