Soft information set for multicriteria decision making

An information source value is perceived differently by different agents. In this paper, we present a new knowledge representation structure, termed as soft information set (SIS), to provide a parameterized representation of the information values, as perceived by an agent. The properties of SIS are investigated and the notion of relations in SIS is devised. The significance of the proposed SIS in aiding multi criteria decision making is illustrated through real case-studies.     

An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms

The present study introduces an efficient algorithm for automatic segmentation and detection of mass present in the mammograms. The problem of over and under-segmentation of low-contrast mammographic images has been solved by applying preprocessing on original mammograms. Subtraction operation performed between enhanced and enhanced inverted mammogram significantly highlights the suspicious mass region in mammograms. The segmentation accuracy of suspicious region has been improved by combining wavelet transform and fast fuzzy c-means clustering algorithm. The accuracy of mass segmentation has been quantified by means of Jaccard coefficients. Better sensitivity, specificity, accuracy, and area under the curve (AUC) are observed with support vector machine using radial basis kernel function. The proposed algorithm is validated on Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. Highest 91.76% sensitivity, 96.26% specificity, 95.46% accuracy, and 96.29% AUC on DDSM dataset and 94.63% sensitivity, 92.74% specificity, 92.02% accuracy, and 95.33% AUC on MIAS dataset are observed. Also, shape analysis of mass is performed by using moment invariant and Radon transform based features. The best results are obtained with Radon based features and achieved accuracies for round, oval, lobulated, and irregular shape of mass are 100%, 70%, 64%, and 96%, respectively.     

Near‐Infrared Intraoperative Chemiluminescence Imaging

Intraoperative imaging technologies recently entered the operating room, and their implementation is revolutionizing how physicians plan, monitor, and perform surgical interventions. In this work, we present a novel surgical imaging reporter system: intraoperative chemiluminescence imaging (ICI). To this end, we have leveraged the ability of a chemiluminescent metal complex to generate near‐infrared light upon exposure to an aqueous solution of Ce⁴⁺ in the presence of reducing tissue or blood components. An optical camera spatially resolves the resulting photon flux. We describe the construction and application of a prototype imaging setup, which achieves a detection limit as low as 6.9 pmol cm^?2 of the transition‐metal‐based ICI agent. As a proof of concept, we use ICI for the in vivo detection of our transition metal tracer following both systemic and subdermal injections. The very high signal‐to‐noise ratios make ICI an interesting candidate for the development of new intraoperative imaging technologies.     

Powder Compaction Dies and Compressibility of Various Materials

Powder Metallurgy and Metal Ceramics - Powder compaction is the most crucial process in powder metallurgy since almost all the desired properties of a material, such as a shape, size, density,...     

Image quality assessment based on gradient similarity

In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes.     

Linked Simulation-Optimization based Dedicated Monitoring Network Design for Unknown Pollutant Source Identification using Dynamic Time Warping Distance

Implementation of monitoring strategy for increasing the efficiency of groundwater pollutant source characterization is often necessary, especially when only inadequate and arbitrary concentration measurement data are initially available. Two main parameters that need to be estimated for efficient and accurate characterization of groundwater pollution sources are: location of the source and the time when the source became active. Complexities involved with the explicit estimation of the time of start and source activity have not been addressed so far in previous studies. The main complexity arises due to the fact that the spatial location and time of activity are inter-related. Therefore, specifying one and solving for the other simplifies the source characterization problem. Hence, in this study, both the source location and time of initiation are treated as unknowns. The developed methodology uses dynamic time warping distance in the linked simulation-optimization model to address some complex issues in designing a monitoring network to efficiently estimate source characteristics including the time of first activity of unknown groundwater source. Performance of the developed methodology is evaluated on illustrative contaminated aquifer. These evaluation results demonstrate the potential use of the developed methodology.     

Addressing spatiotemporal and computational heterogeneity in structured adaptive mesh refinement

Structured adaptive mesh refinement (SAMR) techniques can provide accurate and cost- effective solutions to realistic scientific and engineering simulations modeling complex physical phenomena. However, the adaptive nature and inherent space–time heterogeneity of SAMR applications result in significant runtime management challenges. Moreover, certain SAMR applications involving reactive flows exhibit pointwise varying workloads and cannot be addressed by traditional parallelization approaches, which assume homogeneous loads. This paper presents hierarchical partitioning, bin-packing based load balancing, and Dispatch structured partitioning strategies to manage the spatiotemporal and computational heterogeneity in SAMR applications. Experimental evaluation of these schemes using 3-D Richtmyer–Meshkov compressible turbulence and 2-D reactive-diffusion kernels demonstrates the improvement in overall performance.     

Socket Buffer Auto-Sizing for High-Performance Data Transfers

It is often claimed that TCP is not a suitable transport protocol for data intensive Grid applications in high-performance networks. We argue that this is not necessarily the case. Without changing the TCP protocol, congestion control, or implementation, we show that an appropriately tuned TCP bulk transfer can saturate the available bandwidth of a network path. The proposed technique, called SOBAS, is based on automatic socket buffer sizing at the application layer. In non-congested paths, SOBAS limits the socket buffer size based on direct measurements of the received throughput and of the corresponding round-trip time. The key idea is that the send window should be limited, after the transfer has saturated the available bandwidth in the path, so that the transfer does not cause buffer overflows (self-induced losses). A difference with other socket buffer sizing schemes is that SOBAS does not require prior knowledge of the path characteristics, and it can be performed while the transfer is in progress. Experimental results in several high bandwidth-delay product paths show that SOBAS provides consistently a significant throughput increase (20% to 80%) compared to TCP transfers that use the maximum possible socket buffer size. We expect that SOBAS will be mostly useful for applications such as GridFTP in non-congested wide-area networks.