Multireference object pose retrieval with volume feedback

Pose retrieval of a rigid object from monocular video sequences or images is addressed. Initially, the object pose is estimated in each image assuming flat depth maps. Shape-from-silhouette is then applied to make a 3-D model (volume), which is used for a new round of pose estimations, this time by a model-based method that gives better estimates. Before repeating this process by building a new volume, pose estimates are adjusted to reduce error by maximizing a novel quality factor for shape-from-silhouette volume reconstruction. The feedback loop is terminated when pose estimates do not change much, as compared with those produced by the previous iteration. Based on a theoretical study of the proposed system, a test of convergence to a given set of poses is devised. Reliable performance of the system is also proved by several experiments on both synthetic and real image sequences. No model is assumed for the object and no feature point is detected or tracked as there is no problematic feature matching or correspondence. Our method can be used for 3-D object tracking in video, 3-D modeling, and volume reconstruction from video.     

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

Comparison of deposition models for a TEOS LPCVD process

We present a comparison of models describing the pyrolytic deposition of SiO₂ with a low pressure chemical vapor deposition process. In order to meet industrial simulation requirements, e.g. accuracy and fast delivery of results, we present an overview of established and new models, their use within TCAD applications, and their best results which have been obtained by calibrations according to SEM measurements.     

Nanoelectromechanical Chip (NELMEC) Combination of Nanoelectronics and Microfluidics to Diagnose Epithelial and Mesenchymal Circulating Tumor Cells from Leukocytes

An integrated nano‐electromechanical chip (NELMEC) has been developed for the label‐free distinguishing of both epithelial and mesenchymal circulating tumor cells (ECTCs and MCTCs, respectively) from white blood cells (WBCs). This nanoelectronic microfluidic chip fabricated by silicon micromachining can trap large single cells (>12 µm) at the opening of the analysis microchannel arrays. The nature of the captured cells is detected using silicon nanograss (SiNG) electrodes patterned at the entrance of the channels. There is an observable difference between the membrane capacitance of the ECTCs and MCTCs and that of WBCs (measured using SiNG electrodes), which is the key indication for our diagnosis. The NELMEC chip not only solves the problem of the size overlap between CTCs and WBCs but also detects MCTCs without the need for any markers or tagging processes, which has been an important problem in previously reported CTC detection systems. The great conductivity of the gold‐coated SiNG nanocontacts as well as their safe penetration into the membrane of captured cells, facilitate a precise and direct signal extraction to distinguish the type of captured cell. The results achieved from epithelial (MCF‐7) and mesenchymal (MDA‐MB231) breast cancer cells circulated in unprocessed blood suggest the significant applications for these diagnostic abilities of NELMEC.     

THE EFFICIENCY CHALLENGES OF RESOURCE DISCOVERY IN GRID ENVIRONMENTS

Resource discovery is one of the most important services that significantly affects the efficiency of grid computing systems. The inherent dynamic and large-scale characteristics of grid environments make their resource discovery a challenging task. In recent years, different approaches have been proposed for resource discovery, attempting to tackle the challenges of grid environments and improve the efficiency. Being aware of these challenges and approaches is worthwhile in order to choose an appropriate approach according to the application in different organizations. This study reviews the most important factors that should be considered and challenges to be tackled in order to develop an efficient grid resource discovery system.     

An efficient parallel algorithm for the longest path problem in meshes

The longest path problem is the problem of finding a simple path with the maximum number of vertices in a given graph, and so far it has been solved polynomially only for a few classes of graphs. This problem generalizes the well-known Hamiltonian path problem, hence it is NP-hard in general graphs. In this paper, first we give a sequential linear-time algorithm for the longest path problem in meshes. Then based on this algorithm, we present a constant-time parallel algorithm for the problem, which can be run on every parallel machine.     

Principal component analysis with optimum order sample correlation coefficient for image enhancement

Principal component analysis (PCA) has been commonly used and has played an important role in remote sensing for information extraction. However, the ordinary PCA based on second‐order covariance or correlation is capable of forming components on the basis of the statistical properties of a majority of pixel values – pixel values around mean values. For many applications, principal components should be constructed on the basis of optimum correlation coefficients so that the components can represent low or high values of minority pixels of interest. A new version of the PCA has been proposed on the basis of an optimum order sample correlation coefficient for enhancing the contribution of the image bands including the low or high minority pixel values that can assist in extracting weak information for image classification and pattern recognition. The ordinary PCA becomes the special case of the new version of the PCA introduced in this paper. The new method was validated with a case study of identification of Au/Cu‐associated alteration zones from a Landsat Thematic Mapper (TM) image in the Mitchell‐Sulphurets district, Canada.     

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Journal of Supercomputing - The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare...     

Engineering carbon quantum dots for photomediated theranostics

Carbon quantum dots (CQDs) have emerged as potential alternatives to classical metal-based semiconductor quantum dots (QDs) due to the abundance of their precursors, their ease of synthesis, high biocompatibility, low cost, and particularly their strong photoresponsiveness, tunability, and stability. Light is a versatile, tunable stimulus that can provide spatiotemporal control. Its interaction with CQDs elicits interesting responses such as wavelength-dependent optical emissions, charge/electron transfer, and heat generation, processes that are suitable for a range of photomediated bioapplications. The carbogenic core and surface characteristics of CQDs can be tuned through versatile engineering strategies to endow specific optical and physicochemical properties, while conjugation with specific moieties can enable the design of targeted probes. Fundamental approaches to tune the responses of CQDs to photo-interactions and the design of bionanoprobes are presented, which enable biomedical applications involving diagnostics and therapeutics. These strategies represent comprehensive platforms for engineering multifunctional probes for nanomedicine, and the design of QD probes with a range of metal-free and emerging 2D materials.

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Weighted label propagation based on Local Edge Betweenness

The Journal of Supercomputing - In complex networks, especially social networks, networks could be divided into disjoint partitions. However, nodes could be partitioned such that the number of...