Deformable M-Reps for 3D Medical Image Segmentation

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures—each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure.A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects.The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.     

Deep packet inspection using parallel bloom filters

There is a class of packet processing applications that inspect packets deeper than the protocol headers to analyze content. For instance, network security applications must drop packets containing certain malicious Internet worms or computer viruses carried in a packet payload. Content forwarding applications look at the hypertext transport protocol headers and distribute the requests among the servers for load balancing. Packet inspection applications, when deployed at router ports, must operate at wire speeds. With networking speeds doubling every year, it is becoming increasingly difficult for software-based packet monitors to keep up with the line rates. We describe a hardware-based technique using Bloom filters, which can detect strings in streaming data without degrading network throughput. A Bloom filter is a data structure that stores a set of signatures compactly by computing multiple hash functions on each member of the set. This technique queries a database of strings to check for the membership of a particular string. The answer to this query can be false positive but never a false negative. An important property of this data structure is that the computation time involved in performing the query is independent of the number of strings in the database provided the memory used by the data structure scales linearly with the number of strings stored in it. Furthermore, the amount of storage required by the Bloom filter for each string is independent of its length.     

Speech and noise power estimation using Gamma modeling

In speech enhancement, having an accurate estimation of the power of the speech and noise signals forming the noisy observation is critical, as it can highly affect the performance of the enhancement algorithm. A method is introduced in which the distributions of the power of the speech and noise periodograms are modeled using the Gamma distribution to extract their shape parameters. These shape parameters are later used in the observed noisy speech to estimate the power when forming speech and noise periodograms. This method results in more accurate and faster power estimation with respect to the well‐known minimum statistics power estimation algorithm and together with the maximum a posteriori speech enhancement algorithm exhibits good speech enhancement performance.     

Ag–Sr doped mesoporous bioactive glass nanoparticles loaded chitosan/gelatin coating for orthopedic implants

In this study, we investigated surface and biological properties of Ag–Sr-doped mesoporous bioactive glass nanoparticle (Ag–Sr MBGN) loaded chitosan/gelatin coatings deposited by electrophoretic deposition (EPD) on 316L stainless steel. The EPD parameters, that is, deposition time, applied voltage, and distance between the electrodes was optimized by the Taguchi design of experiment (DoE) approach. Scanning electron microscopy (SEM) images illustrated the spherical morphology of the synthesized Ag–Sr MBGNs with the mean particle size of 160 ± 20 nm. Energy-dispersive X-ray (EDX) spectroscopy results confirmed the presence of Ag and Sr in the synthesized MBGNs. Optimum EPD parameters determined by DoE approach were 5 g/L of Ag–Sr MBGNs, deposition time of 5 min, and applied voltage of 30 V. SEM images confirmed that the coatings were fairly homogenous. Fourier-transform infrared spectroscopy and EDX results confirmed the presence of chitosan, gelatin, and Ag–Sr MBGNs in the coatings. Chitosan/gelatin/Ag–Sr MBGN composite coatings exhibited suitable wettability for the protein attachment and proliferation of osteoblast cells. The composite coatings exhibited suitable adhesion strength with the substrate. The coatings developed HA crystals upon immersion in simulated body fluid. The results of the turbidity test confirmed that the coatings are antibacterial to the Escherichia coli cells.     

Sparse Adaptive Parameterization of Variability in Image Ensembles

This paper introduces a new parameterization of diffeomorphic deformations for the characterization of the variability in image ensembles. Dense diffeomorphic deformations are built by interpolating the motion of a finite set of control points that forms a Hamiltonian flow of self-interacting particles. The proposed approach estimates a template image representative of a given image set, an optimal set of control points that focuses on the most variable parts of the image, and template-to-image registrations that quantify the variability within the image set. The method automatically selects the most relevant control points for the characterization of the image variability and estimates their optimal positions in the template domain. The optimization in position is done during the estimation of the deformations without adding any computational cost at each step of the gradient descent. The selection of the control points is done by adding a L ¹ prior to the objective function, which is optimized using the FISTA algorithm.     

A D2D-Based Solution for MTC Connectivity Problem in NOMA-Based Cellular IoT Networks: Dynamic User Grouping and Resource Allocation

Mobile Networks and Applications - In a cellular network (CN), cellular users (CUs) located nearby machine type communications (MTC) devices (MTC-Ds) may act as uplink gateways to relay data to the...     

Product formulation for ohmic heating: blanching as a pretreatment method to improve uniformity in heating of solid-liquid food mixtures

ABSTRACT:  The electrical conductivity of food components is critical to ohmic heating. Food components of different electrical conductivities heat at different rates. While equal electrical conductivities of all phases are desirable, real food products may behave differently. In the present study involving chicken chow mein consisting of a sauce and different solid components, celery, water chestnuts, mushrooms, bean sprouts, and chicken, it was observed that the sauce was more conductive than all solid components over the measured temperature range. To improve heating uniformity, a blanching method was developed to increase the ionic content of the solid components. By blanching different solid components in a highly conductive sauce at 100 °C for different lengths of time, it was possible to adjust their conductivity to that of the sauce. Chicken chow mein samples containing blanched particulates were compared with untreated samples with respect to ohmic heating uniformity at 60 Hz up to 140 °C. All components of the treated product containing blanched solids heated more uniformly than untreated product. In sensory tests, 3 different formulations of the blanched product showed good quality attributes and overall acceptability, demonstrating the practical feasibility of the blanching protocol.