Survey of denoising,segmentation and classification of magnetic resonance imaging for prostate cancer

Multimedia Tools and Applications - Prostate cancer (PCa) has become the second most dreadful cancer in men after lung cancer. Traditional approaches used for treatment of PCa were manual, time...     

Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.     

Design of a grid service-based platform for in silico protein-ligand screenings

Grid computing offers the powerful alternative of sharing resources on a worldwide scale, across different institutions to run computationally intensive, scientific applications without the need for a centralized supercomputer. Much effort has been put into development of software that deploys legacy applications on a grid-based infrastructure and efficiently uses available resources. One field that can benefit greatly from the use of grid resources is that of drug discovery since molecular docking simulations are an integral part of the discovery process. In this paper, we present a scalable, reusable platform to choreograph large virtual screening experiments over a computational grid using the molecular docking simulation software DOCK. Software components are applied on multiple levels to create automated workflows consisting of input data delivery, job scheduling, status query, and collection of output to be displayed in a manageable fashion for further analysis. This was achieved using Opal OP to wrap the DOCK application as a grid service and PERL for data manipulation purposes, alleviating the requirement for extensive knowledge of grid infrastructure. With the platform in place, a screening of the ZINC 2,066,906 compound "drug-like" subset database against an enzyme's catalytic site was successfully performed using the MPI version of DOCK 5.4 on the PRAGMA grid testbed. The screening required 11.56 days laboratory time and utilized 200 processors over 7 clusters.     

Automated process planning method to machine A B-Spline free-form feature on a mill–turn center

In this paper, we present a methodology for automating the process planning and NC code generation for a widely encountered class of free-form features that can be machined on a 3-axis mill–turn center. The free-form feature family that is considered is that of extruded protrusions whose cross-section is a closed, periodic B-Spline curve. In this methodology, for machining a part with B-Spline protrusion located at the free end, the part is first rough turned to the maximum profile diameter of the B-Spline, followed by rough profile cutting and finish profiling with axially mounted end mill tools. The identification and sequencing of machining volumes is completely automated, as is the generation of actual NC code. The approach supports both convex and non-convex profiles. In the case of non-convex profiles, the process planning algorithm ensures that there is no gouging of the work piece by the tool. The algorithm also identifies when sections of the tool path lie outside the work piece and utilizes rapid traverses in these regions to reduce cutting time. This methodology presents an integrated turn–mill process planning where by making the process fully automated from design with no user intervention making the overall process planning efficient. The algorithm was tested on several examples and test parts using the unmodified NC code obtained from the implementation were run on a Moriseiki mill–turn center. The parts that were produced met the dimensional specifications of the desired part.     

Modeling mineral phase change chemistry of groundwater in a rural-urban fringe

This research paper aims to determine the genetic origin of the chemical elements in groundwater. It deals with the results of physicochemical parameters, to evaluate the hydro-geochemistry of groundwater in rural-urban fringe of district Bareilly, India. Pre- and post-monsoon sampling has been carried out, which reveals inter-seasonal variability effect on the hydro-geochemical processes. Geochemical modeling especially computation of saturation index was undertaken using the WATEQ4F model. Majority of samples fall in the category of undersaturation, which further suggests that groundwater still has potential to dissolve more minerals. Chemical categorizations of groundwater samples were performed with the help of the Aquachem model. Grouping of groundwater on the Piper diagram reveals a common composition and origin. In most of the area, water facies is of Ca(2+)-HCO(3)(-) type in both the seasons. It also indicates that in pre-monsoon, ion exchange is the dominant process, whereas in post-monsoon, both ion exchanges as well as reverse ion exchanges are reported in the groundwater of the study area.     

Nonlinear<Emphasis Type="Italic">I-V</Emphasis> characteristics study of doped SnO<Subscript>2</Subscript>

When tin oxide is doped with Sb₂O₃ and CoO, it shows highly nonlinear current (I)-voltage (V) characteristics. Addition of CoO leads to creation of oxygen vacancies and helps in sintering of SnO₂. Antimony oxide acts as a donor and increases the conductivity. The results are nearly the same when antimony oxide is replaced by tantalum oxide. The observed nonlinear coefficient, α = 30 and the breakdown voltage is 120 V/mm.     

Ferret: An RFID-enabled pervasive multimedia application

This paper presents the design and implementation of Ferret, a system for locating nomadic augmented with RFID tags and visually displaying them to a user in real-time. We present a novel algorithm to infer location of tagged objects using the location of a camera and reader that observes them. We also present techniques to refine location estimates using multiple observations and a method to display and update object locations on a video camera screen. An experimental evaluation of the Ferret prototype shows that (i) Ferret can refine object locations to only 1% of the reader’s coverage region in less than 2 min with small error rate (2.22%); (ii) Ferret can detect nomadic objects with 100% accuracy when the moving distances exceed 20 cm; and (iii) Ferret is robust against different movement patterns of user’s mobility.     

Superparamagnetic Nanostructures for Off‐Resonance Magnetic Resonance Spectroscopic Imaging

This study proposes a new method to generate positive contrast in magnetic resonance imaging (MRI) using superparamagnetic contrast agents. Superparamagnetic nanostructures consisting of octahedron manganese ferrite nanoparticles embedded in spherical nanogels are fabricated using a bottom‐up approach. The composite nanoparticles are strongly magnetized in an external magnetic field and produce a unique NMR frequency shift in water protons, which can be demonstrated in MR spectroscopy and imaging to be different from the bulk pool. Moreover, the particles exhibit excellent colloidal stability in aqueous media and good cell biocompatibility. Hence, these particles are potentially useful as biomarkers by taking advantage of the positive contrast effects produced in MRI.