Discrete contours in multiple views: approximation and recognition

Recognition of discrete planar contours under similarity transformations has received a lot of attention but little work has been reported on recognizing them under more general transformations. Planar object boundaries undergo projective or affine transformations across multiple views. We present two methods to recognize discrete curves in this paper. The first method computes a piecewise parametric approximation of the discrete curve that is projectively invariant. A polygon approximation scheme and a piecewise conic approximation scheme are presented here. The second method computes an invariant sequence directly from the sequence of discrete points on the curve in a Fourier transform space. The sequence is shown to be identical up to a scale factor in all affine related views of the curve. We present the theory and demonstrate its applications to several problems including numeral recognition, aircraft recognition, and homography computation.     

Performance evaluation of cryogenically assisted electric discharge machining (CEDM) process

The performance of cryogenically assisted electric discharge machining (CEDM) process has been evaluated in the presented research paper. The machining of cryogenically treated (CT) and cryogenically untreated (CUT) AISI D2 tool steel work specimens using cryogenically cooled (CC), CT, and CUT copper electrodes have been performed. The effects of various parameters, namely, workpiece condition, tool condition, nozzle flushing, peak current, duty cycle, pulse duration, and gap voltage, have been studied on the performance indicators, viz. the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The best parametric combinations have been suggested to obtain the desired quality characteristics. The interaction effects among various parameters have also been presented. An increase of approximately 18% in MRR and a reduction of 26% and 11% in TWR and SR, respectively, were observed, during the machining through CEDM in contrast to EDM. The confirmatory experiments suggested that experimental values were in permissible agreement with the predicted values for all the performance measures. Finally, the comparison of the CEDM with that of EDM process, in the light of SEM graphs, has been presented.     

Functionalized core-shell nanostructures with inherent magnetic character: Outperforming candidates for the activation of PMS

With a view to engender an advanced oxidation protocol, in the present investigation a very efficient and outperforming catalytic system has been developed which employs peroxymonosulphate (PMS) as oxidant and surface metal functionalized core-shell nanostructures (with ferrite as core coated with shell of dopamine and surface functionalized with transition metal nanoparticles) as catalysts. The present protocol bids fast and facile degradation of organic pollutants without the aid of any promoting radiations. Every component of the developed core-shell nanostructures (M@Dop@CoFe; M?=?Cr, Mn, Fe, Co, Ni, Cu and Zn) has been found to contribute synergistically in the overall oxidative-degradation process. Co@Dop@CoFe presented the best results for the oxidative-degradation of nitrophenols (NP) taken as model pollutants with rate constant values of 8.13?×?10^?1?min^?1, 3.56?×?10^?1?min^?1 and 17.15?×?10^?1?min^?1 for 2-NP, 3-NP and 4-NP respectively. The synthesized core-shell nanocatalysts have been established to be leach-proof and recyclable to a very good extent.     

Crystal Growth and Magneto-transport of Bi<Subscript>2</Subscript>Se<Subscript>3</Subscript> Single Crystals

In this letter, we report on the growth and characterization of bulk Bi ₂Se ₃ single crystals. The studied Bi ₂Se ₃ crystals are grown by the self-flux method through the solid-state reaction from high-temperature (950 °C) melt of constituent elements and slow cooling (2 ℃/h). The resultant crystals are shiny and grown in the [00l] direction, as evidenced from surface XRD. Detailed Reitveld analysis of powder X-ray diffraction (PXRD) of the crystals showed that these are crystallized in the rhombohedral crystal structure with a space group of R3m (D5), and the lattice parameters are a = 4.14 (2), b = 4.14 (2), and c = 28.7010 (7) Å. Temperature versus resistivity (ρ?T) plots revealed metallic conduction down to 2 K, with typical room temperature resistivity (ρ _{300 K}) of around 0.53 m Ω-cm and residual resistivity (ρ _{0 K}) of 0.12 m Ω-cm. Resistivity under magnetic field [ ρ(T)H] measurements exhibited large + ve magneto-resistance right from 2 to 200 K. Isothermal magneto-resistance [ ρH] measurements at 2, 100, and 200 K exhibited magneto-resistance (MR) of up to 240 %, 130 %, and 60 %, respectively, at 14 T. Further, the MR plots are nonsaturating and linear with the field at all temperatures. At 2 K, the MR plots showed clear quantum oscillations at above say 10 T applied field. Also, the Kohler plots, i.e., Δρ/ ρ _oversus B/ ρ, were seen consolidating on one plot. Interestingly, the studied Bi ₂Se ₃ single crystal exhibited the Shubnikov-de Haas (SdH) oscillations at 2 K under different applied magnetic fields ranging from 4 to 14 T.     

Novel Solid-State Growth of <Emphasis Type="Italic">p</Emphasis>-Terphenyl: the Parent High-<Emphasis Type="Italic">T</Emphasis><Subscript>c</Subscript> Organic Superconductor (HTOS)

We report an easy and versatile route for the synthesis of the parent phase of the newest superconducting wonder material, i.e., p-terphenyl. Doped p-terphenyl has recently shown superconductivity with transition temperature as high as 120 K. For crystal growth, the commercially available p-terphenyl powder is pelletized, encapsulated in an evacuated (10^?4 Torr) quartz tube and subjected to high-temperature (260 ^°C) melt followed by slow cooling at 5 ^°C/h. A simple temperature-controlled heating furnace is used during the process. The obtained crystal is one piece, shiny, and plate like. Single crystal surface XRD (X-ray diffraction) showed unidirectional (00l) lines, indicating that the crystal is grown along the c-direction. Powder XRD of the specimen showed that as grown p-terphenyl is crystallized in monoclinic structure with space group P2 ₁/a space group, having lattice parameters a = 7.672 (2) Å, b = 5.772 (5) Å, and c = 13.526(3) Å and β = 91.484 (3)^°. Scanning electron microscopy (SEM) pictures of the crystal showed clear layered slab-like growth without any visible contamination from oxygen. Characteristic reported Raman active modes related to C–C–C bending, C–H bending, C–C stretching, and C–H stretching vibrations are seen clearly for the studied p-terphenyl crystal. The physical properties of the crystal are yet underway. The short letter reports an easy and versatile crystal growth method for obtaining quality p-terphenyl. The same growth method may probably be applied to doped p-terphenyl and to subsequently achieve superconductivity to the tune of as high 120 K for the newest superconductivity wonder, i.e., high- T _c organic superconductor (HTOS).     

Thick-Film Processing for Tl-Oxide Wire and Tape

Thick-film processing techniques such as electrodeposition, electrophoresis, spray pyrolysis, and ink spraying offer promising and economic (<$10/kAm) approaches for fabricating high-temperature superconducting (HTS) wire or tape for the Tl-oxide superconductors. The performance of the Tl system exceeds that for the Bi-oxides and offers easier fabrication than YBCO. Scale up, however, may be difficult because of the need for open tape conductors with aligned microstructures. The Tl-oxides, moreover, offer the potential for operation at 77K in practical magnetic fields of 3-5 T, which is supported by measurements on the irreversibility behavior of the Tl-1223 single layer compound with Pb, Bi, and Sr substitution. Recent advances in the development of biaxial textured electrodeposited films on Ag coated single crystal substrates are discussed. This paper will review the merits for a near-term Tl-oxide based conductor for use in HTS electric power applications, currently under development, such as the transformer and transmission cable. Recent results for the buffer layer development that would provide an oriented ceramic layer or template on a flexible textured nickel substrate utilizing the RABiTS substrates developed by ORNL will then be discussed with a projection for the cost of a long length thick-film process using electrodeposition.     

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.     

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison matrices. Crisp judgments cannot be given for real-life situations, as most of these include some level of fuzziness and complexity. In these situations, judgments are represented by the set of fuzzy numbers. Most of the fuzzy optimization models derive crisp priorities for judgments represented with Triangular Fuzzy Numbers (TFNs) only. They do not work for other types of Triangular Shaped Fuzzy Numbers (TSFNs) and Trapezoidal Fuzzy Numbers (TrFNs). To overcome this problem, a sum of squared error (SSE) based optimization model is proposed. Unlike some other methods, the proposed model derives crisp weights from all of the above-mentioned fuzzy judgments. A fuzzy number is simulated using the Monte Carlo method. A threshold-based constraint is also applied to minimize the deviation from the initial judgments. Genetic Algorithm (GA) is used to solve the optimization model. We have also conducted casestudiesto show the proposed approach’s advantages over the existingmethods. Results show that the proposed model outperforms other models to minimize SSE and deviation from initial judgments. Thus, the proposed model can be applied in various real time scenarios as it can reduce the SSE value upto 29% compared to the existing studies.     

Review of background subtraction methods using Gaussian mixture model for video surveillance systems

Foreground detection or moving object detection is a fundamental and critical task in video surveillance systems. Background subtraction using Gaussian Mixture Model (GMM) is a widely used approach for foreground detection. Many improvements have been proposed over the original GMM developed by Stauffer and Grimson (IEEE Computer Society conference on computer vision and pattern recognition, vol 2, Los Alamitos, pp 246–252, 1999. doi: 10.1109/CVPR.1999.784637) to accommodate various challenges experienced in video surveillance systems. This paper presents a review of various background subtraction algorithms based on GMM and compares them on the basis of quantitative evaluation metrics. Their performance analysis is also presented to determine the most appropriate background subtraction algorithm for the specific application or scenario of video surveillance systems.