Carbon-based TiO2-x heterostructure nanocomposites for enhanced photocatalytic degradation of dye molecules

Application of brown titanium dioxide (TiO_2-x) and its modified composite forms in the photocatalytic decomposition of organic pollutants in the environment is a promising way to provide solutions for environmental redemption. Herein, we report the synthesis of effective and stable TiO_2-x nanoparticles with g-C₃N₄, RGO, and multiwalled carbon nanotubes (CNTs) using a simple hydrothermal method. Among all the as-synthesized samples, excellent photocatalytic degradation activity was observed for RGO-TiO_2-x nanocomposite with high rate constants of 0.075 min^?1_, 0.083 min^?1 and 0.093 min^?1 for methylene blue, rhodamine-B, and rosebengal dyes under UV–Visible light irradiation, respectively. The altered bandgap (1.8 eV) and the large surface area of RGO-TiO_2-x nanocomposite impacts on both absorption of visible light and efficiency of photogenerated charge electron (e^?)/hole (h⁺) pair separation. This resulted in enhanced photocatalytic property of carbon-based TiO_2-x nanocomposites. A systematic study on the influence of different carbon nanostructures on the photocatalytic activity of brown TiO_2-x is carried out.     

A comparative study of the gas sensing behavior of nanostructured nickel ferrite synthesized by hydrothermal and reverse micelle techniques

A comparative study of gas sensing behavior of nanocrystalline nickel ferrite synthesized by micro-emulsion and hydrothermal method to liquefied petroleum gas (LPG) is presented. Nanocrystalline nickel ferrite synthesized by hydrothermal method indicated higher electrical conductivity and gas sensitivity at low operating temperature compared to nanocrystalline nickel ferrite synthesized by reverse micelle technique. This difference in the gas sensing behavior can be attributed to the presence of more oxygen vacancies (i.e. non-stoichiometry) in the hydrothermally synthesized nickel ferrite. Incorporation of palladium had a catalytic effect and the operating temperature was significantly reduced in both the samples. The higher operating temperature of the reverse micelle nickel ferrite material makes the sensor response speed faster (∼10 s) compared to the hydrothermally synthesized material (∼1 min).     

The effect of bandwidth allocation policies on delay inunidirectional bus networks

We consider the problem of allocating bandwidth fairly to each node in a shared, unidirectional bus network. We focus on the p_i persistent protocol, since these are open loop policies designed to operate well in high speed networks, which have a very large bandwidth-delay product and feedback in the upstream direction is not available in a timely manner. First, we introduce an improvement to the basic p_i persistent protocol, in which we replace random coin tosses with a deterministic counting algorithm, and thereby reduce the delays for all nodes for any given choice of {p_i}. We then describe an exact method for calculating average packet delays and queue lengths in both the p_i persistent and our new deterministic n out of m protocols, based on the regenerative approach of Georgiades et al. (1987). These delay results, together with simulation measurements, show that both of these protocols still waste some bandwidth. After presenting a lower bounding argument to show that some wasted bandwidth is inevitable in all such distributed access control schemes, assuming a passive bus without feedback in the upstream direction, we show that changing the bus to unidirectional point-to-point links between (very simple) active interfaces at each node allows us to construct distributed access schemes that require no upstream feedback and are both work conserving and fair. To illustrate how this can be done, we introduce the p_i preemptive protocol, in which each node randomly inserts its own packets into the traffic arriving from upstream. We derive a simple and effective heuristic for calculating the preemption probability for each node, and use simulation to show how well it equalizes the delays at each node     

A stochastic Kaczmarz algorithm for network tomography

We develop a stochastic approximation version of the classical Kaczmarz algorithm that is incremental in nature and takes as input noisy real time data. Our analysis shows that with probability one it mimics the behavior of the original scheme: starting from the same initial point, our algorithm and the corresponding deterministic Kaczmarz algorithm converge to precisely the same point. The motivation for this work comes from network tomography where network parameters are to be estimated based upon end-to-end measurements. Numerical examples via Matlab based simulations demonstrate the efficacy of the algorithm.     

Effects of viscosity ratio and composition on development of morphology in chaotic mixing of polymers

This study investigated the effects of viscosity ratio (p) and composition on morphology development in an immiscible polymer system mixed under chaotic flow conditions. It was seen that morphology of the dispersed phase developed through a widely accepted route involving transitions from lamellas to fibrils and to droplets. It was found in experiments with p≥1 that the dispersed phase converted into droplets very rapidly with narrow droplet size distribution when p∼1. For higher values of p, the speed of morphological transitions slowed down, the droplet size distribution became wider, and much larger droplets were formed. Similar effects were observed at higher concentration of the dispersed phase. No self-similar scaling behavior was observed in the droplet size distribution, which can be attributed to the lack of self-similarity in the breakup of lamellas into fibrils.     

Articulatory and excitation source features for speech recognition in read,extempore and conversation modes

In our previous works, we have explored articulatory and excitation source features to improve the performance of phone recognition systems (PRSs) using read speech corpora. In this work, we have extended the use of articulatory and excitation source features for developing PRSs of extempore and conversation modes of speech, in addition to the read speech. It is well known that the overall performance of speech recognition system heavily depends on accuracy of phone recognition. Therefore, the objective of this paper is to enhance the accuracy of phone recognition systems using articulatory and excitation source features in addition to conventional spectral features. The articulatory features (AFs) are derived from the spectral features using feedforward neural networks (FFNNs). We have considered five AF groups, namely: manner, place, roundness, frontness and height. Five different AF-based tandem PRSs are developed using the combination of Mel frequency cepstral coefficients (MFCCs) and AFs derived from FFNNs. Hybrid PRSs are developed by combining the evidences from AF-based tandem PRSs using weighted combination approach. The excitation source information is derived by processing the linear prediction residual of the speech signal. The vocal tract information is captured using MFCCs. The combination of vocal tract and excitation source features is used for developing PRSs. The PRSs are developed using hidden Markov models. Bengali speech database is used for developing PRSs of read, extempore and conversation modes of speech. The results are analyzed and the performance is compared across different modes of speech. From the results, it is observed that the use of either articulatory or excitation source features along-with to MFCCs will improve the performance of PRSs in all three modes of speech. The improvement in the performance using AFs is much higher compared to the improvement obtained using excitation source features.     

Smart Backhauling for 5G Heterogeneous Network with Millimeter Wave Backhaul Links to Perform Switching Off,Interference Management and Backhaul Routing

Developments made in the fifth generation (5G) and the cellular networks have greatly influenced the lifestyle of the wireless users. Increased demand on higher data rates has also increased the network traffic. In the viewpoint of cellular networks, several Small Cells (SCs) are combined together with the help of microwave communications and millimeter wave communication models, in order to support the heterogeneous environments. In this paper, we have proposed a hybrid communication framework which can efficiently support the interference management, routings in backhaul links and the joint issue during on/off status of the mobile using 5G mmWave backhaul links. A novel cache-enabled technology is designed to develop backhaul links using heuristic search models. Along with that, an effective data access framework is also formulated using distance based cluster head selection that resolves the interference issues. Without modifying the content of the mobile users, the services are offered to the uses associated with backhaul links. Since a fast iterative model is developed, the throughput rate and the energy savings are maximized. A simulation analysis is carried out with a static number of mobile nodes which has proved the efficiency of the proposed framework.

     

Design of interval networks based on neural network and Choquet integral

Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.     

On the Length and Area Regularization for Multiphase Level Set Segmentation

In this paper we introduce novel regularization techniques for level set segmentation that target specifically the problem of multiphase segmentation. When the multiphase model is used to obtain a partitioning of the image in more than two regions, a new set of issues arise with respect to the single phase case in terms of regularization strategies. For example, if smoothing or shrinking each contour individually could be a good model in the single phase case, this is not necessarily true in the multiphase scenario.     

Synthesis of Nanocrystalline Lanthanum Strontium Manganite Powder by the Urea–Formaldehyde Polymer Gel Combustion Route

Nanocrystalline lanthanum strontium manganite (LSM) powder has been synthesized by combustion of a transparent gel obtained by the polymerization of methylol urea and urea in a solution containing La³⁺, Sr²⁺, and Mn²⁺ (LSM ions). Chemistry of the transparent urea–formaldehyde (UF) polymer gel formation and structure of the gel have been proposed such that the LSM ions act in between the growing UF polymer chains by interacting through NH, OH, and CO groups by co-ordination and prevent polymer self-assembly through inter-chain hydrogen bonding as evidenced from infrared spectrum. Thermally stable structures formed by the decomposition of UF polymer below 300°C undergo combustion in the presence of nitrate oxidant in a temperature range from 350°–450°C. A perovskite LSM phase has been formed by self-sustained combustion of the dried gel initiated with little kerosene. The powder obtained after deagglomeration and calcination at 600°C for 2 h has a D ₅₀ value of 0.19 μm, and the particles are aggregates of crystallites 10–25 nm in size.