Multiview point cloud kernels for semisupervised learning [Lecture Notes]

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.     

An optimal scheduling and routing under adaptive spectrum-matching framework for MIMO systems

Multi-users (MUs) along the communication links cause noise and traffic in the channel. The prediction of availability and the optimal usage of channels are the main objectives of the multi-input multi-output (MIMO) system. Several optimisation algorithms select the optimal channel for the users effectively. But the high-error rate and the probability values are the two major problems in traditionally optimised channel selection methods. The bandwidth allotted for information transmission is minimum. Moreover, the outage probability values are maximum in traditional scheduling algorithms. This paper proposes the new optimisation algorithm that predicts the channels for transmission and adaptive spectrum matching concept to predict the suitable channel from allocated bands. Also, the prioritisation on high-spectrum intensity basis assures an efficient data delivery to the receiver. The scheduling of available channels and data prioritisation minimises the error probability rates. This paper investigates the effectiveness of proposed optimal channel utilisation against the different modulation schemes such as three-dimensional complementary codes, linear network coding with the quadrature phase shift keying in terms of the average block error probability and bit error rate.     

Methods for determining agent concentration profiles in agarose gel during convection-enhanced delivery

Convection-enhanced delivery (CED) is a promising technique to deliver large molecular weight drugs to the human brain for treatment of Parkinson's, Alzheimer's, or brain tumors. Researchers have used agarose gels to study mechanisms of agent transport in soft tissues like brain due to its similar mechanical and transport properties. However, inexpensive quantitative techniques to precisely measure achieved agent distribution in agarose gel phantoms during CED are missing. Such precise measurements of concentration distribution are needed to optimize drug delivery. An optical experimental method to accurately quantify agent concentration in agarose is presented. A novel geometry correction algorithm is used to determine real concentrations from observable light intensities captured by a digital camera. We demonstrate the technique in dye infusion experiments that provide cylindrical and spherical distributions when infusing with porous membrane and conventional single-port catheters, respectively. This optical method incorporates important parameters, such as optimum camera exposure, captured camera intensity calibration, and use of collimated light source for maximum precision. We compare experimental results with numerical solutions to the convection diffusion equation. The solutions of convection-diffusion equations in the cylindrical and spherical domains were found to match the experimental data obtained by geometry correction algorithm.     

Parametric Study on Dimensional Control of ZnO Nanowalls and Nanowires by Electrochemical Deposition

A simple electrochemical deposition technique is used to synthesize both two-dimensional (nanowall) and one-dimensional (nanowire) ZnO nanostructures on indium-tin-oxide-coated glass substrates at 70°C. By fine-tuning the deposition conditions, particularly the initial Zn(NO₃)₂·6H₂O electrolyte concentration, the mean ledge thickness of the nanowalls (50–100 nm) and the average diameter of the nanowires (50–120 nm) can be easily varied. The KCl supporting electrolyte used in the electrodeposition also has a pronounced effect on the formation of the nanowalls, due to the adsorption of Cl⁻ ions on the preferred (0001) growth plane of ZnO and thereby redirecting growth on the (10[`1] \bar{1} 0) and (2[`1] \bar{1} [`1] \bar{1} 0) planes. Furthermore, evolution from the formation of ZnO nanowalls to formation of nanowires is observed as the KCl concentration is reduced in the electrolyte. The crystalline properties and growth directions of the as-synthesized ZnO nanostructures are studied in details by glancing-incidence X-ray diffraction and transmission electron microscopy.     

Feature selection in MLPs and SVMs based on maximum output information

This paper presents feature selection algorithms for multilayer perceptrons (MLPs) and multiclass support vector machines (SVMs), using mutual information between class labels and classifier outputs, as an objective function. This objective function involves inexpensive computation of information measures only on discrete variables; provides immunity to prior class probabilities; and brackets the probability of error of the classifier. The maximum output information (MOI) algorithms employ this function for feature subset selection by greedy elimination and directed search. The output of the MOI algorithms is a feature subset of user-defined size and an associated trained classifier (MLP/SVM). These algorithms compare favorably with a number of other methods in terms of performance on various artificial and real-world data sets.     

A Joint Optimization Based Sub-band Expediency Scheduling Technique for MIMO Communication System

In the recent days, the multiple input multiple output (MIMO) is considered as the important technology of wireless due to its characteristics. The channel selection and scheduling plays an important roles in MIMO communication systems. For this purpose, various techniques are proposed in the traditional works, but it has some major drawbacks such as increased bit error rate, inefficient channel selection, and reduced spectral efficiency. In order to overcome these issues, this paper aims to develop a new optimization based scheduling technique for a successful MIMO communication. At first, the Rayleigh fading channel is initialized and its parameters are extracted, then the beamforming technique is used to extract the features. After that, the optimal channel is selected from the available number of channels by implementing a joint optimization (JO) technique. Consequently, the power spectral density is estimated before scheduling the channel for communication. Finally, the sub-band expediency based scheduling (SES) technique is implemented for scheduling the channel based on the priority. The novel concept of this paper are, an optimal channel is selected based on the correlation coefficient value, and the priority based channel scheduling is performed for communication. The experimental results evaluate the performance of the proposed JO-SES technique based on the measures of BET, SNR, and average sum rate. Also, some of the existing techniques are considered in this work for proving the betterment of the proposed technique.

     

Implementation of K-Means Algorithm and Dynamic Routing Protocol in VANET

With the growth of Vehicular Ad-hoc Networks, many services delivery is gaining more attention from the intelligent transportation system. However, mobility characteristics of vehicular networks cause frequent disconnection of routes, especially during the delivery of data. In both developed and developing countries, a lot of time is consumed due to traffic congestion. This has significant negative consequences, including driver stress due to increased time demand, decreased productivity for various personalized and commercial vehicles, and increased emissions of hazardous gases especially air polluting gases are impacting public health in highly populated areas. Clustering is one of the most powerful strategies for achieving a consistent topological structure. Two algorithms are presented in this research work. First, a k-means clustering algorithm in which dynamic grouping by k-implies is performed that fits well with Vehicular network’s dynamic topology characteristics. The suggested clustering reduces overhead and traffic management. Second, for inter and intra-clustering routing, the dynamic routing protocol is proposed, which increases the overall Packet Delivery Ratio and decreases the End-to-End latency. Relative to the cluster-based approach, the proposed protocol achieves improved efficiency in terms of Throughput, Packet Delivery Ratio, and End-to-End delay parameters comparing the situations by taking different number of vehicular nodes in the network.