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Table Of Integrals Series And Products

A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

Matrix Differential Calculus with Applications in Statistics and Econometrics

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The World Bank Group

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

Graph traversal aided detection in uplink MBM massive MIMO based on socio-cognitive knowledge of swarm optimization

In optical metrology, phase shifting is one of the most prominent strategies to retrieve the parameters (like object shape, deformations, thickness, refractive index, focal length, etc) from acquired interferograms However, traditional phase shifting techniques is overly sensitive towards noise (both internal and external), which limits their applicability in accurate interferometric applications To circumvent this drawback, in this paper, a two dimensional moving average filter is combined with phase shifting technique to analyze and extract the phase information from noisy interferograms This novel algorithm acquires the advantages of two existing strategies in terms of accuracy and speed Proposed technique has a remarkable ability to accurately and automatically extract full-field phase distribution from four phase shifted interferograms even in the presence of high-density additive and multiplicative noise The performance estimation and comparison of the technique is demonstrated using controlled computer simulation

Two Dimensional Moving Average Filter for Performance Enhancement of Noisy Phase Shifted Interferograms

An efficient public safety communication network seeks minimal response time, optimal coordination between the disaster site and rescue team, with adequate situational awareness. Such a network requires an enduring and reliable architecture. This paper proposes an architecture for supporting emergency communications using an integrated access backhaul (IAB) in the heterogenous networks (HetNets). The macro base station (MBS) is connected to the wired backhaul, while the small cell base stations (SBSs) are wirelessly connected to the MBS using the IAB. The MBS is the IAB donor, while SBSs are the IAB nodes. As the IAB provides an economically viable solution for dense deployment in the wireless communication networks (WCNs), its use in public warning systems will serve as a robust solution during emergencies. Broadcasting the essential information with a wireless backhaul will involve the multi-hop networks to support minimum throughput requirements of the users within the respective small cell. A major benefit of using IAB is the spectral and infrastructure resource reuse for backhaul as well as the access network. An optimal split is essential for the available bandwidth, between the backhaul and access networks, for adequate system performance. Despite IAB being an attractive scheme, its implementation involves certain research challenges which have been stated in this paper.

Heterogenous Public Safety Wireless Networks with mmWave Small Cells: An IAB-based Approach

In recent years, IEEE 802.22 has emerged as a viable solution to bring broadband access to hard-to-reach, low population density areas, typical of rural environments. There has been successful trials around the world to test the viability of this standard with support from both Governmental and private companies. Since the communication is allowed in the TV White Space band, there has been concerns regarding the effect of interference in these communication channels. For reliable and fast data rates, it is desirable to have low noise and interference. This chapter presents an overview of the standard, from channel interference perspective, followed by a generalised scheme for mitigating the effects of noise and interference in TVWS communication.

TV White Space Channel Estimation and Equalisation: Challenges and Solutions

Spectrum sensing is critical to the advancement of cognitive radio technology in next-generation wireless communication systems. According to FCC policy, the frequency coexistence of GEO (geostationary) as the main user satellite network and NGEO (non-geostationary) as the secondary user satellite network, NGEO as the interference user system, shall not cause harmful interference to the GEO system. In this situation, spectrum sensing is shown as a viable approach to avoid interference. With the increasing number of NGEO satellites in orbit, a NGEO system detecting signals from one GEO system may be affected by another NGEO system. Several sensing technologies such as energy detection, cyclo-stationary features and matching filters have been introduced in the last decade. However, these methods have several drawbacks. Consequently, all of these strategies require the establishment of a threshold, which requires previous knowledge of the noise distribution. As a result, spectrum sensing dependence in wireless communications research remains an unresolved question. In this study, we present a spectrum sensing approach for cognitive radio networks based on machine learning technique. The spectrum sensing problem is studied systematically, and as a result large-scale, exhaustive data collections are created. This dataset is used to train, validate, and test a variety of machine learning algorithms, including random forests, support vector machines, decision trees, k-nearest neighbors, and others. The model was thoroughly tested and evaluated using matrices such as detection probability, false alarm, and missed detection, sensing time, delay, throughput and classification accuracy. Thus, according to the simulation data, random forest models and neural networks outperform all other machine learning methods.

Vimal Bhatia

Papers

Graph traversal aided detection in uplink MBM massive MIMO based on socio-cognitive knowledge of swarm optimization

Two Dimensional Moving Average Filter for Performance Enhancement of Noisy Phase Shifted Interferograms

Heterogenous Public Safety Wireless Networks with mmWave Small Cells: An IAB-based Approach

TV White Space Channel Estimation and Equalisation: Challenges and Solutions

Enhancement of Spectrum Efficiency in Satellite Communication Applying Prediction Model of Machine Learning Technique