Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A surprise announcement by then prime minister Tony Blair in 1998 led to the creation of a new senior clinical role - the nurse consultant.

Work in progress: The evolving nurse consultant role has all-round benefits and should be embraced, finds Frances Pickersgill

Where you can find the array signal processing easily? Is it in the book store? On-line book store? are you sure? Keep in mind that you will find the book in this site. This book is very referred for you because it gives not only the experience but also lesson. The lessons are very valuable to serve for you, that's not about who are reading this array signal processing book. It is about this book that will give wellness for all people from many societies.

Array signal processing

Cognitive users need to detect primary users continuously and rapidly.Cooperative spectrum sensing in cognitive radio(CR) networks is analyzed.Cooperative spectrum sensing in the ideal network with two cognitive users can reduce the mean detection time.Detection probability to primary users can be improved by multi-cooperative users in multi-user networks.Finally,the realization of multi-user networks is considered.

Cooperative Spectrum Sensing in Cognitive Radio Networks

To satisfy the high data demands in future cellular networks, an ultra-densification approach is introduced to shrink the coverage of base station (BS) and improve the frequency reuse. The gain in capacity is expected but at the expense of increased interference, frequent handovers (HOs), increased HO failure (HOF) rates, increased HO delays, increase in ping pong rate, high energy consumption, increased overheads due to frequent HO, high packet losses and bad user experience mostly in high-speed user equipment (UE) scenarios. This paper presents the general concepts of radio access mobility in cellular networks with possible challenges and current research focus. In this article, we provide an overview of HO management in long-term evolution (LTE) and 5G new radio (NR) to highlight the main differences in basic HO scenarios. A detailed literature survey on radio access mobility in LTE, heterogeneous networks (HetNets) and NR is provided. In addition, this paper suggests HO management challenges and enhancing techniques with a discussion on the key points that need to be considered in formulating an efficient HO scheme.

/pdf/a-survey-on-handover-management-from-lte-to-nr-33h0zsjoc2.pdf

A Survey on Handover Management: From LTE to NR

4G wireless networks will integrate heterogeneous technologies such as Wireless LAN and third generation (3G) cellular networks and have the capability to offer various services at any time as per user requirements, anywhere with seamless interoperability at affordable cost. One important challenge in such a heterogeneous wireless environment is to enable network selection mechanisms in order to keep the mobile users always best connected anywhere and at any time. In this paper, a multi-criteria access network selection algorithm is proposed in Worldwide Interoperability for Microwave Access---Wireless Fidelity environment, in order to facilitate the provision of high quality services and at the same time to satisfy different types of user service level agreements. Analytical hierarchy process (AHP) and grey relational analysis (GRA) methods are applying for optimal access network selection. The proposed methodology combines the AHP to decide the relative weights of criteria set according to network's performance, as well as the GRA to rank the network alternatives. The advantages of the GRA method are that the results are based on the original data, the calculations are simple and straightforward, and finally it is one of the best methods to make decision under heterogeneous wireless network environment.

GRA Based Network Selection in Heterogeneous Wireless Networks

With increasing demand of new wireless applications and increasing number of wireless user’s problem of spectrum scarcity arises. Cognitive Radio technology supports dynamic spectrum access to address spectrum scarcity problem. Reliability of cognitive operation entirely depends upon how effectively task of spectrum sensing has been performed. Spectrum sensing is a process of discovering voids in spectrum which can be allocated to cognitive users opportunistically. There exists number of traditional spectrum sensing methods in literature for constant noise floor. Practically, noise spectrum density is uncertain under which performance of spectrum sensing scheme degrades. In this paper a new spectrum sensing adaptive algorithm considering noise uncertainty has been proposed. Simulation results of proposed scheme shows a constant detection probability has been achieved under noise uncertainty.

http://www.sersc.org/journals/IJFGCN/vol7_no3/6.pdf

An Algorithm for Spectrum Sensing in Cognitive Radio under Noise Uncertainty

Mobility management is the most important feature of a wireless cellular communication system. Continuation of an active call is one of the most important quality measurements in the cellular systems. Handover makes it possible for a user to travel between various networks or cells while having a seamless connection. This paper shows that, performance of cellular network can be increased with proposed adaptive soft handoff algorithm, which dynamically calculates the Soft handover margin based on the received signal strength and distance. Performance is evaluated in terms of the performance metrics active set update rate, active set size, soft handover region and probability of outage. Simulation results show that soft handover algorithm gives better performance with adaptive soft handoff margin compared to that of fixed soft handover margin in varied propagation conditions.

Performance Enhancement of Cellular Network Using Adaptive Soft Handover Algorithm

Coverage and capacity analysis of relay-based device-to-device communications underlaid cellular networks

Music emotion classification (MEC) is the multidisciplinary research area that is related to perceive the emotions from the songs and label the songs with particular emotion classes. MEC systems (MECS) extract the features from the songs and then the songs are categorized on the basis of emotions by comparing their features. In this paper an MECS has been proposed that makes use of Convolutional Neural Network (CNN) by converting the music to their visual representation known as spectrograms. By using CNN extraction of specific features of music signals is not necessarily required to classify the songs. In this work two MECS are trained and tested by using Hindi database by using CNN and third MECS system is developed by using SVM. In first MECS spectrograms are obtained by using hamming windows of size 2048 and noverlap factor of 1024 and in second MECS spectrograms are obtained by using hamming windows of size 1024 and noverlap factor of 512. The three combinations of CNN layers are used in order to classify the songs in four, eight and sixteen classes on the basis of emotional tags. The performance of MECS design is analyzed on the basis of training accuracy, validation accuracy, training loss and validation loss. Results show that the two MECS systems developed by CNN has better accuracy and less loss than the third MECS system modeled by SVM.

Niraj Pratap Singh

Papers

GRA Based Network Selection in Heterogeneous Wireless Networks

An Algorithm for Spectrum Sensing in Cognitive Radio under Noise Uncertainty

Performance Enhancement of Cellular Network Using Adaptive Soft Handover Algorithm

Coverage and capacity analysis of relay-based device-to-device communications underlaid cellular networks

Development of music emotion classification system using convolution neural network