Contains research articles by nearly 40 leading mathematicians from North and South America, Europe, Africa, and Asia, presented at the Fourth International Conference on p-adic Functional Analysis held recently in Nijmegen, The Netherlands. Includes numerous new open problems documented with extensive comments and references.
This new edition has been extensively revised to reflect the progress in error control coding over the past few years. Over 60% of the material has been completely reworked, and 30% of the material is original. Convolutional, turbo, and low density parity-check (LDPC) coding and polar codes in a unified framework Advanced research-related developments such as spatial coupling A focus on algorithmic and implementation aspects of error control coding
The armaments of chemical and biological warfare (CBW) are now widely held not just by nation-states, but by terrorist and criminal enterprises. The weapons themselves are relatively inexpensive and very easy to hide, allowing organizations of just a few dozen people to deploy potentially devastating attacks. While in the twentieth century most arms-control efforts focused, rightly, on nuclear arsenals, in the twenty-first century CBW will almost certainly require just as much attention. This book defines the basics of CBW for the concerned citizen, including non-alarmist scientific descriptions of the weapons and their antidotes, methods of deployment and defensive response, and the likelihood in the current global political climate of additional proliferation.
Trellis and turbo coding are used to compress and clean communications signals to allow greater bandwidth and clarity Presents the basics, theory, and applications of these techniques with a focus on potential standard state-of-the art methods in the future Provides a classic basis for anyone who works in the area of digital communications A Wiley-IEEE Press Publication
A veteran real estate agent shares simple but powerful techniques to connect with more customers, close more sales, and maximize success. There are more than 1.7 million real estate agents in the United States and Canada. Thousands of new agents enter the profession each year hoping to make a comfortable living. But more than 80 percent of them will not be successful. To do well in this business, you need to take your career seriously and equip yourself with training, information, and proven strategies. This guidebook provides you with the tools you need, including hundreds of marketing tips to help you ?nd business; advice on responding to objections from clients; the thirty answers to the most common questions youll be asked; strategies to ensure that open houses are successful; tips on how to interact with people on the phone and in person. While this guidebook o?ers hundreds of ideas, youll prefer certain marketing and selling techniques over others. The goal is to ensure that you have every strategy out there so you can sell and succeed. You should know what to say, when to say it, and how to say it. You will get the advice you need to close more sales with Rules for Real Estate Success.
In rangelands and grasslands, land degradation has an immediate and local impact by disrupting ecosystems from functioning, threatening livelihoods and negatively affecting social cohesion. It also threatens productivity while dovetailing with the threats of climate change in these ecologically fragile areas. The understanding of land degradation in rangelands and grasslands is weak, which is attributed to a lack of robust data and a misunderstanding of management objectives. The day-to-day management of land by pastoral communities is intricately linked to local and traditional knowledge that needs to be taken into account when monitoring the health of ecosystems and designing management interventions. Sustainable Development Goal (SDG) 15 Life on land includes Land Degradation Neutrality (LDN) as a target, which requires that the process of degradation is halted and reversed. This publication presents a rationale for participatory approaches to achieve LDN in pastoral areas while showing how this can be achieved using the Participatory Rangelands and Grasslands Assessment (PRAGA) that has been piloted in Kenya, the Niger, Burkina Faso, Uruguay and Kyrgyzstan.
Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.Deep learning has been characterized as a buzzword, or a rebranding of neural networks. This book deeps in big data and deep learning techniques
Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Big Data tools (Parallel Computing Toolbox). Unsupervised learning algorithms, including self-organizing maps and competitive layers-Apps for data-fitting, pattern recognition, and clustering-Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance. his book develops cluster analysis and pattern recognition
Big data analytics is the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data.To analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future. Among all these tools highlights MATLAB. MATLAB implements various toolboxes for working on big data analytics, such as Statistics Toolbox and Neural Network Toolbox (Deep Learning Toolbox for version 18) . This book develops the work capabilities of MATLAB with Neural Networks and Big Data.
This book provides a comprehensive and thorough treatment on fundamentals and applications of light propagation through inhomogeneous media. The authors present a description of the phenomena, components and technology used in GRIN Optics, and analyze various applications.
Trellis and turbo coding are used to compress and clean communications signals to allow greater bandwidth and clarity Presents the basics, theory, and applications of these techniques with a focus on potential standard state-of-the art methods in the future Provides a classic basis for anyone who works in the area of digital communications A Wiley-IEEE Press Publication
This volume contains research articles based on lectures given at the Seventh International Conference on $p$-adic Functional Analysis. The articles, written by leading international experts, provide a complete overview of the latest contributions in basic functional analysis (Hilbert and Banach spaces, locally convex spaces, orthogonality, inductive limits, spaces of continuous functions, strict topologies, operator theory, automatic continuity, measure and integrations, Banach and topological algebras, summability methods, and ultrametric spaces), analytic functions (meromorphic functions, roots of rational functions, characterization of injective holomorphic functions, and Gelfand transforms in algebras of analytic functions), differential equations, Banach-Hopf algebras, Cauchy theory of Levi-Civita fields, finite differences, weighted means, $p$-adic dynamical systems, and non-Archimedean probability theory and stochastic processes. The book is written for graduate students and research mathematicians. It also would make a good reference source for those in related areas, such as classical functional analysis, complex analytic functions, probability theory, dynamical systems, orthomodular spaces, number theory, and representations of $p$-adic groups.
The first installment in the Undead Trilogy, the story follows a young man named Draezon Talon as he seeks to become a member of the Dead Hand, a clan of necromancers that reside in a tower in the desert. During his training, the tower is attacked by a terrifying demon from the Underworld. The unnerving encounter convinces the masters to alert the rulers of the land. But are they ready to combat the might of the Underworld?
What would you do if you were tempted by a true treasure hunt and you could foil one of the most despised men in the world? Kiki Logan is convinced by the well connected Banco to search for a large cache of treasure pulled from the depths of the Caribbean by the Cuban Government. On this perilous adventure, numerous characters join in, most notably David, Kiki's connection in Cuba. Kiki and David try to locate the treasure in Cuba. They find a giant warehouse and upon raiding it, they discover cars, cocaine, and cash, as well as the infamous and elusive Golden Madonna statue. Will the treasure hunters be successful, or will the raid end, as many fear, in their deaths? And what will happen to the famous statue? "Heisting The Beard" is a tale richly entrenched in suspense and intrigue.
MATLAB has the tool Neural Network Toolbox or Deep Learning Tools that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders-Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN)-Unsupervised learning algorithms, including self-organizing maps and competitive layers-Apps for data-fitting, pattern recognition, and clustering
The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models that can be used in segmentation.-Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. This book develops segmentation techniques related to this group of classification techniques with categorical dependent variable.-Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading. Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition.
Cluster analysis, also called segmentation analysis or taxonomy analysis, creates groups, or clusters, of data. Clusters are formed in such a way that objects in the same cluster are very similar and objects in different clusters are very distinct. Measures of similarity depend on the application.Hierarchical Clustering groups data over a variety of scales by creating a cluster tree or dendrogram. The tree is not a single set of clusters, but rather a multilevel hierarchy, where clusters at one level are joined as clusters at the next level. This allows you to decide the level or scale of clustering that is most appropriate for your application. The Statistics and Machine Learning Toolbox function clusterdata performs all of the necessary steps for you. It incorporates the pdist, linkage and cluster functions, which may be used separately for more detailed analysis. The dendrogram function plots the cluster tree.k-Means Clustering is a partitioning method. The function kmeans partitions data into k mutually exclusive clusters, and returns the index of the cluster to which it has assigned each observation. Unlike hierarchical clustering, k-means clustering operates on actual observations (rather than the larger set of dissimilarity measures), and creates a single level of clusters. The distinctions mean that k-means clustering is often more suitable than hierarchical clustering for large amounts of data.Clustering Using Gaussian Mixture Models form clusters by representing the probability density function of observed variables as a mixture of multivariate normal densities. Mixture models of the gmdistribution class use an expectation maximization (EM) algorithm to fit data, which assigns posterior probabilities to each component density with respect to each observation. Clusters are assigned by selecting the component that maximizes the posterior probability. Clustering using Gaussian mixture models is sometimes considered a soft clustering method. The posterior probabilities for each point indicate that each data point has some probability of belonging to each cluster. Like k-means clustering, Gaussian mixture modeling uses an iterative algorithm that converges to a local optimum. Gaussian mixture modeling may be more appropriate than k-means clustering when clusters have different sizes and correlation within them.
Machine learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models.-Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring.-Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops unsupervised learning techniques.
Neural networks theory is inspired from the natural neural network of human nervous system. Is possible define a neural network as a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.This book develops the architecture of the most important neural networks: Perceptron, ADALINE, Radial Basis, Hopfield, Probabilistic, Generalized regression and LVQ neural Networks. It also presents practical examples of the different architectures of neural networks.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.