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IP for 3G - (P1)
Scope of the Book For some years, commentators have been predicting the ‘convergence’ of the Internet and mobile industries. But what does convergence mean? Is it just about mobile phones providing Internet access? Will the coming together of two huge industries actually be much more about collision than convergence? In truth, there are lots of possibilities about what convergence might mean, such as: † † † † Internet providers also supply mobile phones – or vice versa, of course.
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IP for 3G - (P2)
An Introduction to 3G Networks Introduction What exactly are 3G networks? 3G is short for Third Generation (Mobile System). Here is a quick run-down: † 1G, or first generation systems, were analogue and offered only a voice service – each country used a different system, in the UK TACS (Total Access Communications System) was introduced in 1980. 1G systems were not spectrally efficient, were very insecure against eavesdroppers, and offered no roaming possibilities (no use on holidays abroad.)....
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IP for 3G - (P3)
An Introduction to IP Networks The Internet is believed by many to have initiated a revolution that will be as far reaching as the industrial revolution of the 18th and 19th centuries. However, as the collapse of many ‘dot.com’ companies has proven, it is not easy to predict what impact the Internet will have on the future. In part, these problems can be seen to be those normally associated with such a major revolution.
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IP for 3G - (P4)
Multimedia Service Support and Session Management Two of the key new features of 3G networks are their ability to support multimedia applications and the Virtual Home Environment. The former implies a network with the ability to support more than just voice communications (and more than just non-real-time, data applications like the World Wide Web and e-mail).
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IP for 3G - (P5)
IP Mobility This chapter will provide an overview of IP mobility. It aims to be pretty selfcontained, and so should stand alone fairly independently of the other chapters. IP mobility is very important, because it is predicted that the vast majority of terminals will be mobile in a few years and that the vast majority of traffic will originate from IP-based applications. The challenge of ‘IP mobility’ is to deliver IP-based applications to mobile terminals/users, even though, traditionally, IP-protocols have been designed with the assumption that they are stationary...
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IP for 3G - (P6)
Quality of Service What is QoS? The basic definition of QoS is given by the ITU-T in recommendation E.800 as ‘‘the collective effect of service’’ performance, which determines the degree of satisfaction of a user of a service. There are a large number of issues, which affect user satisfaction with any network service. These include: † How much does it cost? † Can a user run the application they want? † Can a user contact any other user they want? None of these is a straightforward technical question....
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IP for 3G - (P7)
IP for 3G In this final chapter, it is appropriate to revisit the theme that started the book. Chapter 1 outlined some of the reasons why IP should be introduced into 3G networks; this chapter will explain in greater detail the technicalities of how IP could be introduced. One result will be that a network is developed that is much more faithful to the original ‘Martini’ vision than current 3G incarnations. This chapter will begin by applying the IP design principles, plus the QoS, mobility management, security and service creation pieces from the preceding chapters, to sketch out a vision of...
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Mạng thần kinh thường xuyên cho dự đoán P1
Artificial neural network (ANN) models have been extensively studied with the aim of achieving human-like performance, especially in the field of pattern recognition. These networks are composed of a number of nonlinear computational elements which operate in parallel and are arranged in a manner reminiscent of biological neural interconnections. ANNs are known by many names such as connectionist models, parallel distributed processing models and neuromorphic systems (Lippmann 1987).
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Mạng thần kinh thường xuyên cho dự đoán P2
Fundamentals Adaptive systems are at the very core of modern digital signal processing. There are many reasons for this, foremost amongst these is that adaptive filtering, prediction or identification do not require explicit a priori statistical knowledge of the input data. Adaptive systems are employed in numerous areas such as biomedicine, communications, control, radar, sonar and video processing (Haykin 1996a).
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Mạng thần kinh thường xuyên cho dự đoán P3
Network Architectures for Prediction Perspective The architecture, or structure, of a predictor underpins its capacity to represent the dynamic properties of a statistically nonstationary discrete time input signal and hence its ability to predict or forecast some future value. This chapter therefore provides an overview of available structures for the prediction of discrete time signals.
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Mạng thần kinh thường xuyên cho dự đoán P4
Activation Functions Used in Neural Networks Perspective The choice of nonlinear activation function has a key influence on the complexity and performance of artificial neural networks, note the term neural network will be used interchangeably with the term artificial neural network. The brief introduction to activation functions given in Chapter 3 is therefore extended. Although sigmoidal nonlinear activation functions are the most common choice, there is no strong a priori justification why models based on such functions should be preferred to others....
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Mạng thần kinh thường xuyên cho dự đoán P5
Recurrent Neural Networks Architectures Perspective In this chapter, the use of neural networks, in particular recurrent neural networks, in system identification, signal processing and forecasting is considered. The ability of neural networks to model nonlinear dynamical systems is demonstrated, and the correspondence between neural networks and block-stochastic models is established. Finally, further discussion of recurrent neural network architectures is provided.
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Mạng thần kinh thường xuyên cho dự đoán P6
Neural Networks as Nonlinear Adaptive Filters Perspective Neural networks, in particular recurrent neural networks, are cast into the framework of nonlinear adaptive filters. In this context, the relation between recurrent neural networks and polynomial filters is first established. Learning strategies and algorithms are then developed for neural adaptive system identifiers and predictors. Finally, issues concerning the choice of a neural architecture with respect to the bias and variance of the prediction performance are discussed....
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Mạng thần kinh thường xuyên cho dự đoán P7
Stability Issues in RNN Architectures Perspective The focus of this chapter is on stability and convergence of relaxation realised through NARMA recurrent neural networks. Unlike other commonly used approaches, which mostly exploit Lyapunov stability theory, the main mathematical tool employed in this analysis is the contraction mapping theorem (CMT), together with the fixed point iteration (FPI) technique. This enables derivation of the asymptotic stability (AS) and global asymptotic stability (GAS) criteria for neural relaxive systems. For rigour, existence, uniqueness, convergence and convergence rate are considered and the analysis is provided for a range of activation functions and recurrent neural networks architectures. ...
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Mạng thần kinh thường xuyên cho dự đoán P8
Data-Reusing Adaptive Learning Algorithms In this chapter, a class of data-reusing learning algorithms for recurrent neural networks is analysed. This is achieved starting from a case of feedforward neurons, through to the case of networks with feedback, trained with gradient descent learning algorithms. It is shown that the class of data-reusing algorithms outperforms the standard (a priori ) algorithms for nonlinear adaptive filtering in terms of the instantaneous prediction error.
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Mạng thần kinh thường xuyên cho dự đoán P9
A Class of Normalised Algorithms for Online Training of Recurrent Neural Networks A normalised version of the real-time recurrent learning (RTRL) algorithm is introduced. This has been achieved via local linearisation of the RTRL around the current point in the state space of the network. Such an algorithm provides an adaptive learning rate normalised by the L2 norm of the gradient vector at the output neuron. The analysis is general and also covers simpler cases of feedforward networks and linear FIR filters...
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Mạng thần kinh thường xuyên cho dự đoán P10
Convergence of Online Learning Algorithms in Neural Networks An analysis of convergence of real-time algorithms for online learning in recurrent neural networks is presented. For convenience, the analysis is focused on the real-time recurrent learning (RTRL) algorithm for a recurrent perceptron. Using the assumption of contractivity of the activation function of a neuron and relaxing the rigid assumptions of the fixed optimal weights of the system, the analysis presented is general and is applicable to a wide range of existing algorithms....
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Mạng thần kinh thường xuyên cho dự đoán P11
Some Practical Considerations of Predictability and Learning Algorithms for Various Signals In this chapter, predictability, detecting nonlinearity and performance with respect to the prediction horizon are considered. Methods for detecting nonlinearity of signals are first discussed. Then, different algorithms are compared for the prediction of nonlinear and nonstationary signals, such as real NO2 air pollutant and heart rate variability signals, together with a synthetic chaotic signal.
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Mạng thần kinh thường xuyên cho dự đoán P12
Exploiting Inherent Relationships Between Parameters in Recurrent Neural Networks Perspective Optimisation of complex neural network parameters is a rather involved task. It becomes particularly difficult for large-scale networks, such as modular networks, and for networks with complex interconnections, such as feedback networks. Therefore, if an inherent relationship between some of the free parameters of a neural network can be found, which holds at every time instant for a dynamical network, it would help to reduce the number of degrees of freedom in the optimisation task of learning in a particular network. ...
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Phân tích tín hiệu P1
Signals and Signal Spaces The goal of this chapter is to give a brief overview of methods for characterizing signals and for describing their properties. Wewill start with a discussion of signal spaces such as Hilbert spaces, normed and metric spaces. Then, the energy density and correlation function of deterministic signals will be discussed. The remainder of this chapter is dedicated to random signals, which are encountered in almost all areas of signal processing. Here, basic concepts such as stationarity, autocorrelation, and power spectral densitywill be discussed. ...
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Phân tích tín hiệu P2
Integral Signal Represent at ions The integral transform is one of the most important tools in signal theory. The best known example is the Fourier transform,buttherearemany other transforms of interest. In the following, W will first discuss the basic concepts of integral transforms. Then we will study the Fourier, Hartley, and Hilbert transforms. Finally, we will focus on real bandpass processes and their representation by means of their complex envelope.
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Phân tích tín hiệu P3
Discrete Signal Representations In this chapter we discuss the fundamental concepts of discrete signal representations. Such representations are also known as discrete transforms, series expansions, or block transforms. Examplesof widely used discrete transforms are given in the next chapter. Moreover, optimal discrete representations will be discussed in Chapter 5. The term “discrete” refers to the fact that the signals are representedby discrete values,whereas the signals themselves may be continuous-time....
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Phân tích tín hiệu P4
Examples of Discrete Transforms In this chapter we discuss the most important fixed discrete transforms. We start with the z-transform, whichis a fundamental tool for describing the input/output relationships in linear time-invariant (LTI) systems. Then we discuss several variants of Fourier series expansions, namely the discrete-time Fourier transform, thediscrete Fourier transform (DFT), and the Fourier fast transform (FFT). Theremainder of this chapter is dedicated to other discrete transforms that are of importance in digital signal processing, such as the discrete cosine transform, the discrete sine transform, the discrete Hartley transform, and the discrete Hadamard and Walsh-Hadamard transform. ...
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Hệ thống cứu nạn và an toàn hàng hải toàn cầu - GMDSS
Điện xoay chiều có dạng hình sin nên khi nó tạo ra điện từ trường truyền lan trong không gian cũng dao động dạng hình sin, gọi là sóng vô tuyến hay sóng radio. Biên độ của sóng là cường độ của nó tính từ điểm 0 đến đỉnh cao nhất của hình sin.
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Radar hàng hải
Thuật ngữ Radar là chữ viết tắt của nhóm từ (Radio detection and ranging) có nghĩa là hệ thống xác định vị trí của mục tiêu bằng cách đo cự ly và phương vị nhờ nguyên lý sóng phản xạ (sóng dội).radda haotj đọng ở tần số vô tuyến siêu cao có bước sóng siêu cực ngắn ,dưới dạng xung ,được phát theo một tần số lặp xung nhất định .
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Sổ tay hàng hải - Hệ thống định vị toàn cầu GPS
Hệ thống NAVSTAR/GPS, viết tắt từ chữ Navigation Satellite Timing and Ranging / Global Poistioning system, là hệ thống dùng vệ tinh đạo hàng đo khoảng cách và thời gian tạo thành hệ thống định vị toàn cầu. Ngày nay gần như thành thói quen, toàn thế giới công nhận và gọi nó là Hệ thống định vị toàn cầu, viết tắt là GPS.
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Hệ thống định vị tầm xa Loran - C
Loran C (Loran là từ viết tắt của Long Range Navigation) là hệ thống đạo hàng hyperbol sử dụng sóng vô tuyến để xác định vị trí ở cự ly cực xa, nó được phát triển dựa trên nền tảng của hệ thống loran A.
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Hệ thống định vị Decca
Hệ thống hàng hải DECCA là hệ thống vô tuyến đạo hàng hyperbol tầm ngắn, dùng sóng vô tuyến tần số thấp từ 70-130kHz và đo hiệu pha của hai sóng liên tục phát từ hai trạm phát trên bờ để xác định hiệu khoảng cách. Tầm xa hoạt động của hệ thống 240 hải lý, độ chính xác xác định vị trí từ vài mét đến vài trăm mét.
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Biến điện thoại 3G thành modem kết nối Internet cho máy tính
Chiếc điện thoại di động không chỉ đơn thuần là công cụ “nghe, gọi và nhắn tin” mà ngày càng hỗ trợ đắc lực với nhiều tính năng như nghe nhạc, chụp ảnh, kết nối internet hay xử lý công việc văn phòng. Bài viết dưới đây sẽ giúp bạn “tối ưu hóa” chức năng chiếc di động qua một vài thủ thuật đơn giản để biến đổi ”dế” thành modem internet cho máy tính.
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MẠNG THÔNG TIN DI ĐỘNG
Mạng thông tin di động đã phát triển mạnh mẽ và rộng khắp trên toàn thế giới trong mười năm vừa qua với khả năng cung cấp đa dạng các loại hình dịch vụ. Hiện nay, nhu cầu sử dụng dịch vụ dữ liệu ngày càng tăng cao, các dịch vụ dữ liệu chiếm một tỉ trọng đáng kể trong tổng doanh thu của nha khai thác mạng thông tin di động.
8/29/2018 6:05:38 PM +00:00