Convergency of Telecommunication and Computer Networks

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Convergency of Telecommunication and Computer
Networks

• Vladislav Skorpil

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• In next pages you can see examples of our
  researches on the theme Convergency of
  telecommunication and computer networks

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GENETIC ALGORITHM AND NEURAL NETWORK
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1. Objective

• application of Genetic algorithm (GA) for design
  of Neural Network (NN)
• control of communication Network Element (NE) by
  NN
• seek an alternative way of increasing the
  performance of NE by parallel data processing

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2. Introduction

• classical version of Genetic algorithm uses three
  genetic operators reproduction, crossover and
  mutation
• Radial Basis Function Network (RBFN) is here
  used
• it is a type of single-direction multilayer
  network

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3. Design of a General Schematic of the Genetic
Algorithm

• Generating of initial population
• Ageing
• Mutation
• Calculation
• Sorting of upward population
• Crossing
• Finalization

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Generating of initial population

• initialization of all bits of all chromosomes in
  initial generation is random
• generator of random numbers which is a standard
  features of the C Builder 5
• Gray code is used to encode the chromosome bits

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Ageing

• ageing shows up variants with limited length of
  life
• all the individuals in the population have their
  age incremented
• if it exceeds a set limit, the element is removed
  from the population and a new element is randomly
  generated in its place.

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Mutation

• classical method
• back-propagation method

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Calculation

• Genetic algorithm performs the minimization of an
  error function

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Sorting of upward population

• we are looking for the function minimum so that a
  chromosome with the least object function value
  will be in the first place
• Quicksort algorithm is used for sorting
• necessity of sorting will not affect the speed of
  algorithm negatively

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Crossing

• uniform crossing is used
• every bit of descendants is with the probability
  0.5 taken from one of the parents
• one half of the population is made by the
  crossing

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Finalization

• return to the Ageing if the finalization
  condition is not realized
• if it is realized then the end of Genetic
  algorithm is made
• they obtain two finalization variation (or their
  combination), they are
• 1) maximal number of iterations
• 2) quality of the best solution, smaller then
  entered

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4. Implementation of GA

• initialization of Genetic algorithm
• calculation of new generation

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Initialization of Genetic algorithm

• we have population in of the number of 100
  chromosomes (randomly generated) ) at the
  beginning
• we preset engaged chromosomes longevity for each
  of them and calculate their value of fitness
  function
• we sort out them according to sizes of fitness
  uplink, by this we set the best chromosome on the
  first position
• classical algorithm QuickSort for sorting is used

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Calculation of new generation

• we expire all chromosomes and recognize its age
  for each of them, if it equals to zero
• if it is true, we generate new chromosome on its
  place and we calculate its fitness
• it is necessary again to order the whole
  population, the best chromosome to be forever on
  the first place.

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Calculation of new generation

• further it follows crossing
• two parents from whole population are selected at
  first, the crossing is made
• two new descendants originate and we calculate
  for them fitness value
• we advance so further, that the number of
  chromosomes, from which we select, is decrease
  about 8
• on the last step the size of population, from
  which parents for crossing will be selected,
  equals to 8, it means, that it will be selected
  from the first 8 (the best) chromosomes

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5. Practical using

• modern possibility, how to change classical
  sequential control of Network Elements NE to
  control using of neural networks
• design a simulation of NE, containing in the
  process of control of switching area artificial
  neural network with GA
• NE switches single data units making provision
  for priority

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Fig. 1 Model of the switch with artificial neural
network
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The basic scheme of the element

• We think over the single-stage switching area,
  which has three inputs and three outputs, it is
  switch on the Fig.2 The switching area is
  realized on the cross-bar switch, i.e. in the
  described case the switching area with 9
  switching points. We can connect arbitrary input
  to arbitrary output.

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Fig.2 Switch
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Fig.3 Switching area
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Fig.4 Switching area with addressing
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Fig.5 Frame structure
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Fig.6 Switching area controlled by control matrix
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4. Conclusion

• crushing majority to learn neural network for
  diagnostic of one object completely on 100 with
  GA
• time of learning is shorter than for classical
  methods
• the results and the learning time highly depends
  on GA parameters setting
• the best results were obtained by GA using the
  D-operator and not using sexual reproduction.
• it is shown modern possibility, how to change
  classical sequential control of network elements
  to control using of neural networks

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Back-Propagation and K-Means Algorithms Comparison
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• The slides describes the application of
  algorithms for object classification by using
  artificial neural networks. The MLP (Multi Layer
  Perceptron) and RBF (Radial Basis Function)
  neural networks were used. We compared results
  obtained by a using of learning algorithms
  Back-Propagation (BP) and K-Means. The real
  technological scene for object classification was
  simulated with digitization of two-dimensional
  pictures.

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1 Introduction

• Pattern recognition consists in sorting objects
  into classes. Class is a subset of objects whose
  elements have common features from the
  classification standtpoint. Object has a physical
  character, which in computer vision is most
  frequently taken to mean a part of segmented
  image.

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• Methods for the classification of objects
  constitute last and upper-most step in computer
  vision theory.
• The following methods were mutually compared
• Recognition with the aid of Back-Propagation
  algorithm.
• Recognition with the aid of K-Means algorithm.

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2 Back-Propagation Algorithm

• Back-Propagation algorithm is an iterative method
  where the network gets from an initial
  non-learned state to the full learned one

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(1)
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(2)
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(3)
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• The following steps can describe the appurtenant
  back-propagation algorithm

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• Initialization. All the weights in the network
  are randomly set at values in the recommended
  range lt0.3, 0.3gt.
• Pattern submitting. A chosen pattern from the
  training set is put in network inputs. Then
  outputs of particular neurons are computed under
  relations (2) and (3).

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• Comparison. This step contains the computation of
  the neural network energy under relation (1) and
  the error for the output layer under the
  relation (4)
• Back-propagation of an error and weight
  modification. The values

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(5)
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(6)
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• Termination of pattern selection from the
  training set. Another pattern from the training
  set is chosen and the step number 2 follows until
  all patterns were submitted.

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• Termination of learning process. The algorithm
  ends when the neural network energy in last
  computation has been less then the criterion
  selected.

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Radial Basis Function Network

• This network belongs to the most recent neural
  networks. It is a type of forward multi-layer
  network with counter-propagation of signal and
  with teacher learning. The network has two
  layers, with different types of neurons in each
  layer. Its advantage is mainly the speed of
  learning.

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• The structure of this two-layer network is
  similar to that of the Back-Propagation type of
  network but the function of output neurons must
  be linear and the transfer functions of hidden
  neurons are the so-called Radial Basis Functions
  hence the name of the network. The
  characteristic feature of these functions is that
  they either decrease monotonically or increase in
  the direction from their centre point. Excerpt
  for the input layer, which only serves the
  purpose of handing over values, an RBF network
  has an RBF layer (hidden layer) and an output
  layer formed by perceptrons.

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4 Problem Solution
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5 Conclusion

• Back propagation algorithm presented very good
  results at classification. The network recognized
  all the patterns submitted. The learning using
  the BPx method (extended Back propagation
  algorithm) was a little slower, but it can be
  succesfully used for networks with lower number
  of neurons. Radial Basis Function networks can be
  designed very quickly.

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• The time necessary for network learning was very
  little. The network was able to classify
  correctly 100 models and at the same time to
  recognize correctly even slightly damaged models.
  As the number of radial basis neurons is
  comparable the input space size and problem
  complexity, RBF networks can be larger than
  back-propagation networks. Recognition with the
  aid of neural network is suitable where
  high-speed classification with randomly rotated
  objects is required and where we need to tolerate
  some differences between learned etalons and
  classified objects.

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Codec G 723.1 by MATLAB simulation
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• The objective of this slides is an introduction
  to multimedia signals compressions under ISDN.
  Compression and following expansion, in agreement
  with standard, will be simulate with the help of
  programme MATLAB. Authors contribution is codec
  G.723.1 by MATLAB simulation. The ITU T block
  structure is observed.

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• Codec realization in MATLAB is applied on test
  speech signal and results were indicated in this
  research and paper. Graphics results are one part
  of research, in the paper is not enough place for
  this description. Presentation paper contains
  results of simulation in MATLAB programme for
  audiocodec by the recommendation G.723.1. This
  recommendation is used for ISDN as example and
  although ISDN is now replaced by xDSL, software
  base used for source encoding is available also
  for the future.

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1. Introduction

• Simulation is made by MATLAB programme. Resulting
  programme is placed on created CD. In MATLAB
  command line must be written encoder(name1.wav,
  name2.bs), where name1 is name of input wav
  file and name2 is encoded sequence, BitStream
  by G.723.1, which will be next suitable as input
  to decoder.

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• For decoding of this sequence and acquirement
  requested wav file must be written
  decoder(name2.bs, name1.wav) where name2 is
  name of input BitStream file which was obtained
  from previous encoding and name1 is resulted
  wav type file. To this resulted file was apllied
  encoder G.723.1.

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• Input encoder file must be 16-bit, singlechannel
  with sampling frequency 8000 Hz, integer type.
• Both bit rates are obligatory parts of encoder
  and decoder. Bit rates can be overswitched also
  in encoder operation, every frame by different
  rate. Encoder G.723.1 is optimalized for speech
  representation. Music or other audiosignals are
  not expressed so truly as speech, but it is
  possible to encoded and decoded them.

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2. Encoder and Decoder G 723.1

• For encoding is used linear prediction, that is
  why codec works on the principle
  analysis-synthesis. For higher bit flow is used
  Multipulse Maximum Likelihood Quantization (MP -
  MLQ) method, for lower bit flow Algebraic Code
  Excited Linear Prediction (ACELP) method. The
  frame has length 30 ms plus adding 7.5 ms. Whole
  algorithm delay is 37.5 ms.

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3. Simulation

• Simulation is made by MATLAB programme. Resulting
  programme is placed on created CD. In MATLAB
  command line must be written encoder(name1.wav,
  name2.bs), where name1 is name of input wav
  file and name2 is encoded sequence, BitStream
  by G.723.1, which will be next suitable as input
  to decoder.

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• For decoding of this sequence and acquirement
  requested wav file must be written
  decoder(name2.bs, name1.wav) where name2 is
  name of input BitStream file which was obtained
  from previous encoding and name1 is resulted
  wav type file. To this resulted file was apllied
  encoder G.723.1.
• Input encoder file must be 16-bit, singlechannel
  with sampling frequency 8000 Hz, integer type.
• Some short speech signals was executed by the
  simulated Codec G. 723.1.

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• The different between the original signal and
  decoded signal is very small. More interesting
  results are on the Fig.2. On this figure is
  displayed time behaviour of the shorter speech
  elements of the e sound. The figure contains
  element of the length 300 samples, it is about
  40ms if the sampling frequency is 8kHz.

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4. Conclusion

• This research is dedicated to introduction to
  multimedia compressions signals problems.
  Following describe ISDN multimedia compressions,
  it is more detailed then before basic describe.
  The next problem is desription of recommendation
  ITU-T G.723.1, which is used in practical part of
  this research.

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• In practical part of this research is codec
  realisation in programme MATLAB. This research
  gives integrated information about compression
  methods, which are used with the most accent to
  audio compression, specify to speech. Codec
  realisation in MATLAB is applied on test speech
  signal and selected results are indicated in this
  paper.

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Chaotic signal for transmission of information

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• Authors contribution is design of CPPM modulator
  (it is contribution for this paper, for complete
  research it is also CPPM demodulator and
  transmission channel). The pulse generator, which
  makes chaotic sequence of impulses, is the basic
  block of CPPM modulator. The impulse is generated
  for every front edge of time signal. Mentioned
  pulse generator is controlled by chaotic time
  signal.

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• The creation of chaotic time signal is next step
  of design. It is known, that necessary condition
  of chaotic system behaviour is the non-linearity
  of this system. The principal results of this
  research is CPPM modulator design and simulation
  of its behaviour in MATLAB and SIMULINK
  environment.

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1. Introduction

• Orbit behaviour in the vicinity of balance state
  of autonomous system is described by the self
  numerous of Jacobins matrix (matrix of
  linearization) and by description of trajectories
  behaviour in the vicinity of closed trajectory ?
  are suitable their multipliers. Trajectory
  multipliers ? are self numerous of Jacobins
  matrix of Poincaré projection in the fix point .

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• For the description of trajectories of generally
  arbitrary trajectory G are used Ljapunov
  exponnets (called also characteristic exponents).
  Ljapunov exponents (LE) are generalization of
  self numerous or multipliers. Ljapunov exponents
  are real numerous and they are suitable for
  classification of chaotic or non-chaotic
  atractors.

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(1)

• x(t)?t(x0)

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• Asymptotic orbits behaviour in vicinity of G(x0)
  is given by asymptotic behaviour of Jacobins
  matrix (matrix of linearization) of the flow
  D?t(x0) of the equation in limits t??.

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(2)
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2. Design of CPPM modulator

• Basic element of CPPM system is pulse generator.
  On its output is chaotic sequence of impulses.
  This generator is controlled by chaotic time
  signal. In every front edge of time signal will
  be generated one impuls. Every impuls has the
  same amplituce and the same duration.

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3. Implementation CPPM in the SIMULINK
environment

• Using programme environment MATLAB and SIMULINK
  was modelled CPPM signal transmission through
  radio environment. Communication system block
  diagram is on the figure 2. Signal CPPM is in the
  radio channel obstructed by additive white noise
  and by interference with obstructing signals.
  Results are in our research work.

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4. Conclusion

• This paper deals with different communication
  techniques which use a chaotic signal for
  transmission of information. These informations
  are studied from the synchronization and noise
  resistance point of view. The main part of this
  work is concentrated on CPPM modulation. The
  final result is to design a communication system
  CPPM by means of MATLAB and SIMULINK
  respectively.

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• Several simulations were done for distinct
  parameters values of CPPMs modulator and
  demodulator. In the end, the multi-user
  transmission has been studied and corresponding
  BER has been confirmed.

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Research of Advanced Multimedia Transmission

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• One period of research of advanced multimedia
  transmission has been finished in our department.
  A lot of papers from this problem were published
  in the previous conferences and this paper
  encloses it. The main problems were mapping the
  actual status of audio signal encoding and bit
  rate compression, focused to MPEG-1 method,
  encoder and decoder MPEG-1 block diagrams and
  design of MPEG-1 model simulated by MATLAB.

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• This model was suitable for testing and
  optimizing of MPEG-1 principle, analysis and
  simulation of the Filter Bank, design and
  modification of scale-factor calculating,
  modelling of FFT for MPEG-1, determination of the
  sound pressure level, study of tonal and
  non-tonal components, decimation of tonal and
  non-tonal masking components design, individual
  masking threshold and global masking threshold,
  determining the minimum masking threshold,
  signal-to-mask ratio, design of bit allocation
  and optimal quantisation and encoding of subbands
  samples.

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I.Introduction

• The research has been divided in several chapters
  that covers all the possible explenation of the
  abstract. Chapter 1 gives a general introduction
  about (MPEG) Motion Picture Experts Group and an
  overview of the current state of this scientific
  issue. Chapter 2 it cover the problem
  definition and research outlines. Chapter 3 ties
  into these and describes in details the algorithm
  used in MPEG standards. Chapter 4 describes the
  creation of an MPEG-1 sound coder/decoder with
  all steps how to design, as it will be used in
  the experiments to be performed.

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• Chapter 5 contains a main results of the
  research and Conclusion we can read at Chapter 6.
  The first problem was to research encoding and
  decoding process of MPEG-1 standard, which gives
  the possibility to program Layer-1. First of all,
  it design the algorihtm for possibility to
  simulate each part of the MPEG-1. MATLAB is used
  for simulation of mathematical calculations. It
  allows to display each variable with a lot of
  format methods. It seems good for displaying
  calculated variables or searching mistakes in
  algorithm, when program is constructed.

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II.Simulation OF MPEG-I IN MATLAB

• A short description of MATLAB programming was
  given in this section. The program consists of a
  number of files. The central unit is mpeg1a.m.
  The important global variables such as bit-rate,
  length of window for analysis (encoding) and
  synthesis (decoding), number of samples in one
  subband in the frame, number of subbands and more
  other parameters are defined in this file. This
  central program unit calls table
  absolute-threshold and mapping.m.

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• These files are used for encoding and decoding
  purposes. For encoder part of the program is much
  longer than decoder part. This is the most
  important part of MPEG-1 standard. The number of
  runs of encoding and decoding are calculated. In
  fact, one run of encoding means calculating of
  one frame. This number is calculated from length
  of input file, which is taken from disk file.
  Number of bits are then calculated for one frame,
  because user can select arbitrary bitrate of list
  taken from norm. This list is included in the
  beginning of the main file.

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• The encode part in first run load many variables
  into memory. In second, third, fourth runs and so
  on, these variables in memory are prepared. It
  gives the encoding process slower in first step
  than in others. This program consists of subpart
  for windowing, partial calculation, matrixing,
  big subpart for psychoacoustic model and
  quantization.

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A. Test Signal

• In this section, the outputs of the software
  calculation in MATLAB are displayed in pictures.
  These outputs are the outcomes of the input-test
  signal. This signal was connected at the input in
  MATLAB programming. With these signals, a lot of
  main parts of compression principles are
  introduced. In addition, it will be introduced on
  real program in future, which is possible to run
  in real time, but now will be used step by step
  in developing software. The input chosen signal
  consists of audio signal which take it from CD.

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B.Filter Bank Analysis

• In the MPEG audio coding, the input signal is
  divided into 32 subbands of equal bandwidth. In
  each subband, the signal is scaled and quantized
  in order to keep the quantizing noise below the
  masking curve. The result of the encoding is a
  series of scale factors, quantizer information
  and coded samples for each subband.

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• According to analysis of subband
  filter
  flowchart, the filter does a time to frequency
  mapping. The 32 subband polyphase filter presents
  optimized characteristics with respect to a
  performance complexity ratio. The filter, of
  order 511, with side lobe rejection better than
  96 dB, is a compromise between two competing
  specifications of the filter response the
  spectral resolution and the transient impulse
  response (TIR). The spectral resolution of the
  filter bank is important, because it corresponds
  to the critical bands found in the human ear.

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• The information yielded by the filter, when
  compared with empirical perceptual data,
  facilitates the reduction of the bit information
  by eliminating masked, unnoticed spectra, and
  reduces the number of bits allocated for spectra
  carrying low amounts of information. The
  time-frequency mapping of the filter allows a
  reasonable emulation of the critical bands of the
  ear, which correspond to a width of about 100 Hz
  in frequencies below 500 Hz, and width of about
  20 of the center frequency at higher
  frequencies.

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C. Subband Analysis in MATLAB

• In the start of the flow chart, there are two
  steps, which are prepared for new input samples.
  Firstly, all the samples in the buffer (512
  samples) are down shifted and then put new 32
  samples at the end of the buffer, which is
  required for further calculations. But in this
  work different algorithm, which contain both in
  only one step is used. One more variable known as
  ofs is present, which shows where is the end of
  the previous data read, because 32 samples into
  512 samples buffer is read, it is different
  number every time

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• . Instead of shifting in each run of flowchart
  (it is 12 times in one frame) only offset is
  shifted. It gives more time for other algorithms.
  When 32 new samples are added, it gives 512
  together, which is all defined length for buffer
  for windowing.

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D. Scale-Factor Calculating

• A scale technique is used in the MPEG-1 coding
  scheme, which provides an effective overall
  dynamic range of 120 dB/subband with a resolution
  of 2dB/scale factor class. The calculation of the
  scale factors is made for every 12 subband
  samples. The maximum value of these samples is
  determined. The lowest value, which is larger
  than this maximum value is used as scale-factor.

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• Scale-factor can be calculated after
  psychoacoustic model according to standard. In
  fact, in MATLAB program, the scale-factor was
  chosen before psychoacoustic model. The algorithm
  contains to looking for maximum value of samples
  and after searching minimum appropriate
  scale-factor.

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E. Fast Fourier Transformation (FFT) Analysis

• The masking threshold is derived from an
  estimate of the power density spectrum that is
  calculated by a 512-point FFT. The FFT is
  calculated directly from the input PCM signal,
  windowed by a Hann window

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• For a coincidence in time between the bit
  allocation and the corresponding subband samples,
  the PCM samples entering the FFT have to be
  displayed. The delay of the analysis subband
  filter in 256 samples, corresponding to 5.3 ms at
  the 48 kHz sampling rate 2. A window shift of
  256 samples is required to compensate for the
  delay in the analysis subband filter. In
  addition, the Hann window must coincide with the
  subband samples of the frame. For Layer-1, this
  amount to an additional window shift of 64
  samples

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III.Determination of the sound pressure level

• The more powerful version was chosen for the
  determination of the sound pressure level in
  program in MATLAB. A maximum is found from all
  spectral lines in each subband and one pressure
  level gives by scalefactors. In fact, it means
  two loops, first is making a searching through
  spectral lines in each subband, second is
  searching just through subbands comparing to an
  appropriate pressure level and maximum from first
  loop.

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A.Finding of Tonal and Non-Tonal Components

• The tonality of a masking component has an
  influence on the masking threshold. For this
  reason, it is worthwhile to discriminate between
  tonal and non-tonal components. For calculating
  the global masking threshold, it is necessary to
  derive the tonal and non-tonal components from
  the FFT.

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B.Decimation of Tonal and Non-Tonal Masking
Components

• Decimation is a procedure used to allow
  considering fewer maskers when calculating the
  global masking threshold.
• The decimation was separated into three steps.
  Each step is represented by one loop, with a
  comparison at the beginning. After the
  comparison, a tone or noise is kept or removed,
  depending on the comparisons result. These three
  parts are decimation for tonal components,
  decimation for non-tonal components, and
  decimation for tonal components within a distance
  of less than 0.5 Bark.

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C.Calculating the Individual Masking Threshold

• The individual masking thresholds of both tonal
  and non-tonal components are calculated according
  to the MPEG-1 standard 1.

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• The masking function vf of a masker is
  characterized by different lower and upper
  slopes, which depend on the distance in Bark
  dz  z (i)  z (j) to the masker. In this
  expression, i is the index of the spectral line
  at which the masking function is calculated and j
  that of the masker. The critical band rates z(j)
  and z(i) can be found in the tables in the
  standard. These are the same tables as before.
  Therefore, in the present project, the table for
  FS  44.1kHz is included in the appendix with the
  MATLAB program.

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D.Quantization and Encoding of Subband Samples

• All computations in the encoder were finished at
  the end of the previous chapter. Now the encoder
  has to transmit the data. The next paragraphs
  describe how the data is transmitted.

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IV.Conclusion

• The main parts of the research was
• Mapping the actual status of audio signal
  encoding and bit rate compression, focusing to
  MPEG-1 method, design of encoder and decoder
  MPEG-1 block diagrams, design of MPEG-1 model
  simulated by MATLAB, analysis and simulation of
  the Filter Bank,

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• design and modification of scale-factor
  calculating, modelling of FFT for MPEG-1,
  determination of the sound pressure level,
  decimation of tonal and non-tonal masking
  components design, individual masking threshold
  and global masking threshold, determining the
  minimum masking threshold, signal-to mask ratio,
  design of bit allocation, optimal quantization
  and encoding of subbands samples.

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• The programming for MPEG-1 audio encoder and
  decoder for Layer-1 is discussed in detail in
  this paper. We use MATLAB for making the proposed
  algorithm, which will be applied later on the
  Digital Signal Processor (DSP). We did not use
  special instructions from MATLAB like

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• In the previous slides you can see possibilities
  of the convergency of telecommunication and
  computer networks.
• Thank you for your interest
• If you have any questions, then write e-mail to
• skorpil_at_feec.vutbr.cz