This document discusses the implementation of arithmetic coding algorithms. It describes how to encode data incrementally by tracking the interval containing the code tag and rescaling the interval as needed. When the interval is confined to either the upper or lower half of the unit interval, the most significant bit of the tag is sent to the decoder and the interval is rescaled. An example demonstrates tracking the interval boundaries and transmitting tag bits during encoding of a sample data sequence.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
Multilayer Perceptron Guided Key Generation through Mutation with Recursive R...pijans
In this paper, a multilayer perceptron guided key generation for encryption/decryption (MLPKG) has been
proposed through recursive replacement using mutated character code generation for wireless
communication of data/information. Multilayer perceptron transmitting systems at both ends accept an
identical input vector, generate an output bit and the network are trained based on the output bit which is
used to form a protected variable length secret-key. For each session, different hidden layer of multilayer
neural network is selected randomly and weights or hidden units of this selected hidden layer help to form
a secret session key. The plain text is encrypted using mutated character code table. Intermediate cipher
text is yet again encrypted through recursive replacement technique to from next intermediate encrypted
text which is again encrypted to form the final cipher text through chaining , cascaded xoring of multilayer
perceptron generated session key. If size of the final block of intermediate cipher text is less than the size of
the key then this block is kept unaltered. Receiver will use identical multilayer perceptron generated
session key for performing deciphering process for getting the recursive replacement encrypted cipher text
and then mutated character code table is used for decoding. Parametric tests have been done and results
are compared in terms of Chi-Square test, response time in transmission with some existing classical
techniques, which shows comparable results for the proposed technique.
Belief Propagation Decoder for LDPC Codes Based on VLSI Implementationinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Investigating the Performance of NoC Using Hierarchical Routing ApproachIJERA Editor
The Network-on-Chip (NoC) model has appeared as a revolutionary methodology for incorporatingmany number of intellectual property (IP) blocks in a die. As said by the International Roadmap for Semiconductors (ITRS), it is must to scale down the device size. In order to reduce the device long interconnection should be avoided. For that, new interconnect patterns are need. Three-dimensional ICs are proficient of achieving superior performance, resistance against noise and lower interconnect power consumption compared to traditional planar ICs. In this paper, network data routed by Hierarchical methodology. We are analyzing total number of logic gates and registers, power consumption and delay when different bits of data transmitted using Quartus II software.
Investigating the Performance of NoC Using Hierarchical Routing ApproachIJERA Editor
The Network-on-Chip (NoC) model has appeared as a revolutionary methodology for incorporatingmany number of intellectual property (IP) blocks in a die. As said by the International Roadmap for Semiconductors (ITRS), it is must to scale down the device size. In order to reduce the device long interconnection should be avoided. For that, new interconnect patterns are need. Three-dimensional ICs are proficient of achieving superior performance, resistance against noise and lower interconnect power consumption compared to traditional planar ICs. In this paper, network data routed by Hierarchical methodology. We are analyzing total number of logic gates and registers, power consumption and delay when different bits of data transmitted using Quartus II software.
Implementation of an arithmetic logic using area efficient carry lookahead adderVLSICS Design
An arithmetic logic unit acts as the basic building blocks or cell of a central processing unit of a computer.
And it is a digital circuit comprised of the basic electronics components, which is used to perform various
function of arithmetic and logic and integral operations further the purpose of this work is to propose the
design of an 8-bit ALU which supports 4-bit multiplication. Thus, the functionalities of the ALU in this
study consist of following main functions like addition also subtraction, increment, decrement, AND, OR,
NOT, XOR, NOR also two complement generation Multiplication. And the functions with the adder in the
airthemetic logic unit are implemented using a Carry Look Ahead adder joined by a ripple carry approach.
The design of the following multiplier is achieved using the Booths Algorithm therefore the proposed ALU
can be designed by using verilog or VHDL and can also be designed on Cadence Virtuoso platform.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
Multilayer Perceptron Guided Key Generation through Mutation with Recursive R...pijans
In this paper, a multilayer perceptron guided key generation for encryption/decryption (MLPKG) has been
proposed through recursive replacement using mutated character code generation for wireless
communication of data/information. Multilayer perceptron transmitting systems at both ends accept an
identical input vector, generate an output bit and the network are trained based on the output bit which is
used to form a protected variable length secret-key. For each session, different hidden layer of multilayer
neural network is selected randomly and weights or hidden units of this selected hidden layer help to form
a secret session key. The plain text is encrypted using mutated character code table. Intermediate cipher
text is yet again encrypted through recursive replacement technique to from next intermediate encrypted
text which is again encrypted to form the final cipher text through chaining , cascaded xoring of multilayer
perceptron generated session key. If size of the final block of intermediate cipher text is less than the size of
the key then this block is kept unaltered. Receiver will use identical multilayer perceptron generated
session key for performing deciphering process for getting the recursive replacement encrypted cipher text
and then mutated character code table is used for decoding. Parametric tests have been done and results
are compared in terms of Chi-Square test, response time in transmission with some existing classical
techniques, which shows comparable results for the proposed technique.
Belief Propagation Decoder for LDPC Codes Based on VLSI Implementationinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Investigating the Performance of NoC Using Hierarchical Routing ApproachIJERA Editor
The Network-on-Chip (NoC) model has appeared as a revolutionary methodology for incorporatingmany number of intellectual property (IP) blocks in a die. As said by the International Roadmap for Semiconductors (ITRS), it is must to scale down the device size. In order to reduce the device long interconnection should be avoided. For that, new interconnect patterns are need. Three-dimensional ICs are proficient of achieving superior performance, resistance against noise and lower interconnect power consumption compared to traditional planar ICs. In this paper, network data routed by Hierarchical methodology. We are analyzing total number of logic gates and registers, power consumption and delay when different bits of data transmitted using Quartus II software.
Investigating the Performance of NoC Using Hierarchical Routing ApproachIJERA Editor
The Network-on-Chip (NoC) model has appeared as a revolutionary methodology for incorporatingmany number of intellectual property (IP) blocks in a die. As said by the International Roadmap for Semiconductors (ITRS), it is must to scale down the device size. In order to reduce the device long interconnection should be avoided. For that, new interconnect patterns are need. Three-dimensional ICs are proficient of achieving superior performance, resistance against noise and lower interconnect power consumption compared to traditional planar ICs. In this paper, network data routed by Hierarchical methodology. We are analyzing total number of logic gates and registers, power consumption and delay when different bits of data transmitted using Quartus II software.
Implementation of an arithmetic logic using area efficient carry lookahead adderVLSICS Design
An arithmetic logic unit acts as the basic building blocks or cell of a central processing unit of a computer.
And it is a digital circuit comprised of the basic electronics components, which is used to perform various
function of arithmetic and logic and integral operations further the purpose of this work is to propose the
design of an 8-bit ALU which supports 4-bit multiplication. Thus, the functionalities of the ALU in this
study consist of following main functions like addition also subtraction, increment, decrement, AND, OR,
NOT, XOR, NOR also two complement generation Multiplication. And the functions with the adder in the
airthemetic logic unit are implemented using a Carry Look Ahead adder joined by a ripple carry approach.
The design of the following multiplier is achieved using the Booths Algorithm therefore the proposed ALU
can be designed by using verilog or VHDL and can also be designed on Cadence Virtuoso platform.
Developing digital signal clustering method using local binary pattern histog...IJECEIAES
In this paper we presented a new approach to manipulate a digital signal in order to create a features array, which can be used as a signature to retrieve the signal. Each digital signal is associated with the local binary pattern (LBP) histogram; this histogram will be calculated based on LBP operator, then k-means clustering was used to generate the required features for each digital signal. The proposed method was implemented, tested and the obtained experimental results were analyzed. The results showed the flexibility and accuracy of the proposed method. Althoug different parameters of the digital signal were changed during implementation, the results obtained showed the robustness of the proposed method.
Design of optimized Interval Arithmetic MultiplierVLSICS Design
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
Analog VLSI Implementation of Neural Network Architecture for Signal ProcessingVLSICS Design
With the advent of new technologies and advancement in medical science we are trying to process the information artificially as our biological system performs inside our body. Artificial intelligence through a biological word is realized based on mathematical equations and artificial neurons. Our main focus is on the implementation of Neural Network Architecture (NNA) with on a chip learning in analog VLSI for generic signal processing applications. In the proposed paper analog components like Gilbert Cell Multiplier (GCM), Neuron activation Function (NAF) are used to implement artificial NNA. The analog components used are comprises of multipliers and adders’ along with the tan-sigmoid function circuit using MOS transistor in subthreshold region. This neural architecture is trained using Back propagation (BP) algorithm in analog domain with new techniques of weight storage. Layout design and verification of the proposed design is carried out using Tanner EDA 14.1 tool and synopsys Tspice. The technology used in designing the layouts is MOSIS/HP 0.5u SCN3M, Tight Metal.
Improving The Performance of Viterbi Decoder using Window System IJECEIAES
An efficient Viterbi decoder is introduced in this paper; it is called Viterbi decoder with window system. The simulation results, over Gaussian channels, are performed from rate 1/2, 1/3 and 2/3 joined to TCM encoder with memory in order of 2, 3. These results show that the proposed scheme outperforms the classical Viterbi by a gain of 1 dB. On the other hand, we propose a function called RSCPOLY2TRELLIS, for recursive systematic convolutional (RSC) encoder which creates the trellis structure of a recursive systematic convolutional encoder from the matrix “H”. Moreover, we present a comparison between the decoding algorithms of the TCM encoder like Viterbi soft and hard, and the variants of the MAP decoder known as BCJR or forward-backward algorithm which is very performant in decoding TCM, but depends on the size of the code, the memory, and the CPU requirements of the application.
Secured transmission through multi layer perceptron in wireless communication...ijmnct
In this paper, a multilayer perceptron guided encryption/decryption (STMLP) in wireless communication
has been proposed for exchange of data/information. Multilayer perceptron transmitting systems at both
ends generate an identical output bit and the network are trained based on the output which is used to
synchronize the network at both ends and thus forms a secret-key at end of synchronizations of the
networks. Weights or hidden units of the hidden layer help to form a secret session key. The plain text is
encrypted through chaining , cascaded xoring of multilayer perceptron generated session key. If size of the
final block of plain text is less than the size of the key then this block is kept unaltered. Receiver will use
identical multilayer perceptron generated session key for performing deciphering process for getting the
plain text. Parametric tests have been done and results are compared in terms of Chi-Square test, response
time in transmission with some existing classical techniques, which shows comparable results for the
proposed technique. Variation numbers of input vectors and hidden layers will increase the confusion
/diffusion of the schemeand hence increase the security. As a result variable energy based techniques may
be achieved which may be applicable devices/interface of the heterogeneous sizes of the network/device.
ENG3104 Engineering Simulations and Computations Semester 2, 2.docxYASHU40
ENG3104 Engineering Simulations and Computations Semester 2, 2015
Assessment: Assignment 3
Due: 23 October 2015
Marks: 300
Value: 30%
1 (worth 40 marks)
1.1 Introduction
To assess how useful the wind power could be as an energy source, use the file ass2data.xls to
calculate the total energy available in the wind for each year of data.
1.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the total energy for each of the years.
2. Reports to the Command Window the energy for each year.
3. Briefly discusses whether there is any trend in the results for annual energy production.
4. Has appropriate comments throughout.
You must also calculate the total energy for the first four hours of power data (i.e. over
the first five data entries) by hand to verify your code; submit this working in a pdf file.
Your MATLAB code must test (verify) whether the computed value of energy is the same as
calculated by hand.
1.3 Assessment Criteria
Your code will be assessed using the following scheme. Note that you are marked based on how
well you perform for each category, so the correct answer determined in a basic way will receive
half marks and the correct answer determined using an excellent method/code will receive full
marks.
Quality of the code 5 marks
Quality of header(s) and comments 5 marks
Quality of calculation of the energy for each year 15 marks
Quality of reporting 5 marks
Quality of discussion 5 marks
Quality of verification based on hand calculations 5 marks
1
ENG3104 Engineering Simulations and Computations Semester 2, 2015
2 (worth 65 marks)
2.1 Introduction
For the wind turbines to operate effectively, they must turn to face into the wind. This could
create large stresses in the structure if the wind changes direction quickly while the wind speed
is high. You are to assess if this is likely to happen using the data in ass2data.xls.
2.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the instantaneous rate of change of wind direction using:
(a) backward differences
(b) forward differences
(c) central differences
2. Plots the three sets of derivatives as functions of time.
3. Produces scatter plots of maximum wind gust as functions of each of the derivatives.
4. Displays a message in the Command Window with a brief discussion of the scatter plots.
Discuss which of the derivatives should be used to compare with the wind gust and why.
Discuss whether you think the wind changes direction too quickly while the wind speed
is high and why.
5. Has appropriate comments throughout.
You must also use a backward difference, forward difference and central difference by hand to
determine the rate of change of wind direction for the twelfth data entry; submit this working
in a pdf file. Your MATLAB code must test (verify) whether these values are the same as
computed by the code for the three differences.
2.3 Assessment Criteria
Your code will ...
A Mixed Binary-Real NSGA II Algorithm Ensuring Both Accuracy and Interpretabi...IJECEIAES
In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binaryreal Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The approach is tested on two control examples: a single input single output (SISO) system and a multivariable (MIMO) system.
A new method for controlling and maintainingIJCNCJournal
Topology Control is an essential technique in a wireless sensor network to extend the operational time of
the sensor nodes. The goal of this technique is to maintain network connectivity and optimize performance
metrics such as network lifetime and throughput. In this paper we presented a new method for controlling
and maintaining topology in wireless sensor networks that show some improvement over the state of art
methods. The results are analyzed based on objective criteria.
Area, Delay and Power Comparison of Adder TopologiesVLSICS Design
Adders form an almost obligatory component of every contemporary integrated circuit. The prerequisite of the adder is that it is primarily fast and secondarily efficient in terms of power consumption and chip area. This paper presents the pertinent choice for selecting the adder topology with the tradeoff between delay, power consumption and area. The adder topology used in this work are ripple carry adder, carry lookahead adder, carry skip adder, carry select adder, carry increment adder, carry save adder and carry bypass adder. The module functionality and performance issues like area, power dissipation and propagation delay are analyzed at 0.12µm 6metal layer CMOS technology using microwind tool.
A New Classifier Based onRecurrent Neural Network Using Multiple Binary-Outpu...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Threshold Enhancement Technique for Chaotic On-Off Keying SchemeCSCJournals
In this paper, an improvement for Chaotic ON-OFF (COOK) Keying scheme is proposed. The scheme enhances Bit Error Rate (BER) performance of standard COOK by keeping the signal elements at fixed distance from the threshold irrespective of noise power. Each transmitted chaotic segment is added to its flipped version before transmission. This reduces the effect of noise contribution at correlator of the receiver. The proposed system is tested in Additive White Gaussian Noise (AWGN) channel and compared with the standard COOK under different Eb/No levels. A theoretical estimate of BER is derived and compared with the simulation results. Effect of spreading factor increment in the proposed system is studied. Results show that the proposed scheme has a considerable advantage over the standard COOK at similar average bit energy and with higher values of spreading factors.
Graphical Visualization of MAC Traces for Wireless Ad-hoc Networks Simulated ...idescitation
Many network simulators (e.g., ns2) are already
being used for performing wired and wireless network
simulations. But, with the current graphical visualization
support in-built in ns2, it is difficult to understand the node
status, packet status and the MAC level events particularly
for Ad-hoc networks. In this paper, we extend the visualization
support in ns-2 that should help research community in the
area of wireless networks to analyze different MAC level
events in an efficient manner. In particular, we have developed
two types of visualizations namely, temporal and spatial.
Temporal visualization helps to analyze success or failure of
a packet with respect to time while spatial visualization helps
to understand the effects due to proximity of nodes. The trace
is made highly configurable in terms of different attributes
like specific nodes and time duration.
Michael Grigoropoulos, MSc Networks and Data Communications COURSEWORK, Kingston University
The purpose of this assignment is to analyze and simulate the physical layer of the 802.11a standard and compare the different modulation and coding schemes it can use. A theoretical approach of the protocol will be presented and also a practical simulation using Matlab and Simulink.
Developing digital signal clustering method using local binary pattern histog...IJECEIAES
In this paper we presented a new approach to manipulate a digital signal in order to create a features array, which can be used as a signature to retrieve the signal. Each digital signal is associated with the local binary pattern (LBP) histogram; this histogram will be calculated based on LBP operator, then k-means clustering was used to generate the required features for each digital signal. The proposed method was implemented, tested and the obtained experimental results were analyzed. The results showed the flexibility and accuracy of the proposed method. Althoug different parameters of the digital signal were changed during implementation, the results obtained showed the robustness of the proposed method.
Design of optimized Interval Arithmetic MultiplierVLSICS Design
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
Analog VLSI Implementation of Neural Network Architecture for Signal ProcessingVLSICS Design
With the advent of new technologies and advancement in medical science we are trying to process the information artificially as our biological system performs inside our body. Artificial intelligence through a biological word is realized based on mathematical equations and artificial neurons. Our main focus is on the implementation of Neural Network Architecture (NNA) with on a chip learning in analog VLSI for generic signal processing applications. In the proposed paper analog components like Gilbert Cell Multiplier (GCM), Neuron activation Function (NAF) are used to implement artificial NNA. The analog components used are comprises of multipliers and adders’ along with the tan-sigmoid function circuit using MOS transistor in subthreshold region. This neural architecture is trained using Back propagation (BP) algorithm in analog domain with new techniques of weight storage. Layout design and verification of the proposed design is carried out using Tanner EDA 14.1 tool and synopsys Tspice. The technology used in designing the layouts is MOSIS/HP 0.5u SCN3M, Tight Metal.
Improving The Performance of Viterbi Decoder using Window System IJECEIAES
An efficient Viterbi decoder is introduced in this paper; it is called Viterbi decoder with window system. The simulation results, over Gaussian channels, are performed from rate 1/2, 1/3 and 2/3 joined to TCM encoder with memory in order of 2, 3. These results show that the proposed scheme outperforms the classical Viterbi by a gain of 1 dB. On the other hand, we propose a function called RSCPOLY2TRELLIS, for recursive systematic convolutional (RSC) encoder which creates the trellis structure of a recursive systematic convolutional encoder from the matrix “H”. Moreover, we present a comparison between the decoding algorithms of the TCM encoder like Viterbi soft and hard, and the variants of the MAP decoder known as BCJR or forward-backward algorithm which is very performant in decoding TCM, but depends on the size of the code, the memory, and the CPU requirements of the application.
Secured transmission through multi layer perceptron in wireless communication...ijmnct
In this paper, a multilayer perceptron guided encryption/decryption (STMLP) in wireless communication
has been proposed for exchange of data/information. Multilayer perceptron transmitting systems at both
ends generate an identical output bit and the network are trained based on the output which is used to
synchronize the network at both ends and thus forms a secret-key at end of synchronizations of the
networks. Weights or hidden units of the hidden layer help to form a secret session key. The plain text is
encrypted through chaining , cascaded xoring of multilayer perceptron generated session key. If size of the
final block of plain text is less than the size of the key then this block is kept unaltered. Receiver will use
identical multilayer perceptron generated session key for performing deciphering process for getting the
plain text. Parametric tests have been done and results are compared in terms of Chi-Square test, response
time in transmission with some existing classical techniques, which shows comparable results for the
proposed technique. Variation numbers of input vectors and hidden layers will increase the confusion
/diffusion of the schemeand hence increase the security. As a result variable energy based techniques may
be achieved which may be applicable devices/interface of the heterogeneous sizes of the network/device.
ENG3104 Engineering Simulations and Computations Semester 2, 2.docxYASHU40
ENG3104 Engineering Simulations and Computations Semester 2, 2015
Assessment: Assignment 3
Due: 23 October 2015
Marks: 300
Value: 30%
1 (worth 40 marks)
1.1 Introduction
To assess how useful the wind power could be as an energy source, use the file ass2data.xls to
calculate the total energy available in the wind for each year of data.
1.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the total energy for each of the years.
2. Reports to the Command Window the energy for each year.
3. Briefly discusses whether there is any trend in the results for annual energy production.
4. Has appropriate comments throughout.
You must also calculate the total energy for the first four hours of power data (i.e. over
the first five data entries) by hand to verify your code; submit this working in a pdf file.
Your MATLAB code must test (verify) whether the computed value of energy is the same as
calculated by hand.
1.3 Assessment Criteria
Your code will be assessed using the following scheme. Note that you are marked based on how
well you perform for each category, so the correct answer determined in a basic way will receive
half marks and the correct answer determined using an excellent method/code will receive full
marks.
Quality of the code 5 marks
Quality of header(s) and comments 5 marks
Quality of calculation of the energy for each year 15 marks
Quality of reporting 5 marks
Quality of discussion 5 marks
Quality of verification based on hand calculations 5 marks
1
ENG3104 Engineering Simulations and Computations Semester 2, 2015
2 (worth 65 marks)
2.1 Introduction
For the wind turbines to operate effectively, they must turn to face into the wind. This could
create large stresses in the structure if the wind changes direction quickly while the wind speed
is high. You are to assess if this is likely to happen using the data in ass2data.xls.
2.2 Requirements
For this assessment item, you must produce MATLAB code which:
1. Calculates the instantaneous rate of change of wind direction using:
(a) backward differences
(b) forward differences
(c) central differences
2. Plots the three sets of derivatives as functions of time.
3. Produces scatter plots of maximum wind gust as functions of each of the derivatives.
4. Displays a message in the Command Window with a brief discussion of the scatter plots.
Discuss which of the derivatives should be used to compare with the wind gust and why.
Discuss whether you think the wind changes direction too quickly while the wind speed
is high and why.
5. Has appropriate comments throughout.
You must also use a backward difference, forward difference and central difference by hand to
determine the rate of change of wind direction for the twelfth data entry; submit this working
in a pdf file. Your MATLAB code must test (verify) whether these values are the same as
computed by the code for the three differences.
2.3 Assessment Criteria
Your code will ...
A Mixed Binary-Real NSGA II Algorithm Ensuring Both Accuracy and Interpretabi...IJECEIAES
In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binaryreal Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The approach is tested on two control examples: a single input single output (SISO) system and a multivariable (MIMO) system.
A new method for controlling and maintainingIJCNCJournal
Topology Control is an essential technique in a wireless sensor network to extend the operational time of
the sensor nodes. The goal of this technique is to maintain network connectivity and optimize performance
metrics such as network lifetime and throughput. In this paper we presented a new method for controlling
and maintaining topology in wireless sensor networks that show some improvement over the state of art
methods. The results are analyzed based on objective criteria.
Area, Delay and Power Comparison of Adder TopologiesVLSICS Design
Adders form an almost obligatory component of every contemporary integrated circuit. The prerequisite of the adder is that it is primarily fast and secondarily efficient in terms of power consumption and chip area. This paper presents the pertinent choice for selecting the adder topology with the tradeoff between delay, power consumption and area. The adder topology used in this work are ripple carry adder, carry lookahead adder, carry skip adder, carry select adder, carry increment adder, carry save adder and carry bypass adder. The module functionality and performance issues like area, power dissipation and propagation delay are analyzed at 0.12µm 6metal layer CMOS technology using microwind tool.
A New Classifier Based onRecurrent Neural Network Using Multiple Binary-Outpu...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Threshold Enhancement Technique for Chaotic On-Off Keying SchemeCSCJournals
In this paper, an improvement for Chaotic ON-OFF (COOK) Keying scheme is proposed. The scheme enhances Bit Error Rate (BER) performance of standard COOK by keeping the signal elements at fixed distance from the threshold irrespective of noise power. Each transmitted chaotic segment is added to its flipped version before transmission. This reduces the effect of noise contribution at correlator of the receiver. The proposed system is tested in Additive White Gaussian Noise (AWGN) channel and compared with the standard COOK under different Eb/No levels. A theoretical estimate of BER is derived and compared with the simulation results. Effect of spreading factor increment in the proposed system is studied. Results show that the proposed scheme has a considerable advantage over the standard COOK at similar average bit energy and with higher values of spreading factors.
Graphical Visualization of MAC Traces for Wireless Ad-hoc Networks Simulated ...idescitation
Many network simulators (e.g., ns2) are already
being used for performing wired and wireless network
simulations. But, with the current graphical visualization
support in-built in ns2, it is difficult to understand the node
status, packet status and the MAC level events particularly
for Ad-hoc networks. In this paper, we extend the visualization
support in ns-2 that should help research community in the
area of wireless networks to analyze different MAC level
events in an efficient manner. In particular, we have developed
two types of visualizations namely, temporal and spatial.
Temporal visualization helps to analyze success or failure of
a packet with respect to time while spatial visualization helps
to understand the effects due to proximity of nodes. The trace
is made highly configurable in terms of different attributes
like specific nodes and time duration.
Michael Grigoropoulos, MSc Networks and Data Communications COURSEWORK, Kingston University
The purpose of this assignment is to analyze and simulate the physical layer of the 802.11a standard and compare the different modulation and coding schemes it can use. A theoretical approach of the protocol will be presented and also a practical simulation using Matlab and Simulink.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
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Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
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Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
1. 1
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
1/46
Lecture 06
Nov. 03, 2022
Instructor:高立人
電子工程研究所
國立臺北科技大學
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
2/46
Arithmetic Code
Algorithm Implementation
November 3, 2022
2. 2
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
3/46
Algorithm Implementation
Algorithm Implementation: An Overview
In previous section, i.e., Section 4.3.1, we developed a
recursive algorithm for the boundaries of the interval
containing the tag for the sequence being encoded as
(4.23)
(4.22)
1
1
1
1
1
1
1
n
X
n
n
n
n
n
X
n
n
n
n
x
F
l
u
l
u
x
F
l
u
l
l
where, xn is the value of the random variable
corresponding to the nth observed symbol, l(n) is the
lower limit of the tag interval at the nth iteration, and u(n)
is the upper limit of the tag interval at the nth iteration.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
4/46
Algorithm Implementation
Algorithm Implementation: An Overview
Before we can implement this algorithm, there is one
major problem we have to resolve.
Recall that the reason for using numbers in the interval [0, 1) as
a tag was that there are an infinite number of numbers in this
interval.
However, in practice the number of numbers that can be
uniquely represented on a machine is limited by the maximum
number of digits (or bits) we can use for representing the
number.
November 3, 2022
3. 3
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
5/46
Algorithm Implementation
Algorithm Implementation: An Overview
Consider the values of l(n) and u(n) in Example 4.3.5.
As n gets larger, these values come closer and closer together.
This means that in order to represent all the subintervals
uniquely we need increasing precision as the length of the
sequence increases.
In a system with finite precision, the two values are bound to
converge, and we will lose all information about the sequence
from the point at which the two values converged.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
6/46
Algorithm Implementation
Algorithm Implementation: An Overview
To avoid this situation, we need to rescale the interval.
However, we have to do it in a way that will preserve the
information that is being transmitted.
We would also like to perform the encoding
incrementally — that is, to transmit portions of the code
as the sequence is being observed, rather than wait
until the entire sequence has been observed before
transmitting the first bit.
The algorithm we describe in this section takes care of
the problems of synchronized rescaling and incremental
encoding.
November 3, 2022
4. 4
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
7/46
Algorithm Implementation
Interval Containing the Tag: An Analysis
As the interval becomes narrower, we have three
possibilities:
The interval is entirely confined to the lower half of the unit
interval [0, 0.5).(全部落在下半部)
The interval is entirely confined to the upper half of the unit
interval [0.5, 1.0).(全部落在上半部)
The interval straddles(跨坐)the midpoint of the unit interval.
(亦即跨坐於0.5的兩端;下限在0.5之下,上限在0.5之上)
We will look at the third case a little later in this section.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
8/46
Algorithm Implementation
Interval Containing the Tag: An Analysis
First, let us examine the first two cases.
Once the interval is confined to either the upper or lower half of
the unit interval, it is forever confined to that half of the unit
interval.
The most significant bit of the binary representation of all
numbers in the interval [0, 0.5) is 0, and the most significant bit
of the binary representation of all numbers in the interval [0.5, 1]
is 1.
Therefore, once the interval gets restricted to either the upper or
lower half of the unit interval, the most significant bit of the tag is
fully determined.
November 3, 2022
5. 5
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
9/46
Algorithm Implementation
Interval Containing the Tag: An Analysis
Therefore, without waiting to see what the rest of the sequence
looks like, we can indicate to the decoder whether the tag is
confined to the upper or lower half of the unit interval by sending
a 1 for the upper half and a 0 for the lower half.
The bit that we send is also the first bit of the tag.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
10/46
Algorithm Implementation
Interval Containing the Tag: An Analysis
Once the encoder and decoder know which half
contains the tag, we can ignore the half of the unit
interval not containing the tag and concentrate on the
half containing the tag.
As our arithmetic is of finite precision, we can do this
best by mapping the half interval containing the tag to
the full [0, 1) interval. The mappings required are
(4.25)
.
5
.
0
2
;
1
,
0
1
,
5
.
0
:
(4.24)
2
;
1
,
0
5
.
0
,
0
:
2
2
1
1
x
x
E
E
x
x
E
E
November 3, 2022
6. 6
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
11/46
Algorithm Implementation
Interval Containing the Tag: An Analysis
As soon as we perform either of these mappings, we
lose all information about the most significant bit.
However, this should not matter because we have
already sent that bit to the decoder.
We can now continue with this process, generating
another bit of the tag every time the tag interval is
restricted to either half of the unit interval.
This process of generating the bits of the tag without
waiting to see the entire sequence is called incremental
encoding.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
12/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2
Let's revisit Example 4.3.5.
Recall that we wish to encode the sequence 1 3 2 1.
The probability model for the source is P(a1)=0.8, P(a2)=
0.02, P(a3)=0.18, i.e., Fx(0)=0, Fx(1)=0.8, Fx(2)=0.82, Fx(3)=1.
Initializing u(0) to 1, and l(0) to 0, the first element of the
sequence, 1, results in the following update:
.
8
.
0
8
.
0
0
1
0
0
0
0
1
0
1
1
u
l
The interval [0, 0.8) is not confined to either the upper or
lower half of the unit interval, so we proceed.
November 3, 2022
7. 7
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
13/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2 Cont.
The second element of the sequence is 3. This results
in the update
.
8
.
0
0
.
1
8
.
0
3
0
8
.
0
0
656
.
0
82
.
0
8
.
0
2
0
8
.
0
0
2
2
X
X
F
u
F
l
The interval [0.656, 0.8) is contained entirely in the
upper half of the unit interval, so we send the binary
code 1 and rescale:
.
6
.
0
5
.
0
8
.
0
2
312
.
0
5
.
0
656
.
0
2
2
2
u
l
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
14/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2 Cont.
The third element, 2, results in the following update
equations:
The interval for the tag is [0.5424, 0.54816), which is
contained entirely in the upper half of the unit interval.
We transmit a 1 and go through another rescaling:
.
54816
.
0
82
.
0
288
.
0
312
.
0
2
312
.
0
8
.
0
312
.
0
5424
.
0
8
.
0
288
.
0
312
.
0
1
312
.
0
6
.
0
312
.
0
3
3
X
X
F
u
F
l
.
09632
.
0
5
.
0
54816
.
0
2
0848
.
0
5
.
0
5424
.
0
2
3
3
u
l
November 3, 2022
8. 8
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
15/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2 Cont.
This interval is contained entirely in the lower half of the
unit interval, so we send a 0 and use the E1 mapping to
rescale:
.
19264
.
0
09632
.
0
2
1696
.
0
0848
.
0
2
3
3
u
l
The interval is still contained entirely in the lower half of
the unit interval, so we send another 0 and go through
another rescaling:
.
38528
.
0
1696
.
0
2
3392
.
0
1696
.
0
2
3
3
u
l
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
16/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2 Cont.
Because the interval containing the tag remains in the
lower half of the unit interval, we send another 0 and
rescale one more time:
Now the interval containing the tag is contained entirely
in the upper half of the unit interval. Therefore, we
transmit a 1 and rescale using the E2 mapping:
.
77056
.
0
38528
.
0
2
6784
.
0
3392
.
0
2
3
3
u
l
.
54112
.
0
5
.
0
77056
.
0
2
3568
.
0
5
.
0
6784
.
0
2
3
3
u
l
November 3, 2022
9. 9
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
17/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2 Cont.
At each stage we are transmitting the most significant
bit that is the same in both the upper and lower limit of
the tag interval.
If the most significant bits in the upper and lower limit
are the same, then the value of this bit will be identical
to the most significant bit of the tag.
Therefore, by sending the most significant bits of the
upper and lower endpoint of the tag whenever they are
identical, we are actually sending the binary
representation of the tag.
The rescaling operations can be viewed as left shifts, which
make the second most significant bit the most significant bit.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
18/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2 Cont.
Continuing with the last element, the upper and lower
limits of the interval containing the tag are
.
504256
.
0
8
.
0
18422
.
0
3568
.
0
1
3568
.
0
54112
.
0
3568
.
0
3568
.
0
0
.
0
18422
.
0
3568
.
0
0
3568
.
0
54112
.
0
3568
.
0
4
4
X
X
F
u
F
l
November 3, 2022
10. 10
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
19/46
Algorithm Implementation
Encoding Termination: How to
At this point, if we wished to stop encoding, all we need
to do is inform the receiver of the final status of the tag
value.
We can do so by sending the binary representation of
any value in the final tag interval.
Generally, this value is taken to be l(n).
In this particular example, it is convenient to use the value of 0.5.
The binary representation of 0.5 is .10....
Thus, we would transmit a 1 followed by as many 0s as
required by the word length of the implementation being used.
(補0不影響數值的大小)
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
20/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2 Cont.
Notice that the tag interval size at this stage is
approximately 64 times the size it was when we were
using the unmodified algorithm.(和Example 4.3.5比較)
Therefore, this technique solves the finite precision
problem.
As we shall soon see, the bits that we have been
sending with each mapping constitute the tag itself,
which satisfies our desire for incremental encoding.
64
7712
.
0
773504
.
0
3568
.
0
504256
.
0
相較於未做Scaling之演算法,
Scale方法讓區間放大了64倍!
November 3, 2022
11. 11
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
21/46
Algorithm Implementation
Tag Generation with Scaling: Example 4.4.2 Cont.
The binary sequence generated during the encoding
process in the previous example is 1100011.
We could simply treat this as the binary expansion of the tag.
A binary number .1100011 corresponds to the decimal number
0.7734375.
Looking back to Example 4.3.5, notice that this number lies
within the final tag interval. Therefore, we could use this to
decode the sequence.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
22/46
Algorithm Implementation
Incremental Decoding:
However, we would like to do incremental decoding as
well as incremental encoding. This raises three
questions:
How do we start decoding?
How do we continue decoding?
How do we stop decoding?
The second question is the easiest to answer.
Once we have started decoding, all we have to do is
mimic the encoder algorithm. That is, once we have
started decoding, we know how to continue decoding.
November 3, 2022
12. 12
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
23/46
Algorithm Implementation
Incremental Decoding: Cont.
To begin the decoding process, we need to have
enough information to decode the first symbol
unambiguously.
In order to guarantee unambiguous decoding, the
number of bits received should point to an interval
smaller than the smallest tag interval(這是指解碼開始
時至少必須有多少位元方可識別第一個符號的區間).
Based on the smallest tag interval, we can determine how many
bits we need before we start the decoding procedure.
We will demonstrate this procedure in Example 4.4.4.
First let's look at other aspects of decoding using the message
from Example 4.4.2.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
24/46
Algorithm Implementation
Example 4.4.3: Deciphering with Scaling
We will use a word length of 6 for this example(在本例
中,先取出從接收端所收到的前6個位元來解第1個符號;在
Integer Implementation時會再提及該選取多少位元之方法).
Note that because we are dealing with real numbers this
word length may not be sufficient for a different
sequence.
As in the encoder, we start with initializing u(0) to 1 and
l(0) to 0.
The sequence of received bits is 110001100. . . 0.
November 3, 2022
13. 13
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
25/46
Algorithm Implementation
Example 4.4.3: Deciphering with Scaling Cont.
The first 6 bits, i.e., 110001, correspond to a tag value of
0.765625, which means that the first element of the
sequence is 1(因為0.76562落在0 ~ 0.8之間,所以解出第
一個符號為1), resulting in the following update:
.
8
.
0
8
.
0
0
1
0
0
0
0
1
0
1
1
u
l
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
26/46
Algorithm Implementation
Example 4.4.3: Deciphering with Scaling Cont.
The interval [0,0.8) is not confined to either the upper or
lower half of the unit interval, so we proceed.
The tag 0.765625 lies in the top 18% of the interval [0,
0.8); therefore, the second element of the sequence is 3.
Updating the tag interval we get
.
8
.
0
0
.
1
8
.
0
3
0
8
.
0
0
656
.
0
82
.
0
8
.
0
2
0
8
.
0
0
2
2
X
X
F
u
F
l
November 3, 2022
14. 14
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
27/46
Algorithm Implementation
Example 4.4.3: Deciphering with Scaling Cont.
The interval [0.656, 0.8) is contained entirely in the
upper half of the unit interval.
At the encoder, we sent the bit 1 and rescaled.
At the decoder, we will shift 1 out of the receive buffer
and move the next bit in to make up the 6 bits in the tag
(這6個bit構成了新的Tag).
We also update the tag interval, resulting in
.
6
.
0
5
.
0
8
.
0
2
312
.
0
5
.
0
656
.
0
2
2
2
u
l
while shifting a bit to give us a tag of 0.546875.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
28/46
Algorithm Implementation
Example 4.4.3: Deciphering with Scaling Cont.
Now, the tag value is 0.546875.
When we compare this value with the tag interval, we
can see that this value lies in the 80-82% range of the
tag interval, so we decode the next element of the
sequence as 2.(See the following for explanation!)
.
54816
.
0
82
.
0
288
.
0
312
.
0
2
312
.
0
8
.
0
312
.
0
5424
.
0
8
.
0
288
.
0
312
.
0
1
312
.
0
6
.
0
312
.
0
3
3
X
X
F
u
F
l
We can then update the equations for the tag interval as
815538
.
0
0.312
-
0.6
0.312
-
0.546875
November 3, 2022
15. 15
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
29/46
Algorithm Implementation
Example 4.4.3: Deciphering with Scaling Cont.
As the tag interval is now contained entirely in the upper
half of the unit interval, we rescale using E2 to obtain
.
09632
.
0
5
.
0
54816
.
0
2
0848
.
0
5
.
0
5424
.
0
2
3
3
u
l
We also shift out a bit from the tag and shift in the next
bit.
The tag is now 000110.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
30/46
Algorithm Implementation
The interval is contained entirely in the lower half of the
unit interval. Therefore, we apply E1 and shift another
bit.
The lower and upper limits of the tag interval become
.
19264
.
0
09632
.
0
2
1696
.
0
0848
.
0
2
3
3
u
l
and the tag becomes 001100.
November 3, 2022
16. 16
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
31/46
Algorithm Implementation
The interval is still contained entirely in the lower half of
the unit interval, so we shift out another 0 to get a tag of
011000 and go through another rescaling:
.
38528
.
0
1696
.
0
2
3392
.
0
1696
.
0
2
3
3
u
l
Because the interval containing the tag remains in the
lower half of the unit interval, we shift out another 0 from
the tag to get 110000 and rescale one more time:
.
77056
.
0
38528
.
0
2
6784
.
0
3392
.
0
2
3
3
u
l
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
32/46
Algorithm Implementation
Now the interval containing the tag is contained entirely
in the upper half of the unit interval. Therefore, we shift
out a 1 from the tag and rescale using the E2 mapping:
.
54112
.
0
5
.
0
77056
.
0
2
3568
.
0
5
.
0
6784
.
0
2
3
3
u
l
Now we compare the tag value to the tag interval to
decode our final element.
The tag is 100000, which corresponds to 0.5.
This value lies in the first 80% of the interval, so we
decode this element as 1.
November 3, 2022
17. 17
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
33/46
Short Conclusions About Scaling
Deciphering with Scaling: A Short Conclusion
If the tag interval is entirely contained in the upper or
lower half of the unit interval, the scaling procedure
described will prevent the interval from continually
shrinking.
Now we consider the case where the diminishing tag
interval straddles the midpoint of the unit interval.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
34/46
As our trigger for rescaling, we check to see if the tag
interval is contained in the interval [0.25, 0.75). This will
happen when l(n) is greater than or equal to 0.25 and u(n)
is less than 0.75, i.e., we check to see if the following
condition holds.
l(n) : 0.01XXX
u(n): 0.10XXX
When this happens, we double the tag interval using the
following mapping:
Short Conclusions About Scaling
(4.26)
.
25
.
0
2
;
1
,
0
75
.
0
,
25
.
0
: 3
3
x
x
E
E
November 3, 2022
18. 18
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
35/46
We have used a 1 to transmit information about an E2
mapping, and a 0 to transmit information about an E1,
mapping.
How do we transfer information about an E3 mapping to
the decoder?
We use a somewhat different strategy in this case.
At the time of the E3 mapping, we do not send any information
to the decoder; instead, we simply record the fact that we have
used the E3 mapping at the encoder.
Short Conclusions About Scaling
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
36/46
Suppose that after this, the tag interval gets confined to
the upper half of the unit interval.(這是指假使沒有做E3
mapping,且其後出現的符號將區間帶至0.5以上的情況)
At this point we would use an E2 mapping and send a 1 to the
receiver.
Note that the tag interval at this stage is at least twice what it
would have been if we had not used the E3 mapping.
Furthermore, the upper limit of the tag interval would have been
less than 0.75.(這是指假使沒有做E3 mapping的情況)
Therefore, if the E3 mapping had not taken place right before
the E2 mapping, the tag interval would have been contained
entirely in the lower half of the unit interval.(這是指做完E2
mapping後的情況)
Short Conclusions About Scaling
November 3, 2022
19. 19
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
37/46
At this point we would have used an E1 mapping and
transmitted a 0 to the receiver.
In fact, the effect of the earlier E3 mapping can be mimicked at
the decoder by following the E2 mapping with an E1 mapping.
At the encoder, right after we send a 1 to announce the E2
mapping, we send a 0 to help the decoder track the changes in
the tag interval at the decoder.(這是指假使做了E3 mapping後
的情況)
If the first rescaling after the E3 mapping happens to be
an E1 mapping, we do exactly the opposite. That is, we
follow the 0 announcing an E1 mapping with a 1 to
mimic the effect of the E3 mapping at the encoder.
Short Conclusions About Scaling
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
38/46
Deciphering with Scaling: A Short Conclusion Cont.
What happens if we have to go through a series of E3
mappings at the encoder?
We simply keep track of the number of E3 mappings and then
send that many bits of the opposite variety after the first E1 or
E2 mapping.
If we went through three E3 mappings at the encoder, followed
by an E2 mapping, we would transmit a 1 followed by three 0s.
On the other hand, if we went through an E1 mapping after
three E3 mappings, we would transmit a 0 followed by three 1s.
Since the decoder mimics the encoder, the E3 mappings are
also applied at the decoder when the tag interval is contained in
the interval [0.25, 0.75).
Short Conclusions About Scaling
November 3, 2022
20. 20
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
39/46
Comments on E3 mapping:
第36頁,37頁這一段話的意思在於:當E3 Mapping過後,若
下一個Symbol造成整個Interval落在上半部,則我們會做E2
Mapping!
此外,若未做E3 Mapping,則Upper Bound不會超過0.75。
(原來的Upper Bound就已經小於0.75了,在未做放大的情況
下,後來的區間只會愈來愈小,不可能超過前一次的上限)
因此做完E2 Mapping之後一定落在下半部!(因為E2
Mapping會減掉0.5再乘以2)
Comments on Scaling
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
40/46
Comments on Scaling
Questions:
Q1: E3 Mapping過後若有E1 Mapping出現為何要送01?
Q2: E3 Mapping過後若有E2 Mapping出現為何要送10?
Discussions:
若E3 Mapping之後,new symbol added造成Interval跑
到上半部(i.e., 0.5以上),接著便可做E2 Mapping,送
出1。
問:為何還要再送一個0 ?
Pls see next page for explanations.
November 3, 2022
21. 21
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
41/46
Comments on Scaling
l(n)
u(n)
0
0.5
1
0.25
0.75
0.11
0.10
0.01
0
0.5
0.25
0.75
l(n+1)
u(n+1)
l’(n): 假設做過E3 Mapping之後下限來到這裡
u’(n):假設做過E3 Mapping之後上限來到這裡
假設未做E3 Mapping,New added symbol
將Interval之上限帶到這裡(上限一定小於
0.75,but why? )
因為u(n)就已經小於0.75了,後來的Interval
只有愈來愈窄的份!
Comments on E3 mapping: Cont.
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
42/46
Comments on Scaling
Comments on E3 mapping: Cont.
首先考慮未做E3 Mapping的情況(咖啡色邊界符號)
這個情況已經很明顯了,u(n+1)和l(n+1)都在Upper Interval,故
送出1!
且u(n+1) <0.75,故E2 Mapping後,Interval必定小於0.5;因此
又可再送出0!
Now the problem comes to be what happens if we had
applied E3 Mapping?
The induction process will be shown in next page.
November 3, 2022
22. 22
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
43/46
Comments on Scaling
Comments on E3 mapping: Cont.
其次考慮使用E3 Mapping的情況(紅色邊界符號)
25
.
0
2
25
.
0
2
n
l
n
l
n
u
n
u
This is what we get if E3 Mapping had been applied!
先考慮l’(n+1):亦即新的符號進入後
5
.
0
1
2
5
.
0
1
2
1
2
5
.
0
2
1
1
1
1
1
n
l
x
F
n
l
n
u
n
l
x
F
n
l
n
u
n
l
x
F
n
l
n
u
n
l
n
l
n
n
n
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
44/46
Comments on Scaling
Comments on E3 mapping: Cont.
In summary, we can write down
5
.
0
1
2
1
1
5
.
0
1
2
1
1
n
u
n
u
n
l
n
l
Consider the following two cases:
Case 1: 若新的符號將做過E3 Mapping的Interval帶至上半部,
則即便未做E3 Mapping,同樣的符號也會將新的Interval帶至上
半部。
Case 2: 若新的符號將做過E3 Mapping的Interval帶至下半部,
則即便未做E3 Mapping,同樣的符號也會將新的Interval帶至下
半部。
See next page for explanation.
November 3, 2022
23. 23
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
45/46
Comments on Scaling
Comments on E3 mapping: Cont.
Case 1: 若新的符號將做過E3 Mapping的Interval帶至上半部
1
.
0
1
1
.
0
1
n
u
n
l
0
.
1
5
.
0
1
0
.
1
5
.
0
1
n
u
n
l
10
.
0
5
.
0
1
2
1
10
.
0
5
.
0
1
2
1
n
u
n
l
5
.
0
1
2
1
1
5
.
0
1
2
1
1
n
u
n
u
n
l
n
l
即便未做E3 Mapping,新的符號也會將新的Interval帶至上半部。
因此E3 Mapping過後若發生E2 Mapping,要先送1再送0!
November 3, 2022
Lih-Jen Kau
Signal Proc. & Intell. Electron. Group
National Taipei Univ. of Technology
46/46
Comments on Scaling
Comments on E3 mapping: Cont.
Case 2: 若新的符號將做過E3 Mapping的Interval帶至下半部
0
.
0
1
0
.
0
1
n
u
n
l
1
.
0
5
.
0
1
1
.
0
5
.
0
1
n
u
n
l
01
.
0
5
.
0
1
2
1
01
.
0
5
.
0
1
2
1
n
u
n
l
5
.
0
1
2
1
1
5
.
0
1
2
1
1
n
u
n
u
n
l
n
l
即便未做E3 Mapping,新的符號也會將新的Interval帶至下半部。
因此E3 Mapping過後若發生E1 Mapping,要先送0再送1!
November 3, 2022